Journal of Scholastic Inquiry: Business, Volume 1, Issue 1

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Journal of Scholastic Inquiry:

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Business Edition, Volume 1, Issue 1 Fall 2013

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ISSN: 2330-6807 (print) ISSN: 2330-6815 (online)

Journal of Scholastic Inquiry: Business

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JOURNAL OF SCHOLASTIC INQUIRY: BUSINESS Fall 2013, Volume 1, Issue 1 Managing Editor Dr. Tanya McCoss-Yerigan Editor-in-Chief Dr. Jamal Cooks General Editor Daniel J. O’Brien APA Editor Jay Meiners Editorial Advisory Board Shirley Barnes, Alabama State University Joan Berry, University of Mary Hardin-Baylor Brooke Burks, Auburn University at Montgomery Timothy Harrington, Chicago State University Mark Wesolowski, Practitioner-Chicago Public Schools Lucinda Woodward, Indiana University Southeast

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TABLE OF CONTENTS

Publication Agreement and Assurance of Integrity Ethical Standards in Publishing Disclaimer of Liability Research Manuscripts

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8-124

The Inequitable Distribution of Pharmaceutical R&D Costs: Root Causes and Possible Solutions Steve Molloy, Canisius College

8

Business and IT Strategic Alignment: The Impact of an Enterprise Resource Planning System D. Lance Revenaugh, Montana Tech University Myles Muretta

20

An Excel Sort Heuristic for Solving the Travelling Salesman Problem Richard J. Perle, Loyola Marymount University

34

Behavioral Insights Reveal a Consumer of Mixed Rationality Paul A. Stock, University of Mary Hardin-Baylor Jordan W. Ochel, University of Mary Hardin-Baylor Eileen M. Stock, University of Mary Hardin-Baylor & Center for Applied Research

45

China’s Gradualism Approach to Systemic Transformation: Successes & Challenges Raphael Shen, University of Detroit Mercy Victoria Mantzopoulos, University of Detroit Mercy

53

An Intuitive Approach for Teaching the Central Limit Theorem Brian J. Huffman, University of Wisconsin-River Falls Hossein Eftekari- University of Wisconsin-River Falls

75

Should the Policy Goal be Happiness or Economic Growth?

Maria Cornachione Kula, Roger Williams University Priniti Panday, Roger Williams University McKay Gavitt, Roger Williams University

94

Gender Differences in Leading Change Ann Gilley, University of Texas-Tyler Lisa Eshbach, Ferris State University Elies Kouider, Ferris State University Jerry W. Gilley, University of Texas-Tyler

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Manuscript Submission Guide

126

Why Purchase Our Journals

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Journal of Scholastic Inquiry: Business

The Inequitable Distribution of Pharmaceutical R&D Costs: Root Causes and Possible Solutions Steve Molloy Canisius College

Abstract As noted in the debate surrounding the recent passage of the health care reform bill, rising health care costs are a significant political and economic issue in the U.S. The high cost of prescription drugs is a significant contributor to these rising costs. Rising prescription drug prices have stirred up considerable debate over the issue of prescription drug costs and concerns that ‘greedy’ corporations are unfairly taking advantage of patients. Pharmaceutical companies counter that prescription drug prices result from significant research and development (R&D) costs, the risk associated with the development and marketing of these drugs, and the existence of ‘free riders’ in the market, which have led to significant inequities in the distribution of pharmaceutical R&D costs. This paper examines R&D costs and efficiency, sources of revenue to cover R&D costs, the inequitable distribution of these costs, the implications of these inequities, and suggests possible solutions.

Keywords: pharmaceutical industry, prescription drugs, R&D, counterfeit, free riders

R&D Costs Patent protection is an important factor in motivating and rewarding risk taking and innovation. No one seeks to deny legitimate returns to individuals or organizations that assume the risk of innovation.

Pharmaceutical companies justify their prices as necessary to cover the research and development (R&D) and Food and Drug Administration (FDA) approval costs for both successful products and failures. As a result, the cost of any specific prescription drug is unrelated to the actual manufacturing cost, and it may not even be directly related to the total costs (R&D, clinical trials, marketing, etc.) associated with that specific drug. This can lead to public relations nightmares, such as the negative publicity about the pricing of zidovudine for acquired immunodeficiency syndrome (AIDS) patients.

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In recent years, there has been a significant increase in the number of prescription drugs available and their level of use. These drugs can either prolong life or enhance the quality of life for their users. A factor in this growth has been the steadily increasing life expectancy of the population. Prescription drug expenses in the U.S. were $320 billion in 2011; this amount was over 700% greater than the $40.3 billion spent in 1990 (IMS Health, 2012a). The 2011 prescription drug expenses represents 10.5% of the nation’s health care costs. Health care costs make up 14.9% of gross domestic product. From 1999 to 2009 there was a 39% increase in the number of retail prescriptions filled (2.8 billion to 3.9 billion). This compares to a population growth of only 9% (Kaiser Family Foundation, 2012). In 2011, 62% of the population was taking at least one prescription drug (IMS Health, 2012a). IMS Health forecasts that global pharmaceutical spending will grow to $1.2 trillion in 2016 (IMS, 2012b).

No one disputes the significant expenditures on R&D and clinical trials or the risk involved with successfully bringing a new drug to market. In 2011, pharmaceutical companies in the U.S. spent over $49.5 billion on R&D (PhRMA, 2012). It takes an average of 10 to 15 years and more than $1.2 billion per drug (including failures) for development, animal testing, clinical trials, and FDA approval (Wall Street Journal, 2013). Less than 1% of drugs move from the preclinical phase to the marketplace (Wall Street Journal, 2013). Janet Woodcock, the FDA’s former director of drugs, expressed concern that the escalating cost of developing new drugs was threatening “a veritable golden age of drug development” (Dickinson, 2004, p. 14).

Kim and Marschke (2004) found that a decline in the patent yield is creating further difficulties for the pharmaceutical industry. This reduction in yield may be a consequence of the fact that “few, if any, optimization techniques have found their way into the pharmaceutical research portfolio arena” (Coopersmith & Arvesen, 2004).

It is felt that large, potential rewards are needed to compensate for the significant risks taken by pharmaceutical companies in the pursuit of new drugs. Without the potential for significant rewards, funding for R&D will dry up (Donlan, 2000; Glover, 2000; Hess, 2000; Shlaes, 2000; Wall Street Journal, 1999; Wall Street Journal, 2013). Historically, the long lead times for competitors to develop comparable drugs provided patent holders several years of ‘monopoly’ profits. Advances in drug discovery techniques have, in many cases, reduced this time from years to months (Hunt, 2004).

There is disagreement on what constitutes an acceptable return level for the risks taken and with the validity of the R&D numbers released by the pharmaceutical industry.

Pharmaceutical manufacturing was the most profitable U.S. industry from 1995 to 2002. From 2007 to 2009, it was the third most profitable industry with profits (as a percent of revenues) of 19.3%, which is approximately three times the Fortune 500 average for all industries (Kaiser Family Foundation, 2012). Many argue that up to 50% of the industry’s R&D budget is spent on drugs that add nothing but “me too” products in an already crowded market (Angell, 2005) or merely repackaged versions of an older drug backed by an expensive marketing campaign (Gladwell, 2004). Marcia Angell, former editor in chief of the New England Journal of Medicine, feels that drug companies charge too much, engage in deceptive research, produce

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inferior products, borrow the best ideas from government funded scientists, buy the affections of physicians, and are “now primarily a marketing machine to sell drugs of dubious benefit.”

(Angell, 2005).

Another factor affecting drug costs is the relative inefficiency of the pharmaceutical industry. Selling, general, and administrative expenses account for 17% of a typical, large American firm’s revenues, but they are 33% in the drug industry (even when excluding the large number of American sales reps). Between 1999 and 2003, the number of pharmaceutical sales representatives in the U.S. increased by 54% to 90,000. The industry also scores poorly on a number of management and operational efficiency measures (The Economist, 2003).

However, even if one discounts some of the expenses and questions the desired profit levels, substantial resources are still required to justify the risks assumed.

Sources of Revenue Global sales of prescription drugs generate a number of revenue streams. Ideally, revenue is received from all purchasers of the prescription drugs, and all purchasers equally contribute to cover R&D expenses and profit margins. However, not all revenue streams are captured by the patent holder, and these different revenue streams are not equally profitable.

Counterfeit drugs. One segment of global prescription drug revenue not captured by the patent holder is that of counterfeit drugs. Counterfeit prescription drug sales were an estimated $75 billion in 2010 (Gillette, 2013). According to the World Health Organization, approximately 8 to 10% of prescription drugs sold worldwide and up to 30% of prescription drugs sold in Asia and Latin America in 2011 were counterfeit (Perrone, 2012a). It is estimated that, worldwide, upwards of 100,000 people die each year from substandard and counterfeit drugs. China, a leading source of counterfeit drugs, has begun a crackdown on counterfeit drugs and, in 2007, arrested and executed the director of the nation’s food and drug agency for approving counterfeit drugs (Gillette, 2013). In the U.S., the laws against counterfeit drugs are very weak with minimal consequences for those caught and convicted. Also, it is relatively inexpensive to set up a counterfeit operation—only about $50,000, including $20,000 for a pill press (Perrone, 2012a).

Not only do pharmaceutical patent holders lose revenue because of counterfeit products, they also often incur significant costs in an attempt to find and eliminate these products.

Compulsory licensing. A second area that reduces global revenue for a pharmaceutical patent holder is compulsory licensing. A 2003 World Trade Organization agreement on the Trade-Related Aspects of Intellectual Property Rights (TRIPS) allowed developing countries facing a public health emergency to issue ‘compulsory licenses’ for the manufacturing of generic versions of patented drugs. TRIPS, through the Doha Declaration, also allow those same countries to export generic drugs to other developing countries (essentially those on the UN’s list of least developed countries) (World Trade Organization, 2006). The differences in prices can

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be substantial. The price for an antiretroviral, triple-combination AIDS drug was approximately $10,439 from the originator company in an industrial country. In India, a generic version was priced as low as $201 (Subramanian, 2004). In 2004, Malaysia enacted a law allowing it to import generic AIDS drugs for noncommercial or nonprofit distribution (Foley, 2005).

Price controls. For typical products, R&D costs are amortized over the total output.

These costs are then factored into the price of the product. As a result, R&D costs are equally shared among all purchasers of the product or service. However, price controls introduce severe distortions into the international prescription drug pricing structure. The majority of the industrialized world has some form of price controls on prescription drugs.

Because of its close proximity to the U.S., Canada is often held up as an example of the effect of price controls on prescription drug prices. There are a number of obvious differences in health care between the U.S. and Canada. A less obvious difference is the result of differences in U.S. and Canadian laws pertaining to patented prescription drugs. Canada is unique among countries that regulate drug prices because it only regulates the prices of patented drugs.

The Canadian Patented Medicine Prices Review Board (PMPRB) limits price increases of patented drugs already on the market and limits the introductory prices of new patented drugs.

PMPRB guidelines state that the prices of most new patented drugs may not exceed the maximum price of other patented drugs that treat the same disease. The introductory price of ‘breakthrough’ patented drugs may not exceed the median of foreign prices for patented drugs (Dingwall, 1997). Provincial governments negotiate further discounts, and most of them have laws making these low prices available to the 60% of Canadians covered by private insurance (USA Today, 1999). Price increases for existing patented drugs are limited to changes in the consumer price index (Dingwall, 1997). The result is significantly lower prices for a number of patented prescription drugs in Canada when compared to their prices in the U.S. Prilosec, a heartburn medicine, costs $3.30 a pill in the United States but only $1.47 in Canada. A month’s supply of tamoxifen, a cancer drug, costs $15 in Canada but $95 in the United States. Patented drugs are typically 30–80% less expensive in Canada (Consumer Reports, 2005).

In addition to the U.S., the UK and Switzerland are the next two most active countries in pharmaceutical R&D (Shlaes, 2000). The UK does not have standard ‘price controls,’ but mandatory regulation took effect Oct. 1, 1999, after news reports that drug companies were raising prices in violation of a voluntary agreement. The UK does not limit prices (new drugs can be priced freely), but it limits profits to a 21% return on capital. Price increases are allowed only if profits fall below a certain level. The new agreement included a 4.5% price cut on all existing patented drugs followed by a price freeze until 2001 (Cauchon, 1999; USA Today, 1999).

Switzerland’s federal government negotiates prices twice a year with drug companies.

These prices are used for private insurance companies and government programs. Factors considered in setting the price include the drug’s therapeutic value and prices in Denmark, Germany, and the Netherlands. (Cauchon, 1999; USA Today, 1999).

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Overall, prices are 50% higher in the U.S. than in other developed countries. If the U.S. paid what other developed countries pay, it would have saved approximately $94 billion in 2012 (Brill, 2013).

It should also be noted that there are currently price controls in the U.S., but they apply only to the federal government. Pharmaceutical companies are required by law to sell drugs to the government at the best wholesale price given to other large, U.S. customers, such as HMOs and hospitals. Then, the four largest federal customers—the Veterans Affairs Administration, the Coast Guard, the Defense Department, and the Public Health Service/Indian Health Service—get an additional 24% discount. This essentially results in international prices for the federal government (Cauchon, 1999). In addition, large health maintenance organizations negotiate for discounted prices that often rival those found in Canada.

However, both Medicaid and Medicare including Medicare Part D, are prohibited from negotiating prices. The Medicare Part D program, when evaluating drugs, is also barred from considering price (Economist, 2011).

Parallel trade. Drug resellers take advantage of price controls in other countries by buying drugs in low-cost locations and reselling them in high-priced locations. This deprives the patent holder of the potentially higher profits from high-priced locations such as the U.S. (Grootendorst, Hollis, Levine, Pogge, & Edwards, 2011).

Negotiated discounts. U.S. health-care insurance providers negotiate discounts (often substantial) from the ‘list’, or suggested retail, prices of prescription drugs, and these discounts are reflected on the formulary of approved drugs for customers who carry prescription drug coverage (Consumer Reports, 2012).

The preceding revenue streams represent negative (counterfeit drugs) or reduced (compulsory licensing, price controls, parallel imports, and negotiated prices) sources of revenue for prescription drug manufacturers.

Uninsured in the U.S. Approximately 15 %, over 46 million, of Americans have no prescription drug coverage (Consumer Reports, 2012) and are forced to pay out-of-pocket expenses for prescription drugs. Because they do not have the bargaining power of government agencies or large health care companies, uninsured Americans are paying the significantly higher ‘list’ prices noted above. They are often the working poor. Individuals in this group earn too much to qualify for Medicaid but work jobs that do not provide medical benefits. This group also includes the self-employed and small business owners who cannot afford health insurance and prescription drug insurance. Because they lack insurance coverage, they are often the least able to pay the ‘list’ prices for prescription drugs. The lack of prescription drug coverage has both economic and health consequences.

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Implications The first question asks if the existence of free riders (counterfeit drugs, compulsory licensing, and price controls) negatively impacts the level of pharmaceutical R&D. The answer appears to be no. The number of R&D drugs in the U.S. increased by 7.6% from 2011 to 2012— the largest percentage increase since 2003–2004’s 9.0% increase. There has also been an “unprecedented” 13.3% increase in the number of active companies from 2011 to 2012 (Lloyd, 2012).

Because of the free riders problem, the burden of supporting pharmaceutical R&D disproportionately falls on the U.S. consumer. The negotiated prices by large volume purchasers (government agencies and health organizations) cause the burden to disproportionately fall on those without prescription drug coverage. As drug prices escalate, the ability of the uninsured to pay for these price increases is unsustainable. In 2012, 84% of those without prescription drug coverage in the U.S. were forced to cut back other household expenses or take other financial measures to pay for their prescriptions, and 81% took some action to save money on healthcare, including skipping or halving scheduled doses of prescription drugs (Consumer Reports, 2012).

The issue prescription drug affordability has life and death implications. Peter Rost, Vice President of Marketing for the Endocrinology Division of Pfizer Inc., stated “it is obvious to me that probably tens of thousands of Americans are dying today because they can’t afford drugs” (Barry, 2004, p. 10).

This also raises issues of equity or fairness. It is questionable if it is equitable for the U.S. and uninsured consumers in the U.S. to subsidize pharmaceutical research for the rest of the world.

Solutions What is needed is a way to maintain high levels of productive pharmaceutical R&D and a healthy pharmaceutical industry in the U.S. while maintaining efficacy and safety, reducing overall costs, eliminating the inequitable distribution of pharmaceutical R&D costs, and making prescription drugs available and affordable to those who need them. There are two approaches that can resolve this problem. The first is to reduce the overall cost of bringing a new drug to market. The second is to increase the share of revenue flowing to drug companies by reducing the impact of free riders.

Reduce promotional and administrative costs. As noted previously, 33.1% of pharmaceutical industry revenues were devoted to marketing and administration (The Economist, 2003). In 2004, $55.7 billion was spent on promotion and only $31.5 billion was spent on R&D (Gagnon & Lexchin, 2008). While not denying the value of marketing, there appears to be significant opportunities for improvement in promotional and administrative effectiveness.

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Patents and FDA approval. With the current situation, the patent protection period starts once a patent for a new drug is awarded. However, the drug cannot be sold until the clinical trials necessary for FDA approval have been completed, and this can take up to 10 years (Wall Street Journal, 2013). This effectively reduces the patent protection period the developer has to recoup their significant R&D investment. One solution would be to start the patent clock from the FDA approval date. This would be a significant change to current patent law. Also, it would face stiff opposition from the generic drug manufacturing industry and consumer groups who would have to wait much longer until generic drugs became available. There is no assurance that drug companies would not simply look upon this extension as a windfall and charge high prices for the extended patent period.

An alternative solution is to improve the efficiency of the approval process. Efforts have already been made in the industry to more efficiently link the FDA computer systems to the pharmaceutical companies’ systems to improve the approval process’ speed and efficiency. The concern is that any effort to fast-track new prescription drugs could increase the risk to the public. There have been a number of cases in which drugs that were thought to be safe were subsequently found to pose significant risks to users. Often, these risks do not show up for years or affect a specific minority of end users. One of the most public examples was the banning of dexfenfluramine (fen-phen), an anti-obesity treatment that was found to cause fatal heart problems.

As previously noted, “few if any, optimization techniques have found their way into the pharmaceutical research portfolio arena” (Coopersmith & Arvesen, 2004, p. 56). While increasing R&D efficiency does not address the free riders issue directly, it may lead to lower R&D costs and, thus, reduce their impact. There are already signs of R&D being performed in low-cost locations such as India and China. Eastern Europe and Latin American countries have been used more regularly to conduct clinical trials. However, there is some concern as to the ethical and safety oversight of patients in these locations (Harris, 2010).

Industry profits. There are some who argue that current pharmaceutical industry profits are excessive (Brill, 2013) and, as noted previously, that profits for the pharmaceutical industry are three times the Fortune 500 average (Kaiser Family Foundation, 2012). Lowering industry profit margins could reduce costs to those without prescription drug coverage and, thus, lower the R&D cost burden on these individuals. However, such a move would negatively affect pharmaceutical company stock prices, and pharmaceutical executives have a fiduciary obligation to look out for their shareholders’ interests. It is unlikely that the industry would ever take such a move voluntarily.

Free Riders Counterfeits. The existence of counterfeit drugs has both financial and public safety implications. Starting at the source, pressure needs to be put on countries, like China, India, and,

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more recently, Russia, to enforce intellectual property rights and crackdowns on the often flagrant counterfeiting of prescription drugs. One counterfeiter in China was supplying over a quarter of a million counterfeit Viagra pills a month (Fake Prescription, 2004). While China appears to be cracking down on counterfeiters, high-quality, industrial-scale producers operate almost openly in Russia (Kramer, 2006).

Another avenue for reducing the number of counterfeit drugs is to tighten up control of the distribution system. The existence of parallel imports in Europe and ‘closed-door’ pharmacies and secondary wholesalers in the U.S. creates vulnerable openings for the introduction of counterfeit drugs into the distribution system. Pfizer has a global security team tasked with identifying and shutting down counterfeit producers and distributors (Gillette, 2013).

The planned introduction of a radio-frequency identification (RFID) tracking system to ensure the ‘pedigree’ of shipments will go a long way towards tightening up the distribution system.

The use of a RFID tracking system will make it “very difficult, if not impossible” for counterfeit drugs to enter the system (Downs, 2004, p. 3). However, the system has been stalled for more than a decade since its proposal because of bickering over its cost and scope (Perrone, 2012b).

Eliminate or reduce price controls. The objective would be to reduce free riders by getting other countries to reduce or eliminate their prescription drug price controls. If one country were to maintain the current global revenue for the pharmaceutical industry but charge everyone the same price, this would eliminate price controls as a factor leading to free riders. It would also lead to a more equitable and more sustainable allocation of R&D costs among all customers without any reduction in current R&D levels. However, such a move would be political suicide for any country that currently has prescription drug price controls because prices would substantially increase for consumers. Because prices would increase, this proposal is a nonstarter.

Public funding of R&D. One argument is that, with greater public funding of pharmaceutical R&D, the costs would be more equitably shared among all citizens. However, the U.S. government is already a large supporter of R&D with $25 billion in support in 2005 (Congressional Budget Office, 2004). It was found that most major new drugs of the past 40 years were developed with some contribution from the U.S. government (Congressional Budget Office, 2004). While increased government spending helps to reduce private industry R&D expenditures and potentially reduces current price inequities among U.S. consumers, it does nothing to reduce non-U.S. free riders. Increased government spending merely spreads R&D costs among all U.S. taxpayers. Also, additional increases in government spending on R&D have no chance of passing Congress in the current economic and political climate in Washington.

Price controls in the U.S. One suggestion to address the issues of inequity and free riders is to institute price controls in the U.S. As noted earlier, this would bring U.S. prices more in line with prices in other industrialized nations. Price controls would also result in an annual

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savings of about $94 billion while still providing sufficient profit margins to encourage continued R&D (Brill, 2013). Also, as previously noted, the U.S. already effectively has price controls for many consumers with prescription drug coverage.

There are two primary arguments against price controls. The first is a political or ideological argument. Price controls are viewed by many on the right as anti-market and socialist. Price controls are seen as a further growth of ‘big government’ and will be fiercely opposed.

The second argument centers on the impact on R&D of price controls. Golec, Hegde and Vernon (2012) found that price gaps on pharmaceutical drugs resulted in decreases in R&D funding. The concern is that price controls would result in reduced R&D spending and, ultimately, a reduction in the number of new drugs coming to market. As noted, Brill (2013) disagrees with this notion. Also, there are data that suggest that pharmaceutical companies will engage in significant levels of R&D I countries with price controls. As previously noted, the UK and Switzerland were the next two most active countries in pharmaceutical R&D (Shlaes, 2000), and both regulate their respective prescription drug industries.

Compulsory licensing was dropped by Canada in 1987 largely because it hindered completion of the Canada/U.S. Free Trade Agreement. Compulsory licensing was replaced by the PMPRB and price controls. In order to encourage these changes, the Pharmaceutical Manufacturing Association of Canada undertook a commitment to raise its member companies’ R&D-to-sales ratio from 4.9% in 1984 to 8% in 1991 and to 10% by 1996. The actual levels were 9.6% in 1991 and 12.3% in 1996. This increased further to 12.9% in 1997 (Pazderka, 1999). Pazderka (1999) found that this increase was a direct result of the changes in the patent laws. It is interesting to note that, despite the existence of price controls, the level of R&D-tosales significantly exceeded the agreed upon target of 10%. The experiences of the UK, Switzerland and Canada do not support the notion that price controls destroy the incentive to conduct R&D. For 2012, 50% of all companies active in pharmaceutical R&D were headquartered in the U.S. and 26% were from Europe, and the remaining 24% were from Canada, Japan, Asia and Central/South America and Africa (Lloyd, 2012).

As previously noted, the drug’s therapeutic value is factored into the price paid in many countries with price controls (Cauchon, 1999). This is an indication of price elasticity, which forces drug companies to justify their prices. However, U.S. consumers with prescription drug coverage are largely price inelastic because they do not pay the drug’s full price; they only pay a copay. Those without insurance coverage are also price inelastic because they lack information about the comparative effectiveness of different drugs. Patients are often desperate and will pay whatever is necessary to prolong life. However, if insurance companies, doctors, and consumers were able to get past the scare tactics of “death panels” and look at comparative effectiveness, such ‘informed’ consumers would place downward pressure on drug prices.

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Conclusions Ideally, the goal is to promote R&D and a healthy pharmaceutical industry in the U.S. while maintaining efficacy and safety, reducing overall costs, and eliminating the inequitable distribution of pharmaceutical R&D costs resulting from free riders. There is no single, economically and politically viable solution that will accomplish all of this. The ‘solution’ will be a mixture of several factors, and it will likely only reduce and not eliminate the current inequities. Reducing counterfeits will improve safety, increase overall revenue, and reduce free riders. Increasing pharmaceutical R&D efficiency, including the FDA approval process, will reduce the expenses that need to be legitimately covered. Similarly, improving marketing and administrative efficiency will reduce the overhead burden that needs to be covered by successful drugs. While there is evidence to suggest that price controls in the U.S. would not adversely impact R&D, they would reduce the level of inequity and free riders’ impact on the market.

However, the notion of price controls in the U.S. is a politically charged issue in this time of partisan politics. As a society, we must decide how much profit the pharmaceutical companies need to make to motivate and justify continued R&D. References Angel, M. (2005). The truth about the drug companies: How they deceive us and what to do about it. New York, NY: Random House Trade Paperbacks.

Barry, P. (2004), The Insiders. AARP Bulletin, November, p. 10-15.

Brill, Steven. (2013, February 20). Bitter pill: Why medical bills are killing us. Time.

Cauchon, D. (1999, November 10). Americans pay more for medicine. USA Today. Retrieved from http://www.usatoday.com/life/health/drugs/lhdru066.htm Congressional Budget Office. (2004, April 29). Would prescription drug importation reduce U.S. drug spending? Economic and Budget Issue Brief. Washington, DC: Congressional Budget Office.

Consumer Reports. (2005, October). Prescription drugs: The facts about Canada. p. 50-51.

Consumer Reports. (2012). Sluggish economy forces Americans to cut corners to pay for medications. Retrieved from http://www.consumerreports.org/cro/2012/09/sluggisheconomy-forces-americans-to-cut-corners-to-pay-for-medications/index.htm Coopersmith, L. & Arvesen, J. (2004, May). A better way to manage the pipeline.

Pharmaceutical Executive, (24)5, 56-59.

Downs, M. (2004, October 18). Counterfeit drugs: A rising public health problem. Retrieved from http://www.webmd.com/content/Article/95/103346.htm?printing=true Dickinson, J., (2004, Jan.). Development Costs Threaten Golden Era. Medical Marketing and Media. Vol. 39, Iss. 1, p. 14.

Dingwall, D. (1997). Drug costs in Canada. Health Canada.

Donlan, T.G. (2000, January 24). Editorial commentary: Dangerous comparison. Barron’s, 55.

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Fake prescription drugs warning. (2004, June 24). BBC News. Retrieved from http://news.bbc.co.uk/2/hi/health/3834007.stm Foley, M. (2005). WHO urges nations to bypass patent laws. Retrieved from http://apnews.excite.com/article/20050923/D8CPNK300.html Gagnon, M.-A & Lexchin, J. (2008). The cost of pushing pills: A new estimate of pharmaceutical promotion expenditures in the United Sates. PLoS Med, 5(1). doi:10.1371/journal.pmed.0050001 Gillette, F. (2013, January 17). Inside Big Pharma’s fight against the $75 billion counterfeit drug business. Business Week.

Gladwell, M. (2004). High prices: How to think about prescription drugs. The New Yorker.

Retrieved from http://www.newyorker.com/archive/2004/10/25/041025crat_atlarge Glover, M. (2000, July 5). Gore challenges drug industry group. Associated Press. Retrieved from http://news.excite.com/news/ap/000705/13/news-gore?printstory=1 Grootendorst, P., Hollis, A., Levine, D., Pogge, T., & Edwards, A. (2011). New approaches to rewarding pharmaceutical innovation. Canadian Medical Association Journal, 183(6).

Harris, G. (2010, June 21). Concern over foreign trials for drugs sold in U.S. The New York Times.

Hess, G. (2000). Seeking patent extension for branded drugs. Chemical Market Reporter, 257(7), FR26-FR27.

Hunt, T. (2004). Changing role of intellectual property. Drug Discovery & Development, 7(4), 13.

IMS Institute for Healthcare Informatics. (2012a), The use of medicines in the United States: Review of 2011. Retrieved from http://www.imshealth.com/deployedfiles/ims/Global/Content/Insights/IMS%20Institute% 20for%20Healthcare%20Informatics/IHII_Medicines_in_U.S_Report_2011.pdf IMS Institute for Healthcare Informatics. (2012b). The global use of medicines: Outlook through 2016. Retrieved from http://www.imshealth.com/deployedfiles/ims/Global/Content/Insights/IMS%20Institute% 20for%20Healthcare%20Informatics/Global%20Use%20of%20Meds%202011/Medicine s_Outlook_Through_2016_Report.pdf Kaiser Family Foundation. (2012). Health care costs: A primer. Retrieved from http://www.kff.org/insurance/7670.cfm Kramer, A. (2006, September 4). Counterfeit drugs imperil health and profits. International Herald Tribune Business. Retrieved from http://www.iht.com/articles/2006/09/04/business/fake.php Lloyd, I. (2012). Pharma R&D Annual Review 2012. Citeline. Retrieved from http://www.citeline.com/wp-content/uploads/Citeline_2012_RD_Annual_Review1.pdf Pazderka, B. (1999). Patent protection and pharmaceutical R&D spending in Canada. Canadian Public Policy, 25(1).

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Â

Perrone, M. (2012a, February 15). Counterfeit drugs becoming big business worldwide.

Retrieved from http://apnews.excite.com/article/20120215/D9SU3QOG1.html Perrone, M. (2012b, February 16). System to catch fake drugs has idled for years. Retrieved from http://apnews.excite.com/article/20120216/D9SUP7V00.html PhRMA. (2012). PhRMA statement on prescription drug costs. Retrieved from http://www.phrma.org/media/releases/phrma-statement-prescription-drug-costs Shlaes, A. (2000, July 4). Why controlling drug prices poisons innovation. Financial Times, 15.

Subramanian, A. (2004). Medicines, patents, and TRIPS. Finance & Development, 41(1), 22225.

The Economist. (2003, December 6). Business: Big trouble for Big Pharma; The drug industry. p. 67.

The Economist. (2011, May 28). The costly war on cancer. p. 67-68.

USA Today. (1999, November 10). Other countries set drug prices. p. B1.

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Wall Street Journal, (2013, February 26). A call for innovation in drug regulation. p. B12.

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Business and IT Strategic Alignment: The Impact of an Enterprise Resource Planning System D. Lance Revenaugh Montana Tech University Myles Muretta

Abstract Business strategy is important to all organizations. Nearly all Fortune 500 firms are implementing Enterprise Resource Planning (ERP) systems to improve the execution of their business strategy and to improve integration with its information technology (IT) strategy.

Successful implementation of these multimillion dollar software systems is requiring new emphasis on change management and on business and IT strategic alignment. This paper examines business and IT strategic alignment, and it seeks to explore whether an ERP implementation can drive business process reengineering and business/IT strategic alignment.

An overview of business strategy and strategic alignment is followed by an analysis of ERP. The As-Is/To-Be process model is then presented and explained as a simple but vital tool for improving business strategy, strategic alignment, and ERP implementation success.

Keywords: Business strategy, Strategic alignment, Enterprise Resource Planning (ERP), Information strategy

Business strategy is important to all organizations. Over the last 20 years, there has been a growing interest in business strategy and how it is managed. Questions such as, Where is the company now, or where does the company wish to go?, are routinely asked. A good business strategy helps to answer these questions. Business processes are defined by business strategies.

Competitive advantages are gained through solid strategic management and business strategy objectives (Hakanson, 2006).

In 1977, Bostrom and Heinen (1977) suggested that many of the social problems associated with the implementation of information systems (IS) were due to the frames of reference held by system designers. Information technology and technology in general has come a long way since then. Today, technology is a core part of almost every business. Most

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businesses cannot function without some sort of computer and some type of software to run on that computer. The larger the business the more IT infrastructure is needed.

In recent years, many businesses have looked to Enterprise Resource Planning (ERP) to improve the implementation of their business strategy and to improve integration with their information technology (IT) strategy. An ERP system is application software designed to model and automate many of a company’s basic processes, from finance to the shop floor, with the goal of integrating information across the company and eliminating complex, expensive links between computer systems that were never meant to be linked together (Kimberling, 2006; ERP, 1999).

These ERP systems offer online, real-time information, which reduces processing time and frees managers and analysts from gathering decision-making information.

ERP is now being promoted as a desirable and critical link for enhancing integration between all functional areas within an enterprise, and it is promoted between the enterprise and its trading partners (Kyung, 2002). There is a long history of both successes and failures when it comes to ERP systems. The upside is great, but the risk of failure is also great. The current ERP implementation effort at the Department of Defense, which has the world’s largest ERP system, makes this very clear (Perera, 2012).

The question now becomes, what are the critical components of a successful ERP implementation and how is this impacted by an organization’s business and IT strategy? Since both IT strategy and business strategy have proven to be important to the success of an organization, must the two strategies be aligned? And if so, how is this accomplished. To begin to answer these questions, one must first develop a clear understanding of what strategy and alignment are and what they are not.

Business and IT Strategic Alignment Strategy Overview Current research on the modern strategic alignment of information systems reveal that improvement methodologies are becoming increasingly popular while integrating new software into their respective environments. Strategic alignment is touted as the key to achieving the goals established by the CEO and the board of directors (Papp, 2004). Strategic alignment is an ongoing process that has remained a major issue within companies in the United States and across the globe. When the business strategy and the IT department align, the organization seems to run more smoothly and this alignment sets the foundation for improvements in business processes and performance (Papp, 2004; Atkins, 1994).

Business strategy is built upon three principles: business scope, distinctive competencies, and business governance (Kimberling, 2006). The "scope" of business refers to the breadth of activities a business engages in. Business scope includes the markets, products, services, groups of customers and clients, and locations where a business competes as well as the buyers,

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competitors, suppliers, and potential competitors that affect the competitive environment for a business.

Distinctive competencies are the success factors and the core competencies that give businesses potential edges in a competitive market. Examples of distinctive competencies include brand, research, manufacturing and product development, cost and pricing structure, and sales and distribution channels.

Business governance is a set of policies and business processes that define how an organization is run. It guides the creation of the relationship between the board of directors and stockholders. Also, business governance affects the company through government regulations, and it is the mechanism that guides how the organization manages its relationships with strategic partners (Papp, 2001, 2004; Kimberling, 2006).

The business’s organization infrastructure is divided into three groups: administrative structure, processes, and skills. First, the administrative structure of a business is the way that the business organizes its functions within the firm. Examples of administrative structure components include functional, vertical, horizontal, geographic, centralized, and de-centralized.

Second, “processes” are how the operating strategy and business activities flow in a firm.

Process improvement is a major issue within the process groups, which are groups put together by the business to ensure the company’s smooth running, especially during an ERP implementation. Finally, there are skills. Some of the key skills of a company are motivating of employees, hiring and firing employees, handling cultural change and managing the their human resources in general. These skills come in handy when the company may have internal weaknesses. If management is not comfortable with the groups they have, proper arrangement can turn those weaknesses into strengths. All of these three groups occur to make the business run effectively.

Today, information technology is becoming a major part of firms around the world. IT is divided into strategy and infrastructure. IT strategy is divided into technology scope, systemic competencies, and IT governance.

The IT infrastructure is divided into three categories: architecture, processes, and skills.

The architecture of the IT infrastructure composed of the technology priorities, policies, and choices that allow applications, software, networks, hardware, and data management to be integrated into a cohesive platform. The processes of the IT infrastructure are the practices and activities that develop and maintain the applications that manage them. The skills of the IT infrastructure are the IT human resource considerations (Papp, 2001, 2004; Kimberling, 2006; Barnes, 1999).

Strategic Alignment The Strategic Alignment Model (SAM) of Henderson and Venkatraman (1999) continues to be widely used as the basis of Business/IT alignment theories. The model is shown in Figure

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1. The model’s key message, as well as the key message of many other studies, is that the IT strategy needs to be fully aligned with the business strategy for a company to become successful.

The SAM and other alignment models continue to be developed and updated because traditional methods of developing business strategies have failed to take full advantage of technology. In the past, IT was typically treated as a “cost center” or viewed as an “expense” rather than as an enabler of business value. Strategic information systems shed new light on technology and its role in the development of business strategies. In today’s increasingly “flat world” (Friedman, 2006), it is no longer economically feasible to treat IT as a low-level support tool; failure to leverage IT may seriously hamper the firm’s performance and viability in today’s information-intensive world. Alternatively, organizations can concentrate on the application of IT to enable business strategy by understanding and leveraging the Business/IT partnership (Luftman, Papp & Brier, 1999; Papp, 1995). This harmony can be extended and applied throughout the organization as new opportunities are identified.

Over the past several decades, extremely large sums of money have been invested in information systems and IT. Nevertheless, organizations seem to find it difficult or impossible to harness IT’s power for their own long-term benefit, even though there is worldwide evidence (Earl, 1983, 1993; Martin, 1983; Robson, 1994; King, 1995) that IT has the power to transform whole industries and markets. Strategic alignment, a method of applying IT in an appropriate and timely way, in harmony with business strategies, remains a key concern of business executives (King, 1995; Henderson & Venkatraman, 1990, 1996; Earl, 1983, 1993; Luftman, 1993, 1996; Goff, 1993; Liebs, 1992). In fact, alignment’s importance has been well known and documented for more than 30 years (McLean & Soden, 1977; IBM BSP, 1981; Mills, 1986; Brancheau, & Wetherbe, 1987; Dixon, & John, 1991; Niederman, Brancheau, & Wetherbe, 1991; Earl, 1983, 1993).

The importance of alignment has continued as discussed above via its persistent top ranking in the business press by executives. Alignment seems to grow in importance as companies strive to link technology and business in light of dynamic business strategies and continuously evolving technologies (Papp, 1995). Importance aside, it is not clear how to achieve this harmony between business and IT, and it is not clear what impact misalignment might have on the firm (Papp, 1995). The ability to achieve and maintain this synergistic relationship is anything but easy. For years, firms have been channeling billions of dollars into technology in an attempt to successfully incorporate technology into their processes and longterm plans. Many of these efforts have failed despite overwhelming evidence of IT’s ability to transform both individual firms and entire industries.

The alignment of IT and business strategy to leverage the capabilities of IT and transform the business has increased in importance as firms strive for competitive advantage in a diverse, changing marketplace (Faltermayer, 1994; Adcock, Helms, & Wen-Jang, 1993; Cardinali, 1992).

In light of this, there has been a great deal of research and insight into the linkages between business and IT (Chan & Huff, 1993; Luftman, 1996; Earl, 1993; Henderson, Thomas, & Venkatraman, 1992), the role of partnerships between IT and business management (Keen, 1996;

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Ives, Jarvenpaa, & Mason, 1993), and the need to understand the transformation of business strategies resulting from the competitive use of IT (Boynton, Victor, & Pine, 1996; Davidson, 1996). Firms have been able to not only change their business scope, but also change their infrastructure as a result of innovation regarding IT (Keen, 1991; Foster, 1986).

Simply put, aligning information technology with business strategy and business plans means that a positive relationship between information technologies and the organization’s accepted financial measures of performance exists (Strassman, 1998). In order to achieve alignment, an organization must first identify the sources of misalignment. Then, with the assistance of the models discussed earlier in this paper, progress can be made toward a cohesive and consistent alignment of IT/IS strategy and business strategy.

Enterprise Resource Planning Overview of ERP ERP systems are becoming ubiquitous in the corporate world. They also continue to penetrate the small- and medium-sized company, because firms like SAP and Oracle go after these large markets. Although the benefits of these systems are many, businesses today seem to be moving toward this technology primarily because the systems are considered to be a source of competitive advantage or at least a way to keep up with the competition. However, these systems bring with them their share of problems. Implementing these systems usually involves a significant amount of process change and often dictates changes in organizational structure. In fact, many ERP implementations are used as a means for reengineering the firm. Management has a big role in the success and acceptance of these systems. As with the other technologies mentioned, the business process redesign inherent in ERP implementations requires major technical change, organizational change, and cultural change. The biggest associated challenge is fostering a new culture and managing the changes with consistency and coordination (Wen– Hsien, 2012; Cliffe, 1999).

When implementing information systems, there are usually two paths to take: adapting the inherent process to the people or adapting the people to the process. The former view stresses people as a firm’s fundamental resource, while the latter view emphasizes consistency and coordination of corporate-wide information. However, neither path has been proven to be better. More frequently today, these large ERP systems, which are designed around best practices, are being used as a facilitator of change in companies. This point is supported by Dwight Klappich, vice president of industry marketing at Ross Systems of Atlanta: “The key thing when you look at the success or failure of software implementation is whether the client is implementing software or are they implementing change within their business”. (Trommer, 1997, p. 18) In addition, client-server ERP systems feature many advantages over their mainframe counterparts. Most importantly, they can transfer data in real-time between locations worldwide.

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They can also support multiple currencies and multiple languages, so they can be used at various global locations. The move to these systems has been fostered largely because they are industry standard compliant, and they give many companies the ability to avoid the costly conversion of their legacy systems.

ERP impact. Recognizing the firm-wide impact of these systems, ERP market leader SAP started the trend of selling IS to CEOs. Other technology companies are following this same strategy because they have also realized that IT is having a greater impact on strategic capabilities and the bottom line. This strategy also protects against two of the key components of failure for IT projects; namely, the lack of congruence between a company’s business strategy and its IT goals, and the lack of upper management support (Kimberling, 2006; Cliffe, 1999).

The most important factors in today’s business environment are speed and flexibility.

Companies in every industry are constantly under pressure to perform their service or production faster and meet the customer’s needs. To accomplish this, companies need to streamline operations from the time an order is placed to the order’s delivery. ERP enables this streamlined operation.

ERP problems. However, ERP systems do present many problems for companies. The first problem has to do with the fact that ERP systems are usually part of a larger reengineering effort. Therefore, the costs and time expectations of implementation are usually exceeded.

Another problem is that many companies purchase ERP systems to satisfy what they perceive will be a single, integrated solution to all their data processing needs. ERP systems do not solve every IT problem. ERP systems are not good at everything and cannot perform all the processes that a firm may already have in place. This requires firms to either change processes or use additional applications, which run counter to a single-solution philosophy.

No return on investment. Early ERP implementation projects ran into the problem of not providing an easy means for determining the project’s return on investment. Since the typical ERP implementation costs can be anywhere from $30 million to $100 million, managers were very concerned with having a means to measure their investment. However, these projects are not being viewed today as measurable purely by traditional financial analysis. Rather, intangibles are often being used to measure their success. Commenting on SAP, John Donovan, chairman of Cambridge Technology Group, says, There is no return on these projects. Everyone is looking at this investment the wrong way and doing the wrong analysis, calculating the replacement of one system for another.

SAP is the infrastructure. What is my return on putting electricity in this building? There is none; I just have to do it. (Baatz, 1998, p 2).

Examples of some of the intangible measurement criteria being used include customer satisfaction, employee morale, and employee turnover. These factors are all difficult to link

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directly to an IT project and are difficult to measure. Revenue improvement goals are a commonly used financial target.

Impact of ERP on Strategic Alignment ERP systems help organizations streamline processes, improve the flow of information between different business functions as well as other stakeholders, increase productivity, gain competitive advantage, and allow the business to trade at a global level. To take full advantage of an ERP implementation, businesses will need to adopt the best practice processes that ERP can provide. To do this, businesses will need to change their current processes either slightly or drastically; this change will not just happen naturally, it must be managed (Davis, 2005).

When ERP systems are implemented, ERP implementations usually involve broad organizational transformation processes with significant implications on the organization’s management model, its structure, its management style and culture, and particularly on its people. ERP systems do not just run their course and fade away. It is a long-term, major transformation, and it can be daunting at times (Barnes, 1999). In order for a company to succeed in implementing an ERP system, there must be a solid alignment between the business strategy, IT strategies, and the company’s organizational processes. In order to deal with change effectively, a company has to establish the change vision in the given contexts: technical, social, and organizational (Davis, 2005).

Before implementing the actual software of an ERP system, companies should go over several success factors that must happen before starting such a large transformation: 1) Define the corporate strategy and objectives. Companies should challenge themselves to answer the question, "Where do you want the company to be in 5 years?" They should also answer the question, "What operational strategy is required to enable this higher-level corporate strategy?"

2) Once the organization has clearly articulated the company strategy, then it needs to define its “to be” business processes that will enable this corporate and operational strategy.

3) Next, performance measures at the corporate, operational, and business process levels must be established. These measures should help the organization identify how successful it has been in executing against its defined strategy. These measures should also align with reports that come out of the ERP system.

4) Finally, a company can begin ensuring that the system is aligned with factors 1–3 by designing it, configuring it, and testing it (Sabberwhal & Papp, 2001; Papp, 2004; Barnes, 1999).

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Critical Role of Business Process Analysis Since the early 1990s, there has been an increasing trend of organizations moving from developing in-house information systems to purchasing them from proven vendors (Kyung, 2002). Prior to purchasing any long-term software application, companies need to assess the direction their company is currently going and what direction they want it to go in the future.

Specific to ERP, organizations need to first asses the organizational fit of an ERP followed by the implementation contingencies (Brown, 1999). While assessing direction and organizational fit can be a daunting task for any business, the following model is proposed as a simple but vital tool to assist in this process. Some of the model’s concepts come from Kyung’s (2002) work, and the model is based on assessing the organization’s As-Is and To-Be processes.

The graphical model is presented in Figure 2. It illustrates the key components of the AsIs and To-Be processes and the linkage between them. The model states that an organization must first clearly define or reaffirm its mission and purpose for existence before examining its operational components. The As-Is processes are a method for order of operations, and the ToBe processes are critical components that are derived from the As-Is processes. The As-Is processes must be assessed and documented before moving on to the To-Be processes.

In figuring out one’s To-Be processes, companies must align them and distinguish them from their As-Is processes. The As-Is processes need to be defined by the company. The organization must know what they are doing before they can change themselves for the better especially when a large scale software system is to be installed. The three main reasons are listed below: 1) Big Picture: It helps achieve alignment and understanding among various business units and business locations on how things currently operate. Many managers and key stakeholders do not have a big-picture view of what other parts of the organization are doing, especially in very large organizations. Documenting As-Is business processes helps develop clarity on what is working well and what is broken with current business processes.

2) Current Operations: It helps define how employees are doing their work now, which will help define the gaps between the current and future states. This is critical when it comes to organizational change management and training initiatives later on in the project.

3) Operational Improvements: It helps determine the key operational pain points, the To-Be processes, and the business requirements during the software selection process.

This last reason brings us to the To-Be processes. Once the As-Is processes are established and noted throughout all phases and divisions of the company, the company can then move on to To-Be processes. This is what the company must do before they evaluate any software vendor or major software application that may be implemented in their company. The To-Be processes must be carefully thought through and evaluated by all departments in the

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company to ensure that the processes align with each other. Both Kim (2006) and Davis (2005) advocate conducting the To-Be assessment by using the following critical components.  Business Processes: Business processes are what help companies define their future operational models and business processes independently of software. This allows one to think creatively and look for opportunities to succeed by leveraging IT as a tool to enable measurable business improvements. If companies skip this step, they are more likely to be influenced by sales messages instead of functional fit.  Gap Identification: In conjunction with the As-Is processes, gap identification helps one identify the gaps between current and future jobs, roles, and responsibilities. This is critical from an organizational-change, management perspective.  Performance Indicators: It helps define key performance indicators to help drive business improvements and accountability. With new processes come new responsibilities and opportunities for improvement, so one need performance measures to enable this.  Prioritize Needs: After the software is selected, it helps prioritize customization needs, integration needs, and report-writing needs. Without this understanding of where an organization wants to go from an operational perspective, it is very difficult to determine if customization and additional development are appropriate.

Conclusion Business strategy is important to all organizations. According to Forbes (Columbus, 2013), nearly all Fortune 500 firms are implementing ERP systems to improve the execution of their business strategy and to improve integration with their IT strategy. Successful implementation of these multimillion-dollar software systems is requiring new emphasis on change management and on strategic alignment of business and IT. Technology is evolving as is the business world. IT and business strategies are no longer separate entities; they tie together in one way or another. Involving IT management in the overall company strategy is now a must in a technological world. Formally establishing or improving alignment between business strategies and IT strategies is part of successful change management, especially during an ERP implementation. However, the process to combining the strategies is not something that is done overnight.

The application of the As-Is/To-Be model presented in this paper provides a clear foundation for successful strategic alignment and strategy implementation. In this paper, the author has extended this application to the successful implementation of ERP. A critical aspect of the model is that an organization must first clearly define or reaffirm its mission and purpose for existence before examining its operational components. While this mission/purpose may be very simple for an organization to state, it appears difficult to maintain over the strategy’s life much less over the organization’s life. Future research may focus on an organization’s ability to sustain an ongoing evaluation of whether or not its operations continue to support its stated mission and purpose for existence.

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Author Biographies D. Lance Revenaugh serves as a Professor of Business and Information Technology at Montana Tech University in Butte, MT. Education includes a PhD in Decision and Information Systems from Arizona State University and BBA-Management and MBA degrees from Baylor University. He has been in full-time higher education since 1985 having served at the Air Force Institute of Technology, Wilberforce University, Thunderbird--The American Graduate School of International Management, City University of Hong Kong, and Biola University. His consulting and research focus on the areas of business systems analysis, IS strategy implementation, information overload, and global information management.

Myles Muretta is a senior research student at Montana Tech University in Butte, MT. His major is in Business: Information Technology. He will be graduating in May 2013 and has accepted a position at a global staffing agency in Denver, CO. For the last year, he has worked on two major research projects with Montana Tech. His research is focused on business analysis, Enterprise Resource Planning systems, and business strategy alignment.

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Hong, K. K. (2002). Critical success factors for ERP implementation: An organizational fit perspective. Information & Management, 40, 25-40.

Hussain, H., King, M., & Cragg, P. (2002). IT alignment in small firms. European Journal of Information Systems, 11, 108–127.

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IBM. (1981). Business Systems Planning, Planning Guide (GE20-0527). White Plains, NY: IBM Corporation.

Ives, B., Jarvenpaa, S., & Mason, R. (1993). Global business drivers: Aligning information technology to global business strategy. IBM Systems Journal, 32(1), 143-161.

Keen, P. (1991). Shaping the future. Boston, MA: Harvard Business School Press.

Keen, P. (1996). Do you need an IT strategy? In J. Luftman (Ed.), Competing in the Information Age. New York, NY: Oxford University Press.

Kimberling, E. C. (2006a). Aligning ERP with your overall business strategy. Retrieved from http://it.toolbox.com/blogs/erp-roi/aligning-erp-with-your-overall-business-strategy-8747

Kimberling, E. C. (2006b). To-be or not to-be: When and how to design ERP business.

Retrieved from http://it.toolbox.com/blogs/erp-roi/tobe-or-not-tobe-when-and-how-todesign-erp-business-processes-8515 King, J. (1995, Mar 13). Re-engineering focus slips. Computerworld, 6.

Lacy, K. (2003, June). Business/IT alignment concerns - Ask the expert. CIO Magazine, 17.

Liebs, S. (1992, Oct 26). We're all in this together. Information Week, 8.

Luftman, J. (1996). Competing in the Information Age: Practical applications of the Strategic Alignment Model. New York, NY: Oxford University Press.

Luftman J. (1999). Achieving and sustaining business-IT alignment. California Management Review, 42(1) 109-122.

Luftman, J., Lewis, P., & Oldach, S. (1993). Transforming the enterprise: The alignment of business and information technology strategies. IBM Systems Journal, 32(1), 198-221.

Martin, E. (1983). Information needs of top MIS managers. MIS Quarterly, 7(3) 1-11.

McLean, E., & Soden, J. (1977). Strategic planning for MIS. New York, NY: John Wiley & Sons.

Mills, P. (1986). Managing service industries. New York, NY: Ballinger.

Niederman, F., Brancheau, J., & Wetherbe, J. (1991). Information Systems management issues for the 1990s. MIS Quarterly, 15(4), 475-95.

Muscatello, J. R. (2003). Implementing Enterprise Resource Planning (ERP) systems.

International Journal of Operations and Production Management, 23(8), 850-871.

Papp, R. (1995). Determinants of strategically aligned organizations: A multi-industry, multiperspective analysis (Dissertation). Stevens Institute of Technology, Hoboken, New Jersey.

Papp, R. (2001). Strategic information technology: Opportunities for competitive advantage.

Hershey, PA: Idea Group Publishing.

Papp, R. (2004). Assessing strategic alignment in real time. Journal of Informatics Education Research, 6(1).

Perera, D. (2012). Systemic problems with DoD ERP strategy and implementation, warns report.

Retrieved from http://www.fiercegovernmentit.com/story/systemic-problems-dod-erpstrategy-and-implementation-warns-report/2012-09-23

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Reich, B., & Benbasat, I. (2000). Factors that influence the social dimension of alignment between business and IT objectives. MIS Quarterly, 24(1), 81–113.

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London, UK: Pitman Publishing.

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Trommer, D. (1997, Sep 29). SCOR sets supply chain standard. TechWeb News, 17-19.

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Figure 1. Strategic Alignment Model. This figure illustrates the alignment model presented by Henderson and Venkatraman widely used as the basis of business/IT alignment theories.

Adapted from “Strategic Alignment: Leveraging Information Technology for Transforming Organizations”, by J. Henderson and N. Venkatraman, 1999, IBM Systems Journal, 38(2/3), p 475.

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Figure 2. Moving from As-Is to To-Be Processes. This figure illustrates the linkage between the As-Is and To-Be processes along with their key components.

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An Excel Sort Heuristic for Solving the Travelling Salesman Problem Richard J. Perle Loyola Marymount University

Abstract The travelling salesman problem (TSP) is a well-known combinatorial problem that finds many practical business applications in areas such as product distribution and manufacturing. Finding exact solutions to TSPs has always been difficult, so heuristic methods are usually implemented.

This paper develops and tests the performance of a new Excel-based heuristic algorithm for solving symmetric, Euclidian plane TSPs in which the X-Y coordinates of all nodes are known.

Test results show that this method works well for small problems, and it delivers solutions that are usually near optimal. An advantage of the algorithm is that it is easy to implement, so it might be useful in smaller organizations that do not typically employ skilled mathematicians or possess substantial financial resources. It might also be used in an academic setting to demonstrate heuristic solution procedures in general.

Key words: TSP, Heuristic, Excel.

One of the more difficult business operations problems faced by delivery service companies is routing their delivery vehicles from customer to customer in the most efficient, optimal manner, which would translate directly into lower operating costs and higher profits.

This is just as true for very small businesses, such as mom-and-pop pizza shops, that offer home delivery as it is for very large, multinational shipping companies, such as FedEx and UPS. For companies large or small, the most efficient delivery route may be determined by solving an equivalent formulation of the travelling salesman problem (TSP). The classic TSP is where a salesperson wants to travel from his or her home base and visit clients in other cities. The problem is to define a low-cost tour for the salesperson to travel to each city once and end up back at his or her home base. Clearly, the TSP also defines the problem and solution process for routing delivery vehicles as described by Laporte (1992). Interest in and application of the TSP began with the seminal paper by Dantzig, Fulkerson and Johnson (1954), which found the shortest tour route of 49 U.S. cities. TSP equivalent problem formulations can also be used to solve other important, practical problems, such as assigning airliners to routes (Clark, Johnson, Nemhauser, & Zhu, 1997), drilling holes in printed circuit boards (Grotshel, Junger, & Reinelt,

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1991), order picking in a warehouse (Ratlif & Rosenthal, 1983), and sequencing jobs on a machine (Lenstra, Kan, & Brucker, 1977).

A characteristic of any nontrivial TSP is that it is relatively easy to describe, harder to formulate mathematically, and extremely difficult to solve for an exact, provable optimum. The difficulty stems from the fact that the TSP is part of a class of problems known as nondeterministic polynomial-time hard (NP-hard). NP-hard means that the solution computation time is nonpolynomial, possibly factorial or exponential, as a function of the problemâ&#x20AC;&#x2122;s size. The maximum number of possible, unique tours is (n-1)!/2 in which n is the number of interconnected nodes (cities in the classic TSP) in a complete network graph. For a TSP with only 10 nodes, there are 181,440 possible solutions. An exact, provable solution to a TSP can be found by explicit enumeration of all possible solutions, branch-and-bound integer programming methods (Lawler, Lenstra, Kan, & Shmoys, 1985), or branch-and-cut methods (Junger, Reinalt & Rinaldi, 1995). Because of the large number of possible solutions, these methods require considerable computation power sometimes measured in central processing unit (CPU) days or even CPU years for very large problems. These large solution times can be impractical for many real-world business applications in which plans and decisions must be made within a short time horizon.

As a result of the difficulty in finding exact solutions, practical and useful solutions to a TSP are usually obtained by using heuristic algorithms. A heuristic algorithm is a solution procedure that can lead to a good but not necessarily optimal solution. The advantage is that the time to obtain the heuristic can be found in CPU minutes or CPU seconds rather than CPU hours or CPU years. There are three general types of TSP heuristics: (1) construction methods, (2) improvement methods, and (3) metaheuristic methods. Construction methods start at an arbitrary node and then select succeeding nodes according to a criterion, such as cheapest or shortest distance. Well-known examples of construction methods are variations of the Nearest Neighbor Greedy (NNG) algorithm (Rosenkrantz, Stearns, & Lewis, 1977) and the Christophides algorithm (Christophides, 1976). Improvement methods start with a feasible tour and then make changes in an effort to find a shorter tour. The Lin-Kernighan algorithm (Lin & Kernighan, 1973) is an improvement method. Metaheuristic methods, such as simulated annealing, tabu search, genetic algorithms, and artificial neural networks, search neighborhoods for local optima and then use that information to search for better solutions without getting trapped in any one local neighborhood. Getting trapped in a local neighborhood could yield only a local optimum, which may not be the overall optimum. A good description of metaheuristic methods can be found in Reeves (1993). A disadvantage of many of these methods is that they usually require highly specialized software that may be difficult and expensive to acquire and implement, especially for smaller companies.

The Excel Sort Heuristic (ESH) algorithm described in this paper is a hybrid method. It starts with a feasible tour constructed by a simple node-to-node process, and then it systematically searches for an improved tour without necessarily getting trapped in a local optimum like a metaheuristic method. The software may be developed and implemented in a

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Microsoft Excel spreadsheet instead of a procedural programming language. However, a moderate level of training in mathematics and some expertise in Excel Macros and VBA would be necessary. The practical usefulness of any nonoptimal, heuristic solution, of course, would depend on its expected accuracy, which is defined here as the expected percent over the optimum tour.

The next two sections of this paper will describe the process logic and the mathematics of the ESH algorithm. Its accuracy will be then tested against a set of simulated TSP data with an exact, optimal solution.

The Excel Sort Heuristic Algorithm: Description by Example Consider this example situation in which a delivery must be made to each of 10 locations specified by a set of 10 ordered pair, X-Y map coordinate values as shown in Table 1. The X-Y pair values are numbered and sorted by Excel in ascending X-value order. The 10 nodes are then separated into two sets as shown by the dashed line. The nodes 1-5 are designated as the leftmost set, and the nodes 6-10 are designated as the right-most set. This constitutes the first three steps in the ESH algorithm: 1. Assign a unique number to each node 2. Sort the complete set of nodes on the X-values from low to high.

3. Based on the X-values, separate the nodes into two equal size sets.

Left-most set = nodes with the lowest X-values Right-most set = node with the largest X-values The next three steps are listed below, and they are used to create the data in Table 2 and create the node-graph in Figure 1.

4. Sort the left-most set of nodes on the Y-values from low to high.

5. Sort the right-most set of nodes on the Y-values from high to low.

6. Connect the two sets to identify the complete tour, and calculate the tour distance value.

The tour path by node number in Table 2 is 3-5-1-4-2-7-10-6-9-8-3. Node 3 is listed two times because it is the start-and-finish node. The tour length is 314.20 as calculated by the Pythagorean Theorem.

In Figure 1, is the graphic representation of the first six steps of the algorithm applied to the data in Tables 1 and 2. The vertical dashed line separates the 10 nodes into the left-most set and the right-most set (steps 2 and 3). The arrows show the tour path that steps 4, 5, and 6 would find from start to finish. It can also be inferred from Figure 1 that an advantage of the ESH algorithm is that there will be no crossing paths within either set. Crossing paths will add to the tour length and assure that it is not optimal. The only time crossing paths could be generated is on the two paths that connect the sets together—the upper-most arrow and the lower-most arrow in Figure 1. The entire set of nodes is now rotated about its geometric center in search of a better solution. The rotation does not change the distance between any pair of nodes. After rotation,

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steps 2–6 in the ESH algorithm are repeated to generate a new solution. For example, Table 3 shows the X-Y coordinates of the nodes after they have been rotated clockwise 90 degrees from their original position. After applying steps 2–6 to the data in Table 3, the new tour path by node number is found to be 9-10-2-1-3-5-4-8-6-7-9. Node 9 is now the start-and-finish node. Figure 2 shows the rotated tour that has a tour length of 262.83, which is a 31.69 percent improvement over the tour in Figure 1. It is also optimal as proved by explicit enumeration.

Even though 90 degrees is the best rotation angle in the example above, there is no a priori knowledge that this is the case. So, the objective in the ESH algorithm is to rotate the nodes incrementally and attempt to generate a new solution with each incremental rotation until a best solution is found. The condition that creates a new solution path occurs when a node passes from the right-most set to the left-most set and vice versa due to rotation. Consequently, the largest number of different solutions that the rotation process is capable of generating is n, the number of nodes. All solutions that are detected are generated over a total rotation of only 180 degrees. Rotating the nodes beyond 180 and on to 360 degrees simply generates a repeat of the zero to 180 degree rotation solutions; however, the tour path is in the opposite direction.

Nonetheless, the tour length is obviously the same in either direction. There are a number of rules that could be used to determine the incremental angle of rotation, but the simplest rule, which is used here, is to rotate 180/n degrees in contiguous increments starting from zero to (180 - 180/n) degrees. The zero degree position is the original configuration of the nodes. Two additional steps (7 and 8) are now added to complete the ESH algorithm.

7. Incrementally rotate the nodes 180/n degrees, and then repeat steps 3 through 6 until the nodes have been rotated a total of (180 - 180/n) degrees.

8. Select the solution with the best tour length.

The general ESH model and the underlying mathematical relationships that create the rotated values will now be developed.

The Excel Sort Heuristic: Derivations Assume the Cartesian coordinate values, (xi,yi for i = 1 to n), are known for a TSP in a two-dimensional plane. The symmetric Euclidian distance, (dij for j = 1 to n), between any two nodes, i and j, (i ≠ j) are calculated as dij = SQRT[(xi - xj)2 + (yi + yj)2].

(1) The ordered pair, (xc,yc), is defined as the geometric center of all the nodes, and it is calculated as the mean of the X-values and the Y-values: n xc = [∑ xi]/n.

(2) i=1 n yc = [∑ yi]/n.

(3) i=1

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Now, consider a translated coordinate system centered on (xc,yc) in the original Euclidian plane, which is divided into four quadrants, (Q1 to Q4), as shown in Figure 3. An arbitrary node, (xi,yi), and its vector is shown in Quadrant 2 along with its angle, θi, relative to the positive Xaxis, which is defined as zero degrees. The Euclidian distance from (xc,yc) to (xi,yi) is hi.

(4) hi = SQRT[(xi - xc)2 + (yi - yc)2].

The calculation of any value of θi depends on what quadrant the node is in.

If xi - xc >= 0 and yi - yc >= 0, the node is in Q1, and (5) -1 θi = sin [(yi - yc)/hi].

If xi - xc < 0 and yi - yc >= 0, the node is in Q2, and (6) -1 θi = 180 - sin [(yi - yc)/hi].

If xi - xc < 0 and yi - yc < 0, the node is in Q3, and (7) -1 θi = 180 - sin [(yi - yc)/hi].

If xi - xc >= 0 and yi - yc < 0, the node is in Q4, and (8) -1 θi = 360 + sin [(yi - yc)/hi].

Once the set of θi have been calculated, the entire set of nodes is incrementally rotated about the geometric center by angle, φk, (k = 0 to n-1), in which k is the kth iteration out of n total iterations.

(9) φk = (180/n)k.

If k = 0, then φk = 0, and the nodes are in their original, nonrotated positions. After each rotation, each node’s new vector angle relative to the zero degree position in Figure 3 is (θi - φk), assuming clockwise rotation. New coordinates, (xi,yi), in the original Euclidian plane can then be calculated for each rotated node: xi = hi[cos(θi - φk)]xc.

(10) yi = hi[sin(θi - φk)]yc.

(11) Of course, the distance, dij, between any two rotated nodes is the same as the original, nonrotated distances: dij = dij.

(12) And the tour distance value, vk, for the kth contiguous rotational iteration is vk = ∑dijk.

(13) The i and j values are determined by the final sort sequence, which becomes known after step 7 in the ESH algorithm.

The best overall solution, v*, is then selected in step 8 as (14) v* = min{vk}. * This solution occurs at a rotation angle of φ .

Computational Test Results and Discussion In this section, the results are presented on the performance of the ESH algorithm and compared to the performance of the NNG algorithm using simulated TSP instances. The NNG algorithm was selected for comparison purposes because this is the likely rule that a delivery

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vehicle driver might follow for a small business operation without any knowledge of the TSP. The driver will drive to the nearest customer and make the delivery, drive to the customer that is nearest to the delivery just completed, and so on. Values of xi,yi for 10 nodes were randomly generated using the Excel RAND function for each of 30 TSP instances. Explicit enumeration was used to find the optimal minimum tour for each instance. The incremental rotational angle is 180/10 = 18 degrees, which will generate up to 10 different solutions for each instance.

Results are presented in Table 4 and compared to the NNG algorithm. The instances have been sorted by the last column, which is the percent over the optimal tour length that was obtained by the ESH algorithm at the optimal rotation angle.

It can be seen in Table 4 that, on average, the ESH algorithm for the original, nonrotated node position, (φ = 0), performs slightly better than the NNG algorithm with an average error of 10.79% over the optimal tour length in comparison to 12.29% for the NNG algorithm. The NNG algorithm found the true optimal tour for only one of the 30 test instances: instance number 11.

The ESH algorithm before rotation found two optimal tours out of the 30 test instances: instance numbers 2 and 9. However, when the nodes are rotated to φ*, the ESH algorithm finds the optimal solution in 14 out of 30 instances with the average error reduced to 0.68%, which is significantly better than the NNG algorithm. This would indicate that a small delivery company using the ESH algorithm instead of the NNG algorithm to route its delivery vehicles should eventually gain significant savings in delivery costs.

Summary and Conclusion A new heuristic algorithm for solving the TSP has been developed and tested. The algorithm can be implemented with only a moderate knowledge of trigonometry, Excel, Excel macros, and VBA programming. A methodology for converting street addresses to Cartesian coordinates would also be helpful if the ESH algorithm is used to route delivery vehicles. A good start would be something like the Thomas Guide maps (http://store.randmcnally.com) because they have already divided most metropolitan street maps into grid coordinates, which would facilitate the conversion of the street addresses of delivery customers into X-Y coordinate values.

Test results show the ESH algorithm performs well for small problems, and it certainly performs better than the NNG algorithm. However, accuracy would likely decrease as the number of nodes increases due to the nature of the sort solution methodology that naturally searches the outside perimeter of the node-graph for all values of φ. As the number of nodes increases so does the density of the nodes, which would likely cause more left-right “zigging and zagging” along the tour path and a resultant decrease in accuracy. This decrease could likely be diminished by dividing the complete set of nodes into more than two sets. Although this type of partitioning adds complexity, it would allow the Excel rotate, sort, and search procedure to more accurately navigate the dense interior region of the node-graph with less “zigging and zagging.”

The disadvantage is that adding more sets also increases the number of set boundaries, which

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also increases the possibility of creating more crossing paths at the set boundaries. However, the overall net effect might increase accuracy. Therefore, the application of the ESH algorithm to midsized businesses and larger businesses is an area for future research. Additional avenues for future research should address additional or different methodologies for calculating the incremental rotation angle and address the question of how to handle a case in which the number of nodes cannot be equally divided across all sets so that each set has the same number of nodes.

The easiest way to handle this is to arbitrarily assign more nodes to some sets than to others. A more sophisticated method would be to create enough dummy nodes so the real nodes plus the dummy nodes can be assigned in equal numbers to all sets. The dummy nodes would be created as duplicates of existing nodes, which would add nothing to any tour length because the sorting process should always connect the dummy node to its real node for a net distance of zero.

However, this could perturb the geometric center of the nodes leading to unknown effects after rotation.

Author Biography Richard J. Perle is Professor of Operations Management and Chair of the Department of Finance, Computer Information Systems and Operations Management at Loyola Marymount University. He has published and presented papers in Operations Management and written cases in Information Systems Management. He holds M.S. and DBA degrees from the University of Southern California.

References Christophides, N. (1976). Worst case analysis of a new heuristic for the Travelling Salesman Problem (Report 388). Pittsburgh, PA: Graduate School of Industrial Administration, Carnegie-Mellon University.

Clarke, L., Johnson, E., Nemhauser, G., & Zhongxi, Z. (1997). The Aircraft Rotation Problem.

Annals of Operations Research, 69(0), 33-46.

Dantzig, G. B., Fulkerson, D. R., & Johnson, S. M. (1954). Solution of a large-scale Traveling Salesman Problem. Journal of Operations Research, 2(4), 393-410.

Grotschel, M., Junger, M., & Reinelt, G. (1991). Optimal control of plotting and drilling machines: A case study. Mathematical Methods of Operations Research, 35(1), 61-84.

Junger, M. G., Reinelt, G., & Rinaldi, G., (1995). The Travelling Salesman Problem. Handbook in Operations Research and Management Science. M. O. Ball, Magnant, T. L., Monma, C. L., & G. L. Nemhauser (Eds.). Amsterdam: North Holland.

Laporte, G. (1992). The Vehicle Routing Problem: An overview of exact and approximate algorithms. European Journal of Operational Research, 59(3), 345-58.

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Lawler, E. L., Lenstra, J. K., Rinnooy Kan, A. H. J. R., & Shmoys, B. B. (Eds.). (1985). The Travelling Salesman: a Guided Tour of Combinatorial Optimization. Chichester, England: J. Wiley and Sons.

Lenstra, J. K., Kan, A. H. J. R., & Brucker P. (1977). Complexity of machine scheduling problems. Annals of Discrete Mathematics, (343-62). Amsterdam: North Holland.

Lin, S., & Kernighan, B. W. (1973). An effective heuristic algorithm for the Travelling Salesman Problem. Operations Research, (21), 498-516.

Reeves, (Ed.). (1993). Modern heuristic techniques for combinatorial problems. New York, NY: Wiley & Sons.

Ratlif, H. D., & Rosenthal, A. S. (1983). Order-picking in a rectangular warehouse; A solvable case for the Traveling Salesman Problem. Operations Research, 31(3), 507-21.

Rosenkrantz, D. J., Stearns, R. E., & Lewis, P. M. (1977). An analysis of several heuristics for the Travelling Salesman Problem. SIAM Journal of Computing, 6(5), 63-581.

Table 1 X-Y Values Sorted on X _________________________________ Node X-value Y-value _________________________________ 1 7 53 2 15 81 3 20 26 4 26 67 5 32 39 6 54 57 7 61 80 8 68 37 9 87 49 10 93 72 ________________________________

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Table 2 X-Y Values Sorted on Y _________________________________ Node X-value Y-value _________________________________ 3 20 26 5 32 39 1 7 53 4 26 67 2 15 81 7 61 80 10 93 72 6 54 57 9 87 49 8 68 37

Table 3 Rotated X-Y values _________________________________ Node New X-value New Y-value _________________________________ 1 16.6 82.8 2 29.6 70.8 3 43.6 95.8 4 57.6 76.8 5 71.6 83.8 6 70.6 41.8 7 62.6 9.8 8 47.6 48.8 9 39.6 15.8 10 27.6 34.8 _________________________________

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Table 4 Simulated 10 Node instances solved to optimality by explicit enumeration ______________________________________________________________________________ % over % over % over optimum, optimum, optimum, φ*, Instance NNG ESH at φ = 0 degrees ESH at φ = φ* ____________________________________________________________________________ 1 16.69 15.35 18.00 0.00 2 11.42 0.00 0.00 0.00 3 15.01 8.28 126.00 0.00 4 15.81 7.46 72.00 0.00 5 22.64 0.57 36.00 0.00 6 4.27 6.65 90.00 0.00 7 16.98 8.25 54.00 0.00 8 8.25 7.93 54.00 0.00 9 14.61 0.00 54.00 0.00 10 13.29 30.67 126.00 0.00 11 0.00 22.61 36.00 0.00 12 2.47 11.42 36.00 0.00 13 14.95 7.42 108.00 0.00 14 0.31 0.31 18.00 0.00 15 9.32 29.31 54.00 0.01 16 12.39 14.32 90.00 0.11 17 14.20 1.02 162.00 0.15 18 19.65 9.84 108.00 0.26 19 7.20 13.12 54.00 0.42 20 1.20 21.55 18.00 0.49 21 43.76 14.31 54.00 0.62 22 6.52 6.64 72.00 0.81 23 18.30 14.05 126.00 1.14 24 12.42 16.29 36.00 1.15 25 15.36 1.89 54.00 1.25 26 3.14 1.66 0.00 1.66 27 1.76 15.22 54.00 2.39 28 20.44 22.73 36.00 2.44 29 15.41 3.39 36.00 2.82 30 11.06 11.36 54.00 4.62 ______________________________________________________________________________ Average 12.29 10.79 0.68

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Figure 1. Solution Path. Tour = 314.20

Figure 2. Solution Path Rotated 90 Degrees. Tour = 262.83 Y Q2

Q1

xi,yi o hi

θi X xc,yc

Q3

Figure 3. Translated X-Y Coordinates

Q4

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Behavioral Insights Reveal a Consumer of Mixed Rationality Paul A. Stock University of Mary Hardin-Baylor Jordan W. Ochel University of Mary Hardin-Baylor Eileen M. Stock University of Mary Hardin-Baylor & Center for Applied Health Research

Abstract While a fundamental axiom in economics is that consumers act rationally, maximizing their utility by obeying the law of demand, it has become increasingly recognized that these individuals may make irrational decisions in situations of influence. Because not all consumers have the same rationality, this study aimed to identify specific consumer characteristics associated with increased irrational behavior. A survey design was utilized to assess the relationship between consumers’ demographics and their rationality as well as determine the extent of their risk-taking behavior, impulsive buying decisions, and how they self-perceive their own rationality compared to other consumers. Among all study participants (N=402), younger age, unmarried status, a lower income, and having no children were associated with more irrational consumer behavior (p<0.01 for each bivariate association). Nearly one-fourth (24%) of consumers self-reported being a risk taker most of the time, which was generally observed among men (28% vs. 17%, p=0.02). Approximately 40% have purchased a product they wanted when they should have bought a necessity. Twenty-eight percent believed their emotions affected their buying decisions with 88% making impulsive purchases they regretted later.

Influences often stem from family (36%), friends (14%), or product details containing the words “gourmet,” “authentic,” or “handmade” (48%). Additionally, most participants in this study considered themselves to be more rational than the average consumer, which varied by gender (82% male vs. 71% female, p=0.01). Because this study found that consumers have varying degrees of mixed rationality, additional research should examine how these variations develop over time and in what specific circumstances they hold steadfast.

Keywords: Behavioral Economics, Bounded Rationality, Consumer Behavior, Rational Decision-making

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Since the development of behavioral economics, sociologists, psychologists, and economists have debated the validity of the “rational consumers” assumption. Consumers are assumed to act rationally in modern economics, in which every purchase maximizes their utility and obeys the law of demand. Eighteenth-century economist Adam Smith wrote about consumers acting out of self-interest in his classical work, The Wealth of Nations (Smith, 1986).

This idea of a self-serving consumer led nineteenth-century philosopher and economist John Stuart Mill to define the economic actor as one who seeks the greatest level of happiness with the smallest quantity of labor. The founding fathers of economics, Smith and Mill, set the precedent that consumers are perfectly rational and will, in all circumstances, attempt to maximize their utility. This concept became one of the ultimate truisms in economics from which many theoretical models are based (e.g., Pareto and Edgeworth’s Indifference Curve, Jevons’ Utility Theory, and Pareto’s Pareto Optimality).

Opposition to the rational consumer ideology led to the development of two new schools of thought: (1) those supporting the idea of bounded rationality and (2) those arguing for the idea of an irrational agent. In the 20th century, social scientist Herbert Simon contended that a consumer cannot possibly have complete knowledge of economics or the conditions of his or her environment, and, therefore, cannot make every decision the most optimal (McGuire & Radner, 1972). Daniel Kahneman further proposed bounded rationality as a model to elucidate the limits placed on economic agents’ rationality (Kahneman, 2003, 2006). On the other hand, some experts may argue that consumers are not rational at all and deem them irrational by proxy.

However, the general intent of those supporting this theory is merely to account for consumers’ actions that are inexplicable when set in light of the traditional view.

Modern-day entrepreneur Seth Godin further notes the influence of marketing on consumers’ choices, such as believing a more expensive item is superior or will make a person appear better or become more popular (Godin, 2005). Instead of making one’s customers more rational, a marketer should embrace their irrational behavior. At the same time, Godin also believes some situations are, in fact, better approached irrationally based on gut instincts, conviction, or faith.

While some consumers may make hasty and emotional decisions, a consumer’s choice is generally believed to be based on their preferences, wants, and needs. Because the modern consumer may act irrationally at times, this study aimed to identify characteristics that may influence their purchasing rationale. Accordingly, the risk-taking behavior, emotions, impulsive decision making, and other factors that may alter one’s rationality of today’s consumers will be examined. How modern consumers perceive their own rationality to that of others will also be determined.

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Methods Study Design and Sample A survey design was employed by utilizing a carefully constructed web survey of 24 questions. The survey and its URL location were advertised at locations in central Texas. A total of 402 participants responded over one year. Confidentiality of all participants and their responses was maintained throughout the study.

Survey Instrument The first five questions asked participants for their demographic information: age, gender, household salary, marital status, and number of children. Remaining questions assessed the prevalence of irrational behavior and whether participants’ reasoning was irrational or rational and to what extreme. In a previous study on consumer risk-taking behavior, gambling and lotteries were considered irrational behavior (Delfabbro & Winefield, 2000). It is general knowledge that, beyond emotional utility, gambling is almost always unfavorable probabilistically for the gambler; its consumption can, therefore, be considered to be a violation of utility maximization in terms of return on investment. Such behavior was a focus and an underlying theme of the survey.

Another arising theme in the survey pertained to impulse purchases and emotions experienced when making certain consumer decisions. Although emotions are not a form of utility that a perfectly rational agent is motivated by, it is pertinent to how an irrational agent might act. In addition to emotions, consumers tend to make choices based on preferences, tastes, wants, and needs. The survey assessed these factors as the final theme. The three prominent themes and their respective survey questions are listed in Table 1.

Finally, participants were questioned about how rational they considered themselves and how rational they considered others to be as consumers. Potential responses were ordinally scaled, ranging 0 to 10 (Never Rational to Always Rational). These questions were inspired from Kahneman’s paper on over-optimistic perceptions (Kahneman, 2003).

Analysis Plan A combined rationality score was calculated from all themed questions by assigning highly irrational answers a value of -2, moderately irrational a value of -1, neutral responses a value of 0, moderately rational a value of +1, and highly rational answers a value of +2.

Gambling questions pertaining to extreme behaviors were excluded. Dichotomous responses, excluding impulsive purchases, were assigned values of -2 for “Yes” and +1 for “No”. Bivariate relationships were assessed with Spearman’s rank correlation coefficient, the Wilcoxon signed rank test, the Mann-Whitney test, or chi-square tests (when appropriate as based on distributional

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assumptions), assuming a type I error of 0.05. Some categorical variables were transformed onto an ordinal scale to assess correlations. All analyses were performed in the statistical package, SAS Version 9.2 (Cary, NC).

Results Consumers’ median age was 40 years (range: 18 to 90). The study sample was comprised of 30% female consumers, 57% married consumers, and 51% consumers with children. Approximately one-third had a household income under $25,000, and another onethird had a household income over $70,000. Older age (ρ=0.26, p<0.01) was moderately associated with more rational behavior. Increased rationality was also observed among married (median=2.0 vs. -1.0, p<0.01) consumers and those with higher salaries (ρ=0.19, p<0.01), children (median=3.0 vs. 0, p<0.01), and multiple children (ρ=0.22, p<0.01). Men trended toward being more rational (median=2.0 vs. 0, p=0.13).

Nearly one-fifth (18%) of the sample participated in risk-taking behavior (e.g., gambling, lottery) at least monthly. About half of these individuals participated in risk-taking behavior weekly. Approximately one-fourth of participants consider themselves to be a risk taker most of the time, typically among men (28% vs. 17%, p=0.02). A few participants have demonstrated risk-taking behavior by partaking in gambling with borrowed money (4%), and some participants chose to gamble instead of purchasing a needed item (3%).

Emotions reportedly affected purchasing decisions for 28% of the sample at least regularly, and 88% have later regretted impulsively buying a product. These impulsive purchases occur at least monthly for one-fourth of the participants. The opinions of family (36%) and friends (14%) often influence one’s purchases. Thirty-nine percent admitted to buying an item they wanted when something needed should have been bought instead. A majority (56%), usually women (67% vs. 52%, p=0.01), let cravings influence their purchases at least monthly. Many (37%) have bought an item because it was more expensive, luxurious, or prominent. However, fewer (29%) have made such a purchase instead of purchasing a needed item. Nearly half of the sample would buy one brand over another brand because “gourmet,” “authentic” or “handmade” appear on its label despite the other brand being lower-priced.

Consumers have bought a more expensive product because of its appearance (42%), popularity (24%), higher cost (9%), better quality (80%), and trustworthiness (64%).

An acceptable internal consistency of 0.7 was achieved per Cronbach’s alpha.

Collectively, participants’ purchasing rationale trended toward rational behavior (median=1.5; Appendix). However, consumers were found to act irrational in several instances pertaining to gambling (perceived chances of winning), impulsive purchases (product or service regretted later), and letting cravings influence buying decisions. Irrational behavior was found in gambling and lottery purchases with respect to their occurrence, believing luck or some power

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could influence one's outcome, using borrowed money, and putting gambling before purchasing needed items. Consumers were also found to act rationally in the frequency of impulsive purchases, putting needed items before items wanted, and avoiding purchases of unreasonably expensive items.

Most consumers (79%) considered themselves to be more rational than the average consumer (median=8 vs. 5, p<0.01), and this was more common among men (82% vs. 71%, p=0.01). Perceived rationality was greater among male participants (median=8.0 vs. 7.0, p<0.01), older participants (ρ=0.22, p<0.01), and married participants (median=8.0 vs. 7.0, p=0.01). It was also greater among those participants with a higher household income (ρ=0.14, p<0.01) and those participants with children (median=8.0 vs. 7.0, p=0.02).

Discussion Although in economic theory consumers are assumed to act perfectly rational and always maximize their utility, this study found consumer behavior comprised varying degrees of mixed rationality. Consumer characteristics associated with irrational buying decisions included younger age, unmarried status, a lower income, and having no children. Marriage or having children brings extra responsibility to an individual that may lead them to purchase products more cautiously. Rationality increasing with age suggests that such behavior is learned through one’s experiences and environment. As consumers grow older, their knowledge grows from a lifetime of experiences. However, this acquired knowledge still remains limited and dependent on the specific experiences an individual encounters in his or her environment. Knowledge from experiences not encountered may never be gained, supporting Simon and Kahneman’s bounded rationality in which consumers have limited knowledge based on the knowledge obtained from their environment.

Despite their buying behavior being collectively rational, nearly one in four participants considered themselves a risk taker most of the time. Further research should examine possible sources for this believed behavior and in what avenues consumers feel this way. Marketing strategies are always evolving, and they aim at getting consumers to take risks and try new products. Conversely, good experiences associated with these risks may result in the consumer becoming more of a risk taker. This may be responsible for the large number of respondents buying a craved product over another that was needed. Regardless, marketing has a key role because a product’s appearance, popularity, or label can influence consumer behavior.

Because friends and family largely influence consumers’ purchasing decisions, the true impact of these entities should be explored further to determine situations of rational (or irrational) buying influence. Although a survey design may lack generalizability, this study provides useful insight into consumer behavior within the themes of risk taking, impulsive decision making, and product cravings versus needs. Additional studies should examine

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rationality over time as consumers are exposed to more influences today due to media and modern technology in comparison to influences in the past.

Conclusions A fundamental axiom in economics describes consumers as perfectly rational and always maximizing their utility. However, the modern consumer appears to have deviated from this traditional school of thought. Following the examination of consumer characteristics associated with measures of rationality, it was found that today’s consumer is comprised of mixed rationality. The degree of rationality was largely influenced by consumer characteristics (e.g., gender, age), their lifestyle (e.g., married, risk-taking behavior, impulsive), and their environment (e.g., marketing influences, friends, family).

Author Biographies Paul Stock has a BBA in Economics, MBA, and PhD in Economic Education. After retiring with 24 years as an U.S. Army officer, he started teaching economics at UMHB in Belton, TX and is currently the Interim Dean of the McLane College of Business.

Jordan Ochel graduated from UMHB with a BS in Economics. He founded the university’s chapter of Omicron Delta Epsilon, an International Honor Society in Economics.

He has worked as a loan officer for a regional bank and an analyst for Facebook in Austin, TX. Eileen Stock has a PhD in Statistics and teaches Business Statistics at UMHB. References Delfabbro, P. H., & Winefield, A. H. (2000). Predictors of irrational thinking in regular slot machine gamblers. J Psychol, 134(2), 117-128. doi: 10.1080/00223980009600854 Godin, S. (2005). All Marketers are Liars: The power of telling authentic stories in a low-trust world. New York, NY: Portfolio, Penguin Group.

Kahneman, D. (2003). Maps of bounded rationality: Psychology for behavioral economics. The American Economic Review, 93(5), 1449-1475.

Kahneman, D. (2006). Anomalies: Utility maximization and experienced utility. Journal of Economic Perspectives, 20(1), 221-234.

McGuire, C. B., & Radner, R. (1972). Decision and organization, Chapter 8: Theories of bounded rationality. Amsterdam: North-Holland Publishing.

Smith, A. (1986). The wealth of nations. New York, NY: Penguin Classics.

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Table 1 Descriptive responses of survey questions, grouped by primary themes (N=402).

Themes

Median (Min-Max)

Risk-taking/Gambling Behavior:

1 (-8,8)

How often do you play the lottery, buy a scratch-off ticket, or gamble?

1 (-2,2) Never––Once a Year or Less––Once Every 6 Months––Once a Month––Once a Week––Every Day When you play the lottery or gamble, what do you perceive your chances of winning to be?

-1 (-2,2) 0-25%––26-50%––51-75%––76-100%–– Not Applicable When you play the lottery or gamble, do you believe or hope that luck, destiny, God, and/or some other power or deity influences your outcome?

Yes––No––Not Applicable

1 (-2,2)

How much of a risk-taker do you consider yourself to be? I am a risk-taker: 0 (-2,2) Never––Rarely––Occasionally––Often––Always Have you ever gambled or purchased lottery with borrowed money? a 1 (-2,1) Yes––No Have you ever gambled or purchased lottery tickets instead of purchasing things you need (i.e. food, clothing, Yes––No gas, medicinal care, etc.)? a

1 (-2,1)

Emotions/Impulsive Behavior:

0 (-6,9)

How often do you think your emotions affect your decision to purchase or not purchase a product?

0 (-2,2) Never––Rarely––Occasionally––Somewhat––Regularly––Almost Always––Always Have you ever impulsively bought a product or service that you regretted later?

-1 (-1,1) Yes––No How often do you impulsively buy products?

1 (-2,2) Never––Once a Year or Less––Once Every 6 Months––Once a Month––Once a Week––Every Day How often do your family’s opinions influence your purchasing decisions?

0 (-2,2) Never––Rarely––Sometimes––Often––Always How often do your friends' opinions influence your purchasing decisions?

0 (-2,2) Never––Rarely––Sometimes––Often––Always

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Consumer’s Wants, Needs, and Cravings:

1 (-8,5)

Have you ever purchased something you wanted (electronics, luxury items, tobacco/alcohol, etc.) at a time when you should have purchased something you needed instead (medicinal care, hygienic products, pay back debt, etc.)?

Yes––No

1 (-2,1)

How often do your cravings influence your purchase decisions (comfort foods, tobacco, alcohol, gourmet coffee, etc.)?

-1 (-2,2)

Never––Once a Year or Less––Once Every 6 Months––Once a Month––Once a Week––Every Day Have you ever purchased something that you wanted that is relatively expensive just because it is expensive, luxurious, or prominent?

Yes––No

1 (-2,1)

Have you ever purchased a luxury or expensive item instead of purchasing something you needed?

1 (-2,1) Yes––No Total Score

Mean (SD): Median (Min-Max):

Note. a Excluded from combined total rationality score

1.5 (6.9) 1.5 (-17,19)

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China’s Gradualism Approach to Systemic Transformation: Successes & Challenges Raphael Shen University of Detroit Mercy Victoria Mantzopoulos University of Detroit Mercy

Abstract China initiated its process for economic restructuring in 1979. Unlike the ‘big bang’ approach to systemic transformation that was adopted by some Eastern European economies in the early 1990s, China adopted a calculated and circumspect course of action in 1979. The year 1979 was a full decade before systematic transformation became commonplace in former Communist economies of the Soviet bloc. Pilot reform projects preceded every key domain in need of restructuring and/or reorientation. Only when successes in the experimental projects proved incontrovertible was the scope of reform permitted to widen and accelerate in speed. Paralleling reform measures on the domestic front were China’s proactive measures liberalizing its foreign investment and trade policies. The end result was that its reform success has propelled China to being the world’s second-largest economy merely 30 years after reform began. This paper first provides a historical note elucidating the imperative for economic restructuring. It then highlights the administration’s justification for permitting elements of the socialist-market system in a Communist state. An outline form presentation of restructuring on the domestic front is then followed by an examination and analysis of China’s liberalizing policies fronting its external economic relations. The paper concludes with highlights on patent successes as well as veiled challenges that, if untended, would likely compromise China’s envisioned, future successes.

Keywords: decentralization, systematic transformation, China, economy

Deng Xiaoping succeeded Mao Zedong as China’s paramount leader in late 1978. After decades of mismanagement under Mao, Deng’s accession to power brought about economic restructuring and modernization to China. Deng’s approach to systemic transformation was comprised of calculated phases of economic liberalization. Politically, however, the Communist

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Party would remain absolute. Thus, economic freedom would not be accompanied by political liberalization.

The objective of this paper is to highlight China’s unique approach to systemic transformation while also unraveling some of the anomalies and latent dangers that may derail China’s envisioned development path. This paper begins with a background note on the necessity of structural reform. Reform measures on the domestic scene are followed by policies governing external economic relationships. Successes and latent threats are then discussed. The main point of this discussion seems to suggest that tight political control and close macro supervision are prerequisites to ensure the orderliness and stability of the reform process.

A Background Note The People’s Republic of China was founded on October 1, 1949. The West immediately imposed an embargo on China. In response, Mao Zedong promptly allied China with the former USSR and its Warsaw Pact satellite countries. Financial and technical assistance flowed into China primarily from the former Soviet Union. A host of development projects directed by Soviet experts were agreed upon between the two countries. After Stalin’s demise in 1953, Nikita Khrushchev denounced his predecessor’s dictatorial reign. In 1960, Khrushchev’s revisionism was openly denounced by Mao. Immediately thereafter, the USSR unilaterally annulled all treaties on economic assistance to China. Mao’s distrust of foreign powers deepened. “Self-reliance” became Mao’s maxim for China’s development path.

While some economies in the Pacific Rim busied themselves creating preconditions for economic takeoff in the 1950s and 1960s, Mao took China onto a path of economic isolationism.

Mao’s talent resided in his ability to instigate political movements that espoused “class struggle” and “ideological purity.” In the process, tens of millions of people from every social and political stratum ended up in labor camps and prisons. Through fear, Mao nurtured a cult that made him a demigod in China. By the 1970s, the ascending stars over the four Asian Tigers began shining ever brighter while China’s economy lagged farther behind.

Shortly before his demise in 1976, Mao designated Hua Guofeng as his successor. Hua attempted to gain support by adhering to Mao’s ideology. Hua failed to recognize that the firstgeneration revolutionaries—many of whom were purged by Mao during the so-called Cultural Revolution—were setting the stage to dismantle Mao’s legacy. During the Third Plenary Session of the 11th Central Committee meeting of the Communist Party in 1978, numerous leaders who had been purged by Mao were officially “rehabilitated.” Party leaders who had been removed from office, exiled, or publicly humiliated by Mao returned to center stage. Deng became China’s new paramount leader.

Deng was a pragmatist who believed in tangible results. For Deng, truth was to be found in facts and not in ideology. Among the resolutions of the Party’s leadership meeting were eliminating “class struggles,” restoring economic order, and “seeking truth from facts.” The Party’s resolution in 1978 was a veiled rebuke of the cult that Mao had carefully cultivated for

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himself through the fear he had instilled. However, under no circumstance was the public to question the absolute leadership and authority of the Communist Party. It was made clear from the onset that political freedom would not be accompanying economic reform.

The dawning of reform commenced in late 1979 under the reform-minded Premiership of Hu Yaobang with Deng charting the overall direction. A two-pronged reform approach was adopted: (1) reforming facets pertaining to the structure and functioning of the domestic economy and (2) liberalizing foreign economic relations. Integration of China’s domestic economy into the world arena began in 1979 with quasi-free market experimentation and a phased opening up of China’s economy to the outside world.

Paving the Way for a “Socialistic-Market” System Under Deng’s directive, the 2nd session of the 5th People’s Congress adopted the policy that would permit limited reemergence of market activities in China. It was the first time since 1949 that the merits of the market system were given circumscribed and tacit approbation.

Central planning, however, was to retain its pivotal role in mapping China’s development path.

According to Deng, planning does not equate with a socialist system because market economies also resort to planning. Conversely, introducing market mechanism into a socialist economy does not equate with capitalism because markets do exist in socialist economies. Rhetorical jargon aside, Deng was fully cognizant of the command system’s wastefulness and the merits of market efficiency.

Domestic Realms of Reform Having inherited three decades of structural rigidities and functional inefficiencies from Mao, the scope of Deng’s reform scheme was to be wide, and its depth was to be profound.

With varying speeds, intensities, and sequential orders, parallel reform measures were adopted.

All domains required reform, but the initial focus of reform was centered on the most pressing domains and on those that would reap more immediate and bountiful returns than others.

Given the size of China’s population and the complexity of China’s social and cultural fabrics, reform in all major domains began with well-defined and experimental pilot projects. A select few reform domains are briefly highlighted herewith.

Administrative decentralization. Structural rigidities required administrative decentralization. Private initiatives were given tacit permission to surface for the first time since 1949. While administrative decentralization began, the scope and the level of the Central Planning Commission were curtailed. Provincial and regional administrations received increased autonomy for planning their respective investment, production, distribution, and consumption objectives. On the enterprise level, the state-owned enterprises (SOEs) were granted emerging

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rights for self-administration, bringing into play the objective measures of costs, values, prices, and profits in their decision making processes.

Legal reform. With the foreknowledge that foreign investment and foreign trade were to be the catalyst for reawakening China’s vast productive potentials, new legislative measures governing business relations became indispensable. Concurrent with administrative decentralization was reform in the legal sphere. Within the first 10 years of economic reform (1979–1989), China enacted 580 new laws, of which 55 percent dealt with domestic and foreign economic operations and relations.

Agriculture. More than three-quarters of China’s population were either still directly engaged in or working in farm-related industries in 1978. A pilot project began with only 18 farm households in the Anhui province as participants. In late 1978, these 18 households were permitted to work independently from the agricultural communes. The sole stipulation was that they must deliver the assigned production quota to the commune by the season’s end. Yield over and above the assigned quota—instead of going into the commune’s coffer—would be awarded to the participating households. The pilot project was a glaring success. In time, farmers elsewhere were permitted to leave the communes they had been herded into in the late 1950s and be independent producers once again. Because agricultural land legally still belonged to the communes, land-leasing arrangements became an integral part of agricultural reform throughout China. In time, the system of production quota was abolished.

State-owned enterprises. Before reform began, all productive units were state-owned enterprises. The central plan dictated investment, production, and distribution decisions in all spheres of economic activities. Departing from past practices, enterprises were now directed to function as businesses. Turning a profit became the norm, actively divorcing politics and governmental interventions from business decisions. On the part of the state, the new slogan was “close supervision” of strategic SOEs and a looser grip on smaller ones. All SOEs were also mandated to make their assets transparent in preparation for the process of privatizing nonstrategic enterprises. In time, instead of surrendering all profits to the state, the SOEs were given the full right to retain their respective after-tax earnings.

Pricing system. The absence of an objective pricing mechanism in the past resulted in widespread misallocation of resources, including labor. Reform necessitated the introduction of a market-driven pricing system. For products and services whose supplies were adequate at a given phase of reform, their respective prices were liberalized in accordance with changing supply and demand conditions. For goods and services whose supplies were less than adequate but whose supply were sufficiently price elastic, the prices were permitted to fluctuate within given parameters that were periodically determined and adjusted by the state. And for goods and services that were of pivotal importance to the nation’s economic well-being but the supply of

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which were less than adequate, their respective prices remain fixed. The three-tiered pricing system was administered and monitored by the newly created State Pricing Bureau.

Fiscal reform. Prior to 1979, the Central Planning Board determined all sources of government revenues and expenditure channels. Surplus from all producing units streamed upwards while disbursements coursed downwards from the central government to low-level administrators and SOEs. Fiscal reform kept pace with phased administrative decentralization.

Over time, the rights and responsibilities of administrative units from the center down to local levels were streamlined. With stipulated shares of revenues and expenditure responsibilities between higher and lower levels of administrative units clearly defined, each administrative unit was to be henceforth responsible for their respective revenue generation privileges and disbursement obligations. The intent of fiscal decentralization was to individually and thereby collectively maximize the actualization of potentials in investment, production, distribution, and consumption efficiencies by diverse levels of administrative entities.

Financial reform. Prior to reform, banks ranging from the People’s Bank of China (PBC) to special purpose banks, such as The People’s Bank of Construction and the Agricultural Bank, did not function as financial institutions as it was understood elsewhere. Banks functioned as clearing houses, performing the basic tasks of holding deposits and disbursing plan-prescribed outflows. Instead of mobilizing financial resources for productive uses, banks served merely as bookkeepers. The concept and the reality of opportunity cost were not involved in a bank’s operational processes.

The first step for financial reform was for the PBC to begin exercising the traditional functions of a nation’s central bank. Four years after reform began the PBC ceded all functions not relevant to the central bank itself to special-purpose banks and the newly sanctioned commercial banks. At least in theory, state-owned banks were expected to function as financial institutions for profit. Over the decades, private banks and banks with foreign capital began emerging, actively competing with financial institutions in the public sector.

External Realms of Reform Foreign investment. China lacked capital, technology, and skilled labor. Managerial talents were lacking while marketing experience was nonexistent. Deng recognized the indispensable role that foreign capital and technology could and must play in China’s drive towards modernization and development. For more substantive and speedier reform as well as for accelerated growth, China resorted to the two primary engines of economic development: foreign investment and foreign trade.

A host of economic zones, cities, development regions, and technology areas, with respective, well-defined targets and objectives, were opened up to foreign capital inflow. Initial targets were capital from the more developed economies in the region, namely Hong Kong,

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Taiwan, South Korea, and Singapore. Beginning in March 1980 through April of 1988, China designated five special economic zones (SEZs). Shenzhen was the first SEZ. Six months later, Zhuhai and Shantou were added. Two months thereafter, Xiamen was the fourth, and, in April of 1988, Hainan became the fifth. Foreign investments in these designated zones received the customary tax and import/export privileges. Products from foreign investments were destined mostly for exports. The initial phase of open-door policy yielded exceptional dividends. In addition to foreign capital inflow and increased foreign earnings, foreign investments became the catalyst in reawakening the restive spirit of dormant entrepreneurs throughout China.

Early in 1984, Beijing declared 14 cities along the Pacific coast as also open to foreign capital inflow. The 14 cities that had been declared open were Qinhuangdao, Dalian, Yantai, Tianjin, Qingdao, Lianyungang, Nantong, Shanghai, Ningbo, Wenzhou, Fuzhou, Guangzhou, Zhanjiang, and Beihai(Shen and Mantzopoulos, 2011, p. 104). These historically more commercial cities along the Pacific coast helped connect the dots, fronting the Pacific into a frontier of industrial and commercial centers. Following closely upon the heels of SEZs and open cities were six Economic Development Regions, covering China’s most vital delta regions and waterways as well as economic and technology development areas. The six economic development regions were “the Pearl River Deltas, the Southern Fujian, Xiamen, Zhengzhou and Quanzhou delta region, the Yangtze River Deltas, the Shandong Peninsulas, the Beihaiwan region north of the Yellow River, and the Liaodong Peninsulas bordering North Korea” (Shen, 2000, p. 100).

In brief, through its open-door policy, China’s policy to induce foreign capital and technology inflows evolved from initially targeting investments only from the more developed economies in the Pacific Region to targeting developed economies in the West.

The five-year cumulative foreign direct investment (FDI) between 1979 through 1984 approximated $10 billion. Twenty-two years later, in 2006 alone, FDI exceeded $193 billion.

Table 1 provides an overview of China’s successes in attracting foreign investment since reform began. During earlier years of reform, as data in Table 1 illustrate, China depended more on foreign loans for the much needed, convertible currencies. As the pace of reform successes accelerated, China’s induced foreign capital originated increasingly more from FDI while China concurrently reduced its initial, heavy reliance on foreign borrowings. Foreign capital and technology played a pivotal role in thawing and revitalizing China’s long dormant economy.

More important was FDI’s contribution to China’s phenomenal growths in its export sector and, therefore, its foreign reserves. The growth in China’s foreign trade sector is briefly highlighted below both in Table 1 and in Figure 1. Both show the overall decrease in foreign loans into China while there is a glowing increase in the value of FDI. Foreign trade. Economic liberalization unleashed China’s vast productive potentials that had long been suppressed by the rigidly structured command system. Increased productivity and production, however, could not and did not find a ready outlet. China’s per capita income was low; consumers’ purchasing power was nearly nonexistent. Exploring and exploiting

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foreign markets provided the ready solution. Foreign trade reform commenced in tandem with foreign investment reform.

Prior to reform, foreign trade activities were centralized in the Ministry of Foreign Trade.

Other than import quotas and export targets as assigned by the central plan, no producing or consuming units were permitted to trade with external economies. After the official inception of reform, State Trading Companies (STCs) were established under the aegis of the Ministry to help execute China’s import-export orders.

The pace of trade liberalization lagged behind that of foreign investment reforms. Other than the import-export activities undertaken by foreign capital companies, the STCs continued maintaining state monopoly over trade activities. In time, however, import and export quotas progressively declined over the reform years as China’s economy began awakening to increased interactions with external markets. As the quota system began giving way to accelerated integration with external economies, private producers of export-destined products also began to be permitted to market their products abroad through the STCs. On another front, all earnings from exports were initially required to be converted into the Chinese yuan. As an export promotion incentive, however, a dual exchange rate system was introduced. Earnings from exports were converted to the domestic currency at a notably higher exchange rate than the official exchange rate. After 1988, the practice of mandatory turning over of earnings from exports to the government for currency conversion was also discontinued.

As administrative decentralization progressed, so did the foreign trade regime. Low-level administrative jurisdictions began instituting their respective foreign trade departments. In addition to the central government’s STCs, authorized regional trading agencies began mushrooming. Through incentive policies, such as reduced tariffs, hidden subsidies, and undervalued domestic currency, import and export activities initiated by domestic, private enterprises eventually became commonplace. Domestic investment in export-oriented productive activities proliferated. China’s foreign trade volume accelerated over the three decades between 1980 and 2010. With it, the country’s foreign reserve kept its rapid ascent.

Instead of primarily attracting capital inflows as during the early years of reform, China’s immense earnings from abroad began flowing outward in search of investment opportunities by the end of the 20th century.

Table 2 and Figure 2 provide an overview of China’s unparalleled growth in the foreign trade sector between 1978 and 2010. Though export volume tripled during the 1978–1986 period from $980 million to $2.74 billion, gains in exports were still tentative during the early years of reform. Import gains for the same period were slightly more pronounced since China was still in critical need of imports for basic technologies that had long been available in free markets. Negative trade balances during the early years of reform was inevitable. After 1985, as data in Table 2 indicate, growth in the export sector began soaring, resulting in rapidly accelerating trade surpluses as well as foreign reserves. Between 2000 and 2005, exports grew by 205.8% while imports grew by 193.2%. The dramatic increases in both imports and exports were the direct result of China’s entry into the World Trade Organization (WTO) in 2001. By

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2010, China’s exports climbed to $1.58 trillion while imports grew by 38.7% to $1.4 trillion (Ministry of Commerce, People’s Republic of China, 2011).

One major outcome of China’s sustained export growth has been its rapidly growing foreign reserves. China, the world’s most populous nation, had a foreign reserve of only $1.6 billion in 1978. Ten years later, it grew to $18.5 billion. Another decade passed and China’s foreign reserve soared to $149.2 billion. By 2008, China’s foreign reserves had increased by another 1204% to $1.95 trillion. The latest available data indicated that China’s foreign reserves reached a new height of $3.3 trillion by the end of the first quarter in 2012 (Bloomberg News, 2012). China’s meteoritic growth in imports and exports since reform began may well merit a new chapter in the annals of the world’s development history.

Successes: A Summary Overview China’s gradualist approach to systemic transformation was tentative in the beginning.

However, once the merits of reform in a designated realm became conclusive, the scope kept widening and the pace kept hastening. The more fundamental reform fronts that proceeded simultaneously—though at differing paces—comprised of the following reforms: administrative decentralization, decollectivization of the farm sector, rationalization of investment priorities, phased wage and price reform, privatization of state-owned enterprises, emergence of a labor market, and external economic relations that culminated in China’s membership in the WTO. Dismantling the centralized command system resulted in a revived entrepreneurial spirit and pervasive private initiatives. Commodity circulation channels were multiplied, broadened, and invigorated. Value objectification via a market-based pricing system displaced a distorted, decades-old value system fossilized in the central plan mechanism. Value maximization and waste minimization has now become the norm in decision-making processes. China’s efforts at integrating its economy into the global system have posed challenges as well as bountiful opportunities.

Concurrent with economic liberalization has been a significantly more relaxed, societal atmosphere. Vivacity has displaced pessimism and “wants” have supplanted “needs.” Three decades of reform has transformed China from a slumbering giant to a roaring force of global influence. On the day when the People’s Republic of China was officially founded, Mao declared to the world that “the people of China have stood up.” However, the people of China did not. With reform, new life has been breathed into the life of the masses. The people of China, at least collectively, have now, indeed, been standing taller in tandem with sustained reform progressions.

Although 1978 is not indicated in Table 3’s data, production from the primary sector increased from 139.7 billion yuan in 1978 to 6.93 trillion yuan in 2010, a 4861% increase (National Bureau of Statistics of China, 2011, p. 464). Growth in the primary sector was exceptional. Yet, gross domestic product (GDP) shares from the primary sector had declined considerably from 1979’s 31.3% to merely 10.1% by 2010. Growth of GDP shares by the

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tertiary sector nearly doubled during that same period. Such significant and swift structural changes readily shed light on the extent of investment distortions during the Mao era. The data also evince eloquently the phenomenal successes of reform in structural realignment during the past three decades.

On another front, growth in GDP had risen from 406.3 billion yuan in 1979 to 40.1 trillion yuan in 2010, a 9774% upsurge. Taking population increase into account, the per capita increase of income was still an impressive gain of 5558%. Further taking inflation under consideration for that same time period, the per capita real income increased from 419 yuan to 4342.5 yuan, a remarkable increase of 936.4%. Data in Table 3 eloquently defend economic liberalization. Economic successes are readily measurable. However, intangibles such as economic freedom, improvement in individual freedom, and a drastic reduction in fear of being arrested and sent to labor camps could well be more meaningful to victims of ideological fanaticisms than improved living standards. Therefore, combining successes on both the quantifiable front and the less tangible fronts is how China’s reform successes must be evaluated.

The same analysis is highlighted in Figure 3 providing a better visual of the drastic decline in the primary industry’s share in the GDP and the tertiary industry’s share steady increase.

In brief, reform has yielded bountiful dividends to China’s vast population both quantitatively and qualitatively. China’s approach to systemic transformation has been distinctive and its successes are unparalleled. Nevertheless, amidst glittering successes, anomalies have been making appearances alongside the get-rich-quick fever that has caught the people’s imagination throughout China’s vast terrains.

Anomalies and Challenges Behind China’s glaring successes are anomalies and challenges. As an evolving system, the successes often generate challenges both from within and from without. Since China has now emerged as a major international economic powerhouse, its economic well-being and stability are of interest to the world community. The ensuing sections highlight select anomalies that require vital vigilance on the part of policy designers both inside and outside of China.

Anomalies on farm. Reform began in 1979 with the farm sector. When the scope of systemic transformation in nonfarm sectors began widening in the mid-1980s, the rural sector began experiencing overall neglect. For instance, as a result of the grain surpluses, food prices and relative farm incomes declined while factor costs and the cost of living kept rising. With administrative decentralization, a local administration was now responsible for providing basic services and making necessary infrastructural investments. However, excuses abounded for exacting charges, fees, taxes, levies, duties, fines, and obligatory contribution of labor from farmers and rural residents.

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In his annual report to the People’s Congress in 1992, Premier Li Peng outlined the need to “effectively control agricultural factor prices, resolutely end arbitrary imposition of fees or collecting unwarranted charges, (and) realistically reduce burdens borne by farm producers” (Li, 2006a, p. 2). In the following year, the Premier reiterated the warning that “there have been pervasive practices of collecting unreasonable fees, generating unwarranted revenues and assigning uncalled-for obligations” (Zhu, 2006c, p. 3). During the subsequent years, he continued to call for implementing “effective measures and steadfastly help resolve the problem so that the burden on farmers is strictly limited to the parameters as defined by the state” (Li, 2006b, p. 5). But it was not until March 5, 2008, that his successor, Premier Wen, reported that taxes on agriculture, livestock, and special produce had been eliminated. Additionally, Premier Wen reported that a system of farm subsidies had been established to strengthen the foundations of agriculture by directly subsidizing a variety of grain producers, quality products, and purchasers of farm equipment (People’s Daily Online, 2008).

Despite improved conditions on farms since 2008, two points merit clarification. First, the burden that was removed from the farmers in 2007 was the taxes that urban and township populations never had to pay while farmers had been paying them all along (Hexun.com, 2008).

Second, the farm subsidy programs were meant to improve the farmers’ living standards. Rural living conditions have indeed improved in services such as irrigation, roads, and drinking water.

Nevertheless, the goal to eliminate abuses by local officials has not been achieved (People’s Net, 2009). Incidents of social unrest have been commonplace in rural regions. The unrest is systematic and indicative of the lingering, widespread discontent on the part of farm producers.

Investment and demand disparities. China’s phenomenal GDP growth has been fueled by an even speedier growth in exports. Yet, growth in domestic consumption trails is significantly behind that of the growth in GDP and exports. Domestic consumer prices have been high. High-relative prices on the domestic scene have helped reduce consumption while generating more savings. Increased savings have found their ways to fuel increases in investments in the export sector. However, excess investment in one sector produces sectorial imbalances. As an example, excess investment in heavy industry under Mao resulted in severe sectorial imbalances. Now, however, excessive investment in the export sector without comparable investment increases in industries producing goods and services for domestic consumption weakens domestic demand while unduly expanding China’s export industries. On average, for instance, domestic consumption ranges from between 60% to 75% of a given economy’s GDP. A recent study by Will Hutton, however, reports that China’s total consumption of goods and services accounted for only 37% of its GDP (Hutton, 2009). Much of the household savings have been flowing into export-destined industries. Excess investments in the export sector combined with a slow recovery of the worldwide recession can result in underutilized capital goods in the export sector in the near future. When this takes place, unemployment in the export sector can rise. Since a 1% loss in China’s GDP growth can lead to the loss of millions of jobs, China’s envisioned GDP growth must be dependent on increased

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domestic consumption. Greater emphasis, therefore, needs to be placed by the government on effectively expanding domestic consumption in order to compensate for the likely decline in export growth in the foreseeable future.

Select incongruities. The adverse effects of China’s soaring foreign exchange reserves have been a serious concern for many developed economies with mounting trade deficits against China.

Excessive foreign reserve leads to the need for yuan revaluation. Revaluation of the yuan will render Chinese exports as relatively less competitive and render imports as more attractive.

When this materializes, an increase in imports can create pressure to reduce domestic demand for labor. A rise in unemployment can occur. Continual accumulation of foreign reserve while neglecting the need of growth in domestic demand thereby can adversely impact China’s economic growth in the long run.

Macro stability may also be threatened by the excess money supply in the system. China still lacks a well-developed financial system because its stock-and-bond markets have only recently been introduced. Supervisory agencies that monitor financial markets’ operations are still in their embryonic phase. Meanwhile, the excess money supply that is available to the private sector has been flooding the stock-and-bond markets. The expectation of continual upward movement in stock prices has formed ominous bubbles in China’s financial system.

Speculative fever has kept its steady, upward movement. China must effectively prevent the bubble from bursting to avoid the same experience as the near meltdown of financial markets in the U.S. in 2008.

Administrative decentralization and social unrest. Administrative decentralization is an essential component of systemic transformation. However, with administrative decentralization came the responsibility for meeting budgetary needs. From the perspective of what each municipality or province can accomplish with the revenues generated from their respective jurisdictions, the poor provinces or municipalities do not come close to the better endowed jurisdictions. Poorer provinces and jurisdictions do receive special budgetary allotments; however, even with the subsidies from the central administration, substantial disparities persist. Serious inequalities among diverse administrative jurisdictions in terms of fiscal advantages and responsibilities help breed dangerous anomalies. Revenue sources for many low-level administrations were inadequate while needs were more than considerable.

Without hesitation, administrative units in poorer regions began resorting to “creative” methods for revenue generation. Fees and charges mounted.

With unjustifiable and sometimes inexplicable financial burdens being placed on the masses in the form of fees, charges, taxes, and fines, the number of complaints and petitions soared. Demonstrations and even violent confrontations with authorities occurred as security forces were often deployed to quell the demonstrations. The fabric of social calm keeps tearing.

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Another consequence of administrative decentralization is that success is often defined by increased production. This criterion leads to the proliferation of low-end manufactured or valueadded products in the decentralized administrative units. As competition for value-added exports grow, especially from economies like India, Vietnam, and other developing economies in the Pacific Basin, the excess capacity for producing low-tech products in China’s export sector will meet increasingly mounting challenges. Thus, the potential opportunity cost of the current investment trend in low-value-added industries within decentralized administrative regions would be high.

Banking, housing, and stock bubbles. The Bank of China became the central bank in 1983. Instead of functioning as a clearing house for the state’s financial transactions, it began assuming the traditional role of a central bank. In theory, the special purpose banks became independent commercial banks, reaping successes or bearing losses without having recourse to the state. Also in theory, most SOEs became financially independent from the state budget.

Predicaments, however, did arise when many of the less viable SOEs began incurring losses.

Allowing SOEs to bankrupt would mean further increases in unemployment. The articulated objective in decentralization was the separation of the state from enterprises. It was easier to envision than to realize. SOEs incurring losses turned to state-owned financial institutions for assistance. Lacking independence from the central and provincial administrations, state banks routinely extend unsecured credits to noncompetitive SOEs. The unrecoverable loans are then entered into the loss column of the banks’ ledger, but they, nevertheless, help fuel financial bubbles. If and when the bubble does burst, macro disturbances would quickly follow upon financial chaos.

On another front, during the early stages of reform, no market for real estate existed.

Investment opportunities for growing savings had limited opportunities. The social class that became very rich quickly recognized a potential gold mine. For example, in “1978 there were only 3.6 square meters of living space per inhabitant… By 1981, living space in urban housing had increased to 5.3 square meters per person, and by 1985 the figure was 6.7 square meters” (Geographic.org, 2005). The housing supply was still significantly short of demand. Similar to other major reform measures, experimental sites for the housing industry included several southern cities such as Hainan, Beihai, Guanzhou, and Shenzhen. A mortgage system was established in these experimental cities. The Central Bank of China, therefore, issued a communiqué granting permission to commercial banks to extend mortgage loans to creditworthy patrons. A nationwide mortgage system came into existence by 1997. It was through state intervention from 1998 onward that the growth in the housing market accelerated (Credit Web Net, 2010).

The government’s role in creating the housing market is apparent. A 585 billion yuan stimulus package was used to build shopping malls, apartment buildings, office towers, condo towers, and even entire cities. Unfortunately, significant portions of the newly constructed structures have since been left unoccupied (Money Matters, 2010). Another report estimates that

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64.5 million apartments and houses in urban areas have been left vacant (Durden, 2010). An additional fact is that since the central administration announced property tax reform, sales of real estate fell by 70% in Beijing and Shanghai in 2010 (Jubak, 2010).

The 2008 meltdown of the U.S. financial system was triggered by massive defaults and operational irregularities in the financial system. It is a lesson that is worth learning for any economy that is intent on steady economic growth.

The third major area of concern is the stock market’s instability. China’s financial market is still in its embryotic stage. The primary investment outlets for average household savers include banks, stock-and-bond markets, insurance funds, and trust funds. The Shanghai Stock Exchange and the Shenzhen Stock Exchange were established in 1990. Within a year, there were a total of 1625 listed companies with 864 in the Shanghai Exchange and 761 companies in Shenzhen Exchange (PRC, SSB, 2009, p. 780).

Relative stability in the stock market has been absent. Although the government intervened to temper the speculative fever in the stock market, violent fluctuations endured. For instance, the market reached a historical high of 6124 points on October 16, 2007. Shanghai’s Composite Index then plunged by 65% in 2008 alone. The moving average for the month of August still remained at a paltry level of 2627 points by 2010 (Trading Economics, 2010). And the Shanghai Stock Exchange composite index for April 15, 2013 declined yet further to 2169.63 points (Bloomberg, 2013). The volatility of China’s stock market may be viewed from yet another perspective. The price/earnings ratios for shares in the two Exchanges in 2007 were 59.2 and 69.7, respectively. Severe downturns were experienced in subsequent years (PRC, SSB, 2009, p. 779). Another factor contributing to the market’s flaw is the government’s role in the stock market. As of 2007, the seven state banks whose shares are listed on the stock market accounted for nearly one-third of total market value of the entire stock exchange. Yet at the same time, these banks carry combined unrecoverable loans totaling in excess of 600 billion yuan (Xue, 75). The government holds a controlling interest in the listed shares, and there has been no indication that the government intends to privatize the assets of these state-owned concerns. The stock market, therefore, is unable to fulfill its broad function of efficiently allocating resources for value maximization (Lunwenda.com, 2008).

A fourth concern is the central bank’s monetary policy. The supply of M2 consistently exceeds that of GDP growth by a significant margin. For instance, GDP growth for 1994 was 13.1%. The money supply increased by 34.5%, and the stock market gain went from 333 points on August 1, 1994 to 1053 points by September 13 of that same year. Another instance was that M2 inexplicably grew by 47% within a 20-month period between year-end 2008 and early August 2010, far exceeding the GDP growth rate of 27.8 between year-end 2008 and year-end 2010 (Quinn, 2010; National Bureau of Statistics of China, 2011). That such a monetary policy is based on sound economic rationality is questionable at best.

A more cogent issue concerning stable economic growth and social well-being is whether macro policies based on political decisions are in the best, long-term interest of the nation. If monetary policy is being implemented for influencing housing and/or stock market activities for

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the benefit of vested interests with “connections” by fanning the speculative fever of the general public, than the average household investors are being preyed upon. In a time when augmented domestic consumption is a crucial factor for sustained economic growth in China, such macro policies merit reconsideration.

World trade organization. China’s membership into the WTO meant accelerated systemic reform. Benefits from gaining WTO membership are bountiful. Challenges consequent upon being a member of the WTO organization, however, likewise abound. A mandated timetable was established for implementing the required changes. Two primary domains become immediately challenged. On the political front, the WTO may challenge China’s policies pertaining to foreign trade and domestic commerce. Laws for foreign and domestic economic relations must be in sync with accepted international norms, mitigating the influence of political decisions on economic affairs. If not, then challenges may be filed in the WTO against China for unfair trade practices. China has, for the first time, agreed to be subjected to external arbitration. China is also required to remove trade barriers or restrictive practices including, among others, restrictions on foreign communication equipment imports, mass media, and internet services.

Although China has made significant strides in technical and technological fronts, it still pales to those of developed economies. For instance, media has been the tool for promoting government causes and ideology. Upon China’s accession to the WTO, however, the entry of foreign capital, foreign technology, and foreign organizational and operational expertise in communications and mass media industries will pose serious challenges to the established communications industry in China. Permitting foreign media industries to enter into China can, therefore, severely cripple the effectiveness of the state’s monopoly over its propaganda machine.

Viewed from yet another perspective, though there has been strong growth in exports of China’s manufactured goods such as colored TV sets and computing equipment, many key components of these products have either to be imported or licensed by foreign patent holders (Ministry of Science and Technology of the People's Republic of China, 2002). China’s research and development (R&D) capacity still pales in comparison with that of the developed economies, especially when contrasted with research facilities in the U.S., Japan, and some of the European Union countries. Mergers, acquisitions, and similar tactics by foreign capital can slice deeply into China’s intent of fostering a high-tech force of its own. The pace of China’s drive for fostering a viable R&D foundation is likely to be more of a challenge as a result.

Another front that can expect serious challenges from injection of foreign interest into China is its banking and insurance industries. According to agreements with the WTO, China promised to permit financial institutions including banks, insurance firms, and investment firms wholly owned by foreign interests to freely enter the country by 2005. Foreign banks are now permitted to conduct business in local currency, servicing domestic businesses and consumers.

Entry of foreign banks and insurance companies, which are well organized, experienced, and

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efficient, can pose serious challenges to state-owned domestic financial institutions. Other than the entry of foreign financial institutions and foreign service industries, such as entertainment, tourism, and restaurants, all pose compelling challenges to domestic concerns as a result of China’s entry into the WTO. The real challenge is whether the state is prepared to oblige the less efficient SOEs in these industries to undergo the test of market forces by having them either reorganize or be eliminated from the market by competing forces. This will test the state’s avowed intent on deepening reform for improved efficiency. China’s entry in the WTO questions whether economic rationale can prevail over political predilections.

Entry into the WTO also required a reduction in farm subsidy, a reduction in tariffs, and, as a result, likely significant increases in agricultural imports. China’s agricultural exports are mostly unexceptional items. Instances of unsafe agricultural exports from China have already raised the level of apprehension of importing economies. China, therefore, will need to learn how to improve the productive efficiency of its farm sector within the prescribed rules of the WTO. In brief, the long-term benefits hold vast promises. Short- and medium-term challenges arising from WTO membership, however, are manifold. When managed well, stability and sustained growth will prevail. Otherwise, serious disorder and disruptions can either help delay or derail the state’s current drive for continual reform and development.

Conclusion More than a decade before the collapse of the Communist regimes in Eastern Europe, the world’s most populous nation began experimenting with systemic and structural transformation.

Concurrent with institutional reforms on the domestic front, China initiated reforms in the external sectors. On the domestic frontier, reform successes may be attributed to administrative decentralization, to restoring private ownership rights, to privatization of state-owned enterprises, and to the private sector’s rapid emergence. The most pronounced catalysts of China’s meteoric growth, however, may be attributed to reform successes in the realms of foreign investment and foreign trade. China has transited from a foreign-reserve deficient economy to one that has been awash in reserves. One of the predictable consequences is China’s ability and readiness to search for investment opportunities abroad. Three decades of sustained reform has transformed China from a closed and unpretentious economy to one whose performance is capable of impacting the well-being of world economies. China’s reform experience richly merits a chapter in the annals of world development history.

The main body of research into China’s phenomenal successes to date seems to revolve around, among others, topics such as China’s accomplishments in systemic transformation and rapid growth. This paper, aside from presenting the modern history of China’s economic development in a succinct manner, has also shed light on domains that have been seldom researched by China’s scholars. Such topics may include anomalies such as disparate growth patterns between domestic consumption, investment increases in the export sector, growing

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dangers in banking, housing, and the stock market bubbles; increasing incidents of social unrest that accompany economic decentralization, and countless incidents of grievances against China’s trade practices by WTO members.

The researchers do not delve conclusively into the factors that contribute to the unprecedented successes of China’s experiment in systemic transformation. However, one qualitative variable that seems to be instrumental in ensuring the orderliness and stability of the development process is tight political control and close macro supervision. It may, therefore, be suggested that further research be conducted to validate or refute the thesis that “benign dictatorship” may help ensure orderly progression toward envisioned development objectives, especially during the early stages of structural transformation.

Much has been accomplished. Nevertheless, the burgeoning capitalist spirit in China has concurrently revealed institutional flaws and critical financial anomalies. As discussed in this paper, acute irregularity in real estate and in the financial markets are only two of the more serious aberrations faced by China’s policy designers and decision makers. Alternately stated, amidst China’s bountiful accomplishments there are also copious flaws. The flaws require the pressing mindfulness of the central government to ensure China’s continual path towards improving the well-being of its growing population.

Author Biographies Raphael Shen, SJ, is Professor of Economics at the University of Detroit Mercy. He holds a B.A. degree from Berchmans College and M.A. and Ph.D. degrees from Michigan State University. His publications and presentations have dealt mainly with transitional economies in Eastern Europe. Shen’s most recent publication is The Political Economy of China’s Systemic Transformation: 1979-Present: Successes, Obstacles, Anomalies (with Dr. Victoria Mantzopoulos.)

Dr. Victoria Mantzopoulos is Professor and Chair of Political Science, Director of PreLaw, and Chair of Economics Department at the University of Detroit Mercy. She holds a B.A. degree from Ball State University and M.A. and Ph.D. degrees from Wayne State University. Dr. Mantzopoulos’ publications and professional presentations have dealt with China’s political economy and public opinion polling. She has also published several statistics textbooks. Her most recent book is The Political Economy of China’s Systemic Transformation: 1979-Present: Successes, Obstacles, Anomalies (with Raphael Shen, S.J.) Dr. Mantzopoulos is a member of the Midwest Economic Association and the Midwest Political Science Association.

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since Asian ’98 crisis. Bloomberg. Retrieved April 11, 2012 from http://www.bloomberg .com/news/2012-01-13/china-foreign-exchange-reserves-drop-for-first-quarter-in-morethan-decade.html Chen, X. (2008, August 5). Reforming tax burdens in China's rural regions: The first time a change that took place in 2600 years. Hexum.com. Retrieved August 10, 2010, from http://news.hexun.com/2008-08-05/107924192.html Credit Web Net. (2010, August 2). China’s real estate development annals. Retrieved August 12, 2010 from http://www.zhongyian.com/zhongyiandongtai/90/n-12690.html Durden, T. (2010, July 14). China has been covertly funding a housing bubble five times larger than that of the US: 65 million vacant homes uncovered.

Zerohedge.com. Retrieved from http://www.zerohedge.com/article/china-has-beencovertly-funding-housing-bubble-five-times-larger-us-65-million-vacant-homesGeographic.com. (2005, March 27). China housing. Retrieved July, 27, 2010 from http://www. photius.com/countries/china/geography/china_geography_housing.html Hutton, W. (2009). The writing on the wall: China and the West in the 21st Century. Taipei: Yuan-Liou Publishing Co. Ltd. Jubak, J. (2010, June 1). Real estate sales fall by 70% in Beijing and Shanghai on China property tax fears. Jubakpicks.com. Retrieved August 15, 2010 from http:// jubakpicks.com/2010/06/01/real-estate-sales-fall-by-70-in-beijing-and-shanghai-onchina-property-tax-fears.html Li, P. (2006a, February 16). The Prime Minister’s Annual Report in 1992. The Central People’s Government of the People’s Republic of China. Retrieved July 25, 2008 from http://202.123.110.5/test/2006-02/16/content_ 200922.htm Li, P. (2006b, February 16). The Prime Minister’s Annual Report in 1993. The Central People’s Government of the People’s Republic of China. Retrieved July 25, 2008 from http://202.123.110.3/test/2006-02/16/content_200926.htm Lunwenda.com. (2008, April 15). An analysis of ‘The Changing Environment’ phenomenon and ‘The Localization Trend’ characteristics during the process of our economic reform.

Retrieved August 14, 2010 from http://www.lunwenda.com/jingjixue200804/4212/ Mantzopoulos, V., & Shen, R. (2011). China’s economic restructuring through induced capital inflows. Journal of International Business and Cultural Studies, 4, 104.

Ministry of Commerce, People’s Republic of China (2002, May 29). What will become of our high tech products consequent upon WTO membership. MOST.gov.cn. Retrieved August 10, 2007 from http://www.most.gov.cn/gxjscykfq/dtxx/200205/t20020529_ 8990.htm Ministry of Commerce, People’s Republic of China. (2011, Jan. 30). Brief statistics on China’s import & export in December 2010. Ministry of Commerce People’s Republic of China. Retrieved March 7, 2011 from: http://english.mofcom.gov.cn/aarticle/statistic/ BriefStatistics/201101/20110107386812.html Money Matters. (2010, July 23). Asia China housing bubble China miracles. Retrieved

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from http://www.minyanville.com/businessmarkets/articles/asia-china-housingbubble-china-miracles National Bureau of Statistics of China. (2008). China Statistical Yearbook 2008. Beijing: China Statistics Press.

National Bureau of Statistics of China. (2011). China Statistical Yearbook 2011. Beijing: China Statistics Press.

National Bureau of Statistics of China. China Statistical Yearbook 2011. Retrieved October 2, 2012, from http://www.stats.gov.cn/tjsj/ndsj/2011/indexeh.htm People’s Daily Online. (2010, February 24). China’s rural population to halve in 30 years: Economist. Retrieved July 30, 2010, from http://english.peopledaily.com.cn/90001/ 90776/90882/ 6901672.html Quinn, J. (2010, August 9). Why the China miracle is really a debt-financed bubble.

Minyanville.com. Retrieved August 16, 2010 from http://www.minyanville.com/ businessmarkets/articles/asia-china-housing-bubble-china-mriacle/8/9/2010/id/29519 Shen, R. (2000). China’s economic reform: An experiment in pragmatic Socialism. West Port, Connecticut: Praeger.

Trading Economics. (n.d.). China Stock Market Index. Retrieved August 16, 2010 from http://www.tradingeconomics.com/Economics/Stock-Market.aspx?Symbol=CNY. Xue, M. (1982). Current economic problems in China. Boulder, Colorado: Westview Press.

Wen, J. (2008, March 20). The Prime Minister’s Annual Report in 1998. People.com.cn.

Retrieved July 25, 2008 from http://npc.people.com.cn/GB/28320/116286/116587/ 7021687.html Wen, J. (2009, April 9). Strengthen supervisory system, ensuring no abuse of authority.

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Table 1 Capital Inflows (1979-2010)

Year

1979-1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Utilization of Foreign Capital (Unit USD, 100 Million) Foreign Loans Foreign Direct Investments (I) (II) Number of Projects Value 169.78 3724 97.50 35.34 3073 63.33 84.07 1498 33.30 78.17 2233 37.09 98.13 5945 52.97 51.85 5779 56.00 50.99 7273 65.96 71.61 12978 119.77 107.03 48764 581.24 113.06 83437 1114.36 106.68 47549 826.80 112.88 37011 912.82 79.62 24556 732.76 58.72 21001 510.03 83.85 19799 521.02 83.60 16918 412.23 22347 623.80 26140 691.95 34171 827.68 41081 1150.69 43664 1534.79 44001 1890.65 41473 1937.27 37871 27514 23435 27406 27712

Note. National Bureau of Statistics of China, 2011. China’s Statistical Yearbook, 2011.

Beijing: China Statistics Press, p. 240.

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Table 2 China’s Foreign Trade 1978–2010 (Select Years)

Year 1978

Total Exports 9.8

1985 1990 1995 2000 2005 2006 2007 2008 2009 2010 2011

27.4 62.1 148.8 249.2 762.0 969.0 1220.4 1430.7 1201.6 1577.8 1898.4

Billion U.S. $ Total Imports 10.9 42.3 53.4 132.1 225.1 660.0 791.5 956.1 1132.6 1005.9 1396.2 1743.5

Balance –1.1 –14.9 8.7 16.7 24.1 102.0 177.5 264.3 298.1 195.7 181.5 154.9

Note. National Bureau of Statistics of China, 2011. China’s Statistical Yearbook, 2011. Beijing: China Statistics Press, p. 220.

Table 3 Macro Indicators, 1979-2010 (Select Years) Year

1979 1985 1990 1995 2000 2005 2006 2007 2008 2009 2010 2011

GDP (billion Yuan)

406.3 901.6 1866.8 6079.4 9921.5 18493.7 21631.4 26581.0 31404.5 34090.3 40120.2 47288.2

Composition of Gross Domestic Product Primary Industry (%)

Secondary Industry (%)

Tertiary Industry (%)

31.3 28.4 27.1 19.9 15.1 12.1 11.1 10.8 10.7 10.3 10.1 10.0

47.1 42.9 41.3 47.2 45.9 47.4 48.0 47.3 47.5 46.3 46.8 46.6

21.6 28.7 31.6 32.9 39.0 40.5 40.9 41.9 41.8 43.4 43.1 43.3

Per Capita GDP (Yuan)

419 858 1644 5046 7858 14815 16500 20169 23708 25608 29992 35181

Note. National Bureau of Statistics of China, 2011. China’s Statistical Yearbook 2011. Beijing: China Statistics Press, p. 44-45.

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Figure 1. Capital inflows, select years.

Figure 2. China’s foreign trade.

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Figure 3. Composition of gross domestic product.

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An Intuitive Approach for Teaching the Central Limit Theorem Brian J. Huffman University of Wisconsin – River Falls Hossein Eftekari University of Wisconsin – River Falls

Abstract Studies have shown that students do not understand the Central Limit Theorem (CLT) regardless of how it is taught. True understanding of the CLT means that the student has not only acquired the ability to merely do calculations, but also the intuitive understanding of what he or she is actually doing. The best test of a student’s intuitive understanding is whether or not he/she has the graphical insight needed to draw a sketch that roughly predicts the shape and mean of the sampling distribution before any calculations are done.

This research proposes a new instructional method, which is intended to impart this fundamental intuitive understanding of the CLT. The approach incorporates three population distributions, which have been chosen for their widely varying shapes and their simplicity. Students use physical objects (cards and dice) to conduct hands-on experiments with each of the three populations to produce its corresponding sampling distributions. The distinction between the population and sampling distributions is emphasized at every opportunity while the arcane terminology and technical details of the CLT are scrupulously avoided (at least initially).

This new method has been tried in several sections of introductory statistics and operations management (both undergraduate and MBA classes) over three terms from the fall of 2012 to the spring of 2013. Preliminary results suggest that this method is effective.

Key words. Central Limit Theorem, Pedagogy, Statistics Exercises, Active Learning, Hands-On Activities

The Central Limit Theorem (CLT) can be paraphrased as follows: it may be assumed that sample averages taken from an infinite population are normally distributed, even if the samples come from non-normally distributed populations, provided that 1. the sample size is the same for all samples, 2. samples are independent of one another,

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3. the population distribution has a finite mean and standard deviation, and 4. the sample size is large enough.

Studies have shown that students do not understand the CLT regardless of how it is taught. Pfaff and Weinberg (2009) found, for example, that hands-on activities did not lead to improved student understanding of the theorem. Roberts and Pierce (1999) explored three modes of instruction: chalk and talk, hands-on activities, and computer-based simulations; they found no significant difference in student comprehension among the modes. Worse still, they found that none of the three modes was particularly effective for teaching the CLT. Students have difficulty learning the CLT, in part, because it flies in the face of human intuition. For example, Tversky and Kahneman (1974) found that the fact that standard error decreases with sample size is not merely hard to understand but actually counterintuitive. Given the counterintuitive nature of the CLT, it is especially important for instructors to proceed slowly in their lectures because students, grasping to understand, will tend to build mental models that are not only incorrect but nearly impossible to dislodge.

Intuition is the heart of the problem. The literature shows that even students who can correctly calculate the mean and standard error of the sampling distribution may, nevertheless, fail to understand what they are actually doing. Their mental models are completely wrong and, as just mentioned, will remain solidly intact despite the instructor’s futile attempt to change them. The literature calls for an approach to make the CLT more intuitive.

The current (nonintuitive approach) for teaching the CLT imparts either no intuition or the wrong intuition in most students. This is due to many problems in the usual teaching methodology. First, there is often an inadequate emphasis of the distinction between the population and its corresponding sampling distribution. Second, there is often an attempt to teach too much at once. Third, the current methodology usually fails to have students make predictions (of the sampling distribution’s shape and parameters) so they cannot subsequently learn from the errors they would probably make in their predictions. Fourth, the current methodology often incorporates population distributions that students may not thoroughly understand. Fifth, the population distributions used may not represent a wide enough range of possibilities so students may fail to see that the CLT works despite the shape of the population distribution. And finally, instructors may use prepackaged or computer-generated data or distributions, which the students may suspect have been trumped up to prove the CLT. The next section reviews the literature on teaching the CLT. Following that, the intuitive approach to teaching the CLT is described in detail. The paper ends with a discussion of our experimental design and results (at least as far as possible given that the method is still in the process of being evaluated.)

Support for the New Approach for Teaching the CLT Ryan (2006) states (somewhat surprisingly) that it is common for statistics students not to realize that population distributions and sampling distributions are two different things. That is,

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students fail to realize what they must minimally realize to have even the least understanding of the CLT. To correct this problem, he suggests two ways to emphasize the distinction between the distributions. First, he recommends that the instructor avoids trying to teach all the technical details of the CLT at once. When students are simultaneously dealing with new terminology, the characteristics of the sampling distribution, and mathematical tools; it is easy for them to miss essential facts…even something absolutely key to their understanding such as the fact that the sampling distribution is different from the population distribution. Ryan notes that this particular failure to distinguish between the population distribution and the sampling distribution leads to other problems including “the misconception that the standard error measures the variability of individual scores rather than the variability of the sample means” (2006, p. 181). Even worse, once the student has constructed the wrong mental model, he or she will actually disregard correct information to the extent that it contradicts his or her erroneous mental model.

Ryan’s second suggestion for getting students to see that they are dealing with two different distributions is to have them (manually) generate the sampling distribution data themselves. He cites research that shows that self-generated data is especially beneficial for the difficult task of destroying misconceptions because it is much harder for the student to disregard.

The approach to teaching the CLT recommended here incorporates both of Ryan’s suggestions and takes an additional step by having the students generate not only the sampling distributions, but also the original population distributions. Three easy-to-understand population distributions are used. These population distributions concern everyday physical objects, cards and dice, and should be easy for students to generate.

The pedagogical effectiveness of using population distributions dealing with physical objects also has support. Dyck and Gee (1998) and Johnson (1986) all suggest that the use of physical objects is preferred to theoretical distributions from textbooks or computer simulations.

For example, Johnson notes that students have a hard time understanding purely abstract theoretical distributions, and they often express the need for concrete demonstrations.

Although computer simulations could be used to derive both the population distribution and the sampling distribution and, therefore, reduce the class time needed to do these tasks, the computer’s inner processes are unknown to the student. The use of computer simulations makes it too easy for the students to fail at seeing even obvious outcomes, such as the fact that the sampling distribution of the mean turns out to be normal despite the shape of the population distribution. The student’s mind drifts because the computer is doing all the work, and the work it is doing is a mystery.

Rossman and Chance (2000) also argue in favor of physical simulations, and they note that students fail to connect computer output (either graphical or numeric) with the process being simulated. Lunsford, Rowell, and Goodson-Espy (2006) bluntly conclude that computer simulations are simply not enough, and they conclude that students need to have a “directed hands-on experience” (Section 5.1 Conclusions and Conjectures, #1). Likewise, delMas, Garfield, and Chance (1999) found that after using what they considered to be “excellent software” students still demonstrated a lack of understanding and “troubling misconceptions”

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regarding sampling distributions. In that paper, the authors even cite research showing that students have developed incorrect assumptions regarding sampling distributions as a result of their experience with computer simulations. Finally, Velleman and David (1996) commented that students demonstrate greater “ownership” of data if they generate it themselves. That is, students have no doubt that the distributions they are generating are real.

In addition to being understandable, the three population distributions used in this study have another desirable characteristic: they have widely-varying shapes, so students may be especially surprised to see that all three, nevertheless, yield sampling distributions with the same normal shape. Also, because sampling distributions are generated three times (once for each population distribution), there is an increased chance that students will recognize the distinction between population distributions and sampling distributions.

Another feature of the intuitive approach is that students are asked to verbally and graphically predict the sampling distributions’ shapes and parameters. That is, the students are to say what they expect and to draw a sketch of a distribution reflecting that expectation. The students are also asked to explain the differences between their predictions and the sampling distributions’ actual shapes and parameters.

Chi, DeLeeuw, Chiu, and LaVancher (1994) found that students who explain to themselves what they are observing develop better problem-solving skills and understand the material better. They also found that self-explanation is just as effective if it is required by an instructor as it is when done on the student’s own initiative. delMas et al. (1999) discovered, as noted earlier, that it is nearly impossible to get students to change their misconceptions regarding the CLT. In order to solve this problem, they looked to research on conceptual change and found that students will only reject a misconception if they are confronted with the ramifications of that misconception. It is not sufficient for the instructor to merely point out the student’s error.

Thus, in the intuitive approach, students must predict the parameters of the sampling distributions, and then they must explain the differences between those predictions and the actual sampling distributions observed.

The intuitive understanding of the CLT being pursued here is similar to that being sought by delMas et al. (1999). delMas does not use the concept of “intuition” but nevertheless tests the student’s intuitive knowledge via graphically-based questions. It is argued here that these graphically-based questions are the best way to check the student’s intuitive understanding. If a student can produce an accurate graphical prediction of a sampling distribution; that is, if he/she sketches a bell curve with the same mean as the population distribution and a width of about six times the standard error, then it is clear that he or she understands the CLT. Furthermore, if the student sketches a bell-shaped curve half as wide as the first predicted sampling distribution when asked to show what happens when the sample size is quadrupled, then he or she fully comprehends the CLT. On the other hand, students may be able to mathematically predict the mean and standard error of a sampling distribution and still not have the intuitive understanding needed to sketch what they expect to see. Thus, the student’s ability to sketch what he/she expects to see is a

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better test of his/her grasp of the CLT than his/her ability to correctly perform mathematical calculations. Indeed, Lunsford et al. (2006) found that mathematical ability was actually a hindrance when it came to understanding the CLT. It was the more mathematically inclined students who “did not seem to be able to extend their knowledge to a more graphical realm.”

Although these students could calculate better than their less talented contemporaries, they were worse at understanding what they were really doing.

A number of other authors have found that students will learn the CLT better if attention is given to building their graphical intuition. Rossman et al. (2000) promote the importance of what they call visualization. In fact, one of their top three recommendations for teaching statistics is to use visual displays of data throughout the entire statistics course (not just when teaching the CLT) and at all stages of statistical problem solving. They even emphasize the importance of visual displays before carrying out purely mathematical inference procedures (because initial graphical analysis may reveal more than could a significance test or confidence interval…including whether or not the use of a particular test is even appropriate).

Garfield, delMas, and Chance (n.d.) offer pretests and posttests for assessing the student’s intuition. Questions from those tests were used in this study (with some modification). Graphics like those used in the tests are also incorporated into the intuitive method for teaching the CLT. Finally, students have trouble learning the CLT because instructors try to do too much at once. This error is almost unavoidable because the instructor, familiar with every detail of the CLT, speaks reflexively in the language of the CLT. The instructor will often refer to a particular “parameter” of the sampling distribution such as its “standard deviation.” He or she will say that this parameter is now called the “standard error.” The instructor will not be aware that two foreign terms (“standard deviation” and “standard error”) have just been used to describe a foreign aspect (“parameter”) of a foreign distribution (“sampling distribution”). This foreign language, combined with the lack of graphics, is perhaps, the primary reason that students develop the incorrect mental models or false intuitions that are so resistant to change.

To prevent the foreign language problem, the intuitive approach avoids trying to teach too much at once. In the early stages of CLT instruction, the instructor is to avoid most technical terms and qualifications while focusing exclusively on building a graphical intuition. The population distribution and sampling distribution are to be referred to as the “input distribution” and “output distribution,” respectively. The standard deviations of both distributions are to be referred to as the “standard deviation” even though the term “standard error” would be more precise with respect to the sampling distribution. The use of simpler language instead of the arcane terminology of statistics may be uncomfortable for the instructor, but it greatly reduces the number of facts/concepts the student must try to absorb while simultaneously attempting to grasp the CLT’s essential ramifications.

The qualifications that the instructor is to initially avoid include the fact that the CLT requires infinite populations, independent samples, finite means, standard deviations, and adequately large samples. The student cannot simultaneously absorb such minutia while he or she is struggling to understand the big picture. He or she will learn one, the other, or neither. It

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is certainly necessary for students to eventually learn these details, but students will never really learn them if they fail to first internalize the CLT’s essential nature.

The Proposed Teaching Method In order to make claims about the effectiveness of the intuitive method for teaching the CLT, it was necessary to develop a test to assess that effectiveness. The test (“Test Version 1”, Appendix A1) was used to assess the students’ knowledge of the CLT after the CLT was taught in both the experimental groups and control groups in the statistics classes. The test was also used both before and after the CLT was retaught in classes that had statistics as a prerequisite.

All appendices mentioned here are not included with this paper, but are available from the authors upon request.

A better test had been devised by the start of the spring semester of 2013 (“Test Version 2”, Appendix A2). This second version of the test has so far only been used in the Masters in Business Administration (MBA) class taught in the spring 2013 semester.

The tests evaluate both the student’s quantitative (mathematical) and qualitative or intuitive knowledge of the CLT. The student’s qualitative or intuitive knowledge is evaluated via graphical questions based on those found in Garfield (n.d.). The tests show the instructor whether or not the students can both do the calculations (understand the math) and whether or not they intuitively understand what the calculations mean (understand the graphics). It is possible that a student will be able to do the calculations and, nevertheless, not have the deep understanding or intuition needed to answer the graphical questions.

In those courses for which statistics is a prerequisite, the first step in the new method is to conduct either version of the pretest. Next, students are taught the concept of distributions (both discrete and continuous). This lecture is to be confined to distributions in general and intentionally avoid the topic of sampling distributions. The instructor must emphasize that the area under the probability density function corresponds to probability. The instructor must also emphasize that the sum of all the area under that function must be 1. That is, the probability that something happens is 1.00.

The students will learn those facts by determining and drawing for themselves the distributions (the relative frequency histograms) of three widely differing populations. The first distribution is bowed up in the middle, the second distribution is flat (uniform), and the third distribution is bowed down in the middle. Thus, the population distributions will be as different from each other as any three discrete symmetric distributions can possibly be. It should be noted that asymmetrical distributions and continuous distributions (and distributions that are both asymmetrical and continuous) are avoided for several reasons: 1. They are harder for the students to develop. Even most graduate students could not be expected to have the mathematical ability to generate the probability density function for a continuous distribution such as the normal curve.

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2. It is harder for the students to calculate areas (probabilities) in continuous distributions than it is for them to add up the areas in a relative frequency histogram.

The former requires integral calculus, while the latter involves simple addition.

3. Asymmetrical distributions will not yield bell-shaped-curve sampling distributions for small sample sizes. Also, having students draw large samples and then calculate means would take too much class time. In other words, it is possible to get the “right” results with much smaller sample sizes than the CLT requires if symmetric population distributions are used.

The three distributions the students need to develop are the result of a single roll of two dice, a single roll of one die, and the probability of drawing a particular card from a deck of cards (see Appendix B for details). These distributions have four key attributes that make them especially useful for increasing student learning: they are very differently shaped from each other, they involve physical objects, their probabilities are easy for students to determine, and they can be tested empirically in the classroom.

Because the students will be able to develop these theoretical population distributions themselves, they will not suspect them of being trumped up for the purpose of proving the CLT. Because subsequent experimental results will certainly conform to the theoretical distributions, the students will have even more confidence in the theoretical distributions’ validity.

The second distribution (the result of a roll of two dice) will be used to illustrate what has just been asserted. The probability distribution for a single roll of two dice is easy to determine mathematically (again see Appendix B for more details.) The students can enumerate every one of the 36 possible outcomes of a roll of two dice and see how many of them are 2, 3, 4, … 12; the counting will be much easier if the students write every possible outcome on the board.

Thus, the students will have written exactly 6 ways to get a seven on the board. That is, among the 36 outcomes on the board with be 6 in which the first and second die show 1 and 6, 2 and 5, 3 and 4, 4 and 3, 5 and 2, or 6 and 1, respectively. Those will be the only outcomes on the board that total seven. Because there are 36 possible outcomes and only those 6 total seven, the students will have proved to themselves that the probability of getting a seven is 6/36. In this way, the students will find that the population distribution of a single roll of two dice is bowed up in the center with the probability of each outcome increasing in steps of 1/36 from the probability of getting a two (of 1/36) to the probability of getting a seven (of 6/36), and then decreases in steps of 1/36 to the probability of getting a 12 (of 1/36). The students will then roll actual dice to confirm to themselves that the empirical results they experience are very similar to that predicted population distribution.

After the students have developed all three population distributions and convinced themselves of their validity via empirical evidence, they are asked to predict the shape and mean of a distribution of the averages of an infinite number of four observations taken from each of distribution. That is, they will be asked to predict the shape and mean of the sampling distribution for each of the three populations. The term “sampling distribution” will not yet be used. These predictions of the sampling distribution shape and mean must be both verbal and

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graphical; the students must say and draw a sketch of what they expect to see. It does not matter if the students’ predictions are correct; all that matters is that they make the predictions. In fact, they may learn more if their predictions are completely erroneous.

Just as the students experimentally proved the validity of the theoretical distributions they derived for the populations, they conduct experiments to prove (or disprove) their predictions regarding the sampling distributions. The details of these experiments, along with some tools used to save class time and effort (paper forms and an Excel workbook), are described in Appendix C. Once the students have finished compiling data from their experiments, they use it to both draw and verbally describe the sampling distributions. Again, during this time, the population distributions will be referred to as “input” distributions, and the sampling distributions will be referred to as “output” distributions. This has two benefits. First, because the terms are opposites (“input” and “output”) their use emphasizes that there are two distinct distributions. Second, the use of “input” and “output” allows the instructor to postpone the introduction of confusing technical jargon. The students will discover that despite the fact that three widely differing “input” distribution shapes were used, all three “output” (sampling) distributions have the same bell-shape and are narrower than their original corresponding population distribution.

The instructor should not point out the similarity in shape among the “output” distributions; the students themselves must discover that the distribution of the sample averages is bell-shaped regardless of the shape of the population distribution. Note: Technically, the sample sizes should be 30 or larger in order for the CLT to hold. However, because none of the input distributions are skewed (all are symmetrical) the sampling distributions will, nevertheless, be roughly normal even with a small sample size of four. This is important because the time required to do the experiments will be much less than if very large samples were to be used.

The students’ intuitions will only be properly developed if they are guided to discover the ramifications of the CLT for themselves. Students will have a much better chance of giving up their misconceptions if they both discover and correct those misconceptions themselves. Again, the instructor must refrain from any statement regarding the relative shapes of the input and output distributions including the obvious fact that all three output distributions must have the same shape despite having very different input distribution shapes. The students may be led to the correct conclusions regarding these shapes via the following questions: 1. How do the shape and mean of the output distributions compare with your predictions?

Again, the task of recognizing and explaining one’s own errors is the essential component to the model of conceptual change. The students should be able to see that they ended up with “output” (sampling) distributions, which were narrower and more bell-shaped than their corresponding “input” (population) distributions. They will also see that the mean of each output distribution is roughly the same as that of its corresponding input distribution. The differences between these outcomes and the students’ predictions should be heavily emphasized. It is essential that the students

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are made to both recognize and explain their own prediction errors with as little prompting as possible. No words of explanation from the instructor will be as effective in correcting the individual student’s mental model as that student’s successful struggle to explain why he or she is wrong. a. In the MBA class, the students were actually set up to make poor predictions.

The instructor “wondered” aloud about whether or not the shape of the “output” (sampling) distributions might be similar to the “input” (population) distributions. Many students took the bait and drew output distributions with shapes similar to the input distributions. b. Also in the MBA class, the students were asked to look at the sample averages to see if they were clustered around a single number, as must be the case in a normal distribution (which has only one peak…is unimodal). The instructor attempted to mislead the students by suggesting that it would not be true for the sample averages of four cards because that input distribution had its highest frequencies on the edges (is bimodal). That is, the instructor intentionally promoted the false suggestion that the sample means would fall into two clusters (one around a high number and one around a low number) so the output distribution would be bimodal (it would have the same shape as the input distribution). This trick may have enhanced student learning since they discovered that a reasonable expectation (so reasonable it was promoted by the instructor) was clearly false.

2. How do the three output distributions compare with each other? The students should see that all three output distributions are bell-shaped despite having come from a wide range of population distribution shapes. Again, the range of shapes is as “wide” as possible given that there are only three symmetric distributions. The importance of the fact that the “output” distributions all have the same shape should be emphasized only after the students have recognized the similarity of the shapes for themselves.

The instructor can then say that the output distribution will always be approximately normal. The instructor can further state that this is a very important result since the shape of the input distribution may not be known, so it may be impossible to make conclusions based on single observations from that distribution. But it would nevertheless always be possible to make conclusions based on sample means because the distribution of sample means will always be normal. For example, the instructor might say that it would ordinarily be impossible to make any conclusions about an industrial process by looking at a particular dimension of a single unit of output (such as weight, length, and pH) because the shape of the industrial process distribution would generally be unknown; however, it would still be possible to make conclusions about that process by looking at variations in sample means. Again, the phrase “sampling distribution” would not be used yet; that distribution would still be strictly referred to as the “output” distribution or the distribution of sample averages.

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3. If you could not use the term “input” and “output,” what would you call the two distributions to distinguish them from each other? It is worth letting the students make naming suggestions for at least five minutes. This exercise drives home two points: there are two distinct distributions being discussed, and the name “sampling distribution” is not especially evocative. After the students are finished with name suggestions, the real names are given.

4. What would you call the standard deviation of the sampling distribution? Again, it is worth letting the students take plenty of time coming up with names for the same two reasons: it further drives home the point that there are two distributions being discussed, and the name “standard error” is also not especially evocative. Again, only after a thorough discussion is “standard error” finally given as the correct term.

Next, the students are asked what they think would happen to the shape of the sampling distribution as the sample size increases. Their intuition is aided by asking questions like these (in the two-dice-roll case): 1. What is the only way the average of two rolls of two dice can be 2?

2. What is the only way the average of two rolls of two dice can be 12?

3. What is the only way the average of 1,000,000 rolls of two dice can be 2?

4. What is the only way the average of 1,000,000 rolls of two dice can be 12?

The students should be able to see (maybe with some prompting) that the only way these extreme averages can happen is if the same number comes up on every roll of the dice. For example, the only way the average of 1,000,000 rolls of two dice can be 12 is if a 12 comes up on every roll. Hopefully, the students should easily see (again perhaps with a bit of prompting) that the chances of the same number coming up on 1,000,000 rolls is far less than the chances of that happening on just two rolls. In this way, the students will have proved to themselves that the tails of the sampling distributions must shrink as sample sizes get large.

Furthermore, once the students are reminded that the area under all sampling distribution curves must be 1, it should be relatively easy to get them to realize that the sampling distribution of the average of 1,000,000 rolls must have relatively more area in the middle to make up for its very thin tails. The students could be asked questions about how many ways the average of two rolls of dice can be 7. At this point, the students would be asked to make a rough sketch of the sampling distributions for two and 1,000,000 rolls. They should now have the intuition to draw the distribution of average of 1,000,000 rolls with much higher peaks centered much closer to the mean (of 7) and much flatter tails than the distribution of the average of two rolls. Both curves should, of course, be drawn with the same mean.

At this point, the CLT is formally presented with all its qualifications and precise terms.

Because the students should already have an intuitive sense of the theory, they should not develop any misconceptions as its details are filled in. With that in mind, the instructor should use every opportunity to emphasize the distinction between the sampling distribution and the population distribution.

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Experimental Design In this study, student learning from a new, recommended approach to teaching the CLT is compared with student learning from other methods. The CLT has always been taught or, to be honest, retaught as part of a section on statistical process control in an undergraduate Operations Management class at the authors’ university. The students should have understood the CLT on the first day of this class because statistics is a prerequisite, but it has been clear for years that this is simply not the case.

Because all of the professors who teach the statistics prerequisite are generally recognized as great teachers (one is the most recent recipient of this college’s award for teaching excellence), it seemed unlikely that teacher quality had anything to do with the students’ lack of understanding. Indeed, as research into this problem confirmed, students everywhere fail to grasp the CLT. As has been shown, the new intuitive method for teaching the CLT was designed based on the recommendations from the best research to date. It was decided that the best way to test the pedagogical effectiveness of the new method was to try it in three completely different venues. It was, therefore, tested in the statistics course which is a prerequisite for most upper division classes in the authors’ college. It is in that course that the CLT is first introduced. It was also tested in an undergraduate class in operations management, and a Masters in Business Administration (MBA) class in operations management. Both of the undergraduate and graduate operations management classes rely on the students’ having preexisting knowledge of the CLT for the purpose of understanding lectures on statistical process control.

In the statistics class, the new intuitive method of teaching the CLT was used in one section, and the old method of teaching it was used in another. The relative performance of the students in the two sections was used to make conclusions regarding the new method’s efficacy.

This experiment was performed in the fall of 2012. Only one section of the statistics class was taught in each of two terms: the winter and spring of 2013. The new method was used in those terms without a corresponding control group.

The new method was used to reteach the CLT in one undergraduate section of operations management in both the fall of 2012 and the spring of 2013. It was also used in one MBA section of operations management in the spring of 2013. In these three cases, there were no control groups for comparison, but the instructor was still able to make some conclusions based on having taught both classes many times.

The students’ understanding of the CLT in the undergraduate operations class was especially important to the college because that class was used to evaluate student retention of statistical knowledge as part of the maintenance of AACSB (the Association to Advance Collegiate Schools of Business) accreditation. The AACSB is interested not only in whether or not students learn something, but also in whether or not they retain that knowledge after a course is completed. In this case, it was questioned if students in operations management show that they have retained what they had learned in statistics class. The choice of operations

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management as the course in which to measure the retained statistical knowledge was completely incidental: any course that relied on knowledge of statistics could just as well have been chosen.

In addition to the three exams that have always been given in the undergraduate operations management class, a new statistical process control case was developed specifically for the AACSB statistics-knowledge-retention evaluation. Thus, the students’ inadequate understanding of the CLT has been further highlighted by this increased level of assessment.

Experimental Results In the Pretest-Version 1 found in Appendix A1, questions 2, 5, 6, and 7 have to do with the issue of graphical understanding. Questions 8 and 9 have to do with whether or not the students can distinguish between the population distributions and the sampling distributions and whether or not they have learned the basic CLT terminology. The following subsections show the results for testing by using Version 1 over three academic terms: Fall 2012, Winter 2013, and Spring 2013 in the statistics class in which the CLT is ostensibly taught and in the operations management classes that depend on previous knowledge of the CLT. Fall 2012 ECON 226 (Undergraduate – Introduction to Statistics). The pretest was used as a posttest in the introduction to statistics class because the students in the two sections of that class would have no prior knowledge of statistics or the CLT. The two sections were taught in the fall of 2012; one section was taught using the new intuitive method, and the other section (the control group) was taught using the old approach. The pretest was used in this situation because we wanted to use the same test that would be used as a pretest in the operations management classes. Thus, the test served two purposes: to compare learning in the two statistics sections (as can be seen in Table 1), and to show how much knowledge is retained from the statistics class’ end to the beginning of the operations classes that use the statistics class as a prerequisite.

As can be seen in Table 1, the test group did as good as or better than the control group on every question of the pretest with the exception of question 7, which tests whether or not the student realized that the population distributions and the sampling distribution would have the same mean. The test group outperformed the control group on two of the questions regarding the sampling distributions’ shape (questions 5 and 6). Students in the test group were more likely to recognize that the sampling distribution gets more narrow as the sample size increases (question 5), and they were more likely to recognize that the sampling distribution will be normal regardless of the population distribution’s shape (question 6). Thus, it seems that the new approach did impart a superior graphical knowledge of the CLT. The fact that the control group did better on question 7 was due to an oversight in teaching the new method, which was corrected later. The students in the test group were not taught how the means of the population distributions and the sampling distributions compare.

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The control group did better because the fact that the means should be the same was emphasized in that section.

MNGT 361 (Undergraduate - Operations Management). The students were surprised by the pretest (Version 1) because it was given without warning and before any refresher lecture on the CLT. They were, however, assured that the test was not going to be used for grading purposes but rather to merely evaluate where they were at the beginning of the CLT lecture. It was hoped that this would provide enough motivation for them to try to do well. The students only did well on questions 1, 2, 3a, 3b, and 3c. Of these, only question 2 was of interest to this study, and that question was the easiest of the three questions that dealt with the sampling distribution shape. Thus, the students’ performance on the questions of interest (2, 5, 6, 7, 8, and 9) was very poor with the exception of their performance on question 2 (as noted).

The students were surprised with the same test at the end of the semester. The test was given to them more than five weeks after the CLT material was presented and more than four weeks after they had taken the exam that related to statistical process control, so they could reasonably have thought that their knowledge of statistics would no longer be needed.

Although four weeks is not an especially long period of time for the students to begin forgetting knowledge, it was the longest period of time that could reasonably be achieved without completely reordering the class material. Table 2 shows the difference between student performances from the first attempt on the exam and the second attempt on the exam on six questions of interest.

As in the introduction to statistics class in this same semester, the oversight in teaching the CLT via the new method meant that the students were not likely to do any better on question 7 after they were retaught the CLT. The students also did not improve on question 2; this was expected because they had scored very well on that question in the pretest. Furthermore, in retrospect, question 2 was the sort of question a student might answer correctly even if he or she had forgotten everything. Question 2 asks the student to match a graphical representation of a sampling distribution with its corresponding population distribution. Unfortunately, the population distribution in that problem is normal so a student might guess that the sampling distribution would be normal even if he or she incorrectly thought sampling distributions should always look like population distributions.

The students showed significant improvement on questions 5, 6, 8, and 9. The increase in correct responses was 37.0%, 20.1%, 14.3%, and 34.4%, respectively. The improvement on questions 8 and 9 would seem to be especially significant because those questions were not multiple-choice questions.

Winter 2013 ECON 226 (Undergraduate – Introduction to Statistics). There was only one section of the introduction to statistics class in the winter term, so it was not possible to compare an

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experimental and control group. Instead, the students were given a quick lecture on the CLT, tested, and then retaught (via the new method) and retested. Improved student performance on questions 2, 5, 6, 7, 8, and 9 was much more impressive in January than in the fall, but the comparison was quite different. In this case the comparison involved the same students and looked at their understanding of the CLT after having learned it the old way and then having had it retaught the new way. In the fall the comparison was between different students; one section of students taught by the new and one by the old methods. Table 3 shows the improvement in the same student learning measured during the winter 2013 term.

Spring 2013 MNGT 361 (Undergraduate - Operations Management). As in the fall semester, the students were surprised by the pretest (Version 1). Again, they were assured that it was used only to evaluate their knowledge at the beginning of the lecture instead of grading purposes.

As before, the results on the pretest were also grim. The best the students did was 73.7% on question 2. Again, this probably had more to do with the fact that question 2 is too easy.

Students’ performance on the other problems ranged from merely disappointing to terrible. Only 31.6% correctly answered question 6. The students generally did not expect the sampling distribution from a skewed distribution to be normal. Only 52.6% of the students thought the sampling distribution of means would have the same mean as the population distribution (question 7). Only 10.5% thought larger samples would produce narrower sampling distributions (question 5). None of the students knew the right name for sampling distribution or standard error (questions 8 and 9).

The students were evaluated again after they had been retaught the CLT. Again, the second administration of the test came as late as possible. It also came as a surprise to the class.

Table 4 shows the students improvement from the pretest to the posttest. As can be seen, the students scored significantly better on all the tough questions. The 5.0% improvement on the easy question (question 2) was welcome, but it did not seem to mean much for reasons already given.

MBA 705 (Graduate – Operations Management). The MBA students were given the pretest (Version 2 in Appendix A2) at the beginning of the lectures on statistical process control in which the CLT was needed and traditionally retaught. The CLT was covered in the second weekly class, and the exam on statistical process control, which covered the CLT, was given in the fourth week. This exam was one of three normally given in the class and is not to be confused with the tests in Appendix A. The students knew the exam was coming because it was on the syllabus, and they did much better on each aspect of the CLT than they had done in the pretest. However, because they had been taught the CLT prior to this class, it might have been reasonable to expect only a fairly small increase in their CLT knowledge, but they did much better on every question type. They

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did 13.6% better on their prediction of the shape of the sampling distribution, 34.7% better on general knowledge of the normal curve and its parameters, 36.7% better on predicting that sampling distributions would be more narrow as sample size increases, 50.8% better on their ability to recall the term “sampling distribution” and use it correctly, and 51.9% better on their ability to recall the term “standard error” and use it correctly.

The MBA students were further tested by the same surprise retesting that the other classes were given. Just as in the other classes, the MBA students reasonably expected that they would no longer have to worry about being tested on the CLT after the first exam, so it can be assumed that none of them would have made any special effort to retain what they learned. The students were given the pretest, Version 2 in Appendix A2, as a posttest.

Despite the fact that they were unprepared for this test, they showed improvement on all questions. The improvements went from a low of 11.0% on question 2 to a high of 43.9% on question 9. The results are shown in Table 3.

General Conclusions The results in the five tables above are of three types. Table 1 involves the comparison of an experimental and control group. Table 3 shows the improvement in the students’ CLT knowledge from after having just learned it the old way to having just learned it by the intuitive method. This comparison probably overstates the benefits to be expected from the new intuitive method because the students’ second scores were probably better not only because of the new intuitive method, but also better because the testing was after the CLT had been covered twice in rapid succession. Tables 2, 4, and 5 all present the same thing: improvement of student CLT knowledge from before it was retaught to after it was retaught using the new intuitive method. A few further observations can be made by considering these three tables.

If the results from question 7 on Table 2 are ignored; it will be seen that improvements on the remaining, more difficult questions (all remaining questions except question 2) are consistently very good. The percentages of correct answers on the shape questions (questions 5 and 6) all show a solid improvement (an increase in correct answers ranging from 20.1% to a high of 37.0%). Likewise, the percentages of correct answers on the term (vocabulary) questions show a very good improvement in the students’ statistical vocabulary.

Final Comments The initial results from using this new intuitive method in five classes over three terms seem promising. The design of experiments and the analysis of results have not been very sophisticated to date. However, evaluation of the method is ongoing, and better design approaches and better analysis approaches are being developed.

The tests that were used were fairly short. Furthermore, because the new method was not taught correctly during the fall term of 2012 due to an oversight regarding the sampling

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distribution mean, question 7 was not as useful as it should have been. More research is needed with a longer and better pretest for evaluation. Also, more accurate statistical inferences will be possible with larger sample sizes.

However, it is still the researchers’ belief that the new intuitive method is a major improvement in our approach to teaching the CLT. The improvement in the students’ retention of knowledge of the CLT goes well beyond what one would have reasonably expected. For example, one would expect that students who have been taught the CLT should score about the same on the surprise tests regardless of how they are taught, but the results suggest that about 30% more students will retain their knowledge of the CLT if taught by the new method.

The main conclusion is simple enough. Instructors have been fooled into believing that students are learning the CLT because they have been able to do mathematical calculations correctly. The fact is that students who do not have an intuition about the CLT do not understand what they are doing even if they can do the mathematical calculations correctly. The new intuitive approach for teaching the CLT imparts that necessary intuition.

References Chi, M. T. H., DeLeeuw, N., Chiu, M., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439-477. delMas, R. C., Garfield, J., & Chance, B. L. (1999). A model of classroom research in action: Developing simulation activities to improve students’ statistical seasoning. Journal of Statistics Education, 7(3). Retrieved from http://www.amstat.org/publications/jse/secure/v7n3/delmas.cfm Dyck, J. L, & Gee, N. R. (1998). A sweet way to teach students about the sampling distribution of the mean. Teaching of Pyschology, 25(3), 192-195.

Hyndman, R. J. (1995, July 5). The problem with Sturges’ Rule for constructing histograms.

Retrieved November 6, 2012, from http://www.robjhyndman.com/papers/sturges.pdf Johnson, D. E. (1986). Demonstrating the Central Limit Theorem. Teaching of Psychology, 13(3), 155-156. DOI:10.1207/s15328023top1303_18 Garfield, J., delMas, R. C., & Chance, B. L. (n.d.). Tools for teaching and assessing statistical inference. Retrieved November 13, 2012, from http://www.tc.umn.edu/~delma001/stat_tools/ Lunsford, M. L., Rowell, G. H., & Goodson-Espy, T. (2006). Classroom research: Assessment of student understanding of sampling distributions of means and Central Limit Theorem in post-calculus probability and statistics classes. Journal of Statistics Education, 14(3).

Retrieved from http://www.amstat.org/publications/jse/v14n3/lunsford.html Pfaff, T. J., & Weinberg, A. (2009). Do hands-on activities increase student understanding?: A case study. Journal of Statistics Education, 17(3). Retrieved from http://www.amstat.org/publications/jse/v17n3/pfaff.html

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Ryan, R. S. (2006). A hands-on exercise improves understanding of the standard error of the mean. Teaching of Psychology, 33(3), 180-183. doi: 10.1207/s15328023top3303_5 Roberts, L., & Pierce, R. (1999). Some reflections on different ways to teach the Central Limit Theorem. Retrieved September 11, 2012, from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.159.4125 Rossman, A. J., & Chance, B. L. (2000). Teaching the reasoning of statistical interference: A “Top Ten” list. Retrieved September 20, 2012 from http://www.rossmanchance.com/papers/topten.html Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases.

Science, 185, 1124-1131.

Velleman, P. F., & David S. M. (1996). Multimedia for teaching statistics: Promises and pitfalls.

The American Statistician, 50(3), 217-225.

Table 1 Comparison of Test and Control Group in Statistics Class Question Number & General Topic 2 shape 5 shape 6 shape 7 mean 8 term 9 term Over All Sample Size

Correct Answers - Correct Answers Intuitive Approach Controlled Group 78.26% 78.26% 25.46% 13.04% 34.78% 13.04% 4.34% 30.44% 26.09% 25.99% 12.31% 4.30% 55.81% n=23

32.72% n=23

% Improvement 0.00% 12.42% 21.74% -26.10% 0.10% 8.01% 23.09%

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Table 2 Improvement in Student Performance Fall 2012 MNGT 361 Question Number & General Topic 2 shape 5 shape 6 shape 7 mean 8 term 9 term

% Improvement -2.6% 37.0% 20.1% -7.2% 14.3% 34.4%

Sample

n = 39

Size

Table 3 Improvement in Student Performance J-Term ECON 226 Question Number & General Topic 2 shape 5 shape 6 shape 7 mean 8 term 9 term Over All Sample

% Improvement 92.30% 61.15% 46.15% 66.60% 61.15% 84.46% 68.64% n = 13

Size The increased performance here might not be due to the new method so much as having covered the material twice. The percentage improvements are much higher than expected.

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Table 4 Improvement in Student Performance Spring 2013 MNGT 361 Question Number & General Topic 2 shape 5 shape 6 shape 7 mean 8 term 9 term Sample

% Improvement 5.0% 28.2% 25.7% 35.1% 20.5% 30.4% n = 34

Size

Table 5 Improvement in Student Performance Spring 2013 MBA 705 Question Number & General Topic 2 shape 5 shape 6 shape 7 mean 8 term 9 term Sample

% Improvement 11.0% 32.6% 28.4% 38.3% 31.1% 43.9% n = 24

Size

APPENDICES AVAILABLE UPON REQUEST OF THE AUTHOR

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Should the Policy Goal Be Happiness or Economic Growth?

Maria Cornachione Kula Roger Williams University Priniti Panday Roger Williams University McKay Gavitt Roger Williams University

Abstract There have been calls in the public sphere for a movement away from using real gross domestic product (GDP) per capita as a measure of well-being to using a more subjective, survey-based indicator of “happiness” or “life satisfaction” as a direct measure of societal well-being. As recent research finds that increasing GDP per capita corresponds to increasing happiness, contradicting earlier work in this area, it is an open question whether or not the policy focus should be on maximization of the growth rate of real GDP per capita. This paper sheds light on this debate by considering the relationship between happiness and commonly criticized aspects of GDP as not representative of well-being.

Keywords: Subjective Well-being, Economic Growth

In 1972, Bhutan’s King Jigme Singye Wangchuck suggested that a “Gross National Happiness” statistic would be a better indicator of progress than gross national product for his country because it would capture nonmaterial, culturally important aspects of authentic life in his small country. More recently, political leaders of larger, more developed economies have called into question the validity of using real gross domestic product (GDP) per capita as a measure of well-being.

In February 2008, French President Nicolas Sarkozy formed a commission, chaired by Nobel Prize winning economist Joseph Stiglitz, to study and present a report on the limitations of GDP as an indicator of economic performance and social progress, and this report was to suggest alternatives. The resulting “Report by the Commission on the Measurement of Economic

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Performance and Social Progress” was completed in September, 2009 (available at http://www.stiglitz-sen-fitoussi.fr/en/index.htm). Among other suggestions, the report recommends that subjective measures of well-being should be included in a measure of living standards, including people’s self-reports of their “… happiness, satisfaction, positive emotions such as joy and pride, and negative emotions such as pain and worry” (Stiglitz, Sen, & Fitoussi, 2009, p. 16). It should be noted that critics have suggested that the French are interested in alternatives to GDP only because France’s GDP growth has lagged behind other developed countries’ economic growth rates. For example, from 1982 to 2007 France’s economy grew at 2.1% per year while the U.S. economy grew at 3.3%. In 2007, Americans were 33% richer than the French by using GDP as the measuring stick.

In November 2010, British Prime Minister David Cameron tasked the Office of National Statistics with measuring the nation’s well-being. In a speech at the Treasury, Cameron said, we will start measuring our progress as a country not just by how our economy is growing, but by how our lives are improving, not just by our standard of living, but by our quality of life… it is high time we admitted that, taken on its own, GDP is an incomplete way of measuring a country's progress. (D. Bentley & J. Churcher, November 25, 2010) Political calls for a focus on subjective well-being or “happiness” have coincided with a surge in academic interest in the topic. Interest has been fueled by various waves of surveys such as the General Social Survey and the World Values Survey, which encompass dozens of countries and thousands of individuals and ask questions related to subjective well-being.

The debate over the proper measure for societal well-being has important implications, including whether the focus of economic policymaking should be the maximization of the growth rate of real GDP per capita or some other, “truer” measure of well-being. This paper begins an exploration into this area by considering those elements for which real GDP per capita is most often criticized and their relationship to “happiness.”

Background Criticism of real GDP per capita as a proxy for well-being is not new. The widely accepted problems of real GDP per capita, which even those in favor of its use as a proxy agree on, are (1) the exclusion of nonmaterial dimensions of well-being such as spirituality and the benefits of leisure (2) the exclusion of nonmarket activities (which generally decrease the measure), (3) the inclusion of items that are actually harmful and (4), as a measure of the “typical”, it omits anything related to the distribution of income within a country. These inadequacies are so widely accepted as to be noted in most introductory economics textbooks (see, for example, Mankiw, (2012)).

Given these longstanding criticisms, many alternative measures of well-being have been proposed. One set of alternatives generally begins with GDP and then adds missing, valuable items and subtracts disamenities in order to focus on a consumption measure believed to be

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closer to well-being than is production. Papers in this category begin with Nordhaus and Tobin’s (1972) Measure of Economic Welfare. A full summary can be found in Morse (2004). A newer contribution along these lines is Jones and Klenow (2010) “Beyond GDP? Welfare across countries and time”, which calculates a consumption-equivalent measure of welfare based on consumption, leisure, inequality, and mortality data. However, none of these measures have gained traction as a policy actionable alternative to real GDP per capita.

In contrast to attempts to measure consumption more precisely, other alternatives to real GDP per capita construct composite indices based on societal attributes deemed to reflect wellbeing. Perhaps the most well-known alternative is the United Nation’s Human Development Index (HDI), which is constructed by combining measures representing health, education, and living standards. The HDI is supposed to be a more accurate measure of well-being than real GDP per capita. The index proposed by Kula, Panday, and Parrish (2008) is along these lines, but it uses subcomponents of characteristics deemed to be more conducive to the individual attainment of well-being by providing an environment that enables choice and freedom.

However, recent calls for replacing real GDP per capita as the default summative measure of well-being have not focused on maximizing the HDI or other composite indices. Instead, they have explicitly focused on subjective “happiness.”

There have been two conflicting primary results on work focusing on the relationship between happiness and real GDP per capita. Easterlin’s (1974, 1995, 2005a, 2005b) results have been coined the “Easterlin paradox” because they suggest that higher income individuals are happier. However, Easterlin’s results also suggest that people in rich countries are not happier than people in poor countries. The explanation put forward to explain the paradox is rather simple: people are concerned with relative differences in income instead of absolute differences in income – they want to “keep up with the Joneses.” Furthermore, some results have indicated a satiation point with respect to income and happiness. For example, Layard (2003) notes that happiness is independent of income per person at income levels over $15000 per person. These findings lend credence to the view that maximizing GDP per capita should not be a policy goal.

In fact, if relative income differences are the true sources of happiness, then income equality should be the goal.

More recent research has called into question these results, beginning with Stevenson and Wolfers (2008) and extending with Sacks, Stevenson, and Wolfers (2010). Stevenson and Wolfers (2008) find no evidence of a satiation point: increases in real GDP per capita increase happiness regardless of the country’s level of income. They also find that people in rich countries are happier than those in poor countries, and they find that within a country, the poorer an individual is, the less happy that person is. These results stand in stark contrast to those of Easterlin (1974). Stevenson and Wolfers (2008) attribute this to the data used; they use data on a large sample of both rich and poor countries and use several survey sources for happiness and life satisfaction data, while Easterlin (1974) used two international datasets of countries with similar attributes. Stevenson and Wolfers’s (2008) results show that a policy goal of maximizing

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economic growth will maximize happiness, suggesting no need for current calls to replace GDP growth as a policy objective.

The goal of this paper is to explicitly consider three of the most common criticisms of real GDP per capita as a measure of well-being and their relationship to happiness in an effort to determine if economic growth is the proper societal goal for policymakers. Because a policy goal of maximizing economic growth would be consistent with stable prices and unemployment at or near its natural rate, this paper will also investigate how the relationship between happiness and the three criticized areas is affected by inflation rates and unemployment rates.

Literature Review Given economists’ traditional reluctance to rely on survey data, it is somewhat surprising to see the extent to which subjective measures of happiness have been embraced. The validity of the use of happiness survey data has been supported by Kahneman and Krueger (2006), who find that responses to subjective well-being questions are related to health outcomes and other objective physiological measures. Di Tella and MacCulloch (2006) have a nice summary article of additional work on the validity of the use of happiness data in economics research.

Several papers are particularly noteworthy given the current study. Several studies consider how well-being defined in terms of self-reported “happiness” or “life satisfaction” is affected by macro variables, such as the unemployment rate and the inflation rate. Di Tella, MacCulloch, and Oswald (2001) use a sample of 12 European countries over the 1975–1991 period and find that people are happier when inflation and unemployment are low.

In a similar study, Wolfers (2003) investigates business cycle effects on well-being by using a sample of 16 European countries from 1973–1998, and he finds that both unemployment and inflation are negatively correlated with measures of happiness life satisfaction with unemployment having a bigger impact than inflation.

Blanchflower (2007) uses a larger sample of countries over a longer time period, and he also finds that both higher unemployment and higher inflation lower happiness with unemployment having a larger effect than inflation. He also includes interest rates in his study, and he finds a negative association with happiness. Perovic (2008) uses a sample of eight transition economies and reaches similar conclusions. Gandelman and Hernandez-Murillo (2009) also focus on happiness and unemployment and inflation. While they find a negative association between these variables and well-being, they do not find a significantly different impact of unemployment compared to inflation unlike previous studies.

Data Stevenson and Wolfers (2008) use three surveys in their analysis: the “Pew Global Attitudes Survey” and both the “life satisfaction” section and “happiness” section of the “World Values Survey”. Their results on the relationship between income and happiness are consistent

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across the surveys used. Here, the researchers use the “happiness” data Stevenson and Wolfers (2008) derived from the World Values Survey as the well-being variable.

The World Values Survey asked citizens of participating countries questions with responses limited to their levels of happiness and satisfaction. A sample question from The World Values Survey’s happiness section is “taking all things together would you say that you are, ‘very happy,’ ‘quite happy,’ ‘not very happy,’ [or] ‘not at all happy?’” Stevenson and Wolfers (2008) map responses from participants into an ordered point index where 1.5 was the happiest and -1.5 was the least happy. They achieved this by using ordered probit regressions of the ladder ranking on a series of country fixed effects; thus, they estimated average levels of subjective well-being in each country. (See Stevenson and Wolfers (2008) for a complete description of their methods; also Appendix A of Stevenson and Wolfers (2008) for a description of how their index compares favorably to four alternative measures they implemented.) This happiness measure is what is used in this paper as the “happiness” variable.

GDP per capita, the unemployment rate, and the inflation rate are from the World Bank, World Development Indicators (2009). Pollution is measured as CO2 emissions (metric tons per capita) and is from the World Bank, World Development Indicators, 2008.

Income inequality is measured by the Gini coefficient. The Gini coefficient is a number between zero and one, and it summarizes the degree of income inequality in a country. A greater Gini coefficient indicates a more unequal income distribution in a country. The Gini coefficient data is from the United Nations International Human Development Indicators for the year 2000.

Leisure is measured by the residual time not spent in paid work as a share of overall time whether overall time is spent either in paid work or not in paid work. The data is for 2006, and it is from the OECD (2009).

The sample of 16 countries consists of Austria, Belgium, Canada, Finland, Germany, Greece, Hungary, Ireland, Italy, Norway, Poland, the Slovak Republic, Spain, Sweden, Switzerland, and the United States. The sample was selected based on data availability, particularly the availability of consistent data for leisure, and for comparison with previous studies focused on the relationship between happiness, inflation, and unemployment rates.

Analysis To investigate the necessity of policymakers’ use of an alternative, subjective measure of well-being, basic correlations between the variables are examined. Next, ordinary least squares (OLS) regression is used on a cross section of the sample countries. OLS regression is the best approach in this context given the long-term stability of variables like the Gini coefficient and the independence of the regressors.

The first consideration is whether basic data correlations support the expected relationships between the “problem” variables, happiness, and GDP per capita. The correlation between GDP per capita and happiness for the sample is 0.742, which matches the high, positive relationship found in other research.

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Regarding leisure, GDP per capita, and happiness, greater leisure would lead to greater happiness but lower GDP per capita. With respect to pollution, greater pollution would be associated with greater output, so we would expect a positive correlation with GDP per capita.

However, the researchers would presume greater pollution would negatively impact happiness.

For income inequality, lower inequality (a lower Gini) should result in greater happiness, so one would expect a negative relationship between these two variables. Economic research has shown that the relationship between inequality and GDP per capita is complex and dependent on the level of development. As seen in Table 1, the inflation rate, the unemployment rate, and leisure all have the expected relationship with happiness. However, the positive correlations between happiness and pollution and between happiness and income inequality are surprising.

Regarding GDP per capita, the inflation rate, the unemployment rate, and pollution all show the expected relationship. It is surprising that leisure is positively correlated with GDP per capita. It is possible productivity improvements could lead to higher output and more time for leisure, and it is possible this is what the correlation is picking up. The negative correlation between the Gini coefficient and GDP per capita can be explained by the fiscal policy approach to inequality and taxation whereby increased inequality leads to calls for redistribution, which result in higher tax rates. In turn, higher tax rates lower growth and GDP (see, e.g., Kula & Millimet (2010) for a discussion of the fiscal policy approach to growth). Given the positive correlation between pollution and GDP per capita, the positive correlation between pollution and happiness could be picking up the indirect, positive correlation of GDP per capita and happiness.

Next, consider the impact of inequality, leisure, and pollution on happiness. OLS regression results are in Table 2. Cross sectional data is used here for several reasons.

Note that cross sectional data is used due to data limitations. Inequality data, in particular, would take decades to show major changes Only leisure is statistically significant at the 5% level and has the correct sign (positive).

While this might be some evidence to indicate happiness is the correct goal to pursue rather than economic growth, it is important to investigate what happens when two important macro variables, the inflation rate and the unemployment rate, are included in the regression. Results are in Table 3.

Both the unemployment rate and the inflation rate have the correct signs (negative) and are statistically significant at the 5% level with inflation having a greater impact than unemployment. This finding is unlike the findings in previous studies (e.g., Wolfers (2003) and Blanchflower (2007)). However, with the inclusion of the unemployment rate and the inflation rate, leisure loses its significance. These results suggest that it is correct to focus on traditional economic outcomes versus subjective measures of happiness for the industrialized countries.

Doing so will stabilize the unemployment and inflation rates, which benefit happiness anyway.

“Good” unemployment and inflation outcomes result through prudent policies of maximizing economic growth.

Generalizing these results across a broader spectrum of countries requires a larger, more diverse set of countries in the sample. A larger sample requires the construction of a consistent

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set of data on leisure, which is left for further research. Also, further research is needed to see if the results hold specifically for developing countries. In particular, it might be the case that developed countries have reached a level of environmental concern and pollution abatement that environmental degradation is less of a concern than in developing countries. For example, China’s rapid economic growth has produced incredible pollution problems with many Chinese expressing concerns over the state of the country’s environment.

Conclusion Which side of the current debate over the goal of economic policymaking has greater merit can be examined by considering those elements for which real GDP per capita is most often criticized and their relationship to “happiness”. By using a sample of 16 countries, it is shown that leisure time, income inequality, and pollution do not impact happiness when inflation rates and unemployment rates are considered. Given that these three elements are typically what using GDP as a measure of well-being is criticized for, this criticism of GDP as a policy objective is unfounded. Given the importance of inflation and unemployment to happiness, the correct policy focus should be on stable prices and unemployment at or close to its natural rate.

This is what a policy focus on economic growth provides. This evidence, plus the more recent research showing that higher income corresponds to greater happiness, suggests no need to move to an explicit maximization of happiness as a policy goal.

References Bentley, D., & Churcher, J. (November 25, 2010). Cameron defends wellbeing measure. The Independent. Retrieved from http://www.independent.co.uk/news/uk/politics/camerondefends-well-being-measure-2143595.html.

Bjornskov, C., Dreher, A., & Fischer, J. A. (2007). The bigger the better? Evidence of the effect of government size on life satisfaction around the world. Public Choice, 130(3/4), 267292.

Blanchflower, D. (2007). Is unemployment more costly than inflation? (NBER Working Paper No. 13505), Cambridge, MA. Di Tella, R., MacCulloch, R., & Oswald, A. (2001). Preferences over inflation and unemployment: Evidence from surveys of happiness. American Economic Review, 91(1), 335-341.

Di Tella, R., & MacCulloch, R. (2006). Some uses of happiness data in economics. Journal of Economic Perspectives, 20(1), 25-46.

Easterlin, R. (1974). Does economic growth improve the human lot? Some empirical evidence.

In P. A. David & M. W. Reder (Eds.), Nations and Households in Economic Growth: Essays in Honor of Moses Abramowitz (pp. 88-125). New York, NY: Academic Press.

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Easterlin, R. (1995). Will raising the incomes of all increase the happiness of all? Journal of Economic Behavior and Organization, 27(1), 35–48.

Easterlin, R. (2005a). Feeding the illusion of growth and happiness: A reply to Hagerty and Veenhoven. Social Indicators Research, 74(3), 429–443.

Easterlin, R. (2005b). Diminishing marginal utility of income? Caveat Emptor. Social Indicators Research, 70(3), 243–255.

Gandelman, N., & Hernandez-Murillo, R. (2009). The impact of inflation and unemployment on subjective personal and country evaluations. Federal Reserve Bank of St. Louis Review, 91(3), 107-126.

Helliwell, J., & Huang, H. (2008). How’s your government? International evidence linking good government and well-being. British Journal of Political Science, 38(4), 595-619.

Jones, C., & Klenow, P. (2010). Beyond GDP? Welfare across countries and time. (NBER Working Paper, No. 16352).

Kahneman, D., & Krueger, A. (2006). Developments in the measurement of subjective wellbeing. The Journal of Economic Perspectives, 20(1), 3-24.

Knack, S., & Keefer, P. (1997). Does social capital have an economic payoff? A cross-country investigation. The Quarterly Journal of Economics, 112(4), 1251-1288.

Kula, M., & Millimet, D. (2010). Income inequality, taxation, and growth. Atlantic Economic Journal, 38(4), 417-428.

Kula, M., Panday, P., & Parrish, B. (2008). A well-being index based on an enabling environment. International Journal of Social Economics, 35(3), 174-187.

Layard, R. (2003). Happiness: Has social science a clue. Lionel Robbins Memorial Lectures 2002/3, London School of Economics. Retrieved from http://www.cep.lse.ac.uk/events/lectures/layard/RL030303.pdf.

Mankiw, N. G. (2012). Principles of Economics, Sixth Edition. Independence, KY: SouthWestern, Cengage Learning.

Morse, S. (2004). Indices and indicators in development: An unhealthy obsession with numbers.

London, U.K.: Earthscan Publications.

Nordhaus, W., & Tobin, J. (1972). Is growth obsolete? Economic Research: Retrospect and Prospect Volume 5: Economic Growth. NBER, pp. 1-80.

OECD (2009). Society At a Glance, Chapter 2: Special Focus: Measuring Leisure in OECD Countries. Retrieved from http://www.sourceoecd.org/pdf/societyataglance2009/812009011e-02.pdf.

Perovic, L. (2008). Subjective economic well-being in transition countries: Investigating the relative importance of macroeconomic variables, Financial Theory and Practice, 32(4), 519-537.

Ram, R. (2009). Government spending and happiness of the population: Additional evidence from large cross-country samples. Public Choice, 138(3), 483-490.

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Sacks, D., Stevenson, B., & Wolfers, J. (2010). Subjective well-being, income, economic development and growth (NBER Working Paper No. 16441).

Stevenson, B., & Wolfers, J. (2008). Economic growth and subjective well-being: Reassessing the Easterlin Paradox. Brookings Papers on Economic Activity, Spring, 1-87.

Stiglitz, J., Sen, A., & Fitoussi J. (2009). Report by the Commission on the Measurement of Economic Performance and Social Progress. Retrieved from http://www.stiglitz-senfitoussi.fr/en/index.htm.

Wolfers, J. (2003). Is business cycle volatility costly? Evidence from surveys of subjective wellbeing. International Finance, 6(1), 1-26.

Table 1 Correlations Inflation Rate

Unemployment Rate

Gini Coefficient

Pollution

Leisure

Happiness

-0.647

-0.326

0.131

0.449

0.458

GDP per Capita

-0.487

-0.546

-0.151

0.230

0.683

Table 2 Impact of inequality, leisure, and pollution on happiness.

Note. Standard Errors in Parentheses. ** Significant at the 5% level; * significant at the 10% level.

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Table 3 Impact of inequality, leisure, and pollution on happiness, considering inflation and unemployment rates.

Note. Standard Errors in Parentheses. ** Significant at the 5% level.

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Gender Differences in Leading Change Ann Gilley University of Texas at Tyler Lisa Eshbach Ferris State University Elies Kouider Ferris State University Jerry W. Gilley University of Texas at Tyler

Abstract Researchers have examined the female management paradigm as distinct from the male management paradigm for decades. However, the findings related to gender differences are contradictory (Gilligan, 1982; Hatcher, 2003), and, thus, they reinforce the need for additional scrutiny of gender influences on managerial success, particularly in critical areas such as leading change. Managerial competencies previously associated with successfully driving change are primarily interpersonal skills, particularly in communication and the ability to motivate others (Gilley, McMillan, & Gilley, 2009). Given that women have been found to have an advantage in the interpersonal realm (Kabacoff, 1998), the authors question whether they are more successful in driving change. This study examined managerial effectiveness in implementing change with specific emphasis on gender differences. The authors explored whether one gender is more effective than another with change, and whether women have an advantage in driving change due to their perceived interpersonal skills. The authors hypothesized that they do. This quantitative study involved 779 respondents across multiple industries. Multiple, linear, and ordinal regressions and t-tests were used. The results support prior studies that found no significant difference in communication, motivation, and changeinitiative skills between male and female managers. The authors add to the research base by specifically examining effectiveness in change management. The respondents indicated that men and women are, statistically, equally ineffective in implementing change. The rapid pace of change encountered by organizations compels the need for more research in this area.

Key words: managing change, gender differences, communication motivation

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While historically considered an untapped resource, female workplace participation has continually increased since the 1950s (Conlin, 2003). Despite the increase of women in the workplace, fewer than 16% of corporate officer positions in America’s 500 largest companies are held by women (Forsythe, 2004). A woman’s fitness to serve in a leadership or management role has been traditionally judged by how she compared to her male peers.

In 1973, Schein concluded that bias in gender roles created a stereotype. This stereotype made it difficult for women to progress through the managerial chain of command. The perception was that women were inferior to men as managers/leaders. That mindset started to change over the next decade. Values once besmirched as feminine were now being hailed as essential for every manager. Intuition, communication, and social aptitude began to be incorporated into managerial training (Claes, 1999).

Today, women are being welcomed into management in ever increasing numbers because of the values and skills they inherently possess. Overall, supervisors see men and women as equally effective (McGregor & Tweed, 2001). Meanwhile, peer and direct assessments rate women slightly higher than men in terms of effectiveness (Kabacoff, 1998). In a recent study conducted by Catalyst, organizations with the highest percentage of women in top management positions financially outperformed those with the lowest percentage by 35% (Frink, et al., 2003).

Leadership/Management Styles and Competencies All managers bring varying management styles, interpersonal skills, and communication tools based on their unique life experiences. Researchers have begun to look at the female management paradigm as distinct from the male management paradigm (Bryans & Mavin, 2003; Helgesen, 1995). Prior research has repeatedly extolled the fundamental differences gender creates in values, strategies, and management styles (Gilligan, 1982; Hatcher, 2003; Rosener, 1990, 1995). Alimo-Metcalfe (1995) further extended the gender leadership styles, and she identified women with transformational qualities and males with transactional qualities. Usually, these differences are used to justify change (or the lack thereof) in management staffing choices.

Konrad and Kramer’s (2006) study of women on corporate boards found that the presence of three women on a board changed its entire culture to be more supportive and collaborative. Studies have shown a correlation between the proportion of women on top management teams and organizational performance (Krishnan & Park, 2005) and a correlation between gender diversity and financial performance (Catalyst, 2004). Meier, O’Toole, and Goerdel (2006) found that gender did make a difference in managerial practices, but they also found that these differences depended on the practice.

Male management has been the gold standard of business for hundreds of years: effective, archaic, and controversial. Throughout modern history the overwhelming majority of managers have been white, affluent males from similar backgrounds and life experiences. Even in today’s culture, the prevalent attitude is that men make better leaders and better supervisors than women (Duerst-Lahti & Kelly, 1995; Kanter, 1977; Stivers, 2002). Typically, men are

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viewed as more likely to grasp organizational vision and strategic planning as Kabacoff (1998) postulates. He further describes males as more innovative, restrained, and persuasive.

When compared to the male leadership style, females are usually rated higher by their subordinates (Conlin, 2003). Kabacoff (1998) also found that female managers score higher on leadership oriented towards production. In addition, women were rated as more communicative and empathetic, and they were viewed as more democratic, interactive, and conscious of others’ needs. Female managers were generally more helpful, which resulted in more effective coaching and development (Burke & Collins, 2001; Eagly & Johnson, 1990). Other studies show that women may have the edge over men in certain aspects of effective leadership (Anderson, Lievens, van Dam, & Born, 2006; Bornstein, 2007; Eagly, Johannesen-Schmidt, & Van Engen, 2003). Moore, Grunberg, and Greenberg (2004) found that there were benefits to working for a female supervisor for both men and women. Eagly (2007) contended that although women tend to lead in a style most associated with effectiveness—transformational leadership—this does not result in subordinates preferring women to men, nor does it translate to success for women leaders in terms of career advancement (Baxter & Wright, 2000; Guadagno & Cialdini, 2007; Heilman, 2001; Lyness & Thompson, 2000; Maume, 2004; Timberlake, 2005).

It is important to note, however, that abundant research contends there are no significant differences between male and female managers (Donnell & Hall, 1980). For example, Hyde’s (2005) meta-analysis revealed that women and men are more alike than different in their abilities. Arditi and Balci’s (2009) study of construction workers concluded that female project managers do not differ much from male project managers in terms of their managerial behaviors, although they do perform better in “sensitivity,” “customer focus,” and “authority presence.”

Additional meta-analyses (Eagly et al., 2003; Eagly, Karau, & Makhijani, 1995) show that women were more transformational in their leadership style, yet overall differences between male and female leaders were small.

Leading Change Today’s business environment calls for properly implemented change programs aimed at improving operating processes. However, many organizations encounter failure due to the lack of a competent leader to guide them through the process (Gill, 2003). Strong, committed change leaders are critical to implementing any change initiative (Daft, 2008; Hughes, Ginnett, & Curphy, 2009; Yukl, 2010). The change leader provides the motivation and communication necessary to propel the change effort forward.

Increasing emphasis on change’s role as critical to organizational success has prompted numerous investigations of change competencies, practices, methodologies, and results (Ford & Gioia, 2000; Friedman, 2005; Gilley, McMillan, & Gilley, 2009; Johansson, 2004). Despite the popularity of the topic, little empirical investigation has explored the influence of gender on success rates of change leaders.

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Types of Change Leadership Change is often described as transactional or transformational (Burns, 1978) with transformational leadership exerting a positive influence on change (Herold, Fedor, Caldwell, & Liu, 2008). Transactional change represents a traditional managerial give-and-take or quid-proquo approach; change is mandatory, but it is not optimal. Successful change is rewarded, while failure to change is punished. In contrast, transformational change relies on shifts in assumptions, culture, strategy, or other significant paradigms, and it may be considered extreme or revolutionary (Denning, 2005). Burns and Riggio (2006, pp. 5-7) identified four components of transformation leadership as ‘idealized influence’ (role modeling), ‘inspirational motivation,’ ‘intellectual stimulation’ (questioning assumptions, reframing problems, and using new ways to approach old situations), and ‘individualized consideration’ of others’ needs.

According to Rosener (1990), women prefer to use an interactive, collaborative, transformational leadership style whereas men favor an authoritative, transactional leadership style. Women tend to be more personal, encourage participation, share power, energize others, and enhance the self-worth of others (Burns, 1978; Paton & Dempster, 2002). The interactive style uses a collaborative process in which influences for decisions are developed from relationships. Bass (1985) explains that the transformational leadership style brings about significant change in both followers and the organization, which supports Kakabadse, Ludlow, and Vinnicombe’s (1987) findings that more women exhibit traits of visionaries and catalysts.

The transformational leader focuses on shared values, beliefs, and qualities rather than on an exchange process between leaders and followers (Daft, 2008). Transformational leaders easily promote innovation within their organizations. Employing this style also supports subordinate commitment to larger goals that need to be fulfilled by the organization (Bass, 1985, 1995; Yammarino, Spangler, & Bass, 1993).

Men, on the other hand, often view their jobs in transactional terms and rely heavily on formal authority (Paton & Dempster, 2002; Rosener, 1990). According to Burns (1978), the transactional leadership style uses an exchange of services in order to meet each other’s needs.

The transactional leader acknowledges the follower’s needs and clarifies the path to satisfaction in exchange for the follower meeting specific objectives or completing certain tasks. Followers receive rewards contingent upon their performance, while the leader realizes task accomplishment (Bass, 1985, 1995; Burns, 1978). Transactional leadership can be viewed as effective because it ensures that stability is preserved within the organization. Bird and Brush (2002) describe masculine management as strategic and competitive with centralized decision making, low commitment to people, with clear boundaries between managers and employees, and growth leading to hierarchy.

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Change Leadership Competencies Recent research has identified two primary competencies associated with effectively leading change as skill in communication and the ability to motivate others (Gilley, Dixon, & Gilley, 2008; Gilley et al., 2009).

Communication skills. Communication is a process by which information is exchanged between sender and receiver, such as an employee and a leader (Daft, 2008; Hughes et al., 2009).

Lussier and Achua (2010) stress the importance of conveying information so all parties understand the message from the same perspective. Managers and leaders typically spend up to 80% of each working day communicating with others (Mitchell, 2002; Yukl, 2010). Successful communication requires correctly applying the appropriate communication tools to each interaction.

During times of change, effective managers communicate a sense of where the organization is going, develop the skills and abilities of subordinates, and encourage innovative and participative problem solving (Paton & Dempster, 2002). Consistent and frequent communication leads to follower acceptance, understanding, and trust development (McCune, 1998). The results are realized through performance improvement and collaborative employee relationships (Cascio & Boudreau, 2008; Daniels & Daniels, 2004; Hill, 2004; Stone, 1999).

From a cross-cultural perspective, men and women inhabit substantially different worlds and exhibit vastly different styles in communication. According to Claes (1999) and Helgesen (1995), women possess qualities that contribute to improved communication, cooperation, team spirit, and commitment within organizations. However, female speech has been categorized as less rational because it displays greater sensitivity. Female language has been labeled as polite and insecure, while male language is assertive and direct. Tannen (1990) referred to female talk as "rapport talk" that views underlying relationships as the most critical component. Women use fewer abstract words, a smaller vocabulary, and a simpler conversational structure with more adjectives, modal verbs, interjections, and tag questions. The conversational styles of women have been pigeonholed as cooperative, while those of men have been perceived as competitive.

In contrast, male talk was designated as "report talk," in which facts are the essential ingredient.

However, evidence in this area is not fully persuasive. Language differences, with respect to gender, have been demonstrated inconsistently. Moreover, a speakerâ&#x20AC;&#x2122;s status and age relative to the status of the targeted person have also been noted as variables (Brouwer, 1982, 1991; Verbiest, 1990).

In order to exercise leadership and achieve goals, women leaders are more likely to use collaborative, participative communication (Gaur, 2006; Rosener, 1990), while men, in general, engage in more directive, unilateral communication (Eagly & Karau, 1991; Gaur, 2006). Other communication differences found by researchers include sociolinguistic patterns that indicate that women are better listeners than men in conversations (Faes, Swinnen, & Snellix, 2010; Lakoff, 1975), and they are more willing to let other speakers into the conversation or to allow

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another speaker to dominate the conversation (Coates, 2004). They are also very respectful of others and focus on the current discussion topic more than men who change topic constantly (Vine & West, 1978). Lastly, women are perceived as better than men when nonverbal communication is used. Women tend to smile more frequently, have more eye contact and take less space in proportion to their body size than men, while men initiate touch and interrupt more often than women (Henley, 1977; Spangler, 1995).

Skill in motivating others. Motivational theories (e.g., Maslow’s Hierarchy of Needs, Alderfer’s ERG Theory, Herzberg and McClelland’s Learned Needs Theory) specify a number of different needs valued by individuals. Generally, motivational needs are defined as lower order and higher order. An effective leader utilizes a variety of methods to identify and fulfill the needs of subordinates while accomplishing the organization’s goals. Effective employee motivation coupled with superior organizational performance will result in higher profits (Grant 1998; Hawk & Sheridan, 1999).

Efficient communication between leader and follower is central to discovering specific motivations. Skilled leaders create an environment conducive to motivational realization; they match followers to jobs/tasks that can provide the appropriate, intrinsic reward (Daft, 2008).

Appropriate application of motivation techniques influences levels of employee motivational satisfaction, which may directly affect productivity. Leaders incapable of motivating followers will have difficulty building teams and getting results (Hughes et al., 2009).

Specific to change, Armenakis and Harris (2009) developed a model of factors that motivate employees to commit to change. Their five factors clarify an employee’s awareness of the need for change: the discrepancy between the desired state versus the status quo; perceived appropriateness of the change; efficacy of the change; leadership support for the change; and perceived valence of the change for the employee.

Hypothesis Past research has shown that effective change leadership relies heavily on a manager’s ability to communicate with others and motivate them to succeed (Gilley et al., 2009).

Additional studies reveal that women are perceived as more transformational in their leadership, which is a style that relies heavily on interpersonal skills that include communication, treating individuals as unique, coaching, and developing others (Burke & Collins, 2001; Burns & Riggio, 2006; Gibson, 1995). Given the personal, transformational nature of female communication— and its underlying influence on motivation skills—the authors suggest that female managers are, therefore, perceived as being more effective at implementing change. The hypothesis is that female managers are perceived as more effective at implementing change than male managers.

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Method In this study, the authors explored managerial practices in the form of male and female managerial behaviors and effectiveness from the perspective of both male and female employees at all levels of an organization. Subordinate assessments of managerial behavior provide the most accurate ratings of leader performance (Hogan, Curphy, & Hogan, 1994).

A total of 779 respondents completed the ‘Managerial Practices Survey,’ which is a previously validated instrument (Gilley et al., 2008). Descriptive statistics, multiple linear regression, ordinal regression, and logistic regression were used to identify any gender influences on change management effectiveness. The analysis was conducted by using Minitab, which deletes rows containing missing values when using regression; hence, the authors were left with 777 usable instruments in their analysis.

The variables examined in this study were derived from research and literature on managerial behaviors and practices (Gilley & Boughton, 1996). Respondents indicated the frequency with which certain managerial behaviors occur within their organizations using a 5point, Likert-type scale ranging from “never” (1) to “always” (5). Manager’s gender, the exception, was a binary variable (male/female). Variable II-11 ‘Change’ was treated as the response/dependent variable. All variables of interest were III-7 Manager’s gender II-2 My manager treats employees fairly and consistently II-4 My manager coaches employees II-6 My manager effectively evaluates employees II-8 My manager effectively rewards/recognizes employees II-10 My manager appropriately communicates with employees II-11 My manager effectively implements change (dependent) II-12 My manger motivates employees II-14 My manager encourages employees’ growth and development II-15 My manager involves employees in decision making II-16 My manager treats employees as unique individuals II-17 My manager encourages teamwork and collaboration Participants Respondents were nontraditional, working professional students in Master of Business Administration (MBA) and Organizational Development (OD) Master’s and Ph.D. programs from three universities (two public and one private) in diverse locations (Mountain West, Midwest, and South) over 10 semesters, excluding summer terms, from 2004 to 2009. Graduate and Ph.D. students were chosen to maximize industry and position diversity, and they were chosen to ensure robust organizational experience levels; ultimately all organizational and work experience levels were represented (front line to executive) in manufacturing, service, education,

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professional, and government entities. The use of separate sources mitigated common method bias (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003), and a longitudinal timeframe of nearly six years mitigated any economic or seasonal fluctuations. The voluntary survey was given to 844 potential participants; 779 responded for a self-selected response rate of 92.3%. Two instruments contained missing variables and, thus, were not used.

Results The sample size for the survey population was 777 with 40.7% of the respondents being male, 58.4% female, while 0.9% did not indicate gender. By age, 16.4% of respondents were age 25 or under, 33% of respondents were between the ages of 26 and 35, 24.6% were between the ages of 36 and 45, 18.1% were between the ages of 46 and 55, and 7.9% were over the age of 55. Front-line employees comprised 34.3% of the population, 26.6% were supervisors or team leaders, 23.3% were mid-level managers, 11.9% were senior executives, and 3.9% reported “other.” Respondents indicated that 58.40% of their immediate supervisors were male, 40.7% were female, and 0.9% did not report.

Manufacturing represented 14.9% of industries with 26.1% in service, 31.1% in education, 21.2% in professional, 5.8% in government, and 0.9% in nonprofit. With regard to organizational size, 27.3% had fewer than 100 employees, 22.4% had 101 to 500 employees, 10.9% had 501 to 1,000 employees, 10.2% had 1,001 to 2,500 employees, 9.4% had 2,501 to 5,000 employees, 6.75% had 5001 to 10,000 employees, and 13.1% had more than 10,000 employees.

With regard to organizational tenure, 17.4% of respondents had been employed with their firm for less than one year, 45.0% for one to five years, 22.3% for six to ten years, 5.1% for 11 to 15 years, 4.6% for 16 to 20 years, and 5.6% for more than twenty years.

Multiple linear and stepwise regression analyses were used to isolate the factors that most influenced change effectiveness, and they distilled the original variables to four variables (managers appropriately communicate, motivate, involve employees, and treat employees as unique), which were all significant at p<.01. Each type of regression analysis identified two main factors, communicates (II-10) and motivates (II-12), as most significantly influencing the dependent variable, change (see Regression Analysis).

Descriptive Statistics and Correlations In Table 1, descriptive statistics include the mean, standard deviation, frequency, and percentage of all significant predictors as well as the response variable (change). Descriptive statistics for the dependent variable, frequency with ‘my manager effectively implements change,’ are revealed by gender in Table 2. Respondents reported that, overall, their managers effectively implemented change “never,” “rarely,” or “sometimes” with a frequency of 76.9%, and 23.1% effectively implemented change “usually” or “always.” By gender, male and female

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managers effectively implemented change “never,” “rarely,” or “sometimes” with frequencies of 74.7% for men and 78.7% for women leaders. Seven surveys (0.9%) contained no response for this question.

In Tables 3 and 4, descriptive statistics for the two primary independent variables are frequency with which ‘my manager communicates appropriately’ and ‘my manager effectively motivates others,’ by gender. Participants indicated that their managers ‘never,’ ‘rarely,’ or only ‘sometimes’ communicated appropriately 68.1% of the time with frequencies of 68.4% for female managers and frequencies of 67.9% for their male counterparts. Similarly, managers were ‘never,’ ‘rarely, or ‘sometimes’ effective in motivating others 74.1% of the time, with female managers reported at 74.7% and males at 73.8%.

In Table 5, the coefficients of correlation range from 0.53 to 0.71, which indicate that the variables are positively and moderately correlated to strongly correlated with each other.

Regression Analyses All independent variables and gender (III-7) were regressed on the response II-11 (Change) using a multiple linear regression model. Table 6 reveals the predictors that are significant at 5% listed by degree of importance, which are II-10 (communicates), II-12 (motivates), II-15 (involves), II-6 (evaluates), II-2 (treats fairly), and II-16 (treats as unique).

The coefficient of determination R2 is 58.58%, and R2-Adjusted to the degrees of freedom is 58.25%, which means that 58.25% of a manger’s effectiveness with change can be attributed to these six variables.

When restricted to only the predictors that are significant at 1%, the stepwise regression model reduces to II-10, II-12, II-15, and II-6 with p-values .000, .000, .000, and .002, respectively, and the predictors are listed by degree of importance. The R2-Adj drops from 58.00% to 57.77%. See Table 7. It has to be noted that the two predictors II-10 (communicates) and II-12 (motivates) account for 55.53% of the R2-Adj; therefore, the other predictors are not important because they contribute jointly only 2.72% to the variance. Communication and motivation abilities represent 55.53% of a manager’s effectiveness in implementing change.

Manager’s gender was expected to be a good predictor of ‘II-11: Manager effectively implements change.’ However, when forced to the above model, gender had a p-value of 0.119, which is not significant even at p<.01. Concurrently, the R2-Adj dropped to 58.17%, which is another indication that gender is actually having a negative effect on the model. See Table 8.

Logistic regression was also applied by combining the values of 1, 2, and 3 as 0 and by combining the values of 4 and 5 as 1 for the variable II-11 (Change). The retained predictors were II-10 (communicates), II-12 (motivates), and II-16 (treats as unique) with p-values .000, .000, and .001, respectively. Again, the results reaffirm findings from our multiple linear and stepwise regressions—the abilities to communicate with others and motivate others are key to successfully driving change. Neither gender is superior.

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Comparing ‘Change’ by ‘Gender’ A t-test was run to determine whether there were any differences in the variable ‘Change’ (II-11) according to the supervisor’s gender (III-7); the test was significant at only 0.243, which means that the two populations are not dissimilar. The Anderson-Darling normality test did not hold, and because the data are ordinal in nature, the Mann-Whitney test (adjusted for ties) was run, which was significant at only 0.2154. When tested for homogeneity of variance, Levene’s test had a p-value of 0.938, which is an indication of equal variances. However, homogeneity of variance is a mild assumption for the Mann-Whitney test. Both the t-test and the Mann-Whitney tests reveal that the rate of successfully ‘implementing change’ is statistically the same for males and females.

Discussion and Implications The authors’ findings contribute to literature on organizational change in several ways.

First, this study supports previous research suggesting that leaders largely lack effectiveness in driving and implementing change and occasionally make the situation worse (Burns, 2004; Cope, 2003; Gilley et al, 2008).

Second, the authors also confirm previous studies that reveal the importance of communication and motivating skills as contributors to managerial success with change (Gilley et al., 2009). Abundant research has revealed that managerial communication that creates narratives, tells stories, and makes sense of change or gives new meaning to change enhances employee acceptance of change and commitment to change (Brown & Humphreys, 2003; Fiol, 2002; Gioia & Chittipeddi, 1991). Organizations may be well served by examining their managers’ skill levels in communication and by crafting training and development programs to cultivate this talent area.

Finally, the study adds to the research base by revealing the importance of motivation skill in driving change. The longitudinal study sought employee perceptions of successful change frequency and skills among male and female managers. Findings suggest that women are no better at driving and implementing change than their male counterparts despite perceived advantages in communication (Claes, 1999; Gaur, 2006). Furthermore, results reveal a previously ignored dimension of female management and leadership—the ability to motivate others. Although communication skills, long thought to be a female advantage, are necessary for successful change, the findings suggest that the inability to motivate others negates the advantages of communication skills. The respondents indicated that their female managers possessed no motivation advantage; consequently, female managers were no more successful in leading change than their male counterparts.

To better understand this perceived gap in motivational skill level by managers, we ran stepwise regression analysis of II-12 (Motivates). Results revealed that, in order of importance, the frequency with which a manager coaches (II-4) helps employees grow and develop (II-14),

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effectively rewards (II-8), encourages teamwork and collaboration (II-17), and communicates (II-10) explains 64.73% of the variation. Interestingly, the ability to communicate (II-10) explains only 0.9 of the R2-Adj value in motivation. Organizations seeking to enhance their leaders’ ability to effect positive change should attend to the importance of skill in motivation and take steps to enhance this competency among their management teams.

Limitations Any research is subject to limitations, and this study is no exception. The authors consider the following limitations to be particularly important. First, this study examined employees’ perceptions of their managers’ behaviors and effectiveness in driving change, which revealed highly subjective opinions based on potentially inaccurate or incomplete information (Bandura & Wood, 1989). Respondents’ perceptual lenses, biases, stereotypes, and work experiences may cloud their interpretations. For example, the frequency of interaction with a manager or the recency with which an employee engages with a manager, whether positive or negative, may unduly influence one’s opinions and subsequent assessment of his or her manager’s ability to successfully implement change. By surveying employees at all organizational levels, however, including managers and executives, the authors strove to minimize bias against ‘management’ while enhancing a broad perspective of practices in general.

Second, the survey solicited self-ratings, imprecise measures, and perceptual data. These factors raise concern about methods variance and attribution bias. Consequently, the developing picture of managerial effectiveness with change and behaviors attributable to gender may be blurred. Furthermore, self-selection by respondents leads to concerns of skewed results (Podsakoff et al., 2003), which the authors attempted to mitigate through the use of multiple groups at different locations over time. However, the high response rate (92.3%) suggests that self-selection bias (usually negative) was minimal.

Third, multilevel or cross level effects are not reflected or measured in this study. The ripple effect or intensity of change may impact some employees more than their colleagues, both on the job and in their personal lives. Consequently, the change or its consequences may be felt more keenly by some employees than other employees.

Finally, the potential for generalization of results may be diminished due to the scope and convenience sampling methodology utilized in this study. The respondents were working professionals in pursuit of advanced degrees in management and organizational development, and, thus, may have been more sensitive to, or critical of, managerial competence with regard to change. Consequently, MBA and OD graduate and Ph.D. students may not accurately represent the overall population in a manner that yields transferrable conclusions.

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Recommendations for Future Research Results of this study suggest that men and women are more similar than dissimilar in their effectiveness with leading and implementing change. In essence, neither gender is particularly skilled in this critical management area despite the authors’ initial suspicions that female leaders possessed an advantage in communication (Claes, 1999; Helgesen, 1995), which, along with the ability to motivate others, is one of two primary contributors to effectively leading change (Gilley et al., 2009). Consequently, additional study is warranted to further explore the linkages between motivation and change with specific focus on managers’ skills in motivating others.

This research examined employees’ perceptions of their leaders’ skills in driving change without regard to the size (e.g., small-scale, moderate, or large-scale) of the change or its duration (e.g., short-term, long-term), position or level within the firm, or type of organization.

Additional research might explore whether the size, scope, or duration of a change initiative influence employees’ perceptions of their managers’ skill levels, or whether certain types of organizations (e.g., service, manufacturing, nonprofit, government, etc.) report more or less effective change leadership. Data collection at multiple points or across varying organizational levels during a change initiative could occur using a comprehensive, longitudinal design, while premeasures and postmeasures might support causal conclusions. Isolating predictors or points of leader success in driving change might allow organizations to assess and refine their managerial selection and promotion methods, leadership training, development, coaching approaches, and specific change skills needing enhancement at various levels within the firm.

Organizations might benefit by isolating change skill deficiencies in their leaders and subsequently building the talent base in these critical areas.

Future study should also utilize documentable, organizational results, such as revenue, productivity, or market share, to support or refute employee perceptions of leadership change effectiveness and clarify the linkages between the ability to effectively drive change and quantifiable organizational results. The ‘bottom line’ is a powerful catalyst for organizational change. Similarly, a leaders’ ability to motivate others creates an environment more conducive to achieving the change necessary to propel the firm forward.

Conclusion Managerial skills, behaviors, and actions influence the success of organizational change (Herold et al, 2008); therefore, leaders must be skilled in driving and implementing change in order for their organizations to remain competitive (Armenakis & Harris, 2009). The abilities to communicate appropriately and motivate others have been identified as the foundation for successfully driving change, although neither men nor women are perceived to possess or exhibit these skills with any acceptable degree of fluency. Anecdotal evidence of a female advantage in communication has failed to yield that gender any noticeable advantage in successfully driving

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change. Consequently, the challenge before organizations is clear: develop change management competencies in all leaders at all levels with particular emphasis on building skill in communication and motivation techniques.

Author Biographies Ann Gilley, PhD, MBA, is a professor of management at the University of Texas at Tyler, where she teaches organizational development, change, and management. She is the author, coauthor, and editor of numerous books and articles, including Manager as Change Leader, The Performance Challenge, and the Praeger Handbook of Human Resource Management. Her areas of research include change, the organizational immune system, and managerial malpractice.

Lisa Eshbach, PhD, MSA, teaches management courses, including Leadership, Strategic Management, Lean Systems, and Team Dynamics for Ferris State University. Prior to joining Ferris State University, she spent thirteen years working in a variety of leadership roles within the automotive industry, in which she implemented strategies that concentrated on productivity and profitability enhancement through continuous improvement initiatives. Her research and writing interests explore case-based research with implementing lean systems, leadership, and strategy within service and manufacturing organizations.

Elies Kouider, PhD, is a professor of statistics and data mining at Ferris State University, where he teaches different courses in statistics and data mining. His research interests include regression, nonparametrics, and applied statistics. He is a member of The International Environmetrics Society, the American Statistical Association, and the International Biometrical Society Jerry W. Gilley is the Interim Dean of the College of Business and Technology, also serving as Professor and Department Head of Human Resource Development and Technology at the University of Texas at Tyler. He has authored or coauthored more than 120 books, refereed journal articles, book chapters, and monographs. Dr. Gilley served as the Principal (Sr. Partner) of Organizational Development for Mercer Human Resource Consulting, the worldâ&#x20AC;&#x2122;s largest compensation, benefits, and HR consulting firm, and he was responsible for OD, performance management, and change management activities in North America. He is also the Past President of the Academy of Human Resource Development. His research interests include organizational development, performance management, and change.

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Table 1 Descriptive Statistics of variables: II-2, II-6, II-10, II-11, II-12, II-15, and II-16 Number of data values (Percentage) Predictor (My manager…)

II-2 Treats Fairly II-6 Evaluates II-10 Communicates II-11 Implements Change II-12 Motivates II-15 Involves II-16 Treats as Unique

Mean

Std Dev

Never

Rarely

Sometimes

Usually

Always

No Response

3.33

1.01

39 (5.0)

116 (14.9)

246 (31.7)

301 (38.7)

75 (9.7)

0

3.02

0.99

47 (6.1)

194 (25.0)

266(34.2)

230 (29.6)

36 (4.6)

4 (.5)

3.01

0.99

45 (5.8)

195 (25.1)

289 (37.2)

198 (25.5)

50 (6.4)

0

2.82

1.02

72 (9.3)

221 (28.4)

304 (39.1)

134 (17.3)

46 (5.9)

0

2.81

1.03

91 (11.7)

200 (25.7)

285 (36.7)

171 (22.0)

30 (3.9)

0

3.01

1.10

65 (8.4)

191 (24.6)

269 (34.6)

173 (22.3)

78 (10.1)

1 (.0)

3.13

1.10

64 (8.2)

156 (20.1)

249 (32.1)

231 (29.8)

76(9.8)

1 (.0)

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Table 2 Frequency with Which My Manager Effectively Implements Change

Manager’s Gender Female

Item II-11: Frequency with Which My Manager Effectively Implements Change Never Rarely Some-times Usually Always 28

84

124

60

20

%

8.9

26.6

39.2

19.0

6.3

Cum % Male

8.9 43

35.5 134

74.7 180

93.7 71

100.0 26

%

9.5

29.5

39.7

15.6

5.7

Cum %

9.5

39.0

78.7

94.3

100.0

No resp.

1

3

0

3

0

%

14.2

42.9

0

42.9

0

Cum %

14.2

57.1

57.1

100.0

100.0

Totals

72

221

304

134

46

%

9.3

28.4

39.1

17.2

5.9

Cum %

9.3

37.7

76.9

94.1

100.0

Total 316 (40.7%)

454 (58.4%)

7 (0.9%)

777 (100%)

Note: N= 777; Mean: 2.82 SD: 1.02 SE: 0.04 Table 3 Frequency with Which My Manager Communicates Appropriately

Manager’s Gender Female % Cum %

Item II-10: Frequency with Which My Manager Communicates Appropriately Never Rarely Some-times Usually Always

Total

16 5.1 5.1

82 26.0 31.1

118 37.3 68.4

81 25.6 94.0

19 6.0 100.0

316 (41.0%)

Male % Cum %

28 6.2 6.2

112 24.7 30.0

168 37.0 67.9

115 25.3 93.2

31 6.8 100.0

454 (59.0%)

No resp. % Cum %

1 14.3 14.3

1 14.3 28.6

3 42.9 71.4

2 28.6 100.0

0 0 100.0

7 (0.9%)

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Journal of Scholastic Inquiry: Business

Totals % Cum %

45 5.8 5.8

195 25.1 30.9

289 37.2 68.1

198 25.5 93.6

50 6.4 100.0

777 (100%)

Note: N= 777 Mean: 3.02 SD: 0.99 SE: 0.04 Table 4 Frequency with Which My Manager Effectively Motivates Others

Manager’s Gender Female % Cum % Male % Cum % No resp. % Cum % Totals % Cum %

Item II-12: Frequency with Which My Manager Effectively Motivates Others Never Rarely Some-times Usually Always 37 11.7 11.7 53 11.7 11.7

85 26.9 38.6 113 24.9 36.6

114 36.1 74.7 169 37.2 73.8

69 21.8 96.5 100 22.0 95.8

11 3.5 100.0 19 4.2 100.0

1 14.2 14.2 91 11.7 11.7

2 28.6 42.8 200 25.7 37.4

2 28.6 71.4 285 36.7 74.1

2 28.6 100.0 171 22.0 96.1

0 0 100.0 30 3.9 100.0

Total 316 (41.0%) 454 (59.0%) 7 (0.9%)

777 (100%)

Note: N= 777 Mean: 2.81 SD: 1.03 SE: 0.04 Table 5 Correlation Coefficients Correlation II-6 II-10 II-11 II-12 II-15 II-16

II-2 .54 .64 .57 .57 .55 .59

II-6

II-10

II-11

II-12

II-15

.63 .57 .53 .56 .57

.71 .66 .63 .64

.65 .60 .59

.59 .57

.71

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Table 6 Multiple Linear Regression Analysis at p<.05 Predictor Constant (II-11) II-10 II-12 II-15 II-6 II-2 II-16

Coefficient 0.115

T-value

P-value

0.342 0.235 0.107 0.079 0.073 0.068

8.87 7.17 3.19 2.40 2.01 2.10

.000 .000 .002 .017 .029 .045

T-value

P-value

10.35 7.92 4.87 3.12

.000 .000 .000 .002

Note: p<.05; R2=58.58, R2adj=58.25 Table 7 Stepwise Linear Regression Analysis at p<0.01 Predictor Constant II-10 II-12 II-15 II-6

Coefficient 0.2049 0.381 0.255 0.148 0.102

Note: p<.01; R2=58.00%; R2-Adj=57.77% Table 8 Stepwise Linear Regression Analysis at p<0.05 when Gender (III-7) is forced Predictor Constant III-7 (Gender) II-10 II-12 II-15 II-6 II-2

Coefficient 0.1758 -0.076 0.357 0.242 0.133 0.086 0.082

Note: p<.05; R2=58.50%; R2-Adj=58.17%

T-value

P-value

-1.56 9.35 7.40 4.37 2.61 2.52

.119 .000 .000 .000 .009 .012

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NOTES

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