Journal of Scholastic Inquiry: Business
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Journal of Scholastic Inquiry: Business
Business Edition, Volume 6, Issue 1 Spring 2016
Published by: Center for Scholastic Inquiry, LLC ISSN: 2330-6815 (online) ISSN: 2330-6807
Journal of Scholastic Inquiry: Business
Volume 6, Page 1 ISSN: 2330-6815 (online) ISSN: 2330-6807 (print)
Journal of Scholastic Inquiry: Business
Spring 2016
Volume 6, Issue 1
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Journal of Scholastic Inquiry: Business The Center for Scholastic Inquiry (CSI) publishes the Journal of Scholastic Inquiry: Business (JOSI: B) to recognize, celebrate, and highlight scholarly research, discovery, and evidence-based practice in the field of business. Academic and action research emphasizing leading edge inquiry, distinguishing and fostering best practice, and validating promising methods will be considered for publication. Qualitative, quantitative, and mixed method study designs representing diverse philosophical frameworks and perspectives are welcome. The JOSI: B publishes papers that perpetuate thought leadership and represent critical enrichment in the field of business. The JOSI: B is a rigorously juried journal. Relevant research may include topics in business, economics, business information systems, international business, business management, accounting, business law, business ethics, management information systems, finance, foreign trade, international politics, and related fields. If you are interested in publishing in the JOSI: B, feel free to contact our office or visit our website. Sincerely,
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JOURNAL OF SCHOLASTIC INQUIRY: BUSINESS Spring 2016, Volume 6, Issue 1
Managing Editor Dr. Tanya McCoss-Yerigan
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EDITORIAL BOARD Shirley Barnes, Alabama State University Joan Berry, University of Mary Hardin-Baylor Brooke Burks, Auburn University at Montgomery Timothy Harrington, Chicago State University Michelle Beach, Southwest Minnesota State University Kenneth Goldberg, National University Linda Rae Markert, State University of New York at Oswego Lucinda Woodward, Indiana University Southeast Arina Gertseva, Washington State University Robin Davis, Claflin University
PEER REVIEWERS Robin Davis Emily Hause Kristen Cole John Simon Michelle Beach
Joan Berry Ronald Stunda Linda Rae Markert Cathy Ann Tully Rosemarie Michaels
Azim Danesh Lucinda Woodward Howard Lawrence Jodi Brown
Teresa Weaver Judith Richards Joyous Bethel Tanya McCoss-Yerigan
Ronald Stunda
Veronica Guerrero
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TABLE OF CONTENTS Publication Agreement and Assurance of Integrity Ethical Standards in Publishing Disclaimer of Liability Research Manuscripts
6
7-60
Improving Undergraduate Business Communication Skills through Paired Coursework: An Exploratory Study Cathyann D. Tully, Wagner College
7
Influence of Forecasting on Bullwhip Effect John Simon, Governors State University
19
Investing Under Institutional Uncertainty: The Choice and Consequences of Organizational Structures Yi Karnes, California State University—East Bay
35
Manuscript Submission Guide
61
Why Read Our Journals
63
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Improving Undergraduate Business Communication Skills through Paired Coursework: An Exploratory Study Cathyann D. Tully Wagner College Abstract This paper discusses an alternative business curriculum approach in which the unlikely pairing of a required General Education Speech Course with a required Business Core Course is used to improve undergraduate business students’ communication skills, specifically by reducing communication apprehension. The development and implementation of the paired coursework, for the purpose of improving communication skills and reducing communication apprehension, is explained and evaluated for effectiveness. This exploratory study suggests that paired coursework is a meaningful method of reducing communication apprehension and improving undergraduate business students’ communication skills. Keywords: Speech, communication apprehension, business curriculum, pairing, learning community
Introduction
The undergraduate curriculum at most American colleges and universities requires students to complete a speech course. The course is typically offered as a standalone course, often completed in the first year. So why do employers claim that undergraduate business students are lacking communication skills? Perhaps communication apprehension interferes with the student’s ability to communicate effectively? Is there a way to minimize fear and maximize development of communication skills? The author explores the development and implementation of paired coursework as a means to conquer fear, to enhance communication skills, and to “learn by doing.” The evolution and effectiveness of paired coursework as a means
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of improving undergraduate business communication skills is examined. Findings indicate that pairing speech with a business core course is, in fact, a useful method to improve communication skills.
Literature Review
Business curricula should be more realistic, practical and hands-on, placing more emphasis on “soft skills” such as communication skills (Herrington, 2013). Communication skills are listed as a primary requisite for hiring, promotion, and career success, yet organizations report that verbal communication skills are the most lacking ability in their applicant pool (Hartman & LeMay, 2004; Robles, 2012). The literature suggests that fear of public speaking is the number one fear and the most pervasive communication problem we face in contemporary society (Deepa & Seth, 2013; Grenby, 2003; McCroskey, 1976). While people have many fears, common fears—e.g. the fear of heights—are avoidable and will not impede the ability to live a happy, productive life. To the contrary, fear of public speaking is not an avoidable fear. Public speaking is inevitable, particularly in the workplace. If not overcome, the fear of public speaking will impede one’s ability to be hired, to be promoted and to succeed in the business world. Most American colleges and universities require undergraduates to complete a public speaking course. Each semester thousands of college students must face their fear of public speaking by taking the mandated speech course. Fear of public speaking, often referred to as communication apprehension (CA) is an essential topic within the required speech course. Mirroring the increased research developments on communication apprehension, 18% of communication departments encompassed CA in their basic course in 1990, and that number swelled to 48.3% in 1996 (Morreale, Hanna, Berko & Gibson,1999). Thus, communication apprehension is given a more primary focus in curriculum development and training for students in speech courses (Pearson, DeWitt, Child, Kahl & Dandamudi, 2007). Decades of research suggests that communication apprehension is a real issue with farreaching consequences. For many undergraduate students the first (and sometimes the only) instruction received with respect to public speaking takes place in the basic college speech
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course. Therefore, students’ experiences in the basic speech course shape their views on speaking, and the apprehension surrounding it, for the rest of their lives. The existing literature addresses CA in the context of the required speech course, taught as a course that is not connected to other parts of the curriculum. “When there is more intentional integration of liberal learning approaches with business double helix style - faculty can help students achieve more advanced educational goals” (Colby, 2011, p.7). Research has proven that collaborative learning, such as learning communities (LC), whereby a group of students enroll in two classes as a cohort, benefits the students in the LC in various ways (Schroder, 2010). With this information in mind, the author presents an alternative curriculum method, created specifically for business students, targeted to reduce CA through the use of paired coursework within an intermediate learning community. An intermediate learning community is an interdisciplinary experience of “learning by doing” through sophisticated writing, challenging research, and an integrated final project that facilitates critical thinking and communication skills. In this exploratory study, the author investigates the benefits of pairing Principles of Finance with Speech as a way to reduce CA. The existing literature does not address learning communities as a way to reduce CA; therefore, this is a groundbreaking study.
Methodology
The author identified an opportunity within the prescribed undergraduate curriculum to create a learning community whereby one speech course and one business core course, Principles of Finance, are taught as a pair in a learning community titled Public Speaking for Business (PSB); the finance course content is utilized in the speech course. The goal of the creation of this LC is to reduce CA, thereby improving communication skills. Students receive one unit of academic credit for each course. Students enrolled in the learning community were surveyed twice during the semester. The quantitative component of this study includes analysis of a Likert scale written survey administered at the beginning and end of the semester to each student enrolled in the paired Principles of Finance and Speech courses. The survey was conducted for four consecutive semesters. In addition, for the same four semesters the professor recorded
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survey results in a Principles of Finance course (PF) which is not paired with speech in order to ascertain the effectiveness of course pairing as a way to reduce communication apprehension, thereby improving communication skills. The qualitative component of the study includes interviews with students enrolled in PSB as well as students enrolled in PF. Cumulative quantitative and qualitative results were studied to assess viability of the desired goal of reducing CA. Over the span of four semesters, 192 undergraduate students participated in the study, which represents 100% participation. Survey findings are discussed in the latter part of the article.
The Prescribed Business Curriculum – An Example
Wagner College prides itself on its practical liberal arts education. The Wagner Plan is the hallmark of the Wagner College undergraduate curriculum. The unique curriculum is designed to unite deep learning with practical application. The belief is that learning communities and hands-on research lead to satisfying careers. The College has a longstanding commitment to the liberal arts, experiential learning and interdisciplinary education (Undergraduate Studies, n.d.). The Wagner Plan requires undergraduate students to complete a liberal arts core program and a major, totaling 36 units. As such, the undergraduate business curriculum involves 18 units of General Education courses. The General Education requirements include the following: 1. Foundation courses: Writing reflective tutorial, writing intensive course, Math, English Literature, History, Technology, Speech. 2. Intercultural courses: International Perspective, American Diversity. 3. Learning Communities: Freshman LC, Intermediate, LC, Senior LC. 4. Disciplinary Perspectives: Social Sciences, Humanities, The Arts, Science. For Business majors, the balance of the students’ coursework is centered on Business courses consisting of 11 units of core business courses including introductory courses in Accounting, Finance, Marketing, and Management, plus seven units within the chosen area of concentration, which includes Accounting, Finance, Management and Marketing. Students are
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required to declare their major by the end of their sophomore year. The Wagner plan includes three learning communities. In the first semester of the college experience, students take part in the freshman learning community (FLC). Typically, the business student’s FLC consists of Business and Society, a multidisciplinary course that explores the role and influence of business relationships and societal issues, paired with a liberal arts course such as Spanish for Business. This FLC is an opportunity for students to link subjects to field work and real world problems. Students explore the New York City financial district and work as community volunteers to assist in solving business challenges in the Port Richmond community which surrounds the campus, while putting their Spanish speaking skills to use. The Wagner Plan culminates with the senior learning community (SLC), which is a summative experience whereby students bring together the breadth of a liberal education and the depth of specialized knowledge of their concentration. For business students, the senior learning community consists of a business capstone simulation, 100 hours of practicum work in the field and a senior thesis (Undergraduate Studies, (n.d.). The Intermediate Learning Community (ILC) is completed between the freshman LC and senior LC experience. The ILC is comprised of two courses and is often used to fulfill core requirements of the undergraduate curriculum. The goals are to expose students to, and involve them in, an interdisciplinary experience of “learning by doing� through sophisticated writing, challenging research and an integrated final project that facilitates critical thinking. The ILC concludes with a written or an oral presentation (Upcoming ILCs, (n.d.). ILC offerings typically vary from semester to semester, based on faculty interest and availability.
Identifying the Speech Problem
The natural inclination for students beginning their college coursework has been to select from the general education courses, often starting with the Foundation courses - Writing Reflective Tutorial, Writing Intensive Course, Math, English Literature, History, Technology and Speech. Although the college does not require students to proceed through the general
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elective foundations before moving to other disciplinary perspective coursework or major coursework, extensive qualitative interviews with PF students revealed that the tendency was for students to either complete the required Speech class in their freshman year “to get it over with,” or to avoid what was perceived to be a “nerve-wracking course” by enrolling in Speech in the last semester of senior year. Both of the abovementioned scenarios confirm the existence of communication apprehension amongst college students; however, neither approach seems beneficial to the learning process.
Creating a Solution to the Speech Problem
In order to combat the “fear factor” while enhancing the learning process, the author created an intermediate learning community entitled Public Speaking for Business, which pairs Principles of Finance (Business core requirement taught by finance professor) with Public Speaking (General Education Foundation requirement taught by speech professor). The pairing of a business core course with a general education course utilizes the strengths and resources of the general education program to provide business students with some of the competencies employers seek in business graduates (Herrington, 2013). Each course is a standalone unit, with overlapping content. The ILC created by the author is described as a means to address apprehension of public speaking. Utilizing principles of finance concepts, students learn to analyze a publicly held company and present financial information about their company to various stakeholder groups including investors, customers and lenders. Fellow students in the cohort serve as the stakeholder audience. Students learn skills that allow them to speak informatively, persuasively and in groups. Through these techniques, students cultivate their personal speaking style leading to more powerful presentations, which is a skill that is critical to one’s academic and professional advancement (Undergraduate Studies, (n.d.). It is important to note that Principles of Finance course prerequisites are (a) one unit of Economics and (b) one unit of Accounting. The prerequisites prevent the student from registering for Principles of Finance until their sophomore year.
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Linking the General Education and Business Curricula
Principles of Finance is described as a required core business course in which the basic tools and methods of financial analysis and decision making are introduced to all business majors. The course is a general study of financial management to include time value of money, measurement of risk and return, analysis of financial statements, and capital structure. Upon completion of this course students should have improved critical thinking skills, enhanced research skills, developed technology skills, and applied finance textbook concepts to real world financial information. All subsequent finance courses build upon this base; the course serves as a prerequisite to all additional courses in the finance program. At the outset of the semester, Principles of Finance students are assigned a publicly held company, which serves as the backdrop for applying course content during the semester. Using ratio analysis, students identify company strengths and weaknesses, compare the company to others in their respective industry, and determine the company’s ability to maximize shareholder wealth. At the end of the semester, students are required to deliver a written project on their findings. Twenty percent of the final grade for the course is based on this written project. Public Speaking is described as a basic speech course that studies the art of public speaking from a variety of informal and formal perspectives. The course is a General Education foundation requirement for all undergraduate students at the college. The chief objective of the course is to help students overcome the anxiety that arises from public speaking; other goals include helping students effectively communicate ideas and information in public presentations, and providing students with the opportunity to hone their abilities in the planning, development, and performance of speeches. Students begin the semester with a two-minute personal introduction speech. In the third week, students make an informative speech on their company (assigned in Principles of Finance). Later in the semester, each student makes a persuasive speech about a specific aspect of their company. Students end the semester with a group presentation to promote their company based on company financial analysis and findings (Principles of Finance written project). Fifteen percent of the final grade for the Speech course is based on the final presentation. By pairing Principles of Finance and Speech, the finance course principles are reinforced
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in the context of the speech class, providing students the platform to develop public speaking skills. Using learned finance principles and financial market research, students deliver speeches feeling empowered and knowledgeable of their topic, which should enhance confidence and reduce CA.
Building ILC Participation Over Time
The first semester in which Public Speaking for Business was offered as a paired ILC, 29 % of those students who completed freshman LC enrolled in this intermediate LC. In the second semester offering this intermediate, LC enrollment increased to 33%. After reviewing all undergraduate business students’ course history, it became apparent that many of our business students (a) had already completed the required speech course as a standalone course in their freshman year to “get it over with” or (b) had already completed Principles of Finance. In either case, students eliminated themselves from the opportunity to enroll in Public Speaking for Business ILC. To address the abovementioned course sequencing issues, the Speech Department and the Business Department committed to offering this set of paired classes as an ILC in both the fall and spring semesters, giving students a clear understanding of course/ILC availability. Business students are now advised to “save” the required speech course until they have met the prerequisites for Principles of Finance at which time they would be eligible to enroll in Public Speaking for Business ILC. Because students are not required to declare their concentration until the end of sophomore year, it is critical to call this regularly scheduled ILC to the attention of the entire campus community so that students can strategically plan their enrollment in the required speech class. It is worth noting that after the fourth semester offering Public Speaking for Business, ILC enrollment significantly increased to 48% of those business students who completed freshman LC. Based on feedback gathered during registration and academic advising, students indicated they are currently making a conscious effort to plan to partake in PSB, at the appropriate time in their academic program, which confirms the belief that students are interested in paired coursework as a means to reduce CA.
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Findings
To ascertain whether the Public Speaking for Business ILC fulfills the desired goal of reducing communication apprehension while meeting the expectations of the Wagner Plan, at the beginning and end of the semester, students enrolled in PSB were surveyed. The survey included a Likert scale rating of 1 to 5, with 1 being minimal and 5 being extreme. Findings represent four semesters of survey results. Summary findings are as follows: •
At the outset of the semester students were asked if they had completed a speech class prior to enrolling in PSB. All students indicated they had not completed a speech class prior to enrolling in PSB.
•
At the outset of the semester students were asked to rate their present level of communication apprehension. All student responses were equal to or greater than 3, with 78% indicating 5.
•
At the end of the semester students were asked to rate their comfort level in delivering speeches. All student responses were equal to or greater than 3, with 32% indicating 5.
•
At the end of the semester students were asked to rate their present level of communication apprehension. All student responses were equal to or less than 3, with 22% indicating 1.
•
At the end of the semester students were asked if they believe this ILC exposed them to, and involved them in, an interdisciplinary experience of “learning by doing” through sophisticated writing, challenging research and an integrated final project that facilitates critical thinking and communication skills, 89% replied affirmatively. To complement the above findings, survey results were accumulated over the same four
semesters in Principles of Finance course which is not part of an ILC. Summary findings are as follows: •
Students were asked if they had completed a public speaking course. 92% responded affirmatively.
•
Students were asked when they completed public speaking. Seventy-nine percent indicated they completed the speech course in the first semester of freshman year.
•
Students were asked to rate their level of communication apprehension at the beginning
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of their speech class. All student responses were equal to or greater than 3, with 88% indicating 5. •
Students were asked to rate their comfort level in delivering speeches at the end of the speech class. Thirty-seven percent of student responses were equal to or greater than 3; 88% at 3, 22% at 4. No students at 5.
•
Students were asked to rate their present level of communication apprehension, after having completed the speech course. Student responses remained equal to or greater than 3. Thirty-two percent at 3, 40% at 4, 28% at 5.
•
When asked about obstacles to reducing communication apprehension at the time they were enrolled in speech class a sample of students’ comments are as follows: o I felt uncomfortable. o I was new to college. o I was scared and overwhelmed. o I didn’t know anyone in the class. o Why did we make speeches about meaningless topics?
Discussion
The aim in creating this ILC is to make the speech course a meaningful, positive experience rather than a fearful, dreaded required course. Doing so enables students to reduce communication apprehension, thereby improving communication skills. Based on survey results and student feedback, it appears that the pairing of Principles of Finance with Speech is a creative way to work the business curriculum to the students’ advantage, reducing communication apprehension while building necessary communication skills to be used throughout the students’ academic and professional career. The effectiveness of the pairing has evolved over time, with special attention given to curriculum placement, course offering and student advising. This exploratory study is limited to four semesters of observation. The author intends to continue to study the pairing of a business course with speech so as to expand upon the
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foundation put forth in this groundbreaking effort to reduce communication apprehension. The author encourages business professors to seek out similar curriculum opportunities to pair the required speech course with a business core course, such as Principles of Finance. Course pairing affords business students the opportunity to overcome communication apprehension while building the primary requisite for hiring, promotion and career success: communication skills—the timeless, inevitable business skill. This small curriculum change appears to have a lasting impact on undergraduate business students.
Author Biography
Cathyann D. Tully is an Associate Professor of Finance and the Director of Undergraduate Business Programs at Wagner College, located in Staten Island, New York. Dr. Tully teaches various finance courses at the undergraduate and graduate level. She received her undergraduate degree in Accounting from Seton Hall University Stillman School of Business and her MBA in Finance and Doctorate in Finance from Pace University Lubin School of Business.
Acknowledgements
I am most grateful to the journal review board who generously gave their time, guidance and useful commentary. Special thanks to my very first learning community teaching partner, Dr. Marilyn Kiss, who taught me how to effectively deliver paired course work, and to Susan Fenley, my loyal Public Speaking for Business teaching partner.
References
Colby, A. (2011). Rethinking undergraduate business education: Liberal learning for the profession. San Francisco, CA: Jossey-Bass. Deepa, S., & Seth, M. (2013). Do soft skills matter? – Implications for educators based on
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recruiters’ perspective. IUP Journal of Soft Skills, 7(1), 7-20. Grenby, M. (2003). Six ways to overcome your fear of public speaking. Harvard Management Communication Letter, 6(3), 3. Hartman, J. L., & LeMay, E. (2004). Managing presentation anxiety. Delta Pi Epsilon Journal, 46(3), 145-154. Herrington, J. D., & Arnold, D. R. (2013). Undergraduate business education: It's time to think outside the box. Journal of Education for Business, 88(4), 202-209. McCroskey, J. C. (1976). The effects of communication apprehension on nonverbal behavior. Communication Quarterly, 24(1), 39-44. Morreale, S. P., Hanna, M. S., Berko, R. M., & Gibson, J. W. (1999). The basic communication course at U.S. colleges and universities: VI. Basic Communication Course Annual, 11, 136. Pearson, J. C., DeWitt, L., Child, J. T., Kahl, D. H., & Dandamudi, V. (2007). Facing the fear: An analysis of speech-anxiety content in public-speaking textbooks. Communication Research Reports, 24(2), 159-168. Robles, M. M. (2012). Executive perceptions of the top 10 soft skills needed in today’s workplace. Business Communication Quarterly, 75(4), 453-465. Schroder, R. (2010). Teaching across disciplines: Using collaborative instruction in undergraduate education. Journal of Economics & Finance, 34(4), 484-488. Undergraduate Studies. (n.d.). Retrieved from http://www.wagner.edu/academics/undergraduate Upcoming ILCs. (n.d.). Retrieved from http://wagner.edu/academics/undergraduate/ilc/courses
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Influence of Forecasting on Bullwhip Effect John T. Simon Governors State University
Abstract
Supply chains may suffer from the “bullwhip effect”: the increasing variability of demand as one moves upstream along the chain. There are many reasons for this, but it is instructive to see how forecasting methods alone may cause it. For some of the common time-series forecasting methods, we investigate the variance of their output to that of the input – a larger variance of output leads to the bullwhip effect. Our results show that some of the methods do yield an output that has a higher variance compared to their input. For the linear regression method, at least five data points for input are required for the variance of output to be less if the input series is independent, and even more data points if the input series is correlated. The results are useful and offer insight in managing supply chains as well as in the selection of forecasting methods. Keywords: Forecasting, bullwhip effect, supply chain, regression forecast.
Introduction
In some supply chains, the variability of demand increases as we move upstream (i.e. a low variability of demand at the retail end, and a higher variability of demand at the manufacturer), and this has been termed the bullwhip effect (also called the “Forrester effect”, or “demand variability amplification” or “whiplash effect”). Given that manufacturers need stability in their production levels, this artificial increase in variability can wreak havoc on their plans and schedules, leading to higher costs of production. The bullwhip effect has been studied extensively in the recent literature (see for example Derbel, Hachicha, & Masmoudi, 2014; Geary, Disney, & Towill, 2006; Lee, Padmanabhan, & Whang, 1997; Torres & Morán, 2006). It remains a complex phenomenon with multiple causes, and its importance in supply chain design
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cannot be overstated. Overcorrection of supply quantities along the supply chain can amplify the variability of supply. Analogous effects exist in other fields – the housing bubble (when demand for houses increase, too many houses are planned to be constructed, and when demand slows, too few housing starts are planned), export overshooting phenomena described by Liu (2011), the speed fluctuations in traffic (when a vehicle ahead slows down a little, successive vehicles react in an increasingly severe manner), and even the Deming funnel experiment (tampering with a process to correct for the last observed deviance as described in Deming, 1986) are all related to this amplification of variability.
Literature Review
The literature is abound with complex analyses that incorporate multiple causes which create bullwhip effect. From a realistic perspective, this is the right approach since the interplay among the multiple causes can accentuate the effect. Several causes have been identified, including lead times for replenishment, demand forecasting, batching of orders, rationing and consequent gaming of orders, and inventory and replenishment policies (see Lee et al., 1997; Bhattacharya & Bandyopadhyay, 2011; Zhang, 2004). Games have also been developed to demonstrate the impact of these factors on supply chains (such as the beer game of Forrester, 1961). Fransoo and Wouters (2000) have investigated the presence of the bullwhip effect empirically. In other empirical studies, Cachon, Randall, and Schmidt (2007) describe the prevalence of bullwhip effect in wholesale industries; Hammond (1994) discusses the occurrence of the effect in a major pasta manufacturing business; Hewlett-Packard’s experience with bullwhip is outlined in Kuper and Brandvold (2000); and Bray and Mendelson (2012) report that about two-thirds of a sample of over 4500 public US companies exhibit effects of bullwhip. Behavioral aspects that may lead to the creation of bullwhip effect are described in Sterman (1989), and Croson and Donohue (2006). Several methodologies have been employed in the study of this effect, some of which include empirical studies mentioned above, stochastic modeling (Lee et al., 1997), and control
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theory (Dejonckheere, Disney, Lambrecht, & Towill, 2003). Some studies are focused on specific causes, such as ordering policies (Cachon, 1999; Wright & Yuan, 2008), information sharing (Lee, So, & Tang, 2000; Ouyang, 2007) or forecasting methods (Costantino, Di Gravio, Shaban, & Tronci, 2015; Sadeghi, 2015).
Methodology
Our goal in this paper is to understand the effect at a basic level and draw insights. Hence we have employed theoretical models in simple cases, and resorted to Monte Carlo simulation for more complex cases. In this study we focus on a simple conceptual model for bullwhip effect based only on one of the causes, namely demand forecasting. We assume that the forecast of demand at one echelon of the supply chain serves as the demand of its upstream echelon. Even with this cause alone, there are situations where the bullwhip effect can arise, and it is instructive and insightful to see it devoid of other complications. Chen, Ryan, and Simchi-Levi (2000) discuss the impact of exponential smoothing forecasting method on the bullwhip effect, but their analysis includes the impact of ordering policies. Rahimzadeh, Haji, and Makui (2012), and Zhang (2004), also have a similar model, but aside from differences in focus and methodology, our analysis includes linear regression as one of the forecasting methods. Its inclusion here fills a void in the existing literature. A common model of supply chain uses four echelons – manufacturers, distributors, wholesalers, and retailers. The bullwhip effect ripples upstream through the chain. However the effect can be understood by looking at just two echelons. Thus, in order to keep our model simple, we will limit it to two echelons, for example a manufacturer and a retailer (this is similar to the approach in Chen et al., 2000). In the spirit of Richard Feynman who said “what I cannot create I do not understand” (Richard Feynman’s blackboard, 1988), we begin with the construction of a spreadsheet model that makes the bullwhip effect explicit. Subsequently we show the impact of common forecasting methods in the creation (or dampening) of the bullwhip effect. Our spreadsheet and simulation based approach makes the discussion accessible to managers and practitioners in the
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supply chain field. Accordingly, consider a supply chain consisting of a manufacturer and a retailer. In the time period ‘t’, the retailer faces customer demand At , and creates a forecast Ft +1 which serves as the demand to the manufacturer for the next period. A forecasting approach that smooths the time series At will tend to reduce the variation in the forecasts (compared to the variation in the demand sequence), while a poorer choice in forecasting will amplify the variability and thus lead to the bullwhip effect. Common forecasting methods taught in business programs include the naïve approach, moving average, weighted moving average, exponential smoothing, and least squares regression (see Stevenson, 2015). There are a vast variety of patterns of demand faced by the retailer. This study considers stable independent demand, demand with trend, and demand that is serially correlated (autoregressive of order 1, as used in Lee et al., 1997). To explicitly show the effect, we begin our model with the naïve forecasting method and a stable independent demand.
A basic model that exemplifies the bullwhip effect. Assume that the retailer faces a stable but random demand, modeled as At = µ + ε t in period t, where µ is a constant, and ε t are independent and Normally distributed (rounded to the nearest integer) with a mean of zero and a standard deviation of σ . Values of At are known until time t. The retailer creates a forecast for time t+1 based on the naïve forecasting method with trend, i.e., the forecast Ft +1 is based on the actual demands At of the two most current time periods (t and t-1) extrapolated linearly to capture the trend. So Ft +1 = At + ( At – At −1 ). For specificity of the model, we take µ to be 100 and σ to be 5. A simulated set of results for 10 time periods is shown in Table 1. The standard deviations of the demands and forecasts are also shown. Thus for example, F3 in Table 1 would be computed based on A2 and A1 , and is equal to the value of A2 plus the trend seen in the two periods, A2 – A1 . So F3 = 98 + (98 – 102) = 94. A comparison of the standard deviations of At and Ft clearly demonstrates that the variation in Ft , the demand transmitted to the manufacturer, is much higher than the variation
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in At , the demand experienced by the retailer. This is precisely the bullwhip effect. A graph of the data also makes this increase in variability very clear (see Figure 1). (Note: the standard deviation of Ft was computed from the values of F3 , F4 , …, F10 , which is a valid estimate in this case. But in general, one has to generate Ft multiple times for a given value of t and compute the standard deviation from those values.) A common measure of the bullwhip propagation is the ratio of the standard deviation of demand seen by the manufacturer to that of the retailer. Chen et al. (2000) use the ratio of variances, which is the square of this. In the short simulation run in Table 1, this ratio is estimated to be 15.85/5.83 = 2.72 (or 15.85/5 = 3.17, since we took the standard deviation of At to be 5). A ratio that is larger than one implies that the variability is increasing as we move upstream. In the next section, we will compute this ratio for some of the more common forecasting methods. In a few cases, the ratio can be easily computed theoretically; in others, we resort to simulation.
Results/ Findings
In this section, we provide the bullwhip propagation measure (the ratio of the standard deviation of forecasts to that of the demand = SD( Ft ) / SD( At ), where ‘SD’ is the standard deviation) using different forecasting methods, under three different patterns of demand experienced by the retailer.
Assuming a stable independent demand. Demand is modeled as At = µ + ε t as noted above, where µ is 100 and ε t is independent with a Normal distribution having a mean of 0 and standard deviation of 5.
Forecast using naïve approach with trend. The forecasting formula used is
Ft +1 = At + ( At – At −1 ) The bullwhip measure is
(1)
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SD( Ft ) / SD( At ) =
5 = 2.24
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(see Appendix A)
This causes a significant increase in the variability of the forecast demonstrating clearly that the naïve approach with trend is not suitable for forecasting a stable demand.
Forecast using moving average method. The forecasting formula used is
Ft +1 = ( At + At −1 + … + At −n+1 ) / n
(2)
The bullwhip measure is SD( Ft ) / SD( At ) = 1 /
n
(see Appendix B)
Because this is less than one, this actually dampens the bullwhip effect. If n = 5, this ratio is 0.45.
Forecast using weighted moving average method, with three time periods. The forecasting formula used is
Ft +1 = w1 At + w2 At −1 + w3 At −2
(3)
The bullwhip measure is SD( Ft ) / SD( At ) =
w12 + w22 + w32 (see Appendix C)
For weights which are positive and add up to one, this term will always be less than or equal to one, meaning that the variations are dampened as we go upstream in the supply chain. For example, if weights are equal to 0.6, 0.3 and 0.1, the ratio becomes 0.68.
Forecast using exponential smoothing average method. The forecasting formula used is
Ft +1 = α At + (1 – α ) Ft
(4)
The bullwhip measure is SD( Ft ) / SD( At ) =
α2 1 − (1 − α ) 2
(see Appendix D)
A few of these ratios for different values of α are as follows: α
0.1
0.2
0.3
0.4
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SD( Ft ) / SD( At ) 0.23 0.33 0.42 0.50
These too indicate that the bullwhip effect is dampened (upstream variability is less than downstream variability), as expected from a ‘smoothing’ forecast; as α rises to one, this ratio also goes to one.
Forecast using linear regression method. Here we use n recent values of At ( At −n+1 ,
At −n+2 , …, At −1 , At ) to find a regression line, and use that to forecast the next period demand. (If n = 2, this defaults to the naïve approach with trend). Since direct computation of SD( Ft ) / SD( At ) is difficult, we use a simulation approach to approximate this ratio. See Appendix E for details. A few of these ratios for different values of n are as follows: n
2
3
4
5
6
8
10
SD( Ft ) / SD( At ) 2.24 1.53 1.22 1.05 0.93 0.78 0.68
The results indicate that for values of n larger than five, the regression forecasts are less volatile than the original series; however if n is five or below, we see the occurrence of the bullwhip effect. This is a very interesting result, and offers practical guidance to those who use the regression method to forecast demand.
Assuming demand with trend. Here demand is modeled as At = µ + β t + ε t , where
µ is 100 and ε t is independent with a Normal distribution having a mean of 0 and standard deviation of 5. A range of values of β were chosen (from 0.01 to 2), and the resulting ratio SD( Ft ) / SD( At ) were found to be same as those in the stable demand case – meaning that trend has no additional contribution to the bullwhip effect. The ratios are summarized in Table 2 for brevity. These ratios were all computed using simulation.
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Assuming a correlated demand. Now the demand is modeled as At = µ + ρ At −1 + ε t , where µ and ρ are constants (| ρ | < 1) and ε t is independent with a Normal distribution having a mean of 0. To be consistent with the previous results, the values of µ and σ (standard deviation of ε t ) are chosen to make the mean of At to be 100 and the standard deviation of At to be 5. These values of µ and σ are given in Table 3 for different values of ρ (they are computed as µ = 100 (1 – ρ ) and σ = 5 1 − ρ 2 ; for details, see Chen et al., 2000). Table 4 provides the resulting ratio SD( Ft ) / SD( At ) for the different forecasting methods. As in the previous section, these were also computed using simulation. It is interesting to note that in some cases the ratio increases with the serial correlation and in some it decreases. In the regression method, for n = 2, it decreases, while for n = 4 or more, it increases with correlation. However when n = 3, it first increases and then decreases. As seen earlier, we need n to be over five in order to have this ratio below 1 when ρ is 0 or negative, but we need an even larger value of n when ρ is positive. Again these results provide useful guidance in forecasting as well as in the planning of supply chains.
Discussion
Both empirical and theoretical studies of the bullwhip effect are available in the literature. In this study, our focus was exclusively on the role of forecasting in the creation of bullwhip effect. For common time-series forecasting methods including the linear regression method, we have looked at the possibility of the creation of bullwhip effect by computing the ratio of the standard deviation of the forecasts to that of the demand series. Smoothing methods such as moving average, weighted moving average, and exponential smoothing dampen the variability of forecasts compared to that of the demand series. Our study indicates the extent of dampening through the measure ‘bullwhip propagation ratio’ – as this increases, the bullwhip effect becomes more extreme. Results in Table 4 show how the ratio changes with the degree of correlation within the input data. The results show that the change is dependent on the forecasting method as well as the parameters chosen. We also see that a
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deterministic trend in the demand series does not have an impact in changing the variability of forecasts. Literature is scarce in the study of bullwhip effect caused by a linear regression method of forecasting. Our study has obtained interesting results when the regression method of forecasting is used. For uncorrelated or negatively correlated demand series, we see that we need to use five or more data points of the demand series in the regression model to overcome the bullwhip effect. As correlation becomes positive, we need even more than five data points (at least 10 if correlation is 0.5) in our regression model to prevent the bullwhip effect. Also, the bullwhip propagation ratio decreases (bullwhip effect becomes weaker) as correlation increases when n is 2, but for n larger than 3, the ratio increases with correlation. The results of this study should aid managers in appreciating the intricacies of forecasting, provide guidance to forecasters in their choice of forecasting methods and choice of parameters, and also provide insight to supply chain planners. As noted in the introduction, many other factors contribute to the bullwhip effect, and further research is needed for understanding the consequences of using the linear regression method for forecasting in the presence of those factors.
Author Biography
John T. Simon holds a doctorate in Industrial Engineering and Management Sciences from Northwestern University. He is an associate professor in the area of operations management at Governors State University, University Park, Illinois. He has published articles in journals such as Management Research Review, Decision Sciences Journal of Innovative Education, and Advances in Competitiveness Research.
References Bhattacharya, R., & Bandyopadhyay, S. (2011). A review of the causes of bullwhip effect in a supply chain. The International Journal of Advanced Manufacturing Technology, 54(912), 1245-1261.
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Bray, L., & Mendelson, H. (2012). Information transmission and the bullwhip effect. Management Science, 58(5), 860–875 Cachon, G. P. (1999). Managing supply chain demand variability with scheduled ordering policies. Management science, 45(6), 843-856. Cachon, G. P., Randall, T., & Schmidt, G. M. (2007). Search of the bullwhip effect. Manufacturing and Service Operations Management 9(4), 457–479. Chen, F., Ryan, J. K., & Simchi-Levi, D. (2000). The impact of exponential smoothing forecasts on the bullwhip effect. Naval Research Logistics, 47(4), 269-286. Costantino, F., Di Gravio, G., Shaban, A., & Tronci, M. (2015). SPC forecasting system to mitigate the bullwhip effect and inventory variance in supply chains. Expert Systems with Applications, 42(3), 1773-1787. Croson, R., & Donohue, K. (2006). Behavioral causes of the bullwhip effect and the observed value of inventory information. Management Science, 52(3), 323-336. Dejonckheere, J., Disney, S. M., Lambrecht, M. R., & Towill, D. R. (2003). Measuring and avoiding the bullwhip effect: A control theoretic approach. European Journal of Operational Research, 147(3), 567-590. Deming, W. E. (1986). Out of the crisis. Cambridge, MA: Massachusetts Institute of Technology, Center for Advanced Engineering Study. Derbel, M., Hachicha, W., & Masmoudi, F. (2014). A literature survey of bullwhip effect (2010– 2013) according to its causes and evaluation methods. Advanced Logistics and Transport (ICALT), 2014 International Conference on Advanced Logistics and Transport, 173-178. IEEE. Forrester, J. (1961). Industrial Dynamics. Cambridge, MA: MIT Press/Wiley. Fransoo, J. C., & Wouters, M. J. (2000). Measuring the bullwhip effect in the supply chain. Supply Chain Management: An International Journal, 5(2), 78-89. Geary, S., Disney, S. M., & Towill, D. R. (2006). On bullwhip in supply chains—historical review, present practice and expected future impact. International Journal of Production Economics, 101(1), 2-18. Hammond, J. (1994). Barilla SpA (A) and (B). Harvard Business School Case # 9694046. Cambridge, MA: Harvard Business School.
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Kuper, A., & Brandvold, D. (2000). Innovation diffusion at Hewlett Packard. Supply Chain Management: Innovations for Education, 205-218. Lee, H. L., Padmanabhan, V., & Whang, S. (1997). Information distortion in a supply chain: The bullwhip effect. Management Science, 43(4), 546-558. Lee, H. L., So, K. C., & Tang, C. S. (2000). The value of information sharing in a two-level supply chain. Management Science, 46(5), 626-643. Liu, B. J. (2011). Why world exports are so susceptible to the economic crisis – the prevailing "export overshooting" phenomenon (Working Paper No. 16837). Cambridge, MA: National Bureau of Economic Research. Ouyang, Y. (2007). The effect of information sharing on supply chain stability and the bullwhip effect. European Journal of Operational Research, 182(3), 1107-1121. Rahimzadeh, A., Haji, A., & Makui, A. (2012). Bullwhip effect measure when supply chain demand is forecasting. Journal of Basic and Applied Scientific Research, 2(4), 42274232. Richard Feynman's blackboard at time of his death | Caltech. (1988). Retrieved from http://archives-dc.library.caltech.edu/islandora/object/ct1:483 Sadeghi, A. (2015). Providing a measure for bullwhip effect in a two-product supply chain with exponential smoothing forecasts. International Journal of Production Economics, 169, 44-54. Sterman, J. (1989). Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making experiment. Management Science, 35(3), 321-339. Stevenson, W. J. (2015). Operations management (12th ed.). New York, NY: McGraw-Hill Irwin. Torres, O. A. C., & Morán, F. A. V. (Eds.). (2006). The bullwhip effect in supply chains: A review of methods, components and cases. New York, NY: Palgrave Macmillan. Wright, D., & Yuan, X. (2008). Mitigating the bullwhip effect by ordering policies and forecasting methods. International Journal of Production Economics, 113, 587– 597. Zhang, X. (2004). The impact of forecasting methods on the bullwhip effect. International Journal of Production Economics, 88(1), 15-27.
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Tables and Figures Table 1 Bullwhip effect when using naĂŻve forecasting method t
At
Ft
1
102
2
98
3
107
94
4
103
116
5
92
99
6
102
81
7
98
112
8
114
94
9
103
130
10
104
92
standard deviation 5.83 15.85
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Table 2 Bullwhip propagation ratio for different forecasting methods when demand data shows trend Forecasting method Parameter SD( Ft ) / SD( At ) Naïve approach with trend
2.24
5 period moving average
0.45
Weighted moving average
0.68
(weights 0.6, 0.3, 0.1) Exponential smoothing method α = 0.1
0.23
α = 0.2
0.33
α = 0.3
0.42
α = 0.4
0.50
n=2
2.24
n=3
1.53
n=4
1.22
n=5
1.05
n=6
0.93
n=8
0.78
n = 10
0.68
Regression approach
Table 3
µ and σ for different values of serial correlation Serial correlation ρ
-0.5 -0.2
0 0.2
µ
150 120 100
σ
4.33
4.9
80
0.5 50
5 4.9 4.33
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Table 4 Bullwhip propagation ratio for different forecasting methods when demand data shows serial correlation Serial correlation ρ
-0.5
-0.2
0
0.2
0.5
SD( Ft ) / SD( At )
Forecasting Method parameter Naïve approach with trend
2.64
2.41
2.24
2.05
1.74
5 period moving average
0.29
0.38
0.45
0.52
0.67
0.53
0.62
0.68
0.74
0.84
α = 0.1
0.14
0.19
0.23
0.27
0.37
α = 0.2
0.22
0.28
0.33
0.39
0.51
α = 0.3
0.29
0.36
0.42
0.48
0.60
α = 0.4
0.37
0.44
0.50
0.56
0.68
n=2
2.64
2.14
2.24
2.05
1.74
n=3
1.29
1.48
1.53
1.53
1.45
n=4
1.00
1.13
1.23
1.29
1.32
n=5
0.79
0.95
1.05
1.14
1.24
n=6
0.68
0.83
0.93
1.04
1.17
n=8
0.54
0.68
0.78
0.89
1.06
n = 10
0.46
0.59
0.68
0.79
0.98
Weighted moving average (weights 0.6, 0.3, 0.1) Exponential smoothing method
Regression approach
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Figure 1. Bullwhip effect with stable demand values and naïve forecasting method
Appendices We provide algebraic computations only for the case of stable and independent demand. Here the demand and forecast processes are stationary, so Var( Ft +1 ) = Var( Ft ) and Var( At ) = Var( At −1 ). Notation: Var( ) and SD( ) stand for the variance and standard deviation of the variable within parenthesis. Appendix A
Ft +1 = At + ( At – At −1 ) = 2 At – At −1 Var( Ft +1 ) = 4 Var( At ) + Var( At −1 ) = 5 Var( At ) Hence SD( Ft ) / SD( At ) =
5 = 2.24
Appendix B
Ft +1 = ( At + At −1 + … + At −n+1 ) / n Var( Ft +1 ) = (
1 1 ){Var( At ) + … + Var( At −n+1 )} = ( 2 ) n Var( At ) = Var( At ) / n 2 n n
Hence SD( Ft ) / SD( At ) = 1 /
n
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Appendix C
Ft +1 = w1 At + w2 At −1 + w3 At −2 2
2
2 2 2 2 Var( Ft +1 ) = w1 Var( At ) + w 2 Var( At −1 ) + w3 Var( At −2 ) = ( w1 + w 2 + w3 ) Var( At )
Hence SD( Ft ) / SD( At ) =
w12 + w22 + w32
Appendix D
Ft +1 = α At + (1 – α ) Ft Var( Ft +1 ) = α 2 Var( At ) + (1 − α ) 2 Var( Ft ) In the long run, Var( Ft +1 ) and Var( Ft ) will be equal, and hence Var( Ft ) {1 – (1 − α ) 2 } = α 2 Var ( At ) Hence SD( Ft ) / SD( At ) =
α2 1 − (1 − α ) 2
Appendix E When using the linear regression method, and in situations other than stable independent demand, direct computations are difficult, and we have used simulation to find the ratio SD( Ft ) / SD( At ) (since SD( At ) is 5 in all our examples, simulation was used mainly in computing SD( Ft )). For example, for the linear regression method, first we generated random values for At based on the appropriate demand pattern. Next, we selected a value for n (2 to 10), and then using n values of the sequence At (i.e. At −n+1 , At −n+2 , …, At −1 , and At ), we found the regression equation for At as a linear function of t, and used that to extrapolate the function to the next period to find the forecast Ft +1 . This was repeated several times, and from that SD( Ft +1 ) (which is equal to SD ( Ft )) was computed, and then the ratio SD( Ft ) / SD( At ). To avoid excessive notation, we provided point estimates instead of confidence intervals; however +/- 0.02 serves as a better than 99% confidence interval for all these estimates.
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Investing Under Institutional Uncertainty: The Choice and Consequence of Organizational Structures Yi Karnes California State Universityâ&#x20AC;&#x201D;East Bay
Abstract
Economic liberalization in emerging economies has presented many investment opportunities to private investors. Despite greater extent of capital inflow, investments in emerging economies still retain many features conflicting with management of a market system. Drawing from transaction cost economics, knowledge-based view, and institution-based view, we investigate private investorsâ&#x20AC;&#x2122; choice of various organizational structures under institutional uncertainty. We find evidence that different investments vary systematically in their choice of organizational structure to respond to asset specificity and institutional uncertainty. Our findings suggest that success of private investments is affected by the choice of organizational structures.
Keywords: Transaction cost economics, organizational structure, institution-based view, knowledge-based view
Introduction
Many emerging economies have introduced market-based reforms, presenting investment opportunities to private investors. One of the reforms is to invite private entities to invest in state owned enterprises (SOEs), which is encouraged by the World Bank (Cook & Kirkpatrick, 1995). The main change introduced by private investments in SOEs is different transactions and coordination between private investors and SOEs manifested in organizational structures.
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Ownership and control are among the most fundamental reflections of influential organizational structures (Lo, 2015). Owners generally have the authority to decide on the firm’s strategic goals, develop its competitive strategy, and allocate its resources. Recognizing private investors’ ownership and control in former SOEs in emerging economies, this research examines choices of organizational structures of private investments and the consequence of the choices. Private control of former SOEs may internalize transactions between the state and private entities through a managerial contract. In a managerial contract, a private entity takes over the management part of an SOE while ownership remains with the state and investment decisions are made within the SOE hierarchy. The state and private entities resolve transaction disputes internally within the SOE. Transactions between private investors and SOEs in this type of organizational structure can be characterized as internal transaction (Williamson, 1991). When SOEs’ property is acquired by a private entity, the state relinquishes both ownership and control rights. This organizational structure avoids political interference in management’s decision making with clearly defined property rights (Boycko, Shleifer, & Vishny, 1993). Nevertheless, given the politically sensitive nature of private ownership in emerging economies (Vernon, 1971), private firms cannot totally avoid transactions with the state. Newly privatized firms in emerging economies often find it necessary to seek financial, technological, and managerial resources and capabilities from more richly endowed SOEs (Hitt, Dacin, Levitas, Arregle, & Borza, 2000). In addition, the private entity often acquires only part of the SOE’s assets, resulting in external transactions between the private entity and the rest of the SOE after privatization. A private entity and the state may also form a joint venture (JV), in which they both have ownership claims. Both parties maintain autonomy but are bilaterally dependent in this hybrid organizational structure. Investing jointly, the state and the private entity learn a great deal about which investment terms and conditions may be most effective, efficient, and viable in economic, political, and social terms (Doh, Teegen, & Mudambi, 2004), which helps them to adapt to each other’s management style in a coordinated manner. Coordination in hybrid is made neither unilaterally (as with external transaction) nor by fiat (as with internal transaction). Instead, it requires mutual consent (Williamson, 1991). This organizational form foresees unanticipated disputes, requires information disclosure when adaptation occurs, and provides for arbitration
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(prior to resorting to the courts) in the event of disagreement (David & Han, 2004). Drawing from transaction cost economics (TCE) (Williamson, 1985), knowledge-based view (Kogut & Zander, 1996), and institution-based view (North, 1990), we suggest that organizational structures of private investments in SOEs determine transactions and coordination between the government and private entities. Managerial contracts can be conceptualized as internal organizational structure (i.e., the private entities in charge of the management of the “privatized” SOEs under managerial contract are viewed as internal units of the government). Public-private JVs can be viewed as hybrid structure. Straightforward acquisitions result in external organizational structure. Two questions thus arise: (a) Under what circumstances do private entities choose certain organizational structures but not others?, and (b) What are the consequences of such choices? The purpose of this article, therefore, is to address these two important but previously little explored questions. We accomplish this by (a) extending TCE that has a historical emphasis on transactions between private entities to cover transactions between public and private sectors, and (b) further integrating knowledge-based view and institution-based view with TCE to explore how private investors evaluate organizational structures.
Literature Review
Transaction Cost Economics
Transaction cost economics is still seen as the dominant theoretical framework for studying organizational boundary choices (Geyskens, Steenkamp, & Kumar, 2006). Ronald Coase formulated his ideas on transaction costs and their effects on coordination in markets and firms (Coase, 1937). As with Coase, Chester Barnard’s ( 1938) analysis of adaptation within internal organizations stimulated later research on organizational adaptation to changed circumstances. While Coase’s disciples focused on the boundaries of the firm by assessing factors that impacted the make-or-buy decision (Espino-Rodríguez, Lai, & Baum, 2008), those who followed Barnard focused primarily on intra-organizational coordination. Research in the tradition of Barnard focuses on the attributes of complex organizations comprising multiple,
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interdependent subunits that enable them to achieve coordinated adjustments to changes in their environment (Daft, 2001; Galbraith, 1977; Gulati, Lawrence, & Puranam, 2005). Both Barnard (1938) and Hayek (1945) hold that the central problem of economic organization is coordination. Whereas Hayek locates coordination in the market, it was the coordination of internal organization on which Barnard focused attention (Williamson, 1991). We extend this tradition of research initiated by Barnard and Hayek to the context of private investments in SOEs and explore organizational structures of private investments. We combine Hayek and Barnard’s coordination concepts and examine the ability to generate coordinated responses across units, whether it is within or across firm boundaries. We note that transaction concerns both the state and the private entity in the uncertain environment when applied to private investments in emerging economies.
Institution-Based View
TCE focuses on “transactions and the costs that attends completing transactions by one institutional mode rather than another” (Williamson, 1975, p. 2). The two main dimensions of transactions, according to TCE, are asset specificity and uncertainty. Williamson (1975) defines uncertainty in terms of the inability of decision makers to specify a complete decision tree. Transaction uncertainty exists to the degree that transactions are unstandardized or unpredictable. The greater the level of such uncertainty maintains, the greater the amount of information that an organization has to process and thus the higher the cost. As discussed by Williamson (1985) and North (1990), institutions are developed by societies to create order and reduce uncertainty in promoting economic exchange and coordination. Institutions are the “humanly devised constraints that structure human interaction” (North, 1990, p. 3). Institution-based view examines how the institutional environment may influence the strategic behavior of firms (Monteiro & Pianna, 2012). Some recent work integrates TCE and institutional perspective (Martinez & Dacin, 1999) and introduces unanticipated changes as constraints on firm choices (Argyres & Liebeskind, 1999). Some theorists argue that institutional perspective is the most applicable paradigm for explaining firm behavior in emerging economies (Shenkar & Von Glinow, 1994). Research has integrated TCE
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and institution-based view (Martinez & Dacin, 1999) to focus on the diversity of organizational structures across institutional environments. In emerging economies, the level of uncertainty can be magnified because stable institutions have not yet fully developed, while the old order is being eroded at the same time (Peng, 2003). As a new phenomenon in emerging economies, private investments face unstable institutional environments. Transactions between private entities and the government depend on the discretion of the government, which may be unpredictable. Uncertainty in institutions has significant implications for the design and implementation of organizational structures of private investments.
Asset Specificity
The effect of uncertainty on the choice of organizational structure is conditional: in the presence of asset specificity, increases in uncertainty will increase the costs of external transaction between parties (Williamson, 1985). Asset specificity refers to the degree to which the assets used in support of the transaction can be redeployed to alternative uses without sacrifice of productive value (Williamson, 1991). Williamson (1985) identifies three types of asset specificity: (a) site specificity, (b) physical asset specificity, and (c) human asset specificity. Site specificity refers to the situation whereby successive production stages that are immobile in nature are located close to one another. Physical asset specificity refers to transaction-specific capital investments that tailor processes to particular exchange partners. Human asset specificity refers to transaction-specific know-how accumulated by transaction parties through longstanding relationships. The definition of asset specificity has been loosely defined and used inconsistently across theoretical perspectives (Bethelemy & Quelin, 2006; De Vita, Tekaya, & Wang, 2011). Asset specificity, through the perspective of TCE, provides incentives for opportunistic behavior (Lui, Wong, & Liu, 2009). Studies of resource-based view, however, argue that asset specificity generates knowledge and routines that results in core competencies and use the terms “core competence” and “specificity” interchangeably (Espino-Rodríguez & Padrón-Robaina, 2006; Grant, 1996). The interconnectedness of the asset specificity perspectives indicates that a holistic approach is needed to investigate the construct (De Vita et al., 2011; Sambasivan, Siew-
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Phaik, Mohamed, & Leong, 2013). In this study, two parties – the private entity and the government – are locked into transactions because both the physical assets and the human assets invested in the site chosen for the investment projects are specified to a non-trivial degree. If the project fails, assets invested by the private entity and the site will not be redeployed to alternative uses without sacrifice of productive value because the state may simply change to other partners (Henisz & Zelner, 2001). When private entities have a high level of investments, they develop the site and invest in physical assets such as machineries that are specialized to the investment they embarked on. Human assets specialization is also high since dedicated engineers and managers get involved in the firm and develop experiences working with the SOE to accumulate specialized information and know-how. By investing in specified physical, site, and human assets, private entities develop specialized knowledge of managing such assets.
Organizational Structures
In TCE research, scholars have studied two different structures of organizations: firms can either “make” (internal transaction) or “buy” (external transaction) components necessary to complete their product mandates (Coase, 1937; Williamson, 1975). In recent years, researchers have expanded this dichotomous choice to focus on other hybrid forms of organization – alliances – that are an intermediate form between make and buy (Dyer & Singh, 1998; Williamson, 1991). In hybrid organization, all parties to the transaction voluntarily agree to norms of behavior, which results in relational governance facilitated by membership in the same social group (Gereffi, Humphrey, & Sturgeon, 2005). Our study identifies all three organizational structures: (a) Managerial contract, entailing an internal organizational structure; (b) public-private JVs, considered as a hybrid; and (c) acquisition, viewed as an external organizational structure. The level of institutional development varies significantly among emerging economies (De Castro & Uhlenbruck, 1997). Countries differ in the relative influence of authoritative planning vs. market transaction in domestic resource allocation (Murtha & Lenway, 1994). Without uncertainty, even highly specialized assets may be protected contractually (Mahoney,
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1992). However, in emerging economies with a more erratic formal institutional environment (Wright, Filatotchev, Hoskisson, & Peng, 2005), more restricted product markets (Khanna & Palepu, 1997), and weaker formal regulatory regimes (La Porta, Lopez-de-Silanes, Shleifer, & Vishny, 1998), the level of uncertainty is high and transactions between private entities and the government are costly (Brouthers, Brouthers, & Werner, 2008; Tong, Reuer, & Peng, 2008). There are “rational economic reasons” (Williamson, 1985, p. 52) for private entities to choose the means of governing transactions that minimize transaction costs. In the post-privatization era in emerging economies, the institutional environment tends to be relatively unstable and uncertain (Peng, 2003). Private investments depend much on formal institutional frameworks centered on laws and regulations, which are influenced by other reforms, such as the ownership, tax, and administrative reforms (Johnson, McMillan, & Woodruff, 2002; Peng, 2000). The less developed the institutions, the greater uncertainty in transactions between private entities and the government, and the greater information processing requirements and adaptation pressure on privatized firms. Specifically, transactions between private entities and the state may face unanticipated changes of formal institutions. If the government can easily change the regulatory environment in the future, private entities thus have to choose organizational structures that are likely to minimize such risk. The external organizational structure – namely, acquisition in this context – has been recognized as a quick and simple way to create private owners in emerging economies, but has no established mechanisms to provide credible information to private entities (Spicer, McDermott, & Kogut, 2000). External structure is least capable of facilitating coordination in transactions between the private entity and the government in an uncertain institutional environment, and may only work well in a more developed economy. In emerging economies, high uncertainty in the institutional environment renders external transactions subject to costly haggling and maladaptiveness, and increases the relative attractiveness of hierarchies and hybrids (Williamson, 1985). Because of the need for continued cooperation in an internal or hybrid organization, both parties must be assured that the terms of transactions will be enforced (Hennart, 2010). When the private entity chooses internal or hybrid organizational structure, the former SOE facilitates information exchange and feedback with the state, hence contributes to the survival of the private
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investment. The former SOE also learns to take actions through which a private sector pattern can be institutionalized within its managerial ranks (Johnson, Smith, & Coding, 2000), thus gradually adapting to market-based efficiency. Hence we suggest:
Hypothesis 1a: Private investments in institutions with higher uncertainty are more likely to choose internal or hybrid structure over external structure.
Hypothesis 1b: Institutional uncertainty has more adverse effects on the survival of private investments with external structure than with internal or hybrid structure.
Asset specificity often provides a potential opportunity for opportunism. The essential element of the decision as to whether internal or external organizational structure will be more efficient is the extent to which the parties invest in durable, nonmarketable assets to facilitate a transaction (Williamson & Ouchi, 1981). Based on the level of asset specificity, organizations choose appropriate structures to reduce opportunistic behavior incentivized by asset specificity (David & Han, 2004; Lui et al., 2009). Before privatization, the state has to satisfy multiple political claims in managing SOEs, which may result in significant deviation from market-based efficiency (Zahra, Ireland, Gutierrez, & Hitt, 2000). At the onset of privatization, this template may still be deeply embedded. Moreover, deals in emerging economies are likely to include post-privatization conditions such as some form of government presence after privatization (De Castro & Uhlenbruck, 1997). Transaction-specific investment made by private entities provides opportunities for the government to behave opportunistically and limits the ability of the market to govern exchange (Williamson & Ouchi, 1981). A private entity that invests specific physical and human assets in the project is at high risk of any opportunistic hold-ups from the government. By having private entities and the SOE work within an organization (under internal structure or JV), such costly haggling can be minimized, since conflicts can be settled internally rather than through court battles (Conner & Prahalad, 1996). While the TCE literature focuses on efforts of incentive alignment and problems of avoiding or mitigating opportunism as the central theme in the design of group incentives (Holmstrom & Milgrom, 1994), the knowledge-based view has a different perspective on
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organizational structure. According to the knowledge-based view, whether a firm performs activities in-house or through external contracts depends on whether doing so makes the generation and exploitation of knowledge more efficient (Conner & Prahalad, 1996; Grant, 1996; Kogut & Zander, 1996). Knowledge-specific assets are not easily transferable, and are characterized as unique skills and experience in carrying out the activity (Lamminmaki, 2005). Coff (2003) recognizes that the risk of opportunism increases along with knowledge intensity and cannot be analyzed separately. We argue that besides guarding against opportunism of the state, developing knowledge of working with the SOEs is also an important factor in the decision of organizational structure for private entities. Masten, Meehan, and Snyder (1991) provide the link between TCE and the knowledgebased view and argue that firms should integrate to take advantage of a decrease in internal organization costs. That is, a firm should organize activities within itself not so much because of the fear of hold-ups in dealing with partners but because of the ease with which the activities can be performed within the firm. Such a focus on internal organization is most effective when the asset specificity in question is human rather than physical or site. They further argue that two parties should integrate because it may be cheaper to perform their joint activity within a firm even if there were no possibility of haggling when each works autonomously. Conner and Prahalad (1996) also agree that even in the absence of opportunism, transaction costs still exist in knowledge-based transactions. Because knowledge is often tacit and embedded in organizational routines and specific human assets, it is difficult to duplicate and is acquired largely through personal experience, such as learning by doing or by observing. Knowledge is often “sticky” – difficult and costly to transfer, often requiring frequent interaction to “unstuck” (von Hippel, 1994). If knowledge is embedded in an organization, the knowledge-seeking firm may have to access it through the full or partial takeover of the organization or through the formation of joint ventures with them (Hennart, 2009). Thus, if the private entities invest specific human assets to develop knowledge required in the projects, they may have to interact with the state often so as to exploit the asset interdependencies to create a sustainable advantage (Conner & Prahalad, 1996). Because of the shared language and routines that develop within firms, tighter coordination between existing know-how and incoming knowledge can be achieved through internal transaction. In a nutshell, both TCE and the knowledge-based views suggest that when
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asset specificity is high, transactions between the private entity and the government may be better organized within the firm than through the market. By communicating internally within the former SOE, private entities learn to mitigate opportunistic behavior of the state, enhance knowledge generation and cooperate with the state in order to facilitate transactions more efficiently. Hence we suggest: Hypothesis 2a: Private investments with higher asset specificity are more likely to choose internal or hybrid structure over external structure. Hypothesis 2b: Private entityâ&#x20AC;&#x2122;s asset specificity has more adverse effects on the survival of private investments with external structure than with internal or hybrid structure.
Methodology
Data
The infrastructure industries constitute a majority of privatizations in emerging economies (Dyck, 2001). We acquired a data set of investments in infrastructure industries in emerging economies drawn from the World Bankâ&#x20AC;&#x2122;s Database. Country development data are collected from the United Nations Statistics Division. Data on institutional uncertainty is from the Heritage Foundationâ&#x20AC;&#x2122;s Economic Freedom report, which grades 161 countries on aspects of institution conditions. The projects reached closure during the period 1984-2003. Closure occurs when private entities agreed to a legally binding agreement to invest funds or provide services. A total of 174 out of 2,782 projects have missing data and thus are excluded. After further excluding 58 data points with missing data on institutional development, we have 2,550 projects from 94 emerging economies in the data set. Private investments in SOEs are classified into three categories in the original database: managerial contracts, JV projects, and acquisitions. Investments under managerial contract are coded as having an internal organizational structure. A total of 652 out of 2550 projects are of this type. There are 1350 public-private JV projects in our data and they are considered as hybrid structure of private investments in SOEs since both the government and the private entity
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has ownership claims on the projects. In the 548 acquisition projects, a private entity buys an equity stake in a SOE, and they are recognized as organizations with external structures.
Variables
Survival. Private investments in SOEs are identified as (a) under construction, (b) operational, (c) concluded, (d) canceled, and (e) distressed in the original data set. We code investments under construction, operational, and concluded as “survived projects.” In distressed projects, the government or the operator has either requested contract termination or are in international arbitration. Distressed projects and canceled projects are considered as “failed projects”. There are altogether 138 out of the 2550 projects that have failed, and 2412 projects that have survived until the data was collected. The dependent variable, survival of the private investments in SOEs, is coded 1 if the investment is identified as survived and 0 if the project is cancelled or distressed.
Uncertainty. We measure institution uncertainty following Doh et al.’s ( 2004 ) measurement of countries’ institutional development. We average three variables in Economic Freedom report: the extent of state intervention in the economy, the extent of capital flows and foreign investment, and the extent of regulation. These variables represent how stable government regulations are and how developed regulatory and formal institutions are. Each variable is reported on a five-point scale. A higher score indicates a high level of institution uncertainty in that country. Data for year 2003 is used since investments in our data ends in 2003.
Asset specificity. The measurement of asset specificity has been discretionary in many studies because of conceptual differences of the construct (De Vita et al., 2011). Since the three forms of organizational structures have not been studied in previous research, we attempt to measure asset specificity so that it is comparable across these structures. When private entities make an investment, whether it is in the form of equity control in managerial contracts or both equity ownership and control in hybrid and external structures, they invest in physical, site, and
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human assets. Thus, the higher the level of participation, the higher asset specificity in the investments. We use the level of private entities’ participation as a proxy for asset specificity. In private investments with internal organizational structure, asset specificity is measured as the percentage of the former SOE’s equity controlled by the private entity. In hybrid projects, the percentage of the former SOE’s equity owned and controlled by the private entity is used to measure asset specificity. In market transaction projects when the SOE is acquired into private hands, asset specificity is the percentage of the SOE equity owned and controlled by the private entity.
Organizational structures. An external organizational structure is recognized when private entities acquire some ownership and control rights of SOEs from the government. A total of 548 out of 2550 projects are of this type. Internal organizational structure is documented when the government retains ownership rights, while releasing management control of SOEs to private entities. Private entities are in charge of the management of the privatized SOEs in this type of structure. There are 652 projects with internal organizational structure. When the government and private entities jointly invest, own, and manage an enterprise, a hybrid organizational structure is formed. There are 1350 hybrid projects.
Control variables. Market-supporting institutions may become stronger over time because of cumulative reforms undertaken with individual privatization transactions (Ramamurti, 2000). Given the institutional development over time, recent privatization projects may be less likely to fail. It is possible that newly privatized companies, although still under construction now, will have problems in the future given enough time of observation. Since we can only observe investment status till 2003, there might be a failure bias towards earlier investments. It is also possible that new technologies arise over time that lower the transaction costs present in markets (David & Han, 2004). We control for the year lapsed from when the investments were set up till 2003 to reduce this problem. Other control variables include payment to the government, and countries’ economic development measured as per capita GDP in logarithm. A dummy variable is included to control
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for investments with banks loan or syndication. Four primary sectors of infrastructure â&#x20AC;&#x201C; namely, transport, energy, telecommunication, and water and sewerage sectorsâ&#x20AC;&#x201D;are controlled. Water and sewerage sector is used as a base category and is not included in the regression models. We also control six geographic areas: (a) East Asia and Pacific, (b) Europe and Central Asia, (c) Latin America and the Caribbean, (d) Middle East and North Africa, (e) South Asia, and (f) SubSaharan Africa. Middle East and North Africa area is used as a base category and is not included in the regression models.
Analysis Techniques
In strategic management research, we often wish to draw conclusions about the superiority of the strategy compared to alternatives so that we can aid managers with their business decisions (Shaver, 1998). However, a difficulty in making such assessments is that firms purposely choose their strategies based on their capabilities and environmental conditions (Shaver, 1998). A firmâ&#x20AC;&#x2122;s choice of organizational structure is inseparable from its environment and its characteristics. Since private entities self-select the structures we observe, these strategic organization decisions are not random, and are endogenous to the expected performance outcomes. Likewise, private entities self-select organizational structures that may result in a higher possibility of survival. Therefore, if we observe some firms choosing one organizational structure and other firms choosing different structures, it would not appear that one structure unconditionally leads to superior performance. Empirical estimates of strategy performance that do not correct for this problem may be misleading (Masten, 1993). Econometric techniques to correct for endogeneity arising from discrete strategy choices have been available since the 1970s (Heckman, 1979). Many of these econometric estimators were developed in the context of labor economics. Nonetheless, the econometric problems in that field are similar to problems of strategic management (Hamilton & Nickerson, 2003). To test our hypotheses, we use a switching regression model (Hamilton & Nickerson, 2003; Shaver, 1998). We estimate this model in two steps. First, we estimate a multinomial logit model to predict the choice of organizational structures (internal structures, hybrid or external structure) and construct the inverse Mills ratio terms. It is difficult in many strategy
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data sets to find instrumental variables that affect strategy choice but not performance (Hamilton & (Hamilton & Nickerson, 2003). We use a country’s economic development as an instrument since it is likely to affect firm choice of organizational structure but unlikely to directly affect investment survival. In the second step, we estimate the relationship between organizational structures and survival via ordinary least squares (OLS), including the inverse Mills ratio to obtain unbiased estimates of coefficients. White’s robust test is used to correct heteroskedasticity.
Findings
Table 1 shows the number of different organizational structures across geographic areas. Table 2 summarizes the variables - means, standard deviations, and correlations; and Table 3 reports the results for first-stage multinomial model. The base category is external structure, so that the coefficients are interpreted as affecting the odds of investments choosing internal structure or hybrid, relative to the odds of external structure. Our instrumental variable – country’s economic development – does affect organizational structures. Institutional uncertainty appears to increase the odds of private entities choosing internal and hybrid structures, both of which facilitate cooperation in the organization (Hennart, 2010). This finding indicates that firms self-select organizational structures according to the level of uncertainty in the institutional environment, thus supporting Hypothesis 1a. Because of the need for continued cooperation in an internal or hybrid organization, both parties must be assured that the terms of transactions will be enforced (Hennart, 2010). Hypothesis 2a is also supported. Asset-specific investments by private entities increase the odds of choosing internal structure and hybrid than external structure, indicating that firms choose appropriate organizational structures based on the level of asset specificity (David & Han, 2004; Lui et al., 2009). The results for second-stage switching regression model are presented in Table 4. We regress the survival of investments on asset specificity, institutional uncertainty and other control variables. The switching regression model is estimated separately in each subsample of private investments in SOEs. To test Hypothesis 1b, we compare the coefficients of institutional uncertainty across the
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column models for three different organizational structures. We find that the coefficient of institutional uncertainty is not different from zero for internal structure or hybrid at 5% level. The coefficient of institutional uncertainty is -4.9 and significant at 5% level for external structure. This means institutional uncertainty has more adverse effects on survival of private investments in SOEs with external structure than with internal structure or hybrid, supporting Hypothesis 1b. The standard deviation of institutional uncertainty is 0.488. This finding means one standard deviation increase in institutional uncertainty increases the odds of investment failure by 11 times (4.9Ă&#x2014;0.488=2.4, e2.4=11). To test Hypothesis 2b, we compare the coefficients of private investments in SOEs across the column models for three different organizational structures. We find that the coefficient of asset specificity is not different from zero for internal structure or hybrid. The coefficient of asset specificity is -0.184 and significant at 5% level for external structure. It means assetspecific investment has more adverse effects on the survival of private investments with external structure than with internal structure or hybrid, supporting hypothesis 2b. Specifically, for those private entities that choose external structure, a 10% increase of asset-specific investment increases the odds of investment failure by 6 times (0.184Ă&#x2014;10=1.84, e1.84=6).
Discussion
This study explores attributes of institutional environments and private investments. We find evidence that different investments vary systematically in their organizational structure to respond in a coordinative manner to uncertainty in the institutional environments and to asset specificity. In the post-privatization era in emerging economies, the institutional environment tends to be uncertain. We recognize institutional uncertainty as an important factor in emerging economies and integrate TCE and institutional theory to explain firm organizational structure choices (Argyres & Liebeskind, 1999; Geyskens et al., 2006; Martinez & Dacin, 1999; Peng, 2003; Shenkar & Von Glinow, 1994; Wright et al., 2005). TCE suggests firms should integrate to minimize opportunistic behavior (Holmstrom & Milgrom, 1994; Lui et al., 2009) whereas the knowledge-based view suggests firms integrate to facilitate knowledge transfer and coordination
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(Espino-Rodríguez & Padrón-Robaina, 2006; Grant, 1996; Kogut & Zander, 1996). We argue that TCE and the knowledge-based view delineate distinct yet complementary aspects of the effect of organizational structures. There are two major findings in our study. First, we find that private investments in SOEs in an institutional environment with a higher level of uncertainty tend to choose internal or hybrid organizational structure rather than external organizational structure; private entities with a higher level of asset specific investments are also likely to choose internal or hybrid organizational structure. This finding addresses our first research question: Under what circumstances do private entities choose certain organizational structures? It also suggests that in emerging markets, private entities rely more on forming alliances with the government than on market mechanism to cope with the uncertain institutional environment (De Castro & Uhlenbruck, 1997). This may have ramifications for private entities to choose the transaction type and the organizational structure in face of institutional uncertainty and asset specificity. Private entities may gain control in privatization through various self-selected organizational structures. Newly privatized organizations are likely to follow the public sector pattern while anticipating great changes in the institutional environment. It is essential that private investors adopt the organizational structure that assists transactions between the state and the private entities, in order to survive in the uncertain institutional environment. To answer the second research question – what’s the consequence of the choice of organizational structures? – we examine how various organizational structures affect opportunistic behavior in transactions and knowledge transfer in cooperation between private entities and the government. We find that higher institutional uncertainty and asset specificity have more adverse effects on the survival of private investments with external structure than those with internal or hybrid structure. Institutional uncertainty and asset specificity may have assumed the role of providing incentives for opportunistic behavior (David & Han, 2004; Williamson & Ouchi, 1981) – the TCE perspective, and cooperation between different parties – the knowledge-based view (Conner & Prahalad, 1996; Kogut & Zander, 1996), both which may be better controlled and managed by thoughtfully designed organizational structures. Our findings imply that private investments in SOEs may control institutional uncertainty
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and negative effects related with asset specificity through certain internal arrangements, but not through external organizational structure. The empirical evidence suggests that survival of private investments depends on how private investors respond to asset specificity and institutional uncertainty through self-selected organizational structures.
Conclusion
Our study taps into an important and current issue in emerging economies: transactions and cooperation between the government and private entities in private investments. Three contributions emerge. First, we extend TCE beyond the usual consideration of incentive conflicts. TCE have emphasized opportunistic behavior and incentive alignment in transactions. However, in addition to incentive conflict, failures of transaction may arise because parties read and react to signals differently, even though their purpose is to achieve a timely and compatible combined response (Gulati et al., 2005; Williamson, 1991). We focus on the limitations in organizational structure in private investments in SOEs. The novelty of our approach lies in suggesting that different privatization investments used to organize transactions between the government and the private entity differ in their capacity to align actions through processes. We suggest that private entities recognize differences in organizational structures in terms of facilitating knowledge generation and cooperation with the state and self-select the organizational structure that better facilitates transactions with the government. Second, we recognize the level of institutional development as a factor of uncertainty in privatization environments. Uncertainty is usually treated as a trigger to opportunistic behavior in traditional TCE research. We recognize that institutional uncertainty also presents pressure for private entities to adapt to cooperation with the state and requires greater information exchange in transactions. We extend TCE by suggesting that uncertainty in the institutional environments requires the design of organizational structure that better facilitates coordination in transactions. Third, this research provides a timely guide to privatization process in emerging economies. Prior research on privatization does not distinguish organizational structures of private investments (Zahra et al., 2000). We explicitly study three organizational structures in
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private investments and suggest that different organizational structures enable transactions among private investments in SOEs. We argue that private entities need to choose an organizational structure that best facilitates transactions with the state. Our results generally support this argument. The marginal effect of institutional uncertainty and asset specificity is most adverse on private investments with external structures that least facilitate transactions. Although we are unable to detect any difference between internal and hybrid structures in terms of the effects of environmental uncertainty, our results recommend a caution for choosing external organizational structure in an environment with high institutional uncertainty. Since this study is about private investments in emerging economies, the findings may not be generalized to all economies or public investments. Future research on coordination within or between different types of organizations needs to be conducted at a level of detail that enables us to distinguish the actual coordination mechanisms used to manage transactions. We hope that future research will challenge and extend what we have found in this research. Doing so will help ensure that research in this area ultimately contributes to the understanding of how investments in SOEs evolve, perform, and hopefully prosper in the future.
Author Biography
Dr. Yi Karnes is an Associate Professor of Management at California State University, East Bay. She received PhD in Strategy and International Business from the Ohio State University. She has published in Journals such as Journal of International Business Studies, Journal of Management Studies, Journal of World Business and Asia Pacific Journal of Management. Her research and teaching interests are in the areas of international business and strategic management. ( yi.karnes@csueastbay.edu)
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international ownership strategy. Journal of Business Research, 68(11), 2312-2316. Lui, S. S., Wong, Y.-Y., & Liu, W. (2009). Asset specificity roles in interfirm cooperation: Reducing opportunistic behavior or increasing cooperative behavior? Journal of Business Research, 62(11), 1214-1219. Mahoney, J. T. (1992). The choice of organizational form: Vertical financial ownership versus other methods of vertical integration. Strategic Management Journal, 13(8), 559-584. Martinez, R. J., & Dacin, M. T. (1999). Efficiency motives and normative logic: Combining transaction costs and institutional logic. Journal of Management, 25, 75-95. Masten, S. E. (1993). Transaction costs, mistakes, and performance: Assessing the importance of governance. Managerial and Decision Economics, 14, 119-129. Masten, S. E., Meehan, J., & Snyder, E. (1991). The costs of organization. Journal of Law, Economics, and organization, 7, 1-25. Monteiro, G. F., & Pianna, A. (2012). Institutional change and capability building: Some remarks on the institution-based view of strategy. International Journal of Strategic Change Management, 4(1), 52-67. Murtha, T. P., & Lenway, S. A. (1994). Country capabilities and the strategic state: How national political institutions affect multinational corporations' strategies. Strategic Management Journal, 15, 113-130. North, D. C. (1990). Institutions, institutional change, and economic preference. New York, NY: Norton. Peng, M. W. (2000). Business strategies in transition economies. Thousand Oaks, CA: Sage. Peng, M. W. (2003). Institutional transitions and strategic choices. Academy of Management Review, 28(2), 275-296. Ramamurti, R. (2000). A multilevel model of privatization in emerging economies. Academy of Management Review, 25, 525-550. Sambasivan, M., Siew-Phaik, L., Mohamed, Z., & Leong, Y. (2013). Factors influencing strategic alliance outcomes in a manufacturing supply chain: Role of alliance motives, interdependence, asset specificity and relational capital. International Journal of Production Economics, 141(1), 339-351. Shaver, J. M. (1998). Accounting for endogeneity when assessing strategy performance: Does entry mode choice affect FDI survival? Management Science, 44(4), 571-585. Shenkar, O., & Von Glinow, M. A. (1994). Paradoxes of organizational theory and research: Using the case of China to illustrate national contingency. Management Science, 40, 5671. Spicer, A., McDermott, G. A., & Kogut, B. (2000). Entrepreneurship and privatization in central Europe. Academy of Management Review, 25, 630-649. Tong, T. W., Reuer, J. J., & Peng, M. W. (2008). International joint ventures and the value of growth options. Academy of Managment Journal, 51, 1014-1029. Vernon, R. (1971). Sovereignty at bay: The multinational spread of U.S. enterprises. New York,
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Table 1 Organizational structure of private investments in 94 Emerging Economies Total
389
External structure 86
59
207
186
452
330
445
240
1015
19
43
3
65
20
161
12
193
Sub-Saharan Africa
61
105
21
187
Total (94 countries)
652
1350
548
2550
East Asia and Pacific Europe and Central Asia Latin America and Caribbean Middle East and North Africa South Asia
Internal structure 163
Hybrid
638
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Table 2 Descriptive statistics 1 Survival
Mean S.D. 0.946 0.226
2 Internal structure 3 Hybrid
0.256 0.436 0.529 0.499
4 External structure
0.215 0.411
5 Asset specificity
84.75 23.63
6 Uncertainty
3.078 0.488
7 Age
7.197 3.403
8 Per capita GDP (log)
3.29 0.388
9 Bank loan
0.16 0.367
10 Payment to gov
72.31
11 Energy 12 Telecommunication
0.405 0.491 0.222 0.416
13 Transport
0.279 0.449
1 Survival 2 Internal structure 3 Hybrid 4 External structure 5 Asset specificity 6 Uncertainty 7 Age 8 Per capita GDP (log) 9 Bank loan 10 Payment to gov
Mean 0.946 0.256 0.529 0.215 84.75 3.078 7.197 3.29 0.16 72.31
11 Energy 12 Telecommunication 13 Transport Descriptive statistics
289
S.D. 0.226 0.436 0.499 0.411 23.63 0.488 3.403 0.388 0.367 289
0.405 0.491 0.222 0.416 0.279 0.449
1
2
0.023 0.038 -0.622 0.023 -0.307 0.002 0.061 0.004 0.0004 0.116 -0.003 0.024 0.053 0.018 -0.038 0.021 -0.104 0.008 -0.388 0.049 -0.289 0.029 0.5156 7
0.142 0.018 0.006 0.067 0.104 0.043
3
4
5
6
0.555 0.213
-0.324
0.159 0.058 0.182
-0.194
-0.164
0.073
0.015
-0.171
0.165
0.096
-0.572
0.033 0.000 0.185 0.335
0.089
-0.101
-0.064
0.004
0.096 0.279 0.267
0.296 -0.031
-0.044 0.035
-0.069 0.041
-0.223
0.038
0.052
8
9
10
11
12
-0.021 0.073
0.077
-0.017 -0.105 0.049
0.051 0.037 -0.08
0.006 0.149 -0.107
-0.44 -0.513
-0.332
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Table 3 Multinomial Logit Regression of organizational structures
Asset specificity Uncertainty Year elapsed Per capita GDP (log) Bank loan Energy Telecommunication Transport East Asia and Pacific Europe and Central Asia Latin America South Asia Sub-Saharan Africa Constant N Wald chi2 Pseudo R2
Internal structure 0.04*** (0.004) 0.738*** (0.224) -0.032 (0.023) -1.032** (0.361) 0.057 (0.218) -4.817*** (0.347) -5.269*** (0.506) -0.488 (0.352) -1.373† (0.809)
Hybrid 0.045*** (0.003) 0.984*** (0.172) -0.07*** (0.019) 0.158 (0.262) 0.146 (0.171) -1.576*** (0.327) -0.178 (0.351) -0.572† (0.359) -0.33 (0.669)
-1.83* (0.821) -1.683* (0.79) -2.127* (0.925) -0.533 (0.922) 2.586 (1.91) 2550 1065.36*** 0.357
-2.2*** (0.67) -1.828* (0.656) 0.075* (0.743) -1.087 (0.728) -3.327* (1.468) 2550 1065.36*** 0.357
Note. The base category is external structure. Numbers in the brackets are standard errors. † p<0.1, *p<0.05, ** p<0.01, *** p<0.001.
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Table 4 Survival of private investments in SOEs Model 1 External structure Asset specificity -0.184* (0.074) Uncertainty -4.923* (1.788) Years elapsed 0.191 (0.107) Bank loan -1.084* (0.504) Payment to gov -0.0003 (0.0002) Energy 12.273* (5.060) Telecommunication 7.636* (2.948) Transport 3.741 (2.314) East Asia and Pacific 2.618â&#x20AC; (1.372) Europe and Central Asia 9.898** (3.392) Latin America 6.790* (3.001) South Asia
correction for self-selection N Wald chi2 Pseudo R2
-0.860 (0.841) -0.422 (1.601)
Model 3 Hybrid 0.010 (0.012) -0.296 (0.375) -0.204*** (0.040) 0.344 (0.445) -0.0005* (0.0002) -0.194 (0.757) 0.918 (0.962) -1.259 (0.784) 1.120 (0.911)
1.789 (1.820)
-0.955 (1.08) 5.24* (2.633)
0.321 (0.684) 0.026 (0.590) -0.249 (0.889) 1.658 (1.038) 3.994 (2.907)
11.307** (4.066) 524 44.67*** 0.1433
-0.096 (1.434) 633 33.87** 0.1159
0. 715 (0.903) 1350 57.79*** 0.1096
Sub-Saharan Africa Constant
Model 2 Internal structure 0.009 (0.010) -0.670 (0.457) -0.082* (0.039) -0.001 (0.529) -0.001*** (0.0003) -0.254 (2.044) 0.085 (2.831) 0. 554 (0.443) 0.058 (0.905)
Note. In Model 1, variable Sub-Saharan Africa and South Asia are dropped since they predict survival perfectly, 24 observations are not used. In Model 2, variable South Asia is dropped due to collinearity, 19 observations are not used. Numbers in the brackets are standard errors. â&#x20AC; p<0.1, * p<0.05, ** p<0.01, *** p<0.001.
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Improving Undergraduate Business Communication Skills through Paired Coursework: An Exploratory Study Cathyann D. Tully, Wagner College
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