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Center for Scholastic Inquiry (CSI) offers electronic presentation portfolios as a courtesy to the attendees of our conferences. Materials are the sole intellectual property of the presenters and are displayed only with their permission. All questions about content should be directed to the presenters. Pages
Table of Contents
4-19
Gender Differences in Leading Change – Dr. Lisa Eshbach
20-46
An Examination of the Relationship Between Teaching Presence, Social Presence, Learner Motivation, and Self-Reported Learning Among Face-to-Face MBA Students – Dr. Herbert Pollard, Dr. Lorrie McGovern, and Dr. Mary Connor
47-78
Should the Policy Goal be Happiness or Economic Growth? – Dr. Maria Kula
79-129
The Problem of Spatial Inequality in the Regions of Turkey: An Exploratory Spatial Data Analysis – Dr. Faith Celebioglu
130-153
Dissecting the Turtle: An Examination of the Turtle Trading System – Dr. David Rayome
154-183
A Framework for Specifying Business Models – Dr. Brad Barbeau
184-196
The Influence of Enterprise Systems on Business and Information Technology Strategic Alignment – Myles Muretta and Dr. Lance Revenaugh
197-218
Behavioral Insights Reveal a Consumer of Mixed Rationality – Dr. Paul Stock
220-235
The Threats to Pharmaceutical R & D from Free Riders – Dr. Steve Molloy
236-244
China’s Gradualism Approach to Systemic Transformation: Successes, Challenges – Dr. Raphael Shen and Dr. Victoria Mantzopoulos
245-258
Social Security and Medicare: Earned Entitlements – Dr. Arthur Young
259-280
Enhancing Student Participation through Technology and Collaborative Learning – Dr. Barbara Lamberton
Lisa Eshbach, PhD
Increasingly,
change is seen as critical to organizational success.
Interpersonal
skills have been associated with success in driving change (Gilley, McMillan, & Gilley, 2009);
Females
perceived as having advantages in communications, intuition, and social aptitude (Claes, 1999).
Peers
and subordinates rate female managers slightly higher than men (Conlin, 2003; Kabacoff, 1998).
Women
described as transformational in leadership style, men as transactional (AlimoMetcalfe, 1995).
Abundant
research finds no significant differences between male and female managers (Donnell & Hall, 1980; Hyde, 2005)
Early
research of change competencies
Communications skill Ability to motivate others
Limited
empirical investigation of the competence and effectiveness of female vs male leaders/managers.
HYPOTHESIS: Female managers are perceived as more effective at implementing change than male managers.
Population:
OD Master’s and PhD students
Higher level analytical and critical thinking skills Represented a broad range of participants, industries, companies, and positions
Convenience
sample
Three 4-year universities (2 public, 1 private) N=777; 92.3% response rate
Time
frame: Longitudinal, 2004-2010
Questions
about frequency of managerial behaviors/practices related to change, including:
Motivates employees
Appropriately communicates
Coaches
Promotes employee growth and development
Effectively rewards/recognizes others
Encourages teamwork and collaboration
Possesses appropriate skills
Treats employees fairly
Effectively evaluates
Effectively implements change
Involves employees in decision making
Treats employees as unique individuals
Range:
Never (1) – Always (5)
Simple
statistics (frequencies, percentages, correlations)
Multiple
T-test
linear and step-wise regressions
and Mann-Whitney
Respondent
Respondent Gender
53.7% male / 45.5% female
(0.8% not reported)
Supervisor Gender
Population
58.4% male / 40.7% female
(.9% not reported)
Job Roles
34.34% Front-line employees 26.6% Supervisors, team leaders 23.3% Mid-level managers 11.9% Senior executives 3.9% Other (CEO, owner, self-employed)
Industry Type
14.9% manufacturing 26.1% service 31.1% education 21.2% professional 5.8% government 0.9% non-profit
Mgr’s Gender
Never
Rarely
Sometimes
Usually
Always
Female % Cum %
28 8.9 8.9
84 26.6 35.5
124 39.2 74.7
60 19.0 93.7
20 6.3 100.0
Male % Cum %
43 9.5 9.5
134 29.5 39.0
180 39.7 78.7
71 15.6 94.3
26 5.7 100.0
No Resp
1
3
0
3
0
TOTALS % Cum %
72 9.3 9.3
221 28.4 37.7
304 39.1 76.9
134 17.2 94.1
46 5.9 100.0
Mgr’s Gender
Never
Rarely
Sometimes
Usually
Always
Female % Cum %
16 5.1 5.1
82 26.0 31.1
118 37.3 68.4
81 25.6 94.0
19 6.0 100.0
Male % Cum %
28 6.2 6.2
112 24.7 30.0
168 37.0 67.9
115 25.3 93.2
31 6.8 100.0
No Resp
1
1
3
2
0
TOTALS % Cum %
45 5.8 5.8
195 25.1 30.9
289 37.2 68.1
198 25.5 93.6
50 6.4 100.0
Mgr’s Gender
Never
Rarely
Sometimes
Usually
Always
Female % Cum %
37 11.7 11.7
85 26.9 38.6
114 36.1 74.7
69 21.8 96.5
11 3.5 100.0
Male % Cum %
53 11.7 11.7
113 24.9 36.6
169 37.2 73.8
100 22.0 95.8
19 4.2 100.0
No Resp
1
2
2
2
0
TOTALS % Cum %
91 11.7 11.7
200 25.7 37.4
285 36.7 74.1
171 22.0 96.1
30 3.9 100.0
Managers (both male and female) are largely perceived as being ineffective in leading change.
No significant difference in change effectiveness between males and females.
Donnell & Hall, 1980; Hyde 2005
Although women are perceived as being more effective with communications anecdotally, the research reveals no statistically significant advantage.
Gilley et. al 2008; Cope 2003; Burns 2004
Claes 1999; Gaur, 2006
This initial empirical research on “motivation of others� reveals no statistically significant advantage to either gender.
Step-Wise
Focus on developing key managerial skills
Regression for Motivates
Motivation Management coaching, mentoring Employee growth and development Effective Reward Process Teamwork and Collaboration Communication
Impact of Interpersonal skills Ability to secure results through others Methods: Hiring and Selection process Management /leadership training and development Performance evaluation and promotion practices
Future research
Impact of selection and training methods on change effectiveness Different types of organizations and level of leading change effectiveness Investigation at the individual level (vs organization); which levels of leadership / management most need to improve? What skills most lacking? Data collection at multiple points in ones career Comparison with quantifiable organization results
Managers actions and organization outcomes
etc
An Examination of the Relationship between Teaching Presence, Social Presence, Learner Motivation, and Self-Reported Learning among Face-to-Face MBA Students Dr. Herbert Pollard, Dr. Lorrie McGovern, Dr. Mary Connor, and Dr. Randall Blevins
Community of Inquiry Framework Social Presence
Teaching Presence
Cognitive Presence
Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text- based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2-3), 87-105.
Teaching Presence – Focus on instruction through course structure and instructor behavior. 1. Course design and organization 2. Facilitated discourse 3. Direct instruction
Social Presence – Focus on peer social attitudes and interactions. 1. Emotional expression 2. Open communications 3. Group cohesion
Cognitive Presence – Focus on levels of learning. 1. Triggering event 2. Exploration 3. Integration 4. Resolution
Research Model Teaching Presence
Learner Motivation
Selfreported Learning
Social Presence
Shea, P. & Bidjerano, T. (2010). Learning presence: Towards a theory of self-efficacy, self-regulation, and the development of a communities of inquiry in online and blended learning environments. Computers & Education, 55, 1721-1731.
Hypotheses • H1 Increases in the level of teaching presence will be associated with an increased level of learner motivation. • H2 Increases in the level of social presence will be associated with an increased level of learner motivation.
Hypotheses • H3 Increases in the level of learner motivation will be associated with an increased level of self-reported learning. • H4 Teaching presence, social presence, and learner motivation can explain selfreported learning.
Instruments Teaching Presence Thirteen items adapted from Arbaugh et al. (2008) The instructor clearly communicated important course topics.
Social Presence Seven items adapted from Arbaugh et al. (2008) Getting to know other course participants gave me a sense of belonging in the course.
Cognitive Presence Twelve items adapted from Arbaugh et al. (2008). Problems posed in the course increased my interest in course issues. Arbaugh, J. B., Cleveland-Innes, M., Diaz, S. R., Garrison, D. R., Ice, P., Richardson, J. C., & Swan, K. P. (2008). Developing a community of inquiry instrument: Testing a measure of the community of inquiry framework using a multi-institutional sample. Internet and Higher Education, 11(3-4), 133-136.
Instruments Learner Motivation Sixteen items adapted from Abodous and Yen (2010), Artino (2008), Artino and McCoach (2008), and Deimann and Bastiaens (2010).
Motivation items included four items each for: • • • •
task value - It was important for me to learn the material in this course. self-efficacy - I am certain that I understand the material presented in this course. satisfaction - Overall, I am satisfied with my experience in this course. self-regulation - When I encountered challenges during the course, I always felt that I could overcome the challenges and do well in the course.
Abdous, M. & Yen, C. (2010). A predictive study of learner satisfaction and outcomes in face-to-face, satellite broadcast, and live video-streaming learning environments. The Internet and Higher Education, 13(4), 248-257. Artino, A. (2008). Promoting academic motivation and self-regulation: Practical guidelines for online instructors. Techtrends: Linking Research & Practice to Improve Learning, 52(3), 37-45. Artino, A. R., Jr., & McCoach, D. B. (2008). Development and initial validation of the online learning value and efficacy scale. Journal of Educational Computing Research, 38(3), 279-303. Deimann, M. & Bastiaens, T. (2010). The role of volition in distance education: An exploration of its capacities. International Review of Research in Open and Distance Learning, 11(1), 1-16.
Instruments Self-reported Learning Five items adapted from Akyol and Garrison (2008) and Arbaugh (2005). I learned much in this class. I gained a good understanding of the basic concepts of the material.
Akyol, Z. & Garrison, D. R., (2008). The development of a community of inquiry over time in an online course: Understanding the progression and integration of social, cognitive, and teaching presence. Journal of Asynchronous Learning Networks, 12(3-4), 3-22. Arbaugh, J. B. (2005). How much does "subject matter" matter? A study of disciplinary effects in on-line MBA courses. Academy of Management Learning and Education, 4(1), 57-73.
Methodology Survey:
• Distributed to MBA students during Week 7 of an 8-week face-to-face class. • Asked students to use their personal beliefs to guide their answers. • A Likert 5-point scale was used for responses, – “Strongly Disagree” to “Strongly Agree.”
Methodology Response:
• 126 students offered the survey: – 115 elected to respond to the request with 114 completing the entire survey, – 90% response rate.
• Gender of the respondents: – 53 males, 60 females, and one chose not to indicate gender.
Findings Factor Analysis- Teaching, Social, and Cognitive presences Varimax rotated factor analysis demonstrated a strong separation between three factors: teaching, social, and cognitive. These findings are consistent with prior studies. Varimax Rotated Component Matrix TP1 TP2 TP3 TP4 TP5 TP6 TP7 TP8 TP9 TP10 TP11 TP12 TP13 SP1 SP2 SP3 SP3 SP4 SP5 SP6 SP7 CP1 CP2 CP3 CP4 CP5 CP6 CP7 CP8 CP9 CP10 CP11 CP12
Teaching
Cognitive
Social
.832 .817 .832 .599 .782 .791 .772 .838 .695 .596 .722 .671 .566 .131 .162 .151 .318 .230 .135 .090 .154 .276 .413 .348 .191 .341 .333 .400 .498 .404 .432 .310 .243
.222 .172 .268 -.001 .386 .327 .271 .241 .407 .375 .374 .418 .303 .262 .234 .168 .201 .166 .221 .298 .233 .588 .594 .727 .686 .574 .564 .562 .621 .714 .758 .792 .733
.197 .143 .195 .380 .117 .201 .264 .167 .252 .353 .234 .165 .036 .716 .546 .510 .826 .856 .837 .699 .780 .552 .416 .295 .353 .384 .380 .501 .318 .247 .204 .218 .286
Findings Factor Analysis- Learner Motivation High unrotated factor loadings were found in all motivation item analysis, supporting single-factor loading. Unrotated Component Matrix Learner Motivation MOT1 MOT2 MOT3 MOT4 MOT5 MOT6 MOT7 MOT8 MOT9 MOT10 MOT11 MOT12 MOT13 MOT14 MOT15 MOT16
.759 .828 .736 .663 .867 .818 .804 .787 .862 .889 .881 .907 .846 .852 .621 .717
Findings Factor Analysis- Self-reported Learning High unrotated factor loadings were found in all self-reported learning item analysis, supporting single-factor loading. Unrotated Component Matrix Self-reported Learning Learn 1 .890 Learn 2 .916 Learn 3 .903 Learn 4 .869 Learn 5 .869
Hypothesis 1 H10 Increases in the level of teaching presence will not be associated with an increased level of learner motivation. â&#x20AC;&#x201C; A statistically significant and extremely strong positive correlation was found between the levels of teaching presence and learner motivation, r(109) = .491, p < .001. â&#x20AC;&#x201C; These results strongly indicated that increases in the level of TP were associated with increases in the level of learner motivation.
Hypothesis 2 H20 Increases in the level of social presence will not be associated with an increased level of learner motivation. â&#x20AC;&#x201C; A statistically significant correlation was found between the levels of social presence and learner motivation, r(109) = .235, p < .05. â&#x20AC;&#x201C; These results indicated that increases in the level of SP were associated with increases in the level of learner motivation.
Hypothesis 3 H30 Increases in the level of learner motivation will not be associated with an increased level of self-reported learning. â&#x20AC;&#x201C; A statistically significant and extremely strong positive correlation was found between the levels of learner motivation and self-reported learning, r(111) = .866, p < .001. â&#x20AC;&#x201C; These results strongly indicated that increases in the level of learner motivation were associated with increases in the level of self-reported learning.
Hypothesis 4 H40 Teaching presence, social presence, and learner motivation cannot explain self-reported learning. In order to test this hypothesis, a linear regression analysis was conducted including teaching presence, social presence, and motivation as predictors of self-reported learning. The table below summarizes the results of this analysis. Regression Analysis of Self-Reported Learning Measure
Unstd. Coefficients Beta t Sig. B Std. Error (Constant) .001 .046 .018 .985 Teaching Presence .188 .053 .187 3.521 .001 Social Presence .031 .048 .031 .657 .512 Motivation .767 .055 .766 14.018 .000 Notes: R = .880, R2 = .775, Adj. R2 = .769; F(3, 107) = 123.002, p < .001.
Teaching presence and motivation demonstrated a significant and positive impact on self-reported learning. However, social presence was not found to achieve significance in this analysis.
Supplemental Analysis Following the suggestions of Arbaugh (2005) and Arbaugh, Bangert, and Cleveland-Innes (2010) , additional analyses were conducted to examine the impact of discipline type. Courses were grouped as either â&#x20AC;˘ quantitative or â&#x20AC;˘ non-quantitative.
Supplemental Analysis In an analysis of non-quantitative courses, â&#x20AC;˘ teaching presence and motivation were found to have significant, positive impacts upon learning. In an analysis of quantitative courses, â&#x20AC;˘ teaching presence was not found to have a significant impact, while â&#x20AC;˘ both social presence and motivation were found to have a significant, positive impact upon learning.
Supplemental Analysis The effect of learner motivation was found to be very similar in both regression analyses. Additionally, R-squared measures were found to be very similar between both models. This comparison of results clearly indicates that an analysis within the COI framework which combines disciplines is problematic. Arbaugh, J. B. (2005). How much does "subject matter" matter? A study of disciplinary effects in on-line MBA courses. Academy of Management Learning and Education, 4(1), 57-73. Arbaugh, J. B., Bangert, A., & Cleveland-Innes, M. (2010). Subject matter effects and the community of inquiry (COI) framework: An exploratory study. The Internet and Higher Education, 13(1-2), 37-44.
Supplemental Analyses Regression Analysis on Self-Reported Learning by Type of Course Variable Unstd. Coef. Std. Coef. t p 95% CI for B B SE Beta Lower Upper Non-Quantitative (Constant) .057 .075 Teach .199 .092 Social -.203 .103 Motivation .761 .080
.186 -.141 .757
.761 2.169 -1.970 9.499
.450 .035 .055 <.001
-.093 .015 -.409 .600
.207 .384 .004 .923
Collinearity Tol. VIF
.555 .799 .645
1.802 1.252 1.551
Quantitative (Constant) .092 .060 1.532 .131 -.028 .212 Teach .042 .068 .042 .624 .535 -.094 .178 .780 1.282 Social .182 .057 .221 3.210 .002 .068 .295 .765 1.307 Motivation .742 .070 .756 10.666 <.001 .603 .882 .720 1.388 Notes: Model 1: F(3, 51) = 65.370, p < .001; R = .896, R2 = .803, Adjusted R2 = .791; Model 2: F(3, 57) = 74.134, p < .001; R = .897, R2 = .805, Adjusted R2 = .794.
Discussion and Conclusion This study found significant associations between • teaching presence and learner motivation, and • social presence and learner motivation. Additionally, the study identified a significant association between • learner motivation and self-reported learning. The study supports the inclusion of learner motivation within the Community of Inquiry framework.
Discussion and Conclusion A major finding of the study was the collective predictive strength of teaching presence and motivation for self-reported learning. However, social presence did not have a significant impact on self-reported learning.
Discussion and Conclusion Supplemental analysis with discipline groups as quantitative or non-quantitative indicated that â&#x20AC;˘Teaching presence has a significant impact on learning for non-quantitative courses only. â&#x20AC;˘Social presence has a significant impact on learning for quantitative courses only.
Should the Policy Goal be Happiness or Economic Growth? Maria Cornachione Kula Gabelli School of Business Roger Williams University Priniti Panday Gabelli School of Business Roger Williams University McKay Gavitt Gabelli School of Business Roger Williams University
Intro • Calls for moving away from GDP per capita as a measure of well-being • Replacement: subjective “happiness” or “life satisfaction” measures
Some Examples • In 1972 Bhutan’s King:“Gross National Happiness” statistic • In 2008 French President Sarkozy: commission to study limitations of GDP as an indicator of economic performance & social progress & to suggest alternatives. – chaired by economist Joseph Stiglitz
Report by the Commission on the Measurement of Economic Performance and Social Progress • subjective measures of well-being should be included in a measure of living standards, including people’s self reports of their “… happiness, satisfaction, positive emotions such as joy and pride, and negative emotions such as pain and worry”
• In 2010 British Prime Minister David Cameron tasked the Office of National Statistics with measuring the nation’s well-being. • “…we will start measuring our progress as a country not just by how our economy is growing, but by how our lives are improving, not just by our standard of living, but by our quality of life… it is high time we admitted that, taken on its own, GDP is an incomplete way of measuring a country's progress”
Academic Interest in “Happiness” • Large sources of data • The General Social Survey and the World Values Survey • Encompass dozens of countries and thousands of individuals • Ask questions related to subjective well-being.
Importance â&#x20AC;˘ What should the focus of economic policymaking be? â&#x20AC;˘ The maximization of the growth rate of real GDP per capita, or some other measure of well-being?
This Paper • Considers elements for which real GDP per capita is most often criticized and their relationship to “happiness”.
Criticism of real GDP per capita as a proxy for well-being is not new • the exclusion of non-material dimensions: leisure • the inclusion of harmful items (e.g. negative environmental externalities) • omission of income distribution
Many alternative measures of wellbeing • One set: begin with GDP and add missing valuable items and subtract disamenities to focus on consumption – Nordhaus and Tobin (1972): Measure of Economic Welfare – Jones and Klenow (2010): based on consumption, leisure, inequality, and mortality data.
• Others construct composite indices based on societal attributes deemed to reflect wellbeing – UN’s Human Development Index (HDI): combines measures of health, education, & living standards (the logarithm of income: increases in income are of diminished importance). – Kula, Panday, & Parrish (2008): subcomponents of environmental characteristics conducive to individual choice & freedom and thus attainment of well-being.
Happiness and Real GDP per Capita • “Easterlin paradox”: within countries, higher income individuals are happier, but people in rich countries are not happier than those in poor countries. • People concerned with relative differences in income, not absolute – they want to “keep up with the Joneses”.
• A satiation point with respect to income and happiness • Layard (2003): at income over $15000 per person, happiness is independent of income per person. • Suggest: income equality, not maximization of GDP per capita, should be policy goal
More Recent Research • Stevenson and Wolfers (2008) – find no evidence of a satiation point: ↑ real GDP per capita → ↑ happiness – find that people in rich countries are happier than those in poor countries & that the poorer you are, the less happy you are – a policy goal of maximizing economic growth will maximize happiness
â&#x20AC;˘ Differing results attributed to data used â&#x20AC;&#x201C; Easterlin (1974): two international datasets of countries with similar attributes. â&#x20AC;&#x201C; Stevenson and Wolfers (2008): data on a large sample of countries, both rich and poor, and use several survey sources for happiness and life satisfaction data
This Paper • Consider common criticisms of Real GDP per Capita & their relationship to happiness • Is economic growth proper goal for policy? • Maximizing economic growth: consistent with stable prices & unemployment at its natural rate • How is relationship between happiness and criticized areas affected by inflation & unemployment rates
Literature Review • The validity of the use of happiness survey data: Kahneman and Krueger (2006) – responses to subjective well-being questions are related to health outcomes and other objective physiological measures
• Di Tella and MacCulloch (2006): summary of articles on the validity of the use of happiness data in economics research
“Happiness”, Unemployment & Inflation Rates • Di Tella, MacCulloch, and Oswald (2001): 12 European countries, 1975-1991; people are happier when inflation & unemployment are low. • Wolfers (2003): 16 European countries, 19731998; unemployment & inflation negatively correlated with happiness; unemployment having a bigger impact than inflation.
• Blanchflower (2007): larger sample, longer period; higher unemployment & higher inflation lower happiness; larger effect for unemployment • Perovic (2008): 8 transition economies; similar conclusions. • Gandelman and Hernandez-Murillo (2009): do not find differing impact of unemployment & inflation
Data • Stevenson and Wolfers (2008) use 3 surveys: the “Pew Global Attitudes Survey”, the “life satisfaction” section and “happiness” section of the “World Values Survey”. • Results on relationship between income & happiness consistent across surveys used.
• We use “happiness” data they derived from the World Values Survey as our “happy” (or well-being) variable. • A sample question from The World Values Survey: “taking all things together would you say that you are, ‘very happy,’ ‘quite happy,’ ‘not very happy,’ [or] ‘not at all happy?’”
â&#x20AC;˘ Stevenson and Wolfers (2008) map responses from participants into an index where 1.5 is the happiest and -1.5 is the least happy. â&#x20AC;˘ Used ordered probit regressions of the ladder ranking on a series of country fixed effects and thus estimated average levels of subjective well being in each country
â&#x20AC;˘ GDP per capita, the unemployment rate, and the inflation rate are from the World Bank, World Development Indicators (2009). â&#x20AC;˘ Pollution is measured as CO2 emissions (metric tons per capita) and is from the World Bank, World Development Indicators, 2008
• Income inequality is measured by the Gini coefficient. • a number between 0 and 1 • The greater the Gini coefficient, the more unequal the income distribution • The Gini coefficient data is from the UN International Human Development Indicators, 2000.
â&#x20AC;˘ Leisure: residual time not spent in paid work as a share of overall time, where overall time is spent either in paid work or not. The data is for 2006, from the OECD (2009). â&#x20AC;˘ Sixteen countries: Austria, Belgium, Canada, Finland, Germany, Greece, Hungary, Ireland, Italy, Norway, Poland, Slovak Republic, Spain, Sweden, Switzerland, and the United States
Table 1. Correlations
Happiness
Inflation Rate -0.647
GDP per Capita -0.487
Unemployment Rate -0.326
Gini Coefficient Pollution
Leisure
0.131
0.449
0.458
-0.546
-0.151
0.230
0.683
•Happiness correlations as expected EXCEPT • positive correlations between happiness & pollution • positive correlations between happiness & income inequality
• Leisure is positively correlated with GDP per capita. – may be picking up: productivity improvements could lead to higher output and more time for leisure
• Negative correlation between Gini & GDP per capita – fiscal policy approach to inequality and taxation: increased inequality => calls for redistribution, resulting in higher tax rates, which lower growth and GDP
• Positive correlation between pollution & GDP per capita (matching expectations): – positive correlation between pollution & happiness could be picking up indirect, positive correlation of GDP per capita & happiness.
Table 2. ℎܽ ߙ = ݕ+ ߚ1 ݃݅݊݅ + ߚ2 ݈݁݅ ݁ݎݑݏ+ ߚ3 ݊݅ݐݑ݈݈+ ߝ ߙො 1 ߚ 2 ߚ 3 ߚ ܴ2
-11.14 (3.85) -2.89 t-stat 0.03 (0.02) 1.40 t-stat 12.29** (4.25) 2.89 t-stat 0.04* (0.02) 1.72 t-stat 0.53
Standard Errors in Parentheses ** significant at the 5% level; * significant at the 10% level
â&#x20AC;˘ Only leisure is statistically significant at the 5% level and has the correct sign (positive). â&#x20AC;˘ Some evidence to support happiness as the correct goal â&#x20AC;˘ Next: what happens when the inflation rate & unemployment rate are included
Table 3. ℎܽ ߙ = ݕ+ ߚ1 ݃݅݊݅ + ߚ2 ݈݁݅ ݁ݎݑݏ+ ߚ3 ݊݅ݐݑ݈݈+ ߚ4 ݂݅݊ + ߚ5 ݉݁݊ݑ+ ߝ
ߙො 1 ߚ 2 ߚ 3 ߚ 4 ߚ 5 ߚ ܴ2
-3.74 (4.21) -0.89 t-stat 0.01 (0.02) 0.69 t-stat 4.50 (4.60) 0.98 t-stat 0.03 (0.02) 1.48 t-stat -0.10** (0.04) -2.49 t-stat -0.04** (0.02) -1.97 t-stat 0.73
Standard Errors in Parentheses ** significant at the 5% level
• Unemployment & inflation rates have correct signs (negative) & are statistically significant at 5% level – inflation has greater impact, unlike in previous studies – With inclusion of unemployment & inflation rates, leisure loses its significance.
• Results: correct to focus on economic outcomes. – Doing so will stabilize unemployment & inflation rates, which benefit happiness – “good” outcomes with unemployment and inflation result of prudent policy of maximizing economic growth.
Conclusion • 16 country sample: when inflation & unemployment rates considered – Leisure, income inequality, & pollution do not impact happiness.
• These 3 elements typically what GDP is criticized for – this criticism of GDP as a policy objective is unfounded.
• Given importance of inflation & unemployment to happiness: correct policy focus should be on stable prices and unemployment close to its natural rate. – This is what a policy focus on economic growth provides.
• Results here, plus recent research showing higher income corresponds to greater happiness – no need to move to maximization of happiness as a policy goal.
THE PROBLEM OF SPATIAL INEQUALITY IN THE REGIONS OF TURKEY: AN EXPLORATORY SPATIAL DATA ANALYSIS
Fatih ÇELEBİOĞLU (Ph.D) Associate Professor Dumlupınar University Dept. of Economics Kutahya/TURKEY fcelebi@dpu.edu.tr
Where is Turkey in the world?
Turkey (Turkish: T端rkiye), officially the Republic of Turkey is a transcontinental country, located mostly on Anatolia in Western Asia and on East Thrace in Southeastern Europe.
Turkey is formally composed of 81 provinces used as administrative units.
Province Level Turkey Map
The definition of regions is only used for geographic classification purposes (for example Marmara, Aegean, Southeastern areas). Geographic Regions in Turkey
As a candidate country of the European Union, Turkey (TR) is included in the Nomenclature of Territorial Units for Statistics (NUTS). According to the NUTS2, Turkey has 26 sub-regions.
The regions have very important spatial disparities. For instance, the provinces located in the Southeastern and Eastern Anatolia areas are known to be lagging behind in economic and social terms.
To show regional disparity in Turkey, we start our analysis with the quartile maps of the distribution of some variables for each province.
Data Our dataset comes from the Turkish Statistical Institute, the Ministry of Development and Turkish Patent Institute. Our dataset has been composed for 81 provinces. Socio-Economic Development Index: The Ministry of Development calculates Socio-Economic Development Index to understand different levels of socio-economic development in Turkey. The index values explained two times in the last decade (2004 and 2011). Per capita Patent Applications Net Migration Rates (Per Thousand) University Degrees (%) To analyze spatial relations in Turkey, we use GeoDa (Geographic Data Analysis) software package which conducts Spatial Data Analysis, geovisualization, spatial autocorrelation.
Quantile Maps
Distribution of province level per capita GDP in 2001 (76 Province)
Distribution of province level Socio-Economic Development Index values in 2004
Distribution of province level Socio-Economic Development Index values in 2011
Distribution of province level Socio-Economic Development Index values from 2004 to 2011
Per capita entrepreneurship in 2008
Unemployment rates (%) in 2008
Province level net migration rates in 2009
Province level per capita public investment from 2004 to 2011
Distribution of per capita patent applications in 2009
Per capita electricity consumption in 2008
Literacy rates in provinces of Turkey in 2009
University degrees (%) in 2009
Stages of ESDA
Spatial Weight Matrix Moranâ&#x20AC;&#x2122;s I for Global Spatial Autocorrelation Moranâ&#x20AC;&#x2122;s Scatterplots LISA Statistics for Local Spatial Autocorrelation
Spatial Weight Matrix
A spatial weight matrix is the necessary tool to impose a neighborhood structure on a spatial dataset. As usual in the spatial statistics literature, neighbors are defined by a binary relationship (0 for non-neighbors, 1 for neighbors). Weight matrix calculation is performed under GeoDa. It can be used two basic approaches for defining neighborhood: contiguity (shared borders) and distance.
Contiguity-based weights matrices include rook and queen. Areas are neighbors under the rook criterion if they share a common border, not vertices. Distance-based weights matrices include distance bands and k nearest neighbors. Based on these two concepts, we decided to create weight matrices to investigate the distribution of our variables of interest: k_8 nearest neighbor matrix.
w ( k ) = 0 if i = j ij * wij ( k ) = 1 if d ij ≤ Di ( k ) and wij ( k ) = wij ( k ) / ∑ wij ( k ) for k = 8 j w ( k ) = 0 if d > D ( k ) ij i ij where dij is great circle distance between centroids of country i and j and Di(k) is the 8th order smallest distance between regions i and j such that each region i has exactly 8 neighbors. Now that the weight matrix has been defined, we estimate a couple of spatial statistics that will shed some light on the spatial distribution of our variables. The most common of them is Moran’s I which is a measure of global spatial autocorrelation.
Moranâ&#x20AC;&#x2122;s I for Global Spatial Autocorrelation
Spatial autocorrelation refers to the correlation of a variable with itself in space. It can be positive (when high values correlate with high neighboring values or when low values correlate with low neighboring values) or negative (spatial outliers for high-low or low-high values).
Note that positive spatial autocorrelation can be associated with a small negative value (e.g., -0.01) since the mean in finite samples is not centered on 1. Spatial autocorrelation analysis includes tests and visualization of both global (test for clustering) and local (test for clusters) Moranâ&#x20AC;&#x2122;s I statistic.
Global spatial autocorrelation is a measure of overall clustering and it is measured here by Moran's I. It captures the extent of overall clustering that exists in a dataset. It is assessed by means of a test of a null hypothesis of random location.
Rejection of this null hypothesis suggests a spatial pattern or spatial structure, which provides more insights about a data distribution that what a quartile map. For each variable, it measures the degree of linear association between its value at one location and the spatially weighted average of neighboring values.
n
It =
n
*
w ij ( k ) xit x jt ∑ ∑ i =1 j =1 n
n
xit x jt ∑ ∑ i =1 j =1
Where is the (row-standardized) degree of connection between the spatial units i and j and Xi,j is the variable of interest in country i at year t (measured as a deviation from the mean value for that year). Values of I larger (smaller) than the expected value E(I) = -1/(n-1) indicate positive (negative) spatial autocorrelation. In our study, this value is (-0.0125). There are different ways to draw inference here. The approach we use is a permutation approach with 999 permutations. It means that 999 resampled datasets were automatically created for which the I statistics are computed. The value obtained for the actual dataset has then been compared to the empirical distribution obtained from these re-sampled datasets.
The results of Moranâ&#x20AC;&#x2122;s I are presented in the next slide below. All the results indicate a positive spatial autocorrelation, i.e. the value of a variable in one location depends positively on the value of the same variable in neighboring locations.
Variables Socio-Economic Development Index (SEGE2004) Socio-Economic Development Index (SEGE2011) Socio-Economic Development Index (Rate of Change 2004-2011) Per capita Patent Applications in 2009 Net Migration Rates (per thousand) in 2009 University Degrees (%) in 2009 Note: P-Values are into brackets
K_8 0.6670 (0.001) 0.7235 (0.001) 0.2410 (0.001) 0.3403 (0.001) 0.3400 (0.001) 0.4203 (0.001)
For instance, when the Socio-Economic Development Index (2004) in one country increases by 1%, the one of its neighbors increases by slightly more than 72%. All of our three variables of interest are significant (at 1%) with the k_8 nearest neighbor matrix. For this reason, this is the weight matrix we will use in the rest of our study.
Moranâ&#x20AC;&#x2122;s Scatterplots
The Moran scatter plot complements Moranâ&#x20AC;&#x2122;s I because it provides to categorize the nature of spatial autocorrelation into four types: low-low (LL), low-high (LH), high-low (HL) and high-high (HH). The x-axis captures the value of a variable compared to the average value of the sample. For example, all the points on the right hand side of the figure mean (the vertical axis in the middle) that in the corresponding provinces, the value of the variable under study was above the sampleâ&#x20AC;&#x2122;s average.
On the other hand, the y-axis captures the average value of the same variable in the neighboring locations (with the neighbors being defined by the weight matrix). For instance, all the points below the mean (the horizontal axis in the middle of the figure) represent provinces of which neighbors display, on average, a lower value than the sampleâ&#x20AC;&#x2122;s mean.
The result of this approach is a figure with four windows which reflect the correlation between the relative (to the mean) value of a variable in one location and the relative value of the same variable in neighboring locations. For instance, the quadrant HH means a high value in the studied area and a high value in the neighboring areas.
Countries located in quadrants I and III refer to positive spatial autocorrelation, i.e. the spatial clustering of similar values, whereas quadrants II and IV represent negative spatial autocorrelation, i.e. the spatial clustering of dissimilar values. Note also that the link between a scatter plot and Moranâ&#x20AC;&#x2122;s I is reflected by a line of which slope is the value of Moranâ&#x20AC;&#x2122;s I statistic.
LISA Statistics for Local Spatial Autocorrelation
LISA statistics (Local Indicators of Spatial Association) measure, by definition, the presence of spatial autocorrelation for each of the location of our sample. It captures the presence or absence of significant spatial clusters or outliers for each location. Combined with the classification into four types defined in the Moran scatter plot above, LISA statistics indicates significant local clusters (high–high or low– low) or local spatial outliers (high–low or low–high). The average of the Local Moran statistics is proportional to the Global Moran's I value.
xi I i = ∑ wij x j m0 j
with m0 = ∑ xi2 / n i
where wij is the elements of the row-standardized weights matrix W and xi(xj) is the observation in country i(j). Their significance level is based on a randomization approach with 999 permutations of the neighboring provinces for each observation. The randomization approach is used in the context of a numeric permutation approach to describe the computation of pseudo significance levels for global and local spatial autocorrelation statistics. In order to determine how likely it would be to observe the actual spatial distribution at hand, the actual values are randomly reshuffled over space 999 times.
LISA Cluster Map of SEGE 2004
LISA Cluster Map of SEGE2011
LISA Cluster Map of SEGE from 2004 to 2011
LISA Cluster Map of Patent Applications
LISA Cluster Map of Net Migration Rates
LISA Cluster Map of University Degrees
Provinces that are in HH area (red color) in the figures are mostly from west part of Turkey . HH type autocorrelation is very strong in the west part of Turkey. Provinces that are in LL area (blue color) in the figures are mostly from east part of Turkey. LL type autocorrelation is very strong in the east part of Turkey.
CONCLUSIONS First, our quartile maps have revealed the gap between East and West when it comes to SEGE, patent applications, net migration rates and university degrees. When we measure spatial autocorrelation by means of Moranâ&#x20AC;&#x2122;s I, our results indicate positive (and significant) global autocorrelation for all our variables, and thus indicating the geographical location of a province influences its level of SEGE, patent applications, net migration rates and university degrees. These results are corroborated by the corresponding Moranâ&#x20AC;&#x2122;s Scatterplots that display most of the eastern provinces in the Low-Low quadrant and the western ones in the High-High quadrant. Finally, LISA statistics confirm the significant presence of local spatial autocorrelation and highlight spatial heterogeneity in the form of two distinct spatial clusters of high and low values of SEGE.
CONCLUSIONS Overall, these results confirm the dualistic structure of Turkeyâ&#x20AC;&#x2122;s economic geography, as many previous studies had showed. However, our results also show that this form of spatial heterogeneity goes along with the presence of spatial autocorrelation among provinces. Based on our results, we recommend fighting internal imbalances by promoting investments in education and the training of unemployed in the poorest areas. I also believe that developing the social and economic conditions in the East should be on the governmentâ&#x20AC;&#x2122;s priority list so that migration to the West and eventually ethnic terrorism in the East will be reduced to a minimum.
CONCLUSIONS In addition, if future results indicate that spatial autocorrelation continues to decrease over time, we recommend policies that support a sector rather than a province in particular. Because Eastern Turkeyâ&#x20AC;&#x2122;s provinces tend to display the same sectoral composition, this should guarantee to promote the development of several provinces at once.
Thanks for your attention.
Dissecting the Turtle: An Examination of the Turtle Trading System Ken Janson and David Rayome Northern Michigan University
290
Exhibit 1 - The Turtle Trader System 1979H Corn Contract
280
270
260 cents/bushel
55day Breakout LONG 20dayBreakout LONG 240 LONG Candidates not Traded LONG Trades 220 10dayEXIT LONG 55day Breakout SHORT 20dayBreakout SHORT SHORT Candidates not Traded 200 SHORT Trades 10dayEXIT SHORT 180 HIGH LOW LONG Position Size => Right Axis 160 SHORT Position Size => Right Axis 140
250
120 240 100 230
80 60
220
# contracts 40
210
LONG 20
200
0
SHORT 1
31
61
91
121
151
181
211
241
271
301
290
Exhibit 2 - Price Action in 1979H Corn Contract
280
HIGH
270
LOW 260 cents/bushel 250
240
230
220
210
200 1
31
61
91
121
151
181
211
241
271
301
290
Exhibit 3 - Add 20day Breakout LONG signals 20dayBreakou t LONG
280
HIGH 270 LOW 260 cents/bushel 250
240
230
220
210
200 1
31
61
91
121
151
181
211
241
271
301
290
Exhibit 4 - Add 55day Breakout LONG signals
280 55day Breakout LONG 270
20dayBreakout LONG HIGH LOW
260 cents/bushel 250
240
230
220
210
200 1
31
61
91
121
151
181
211
241
271
301
290
Exhibit 5 - Add 10day LONG Trade Exit signals
280
55day Breakout LONG 20dayBreakout LONG 10dayEXIT LONG
270
HIGH LOW 260 cents/bushel 250
240
230
220
210
200 1
31
61
91
121
151
181
211
241
271
301
290
Exhibit 6 - Make LONG trades based on 20/55day Entry & 10day Exit signals 55day Breakout LONG
280
20dayBreakout LONG LONG Candidates not Traded LONG Trades 10dayEXIT LONG HIGH LOW LONG Position Size => Right Axis
270
260 cents/bushel
240 220 200 180 160 140
250
120 240 100 230
80 60
220
# contracts 40 LONG 20
210
200
0 1
31
61
91
121
151
181
211
241
271
301
290
Exhibit 7 - Replicate Exhibit 6 for SHORT signals and trades
280
240
55day Breakout SHORT 20dayBreakout SHORT SHORT Candidates not Traded SHORT Trades 10dayEXIT SHORT HIGH LOW SHORT Position Size => Right Axis
270
260 cents/bushel
220 200 180 160 140
250
120 240 100 230
80 60
220
# contracts 40
210 20
SHORT 200
0 1
31
61
91
121
151
181
211
241
271
301
290
Exhibit 8 - LONG & SHORT Strategies combined
280
270
260 cents/bushel
55day Breakout LONG 20dayBreakout LONG 240 LONG Candidates not Traded LONG Trades 220 10dayEXIT LONG 55day Breakout SHORT 20dayBreakout SHORT SHORT Candidates not Traded 200 SHORT Trades 10dayEXIT SHORT 180 HIGH LOW LONG Position Size => Right Axis160 SHORT Position Size => Right Axis 140
250
120 240 100 230
80 60
220
# contracts 40
210
LONG 20
200
0
SHORT 1
31
61
91
121
151
181
211
241
271
301
Exhibit 9 - Portfolio Performance for 1979H Corn
Questions?? Thank you!
A Framework for Specifying Business Models Brad Barbeau, Ph.D. CSU Monterey Bay College of Business bbarbeau@csumb.edu
Goals/Objectives of the Research • Develop a more complete “template” for designing new businesses • Develop a useful template for analyzing/managing existing businesses • Develop a rich framework for investigating competition and competitive processes
Outline • What is a “Business Model”? • The Core Business Model • Extending the Business Model • Business Model Outcomes • Applications of Business Models • Future Research/Questions
Distinction: Business vs. Firm • A firm is an organization designed to undertake a business • “Business model” is a model of the business, not the firm • The business is a tangible and intangible set of objectives, relationships, resources, processes, etc. • The firm is a tangible organization that is implementing the business • A firm might have multiple business models operating simultaneously
What is a Business Model?
A Business Model… • Describes the business and economic logic of the business • Provides an architecture for the business • Describes the activities necessary for the business • Describes the functions of the business
Business Models and Business Strategies â&#x20AC;˘ A business strategy may address a single element of how a business works; a business model is allencompassing â&#x20AC;˘ A business model embodies and implements business strategies
Structure of a Business Model
The Value Proposition • The value proposition is the foundation for the Core Business Model • States a customer problem and solution to that problem • Creating and capturing value is central to a business • Uniqueness is critical
Customer Need A unique way to solve an important customer need or problem Available Technologies
The Core Business Model • Marketing Model • How do we recruit customers and deliver value to them?
• Operations Model • How do we produce value?
• Economic Model • How do we make money at this?
Marketing Model How does the business recruit and serve customers? • Identify the value proposition and the customer target market • Customer Recruitment • Value Delivery/Go To Market Strategy
Operations Model How does the business produce value? • Key Business Processes • Key Inputs • Key Resources • Key Partners
Economic Model How does the business make money? • Revenue Model: How do we capture value? • Choosing what to charge for • Setting prices
• Cost Model: How much does it cost to create the value? • Identify cost drivers
• Profit = Revenue - Cost
The Core Business Model Target Customers
Marketing Model • Value Proposition and Positioning • Customer Recruitment • Go To Market Strategy
Revenues
Operations Model • • • •
Key Business Processes Key Inputs Key Resources Key Partners
Costs
• Revenues • Costs • Profits
Economic Model
Osterwalderâ&#x20AC;&#x2122;s Business Model Canvas Key Partners
Key Activities
Operations Key Resources Model
Cost Structure
Value Propositions
Customer Relationships
Customer Segments
Marketing Channels Model
Economic Model
Revenue Streams
Extending The Business Model Organizing, Financing, And Growing The Business
The Core Model is incomplete… • Previous discussions of business models have focused on the Core Model • But there are other sources of competitive advantage • Building superior organizations • New sources of financing or novel capital structures • Innovative growth approaches
• And organizing, financing, and growing are necessary to the functioning of the business
The Organization Model • Legal Structure • Governance Structure • Management Structure • Vision/Mission • Culture
The Financing Model • Distinguish “economics” of the business from financing the business • Choosing a capital structure (debt/equity) • Finding sources of financing
MacDonald’s vs. In-and-Out Burger
The Business Development Model Building the business from a single-market, single-product startup to a multi-market, multi-product mature firm • Internal business development • Adding geographical coverage • Adding new products/services • Adding new markets
• External Business Development • M&A • Strategic Partners
Profitability
Scalability
Competitive Advantage
Leverage
Risk Management
Outcomes Assessing a Business Model
Business Model Outcomes Profitability Scalability Leverage Competitive Advantage Risk Management
22
Business Model Framework Target Customers Marketing Model Profitability
• Value Proposition and Positioning • Customer Recruitment • Go To Market Strategy
Scalability
Revenues
Operations Model • • • •
Key Business Processes Key Inputs Key Resources Key Partners
Organization Model
Costs
Operating Leverage
• Revenues • Costs • Profits
Economic Model Financial Model
Business Development Model
Competitive Advantage Risk Management
Example: Blockbuster, Inc. • Core Business Model: renting videotapes via local stores • Model was developed by the Cooks in Texas • Based on • “Superstore” with large selection • Greatly improved shopping experience through professional store design • Implementation of database technology to handle large checkout volume and manage inventory
Example: Blockbuster, Inc. • Business Development Model: A Store-Opening Powerhouse • Huizingua applied learnings from Waste Management to turn BBI into a “store opening” business • Able to put together a new store in 17 hours • At its peak, BBI was opening a store a day
• BBI’s competitive advantages: Marketing, Purchasing BBI’s competitive advantage came from using its core business model to win local market battles, and its business development model to win the war
Netflix vs. Blockbuster • Netflix introduced a different business model • Value: “convenience” is ordering and receiving without leaving your home • Operating Model: Internet/through-the-mail • Revenue Model: Subscription
• Blockbuster was unable to respond without destroying its largest asset • Dominating the store-opening business was a liability in responding to the disruptive NFLX business model
Applications of the Framework
Applications of the Framework • To designing new businesses • To analyzing and improving existing businesses • To analyzing competition in a rich framework • To analyzing how competition evolves over time
Thank You Brad Barbeau, Ph.D. CSU Monterey Bay College of Business bbarbeau@csumb.edu
Business Model Framework
Target Customers
Marketing Model Profitability
• Value Proposition and Positioning • Customer Recruitment • Go To Market Strategy
Scalability
Revenues
Operations Model • • • •
Key Business Processes Key Inputs Key Resources Key Partners
Organization Model
Costs
Operating Leverage
• Revenues • Costs • Profits
Economic Model Financial Model
Business Development Model
Competitive Advantage Risk Management
Presentation: The Influence of Enterprise Systems on Business and Information Technology strategic alignment Myles Muretta Dr. Lance Revenaugh Montana Tech of the University of Montana
Introduction Business Strategy is important to all
organizations Nearly all fortune 500 companies rely on some sort of an ERP system. ERP systems are becoming more popular and useful. Success with ERP today requires emphasis on change management and business and IT strategy alignment.
What is the Goal of This Research? Overview of Business Strategy and Strategic
Alignment. ERP overview and analysis. Explanation of the As-Is and To-Be process model. How this simple tool is vital piece for improving business strategy, strategic alignment and ERP implementation success.
Strategy Overview Strategic alignment is an ongoing process that has
remained a major issue within companies across the globe. Business strategy is built upon 3 principles which include: Business Scope: "Scope" of business refers to the breadth of activities your business engages in. 2. Distinctive Competencies: Core or simple Success factors that give firms the edge over their competitors. 3. Business Governance: The set of policies and business processes that create the â&#x20AC;&#x153;blueprintâ&#x20AC;? for how the business will operate. 1.
Information Technology Strategy IT strategy is divided into three components just like business strategy: Technology Scope, systemic competencies, and IT governance. Technology Scope: The most important applications and technologies that each company uses within their respective firms. 2. Systemic Competencies: What the information system is capable of that distinguish the services that the IT department has to offer. 3. IT Governance: assessment of the authority of risk, resources, and responsibility of the IT that is shared between business partners, IT management, and service providers. 1.
Strategic Alignment The purpose of strategic alignment is to leverage the
capabilities of both the business and the IT department. Firms have been able to change not only their business scope, but also their infrastructure for the better as a result of innovation regarding IT. The Strategic Alignment Model accurately portrays the way to align business and IT and how they must be cohesive from cover to cover to best utilize alignment.
The Strategic Alignment Model
ERP Systems Overview ERP systems are becoming omnipresent in the
corporate world. Companies like SAP and Oracle target larger firm operations. The market for ERP is expanding into small and medium sized companies as well. There are two paths to take when implementing ERP: Adapting the inherent process to the people or the people to the process.
ERP Impact ERP Systems: help organizations streamline processes improve the flow of information between different business functions increase productivity gain competitive advantage
Though a simple, yet vital tool for business process analysis is utilizing As-Is and To-Be processes before considering any sort of implementation, let alone ERP.
Moving From As-Is to To-Be Model
As-Is Processes There are three questions that companies need to ask themselves
to figure out their As-Is Processes. 1.
Big Picture: Who are you? This helps achieve alignment and understanding among various business units and geographies on how things currently operate.
2.
Current Operations: What are you doing? This helps define how employees are doing their work now, which will help define the gaps between the current and future states.
3.
Operational Improvements: What do we need to get better at? It helps determine the key operational pain points, and therefore the to-be processes and business requirements during the software selection process.
To-Be Processes The To-Be processes have 4 categories and do not belong in a specific
order. 1.
Business Processes: Business processes are what help companies define their future operational models and business processes independent of software.
2.
Gap Identification: In conjunction with the as-is processes, Gap Identification helps you identify the gaps between the current and future jobs, roles, and responsibilities.
3.
Performance Indicators: They help define key performance indicators to help drive business improvements and accountability.
4.
Prioritize Needs: They help prioritize customization, integration, and report-writing needs after the software is selected.
Conclusions Business strategy is important to all organizations. Nearly all fortune 500 companies use ERP systems. Successful implementation of these multi-million
dollar software systems are requiring new emphasis on change management and on Business and IT strategic alignment. IT and business strategies are no longer separate entities. The â&#x20AC;&#x153;As-Is/To-Beâ&#x20AC;? model presented in this paper provides a clear foundation for successful alignment and successful ERP implementation.
Behavioral Insights Reveal a Consumer of Mixed Rationality
Paul A. Stock, PhD Associate Professor, Economics University of Mary Hardin-Baylor Belton, TX
Jordan Ochel System Analyst Facebook Austin, TX
Basic Economic Assumption: Consumers Act Rationally
Many Economics textbooks assume that ‘consumers act rationally’. Here are some examples:
“Economists make assumptions about how people behave. Rational consumers buy the products expected to maximize their level of satisfaction…” ~ “ECON”, McEachern, 2nd Edition, page 10
“The most basic idea of economics is that in making choices, people act rationally. A rational choice uses the available resources to best achieve the object of the consumer…” ~ “Foundations of Economics”, Bade & Parkin, 4th Edition, page 10
Basic Economic Assumption: Consumers Act Rationally
More examples from Economic textbooks:
“A rational consumer knows what they want and makes the most of the available opportunities.” ~ “Economics”, Krugman, 2nd Edition, page 249
“Economists normally assume that people are rational. Rational people purposefully do the best they can to achieve their objectives, given the opportunities they have.” ~ “Brief Principles of Macroeconomics”, Mankiw, 4th Edition, page 6
Purpose and Literature Review Purpose: This research wanted to study the Economic assumption that consumers act rationally. â&#x20AC;&#x2DC;Acting rationallyâ&#x20AC;&#x2122; means consumers will follow the Law of Demand and make decisions that will maximize their utility (i.e. satisfaction). Literature Review: In 1776, Adam Smith wrote that consumers act out of self-interest. In 1861, John Stuart Mill justified the freedom of individual choice. He wrote that individuals should act to produce the greatest level of happiness. This concept of self-interest, a rational consumer, and maximizing satisfaction became an accepted truism in the field of Economics.
Literature Review
Economists who came after Smith & Mill based their models on this assumption, including Vilfredo Pareto, Francis Edgeworth, Leon Walras, and William Stanley Jevon. However, there were a couple critics of this assumption. In 1947, Herbert Simon said a consumer can’t possibly have complete knowledge and therefore can’t possibly make the best decision every time. Daniel Kahneman said people don’t always make the most sensible decision, even though they want to do so.
Bottom Line: Most economists and most economic textbooks support the assumption that ‘consumers act rationally’. But some students question this assumption. We need modern data.
Research Method
A survey with 24 questions was designed and administered online. 5 questions about demographics (gender, age, income, marital status, and children) 19 Questions cover: o o o o o o o o
Perception of the Participantâ&#x20AC;&#x2122;s Rationality Perception of other Consumerâ&#x20AC;&#x2122;s Rationality Role of Emotions Impulse Purchases Influence of Family & Friends Lottery & Gambling decisions Wants versus Needs Expensive and Luxury Products
Descriptive Statistics Total Participants:
402
Gender:
Male Female
264 138
66% 34%
Age:
18-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90
18 37 64 114 88 71 9 1
4.3% 9.2% 16% 28% 22% 18% 2.2% 0.3%
Marital Status:
Single Engaged Married Divorced Widowed
70 5 292 29 6
17% 1.3% 73% 7.2% 1.5%
Descriptive Statistics Income:
< $12,000 $12,001 - $25,000 $25,001 - $45,000 $45,001- $70,000 $70,001 - $120,000 > $120,000
45 35 66 72 104 80
11% 9% 16% 18% 26% 20%
Questions # 6 - # 7 6. On a scale of 1 â&#x20AC;&#x201C; 10, how rational do you consider yourself to be when making a purchase decision? 1
2
3
4
5
6
7
8
9
10
7. On a scale of 1 â&#x20AC;&#x201C; 10, how rational do you consider other people to be when making a purchase decision? 1
2
3
4
5
6
7
8
9
10
Questions # 8 - # 11
8. How often do you think your emotions affect your decision to purchase or not purchase a product? Never / Rarely / Occasionally
/ Somewhat /
Regularly /
Almost Always
/ Always
9. Have you ever impulsely bought a product or service that you regretted later? Yes
/
No
10. How often do you impulsely buy products? Never / Once a Year or Less / Once Every 6 Months / Once a Month / Once a Week / Every Day
11. How often does your familyâ&#x20AC;&#x2122;s opinions influence your purchase decisions? Never / Rarely / Sometimes / Often / Always
Questions # 12 - # 15 12. How often do your friendâ&#x20AC;&#x2122;s opinions influence your purchase decisions? Never / Rarely / Sometimes / Often / Always
13. How often do you play a lottery ticket, buy a scratch-off ticket, or gamble? Never / Once a Year or Less / Once Every 6 Months / Once a Month / Once a Week / Every Day
14. Have you ever gambled or purchased the lottery with borrowed money? Yes
/
No
15. Have you ever gambled or purchased the lottery instead of purchasing things you need? Yes / No
Questions # 16 - # 19 16. Have you ever purchased something you wanted at a time when you should have purchased something you needed instead? Yes
/
No
17. How often do your cravings influence your purchase decisions? Never / Once a Year or Less / Once Every 6 Months / Once a Month / Once a Week / Every Day
18. Have you ever purchased something that you wanted that is relatively expensive just because it is expensive or luxurious? Yes
/
No
19. Have you ever purchased a luxury or expensive item instead of something you needed? Yes
/
No
Questions # 20 - # 23 20. When you play the lottery or gamble, what do you perceive your chance of winning to be? 0-25%
/
26-50%
/
51-75%
/
76-100%
/
Not Applicable
21. When you play the lottery or gamble, do you believe or hope that luck, destiny, God, or some other power or deity influences the outcome? Yes
/
No
/
Not Applicable
22. How much of a risk-taker do you consider yourself to be? Never / Rarely / Occasionally / Often / Always
23. Have word like gourmet or authentic ever caused you to choose one brand over another? Yes
/
No
Question # 24 24. Do you ever buy a product that is more expensive than other brands because of its appearance, because it is popular, more expensive, or you think it is higher quality ? (Circle all that apply) Appearance / Popularity / More Expensive / Higher Quality / Not Applicable
Findings Participants view themselves more rational than other consumers *Males consider themselves more rational than Females Participants over age 60 consider themselves more rational than other age groups *Participants with income over $70,000 consider themselves more rational than other income levels *Participants over age 60 were less likely to think emotions affected their decisions than other age groups *Males were less likely to think emotions affected their decisions than Females * Statistically Significant
Findings Participants over age 30 were less likely to make an impulse purchase Participants with an income over $70,000 were less likely to make an impulse purchase *Participants aged 18 to 30 were more likely to be influenced by friends when making purchases *Participants with an income less than $25,000 were more likely to be influenced by friends *Participants aged 18 to 30 were more likely to purchase a ‘want” instead of a ‘need’ than other age groups *Participants aged 18 to 30 purchased an expensive item just because it was expensive more than other age groups * Statistically Significant
Findings
*Participants aged 18 to 30 thought their odds of winning at lottery or gambling was greater than 25% Participants with income over $70,000 purchased an item that was more expensive because it appeared to be higher quality Participants aged 18 to 30 purchased a more expensive item if it was more popular 46% of participants believe that luck, destiny, or God influences the outcome of their decisions
* Statistically Significant
Conclusions
Many Economic textbooks assume that â&#x20AC;&#x153;consumers act rationallyâ&#x20AC;?, yet: 90% of participants have purchased an item/service they later regretted 30% purchased something they wanted when they should have purchased something they needed instead 30% purchased something just because it was expensive or luxurious 47% of participants consider themselves to be risk-takers Over 18% think their chance at winning the lottery is over 25% Over 45% think that luck, destiny, God, or some other power influences their outcome when gambling or playing the lottery
Conclusions
Our research does not support the assumption that “consumers act rationally” all the time Perfect Rationality does not seem to exist Perfectly Irrationality does not seem to exist, either This research supports the concept of a consumer with “mixed rationality” Consumers do not always follow the Law of Demand Consumers do not always maximize their utility (i.e. satisfaction) A consumer’s age, gender, and income level do influence their decisions
Future Research
Possible topics for future research include: Why consumers act irrationally under certain circumstances? Why do most consumers think they are more rational than others? Is there a maturity process that causes consumers to be more rational as they age? Does culture affect consumer rationality? Why do some consumers buy more expensive products just because they are expensive?
Irrational Anonymous
Two Step Process: Step 1 – Turn to the person to your right or left Step 2 – Admit that you made an Irrational decision once
My example: Coach purse for $200 for daughter’s birthday
Recommendations
1. Economists should hang out with Marketing professors more often 2. Economic textbooks should not imply that all consumers act rational all the time 3. Economic textbooks should say that most people act rationally most of the time 4. Don’t feel guilty about acting irrational 5. Don’t lose sleep about acting irrational (unless it was unlawful) 6. Look for an “Irrational Anonymous” group where you live and attend meetings regularly)
Behavioral Insights Reveal a Consumer of Mixed Rationality
Questions?
Paul A. Stock, PhD Associate Professor, Economics University of Mary Hardin-Baylor Belton, TX
Jordan Ochel System Analyst Facebook Austin, TX
Behavioral Insights Reveal a Consumer of Mixed Rationality
Paul A. Stock, PhD Associate Professor, Economics University of Mary Hardin-Baylor Belton, TX
Jordan Ochel System Analyst Facebook Austin, TX
The Threats to Pharmaceutical R & D from Free Riders Steve Molloy, Canisius College, Buffalo, NY
R&D Costs • Patent protection • Significant R&D costs – prices unrelated to manufacturing costs – PR nightmares
• Growth in market – 1999 – 2009 — 39% increase in number of retail prescriptions filled vs. 9% increase in population – spending in U.S. was $320 billion in 2011 — 10.5% of total health care spending – Medicare Part D $51 billion in 2010
• Approximately ¼ of profits spent on R&D – $24 billion in 1999 — $50 billion in 2004
• Pharmaceutical industry spent > $49.5 billion on R&D in 2011 • Takes 10 – 15 years and $1.2 billion per drug, including failures • <1% of drugs move from preclinical phase to marketplace
• Reduction in lead times for competitors • One in three earn back investment • Disagreement on what is an ‘acceptable’ level of return for risk • Industry was most profitable 1995-02 and third most profitable 2007-09 • Profits, as percentage or revenues, 3X Fortune 500 average • Part of costs due to industry inefficiency • 33% SG&A vs. typical 17%
Revenue Streams • Counterfeit drugs – Globally — 8-10% counterfeit – Up to 40% Mexican made are counterfeit – approximately $75 billion loss in 2010
• Compulsory licensing – Antiretroviral AIDS — $10, 439 vs. $201
Price Controls • Without price controls — R&D amortized over entire production run and costs equitably distributed among all customers • Canada • Patented drugs typically 30-80% less • UK • Switzerland
Price Controls in U.S. • Federal Government – Veterans Affairs, Coast Guard, Defense Dept. and Public Health/Indian Health — additional 24% discount
• HMOs and Health Insurers – Negotiate discount prices — approve formulary
• Who is paying? – Uninsured in the U.S.
Implications • Free Riders – – – –
counterfeits compulsory licensing price controls negotiated discounts
• Can high levels of R&D be maintained? • Number of R&D dugs increased 7.6% from 2011 to 2012 — largest increase since 2003-04 • “Unprecedented” 13.9% increase in number of active companies from 2011 to 2012 • Shift to less risky ‘sure things’? • patent extensions, etc.
• Inequitable & unsustainable(?) distribution of R&D costs • In 2012, 84% of those without drug coverage in U.S. forced to cut back on expenditures or take other measures to pay for drugs • 81% saved $ by skipping or halving scheduled doses • “it is obvious to me that probably tens of thousands of Americans are dying today because they can’t afford drugs.” V.P. Mktg., Pfizer
Remedies? â&#x20AC;˘ Decrease costs of bringing new drugs to market â&#x20AC;˘ Increase revenue to pharmaceutical companies by decreasing free riders
Decrease Costs • 33.1% of revenues devoted to marketing • In 2004, $55.7 billion spent on promotion, but only $31.5 spent on R&D • Reduce R&D costs – offshore R&D and clinical trials – lowers prices, but doesn’t address fairness
Increase Revenue • Counterfeits – enforce intellectual property rights – tighten control of distribution system • • • •
parallel imports closed door pharmacies secondary wholesalers RFID
• Eliminate price controls elsewhere – political suicide
• Public Funding of R&D – Government already big supporter of R&D – doesn’t address issue of free riders
Remedies cont. • Government Funded Prescription Drug Plan in U.S. – expanded Medicare Part D – reduces inequity and increases access – reduces issue of free riders IF negotiate for international prices – growth in ‘Big Government’ – significant costs — can we afford it?
Remedies cont. • Accelerated tax breaks – may reduce prices, but does not address issue of free riders — shifts costs to all U.S. taxpayers
• Status Quo – currently a net benefit to U.S. • multiplier effect of high value added R&D activities in the U.S.
– ignores issues of free riders and inequity to uninsured
Remedies cont. • Price Controls in U.S. – Anti-market and “socialism” – Reduces free rider issue by bringing U.S. in line with other countries
• Recently passed Health Care legislation may effectively result in this – HMOs and insurers negotiate for lower prices
Conclusions • Current inequity will likely never totally disappear • A combination of: improved R&D efficiency improved marketing efficiency reduce counterfeits — cost and safety implications prescription coverage through universal health care coverage – price negotiations by HMOs, insurers and Govt. – Govt. incentives for R&D – learn to live with inequities and free riders as there is an overall net benefit of R&D to U.S. – – – –
Chinaâ&#x20AC;&#x2122;s Gradualism Approach to Systemic Transformation: Successes, Challenges. Raphael Shen, Ph.D. Professor, Department of Economics University of Detroit Mercy Victoria Mantzopoulos, Ph.D. Professor and Chairperson University of Detroit Mercy
Abstract China initiated its process for economic restructuring in 1979. Unlike the ‘big bang’ approach to systemic transformation adopted by some Eastern Europe economies that began in the early 1990’s, China adopted a calculated and circumspect course of actions in 1979. That was a full decade before systematic transformation became commonplace in former Communist economies of the Soviet bloc. Pilot reform projects preceded every key domain in need of restructuring and/or reorientation. Only when successes in the experimental projects proved incontrovertible was the scope of reform permitted to widen and speed accelerate. Paralleling reform measures on the domestic front were China’s proactive measures liberalizing its foreign investment and foreign trade policies. The end result is that its reform success has propelled China to being the world’s second largest economy merely 30 years after reform began. This paper first provides a historical note elucidating the imperative for economic restructuring. It then highlights the administration’s justification for permitting elements of the socialist-market system in a Communist state. An outline form presentation of restructuring on the domestic front is then followed by an examination and analysis of China’s liberalizing policies fronting its external economic relations. The paper concludes with highlights on patent successes as well as veiled challenges which, if untended, would likely compromise China’s envisioned future successes.
A Background Note
Mao Zedong Hua Guofeng Deng Xiaoping Paving the Way for a “Socialistic-Market” System
Domestic Realms of Reform • • • • • • • •
Administrative Decentralization Legal Reform Agriculture State-owned Enterprises Pricing System Fiscal Reform Financial Reform
Table 1: Capital Inflows (1979-2010) Year
1979-1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Utilization of Foreign Capital Unite USD, 100 Million Foreign Loans Foreign Direct Investments (I) (II) Number of Projects Value 169.78 3724 97.50 35.34 3073 63.33 84.07 1498 33.30 78.17 2233 37.09 98.13 5945 52.97 51.85 5779 56.00 50.99 7273 65.96 71.61 12978 119.77 107.03 48764 581.24 113.06 83437 1114.36 106.68 47549 826.80 112.88 37011 912.82 79.62 24556 732.76 58.72 21001 510.03 83.85 19799 521.02 83.60 16918 412.23 22347 623.80 26140 691.95 34171 827.68 41081 1150.69 43664 1534.79 44001 1890.65 41473 1937.27 37871 27514 23435 27406 27712
Foreign Trade Table 2: China’s Foreign Trade 1978–2010 (Select Years) Billion U.S. $ Year
Total Exports
1978
Total Imports
Balance
9.8
10.9
–1.1
1985
27.4
42.3
–14.9
1990
62.1
53.4
8.7
1995
148.8
132.1
16.7
2000
249.2
225.1
24.1
2005
762.0
660.0
102.0
2006
969.0
791.5
177.5
2007
1220.4
956.1
264.3
2008
1430.7
1132.6
298.1
2009
1201.6
1005.9
195.7
2010
1577.8
1396.2
181.5
2011
1898.4
1743.5
154.9
Successes: A Summary Overview Table 3: Macro Indicators, 1979-2010 (Select Years)
Year
GDP (billion Yuan)
1979 1985 1990 1995 2000 2005 2006 2007 2008 2009 2010 2011
406.3 901.6 1866.8 6079.4 9921.5 18493.7 21631.4 26581.0 31404.5 34090.3 40120.2 47288.2
Composition of Gross Domestic Product Primary Secondary Tertiary Industry Industry Industry (%) (%) (%) 31.3 47.1 21.6 28.4 42.9 28.7 27.1 41.3 31.6 19.9 47.2 32.9 15.1 45.9 39.0 12.1 47.4 40.5 11.1 48.0 40.9 10.8 47.3 41.9 10.7 47.5 41.8 10.3 46.3 43.4 10.1 46.8 43.1 10.0 46.6 43.3
Per Capita GDP (Yuan)
419 858 1644 5046 7858 14815 16500 20169 23708 25608 29992 35181
Anomalies and Challenges • • • • • •
Anomalies on Farm Investment and Demand Disparities Select Incongruities Administrative Decentralization and Social Unrest Banking, Housing, and Stock Bubbles World Trade Organization
Conclusion More than a decade before the collapse of the Communist regimes in Eastern Europe, the world’s most populous nation began experimenting with systemic and structural transformation. Concurrent with institutional reforms on the domestic front, China initiated reforms in the external sectors. On the domestic frontier, reform successes may be attributed to administrative decentralization, to restoring rights to private ownership, to privatization of state-owned enterprises, and to the rapid emergence of the private sector. The most pronounced catalysts of China’s meteoritic growth, however, may be attributed to reform successes in the realms of foreign investment and foreign trade. China has transited from a foreign-reserve deficient economy to one that has been awash in reserves. One of the predictable consequences is China’s ability and readiness to search for investment opportunities abroad. Three decades of sustained reform has transformed China from a closed and unpretentious economy to one whose performance is capable of impacting the well-being of world economies. China’s reform experience richly merits a chapter in the annals of world development history. Much has been accomplished. Nevertheless, the burgeoning capitalist spirit in China has concurrently revealed institutional flaws and critical financial anomalies. As discussed in this paper, acute irregularity in the real estate and in the financial markets are only two of the more serious aberrations faced by China’s policy designers and decision makers. Alternately stated, amidst bountiful accomplishments are also copious flaws that require pressing mindfulness of the central government to ensure China’s continual path towards improved well-being of its growing population.
SOCIAL SECURITY AND MEDICARE: EARNED ENTITLEMENTS
DR. ARTHUR E YOUNG, PHD TARLETON STATE UNIVERSITY
ENTITLEMENT SPENDING (WSJ JAN 25 2013 pg a13) Entitlement Transfers Currently = $2.3 Trillion/Year (this is about 2/3 of the federal budget!!!!!!) Over 100 million people –get means tested assistance Social Security, Medicare and Medicaid ---42% of federal budget (WSJ----Jan 16 2013…pg a11) Annual federal government payments for: Food Stamps = $75 Billion Medicaid = $275 Billion
ENTITLEMENT PROGRAMS ----TWO CATEGORTIES 1. “GIFT” ENTITLEMENTS FOOD STAMPS, MEDICAID, SECTION 8 HOUSING, WELFARE, ETC 2. “EARNED” ENTITLEMENTS SOCIAL SECURITY AND MEDICARE
SPREADSHEET….(1973-2012) SOCIAL SECURITY TAX RATES AVERAGE WAGE MAXIMUM WAGE SUBJECT TO SOCIAL SECURITY
WHAT IF FICA---INSTEAD OF INVESTED IN STOCK MARKET??? CONTRIBUTIONS TO “401K PLAN” MADE AT YEAR END (BOTH EMPLOYEE AND EMPLOYER “CONTRIBUTIONS”) DIVIDENDS ADDED AT YEAR END (PER DIV YIELD STATISTICS) 1%MANAGEMENT FEE DEDUCTED INVESTMENT RESULTS EQUAL TO PERFORMANCE OF S&P 500 (DATA IS FROM SPY (ETF) PROSPECTUS)
DATA SET USED Calendar
Change in
Dividend Yield
Year End Value
Index for Year
at Year-End
2010
1258
+12.78%
+1.87%
2011
1258
-0.00%
+2.23%
2012
1426
+13.41%
+2.19%
OVERVIEW OF WORKSHEET Total Start of Year 593589
Stock Market Increase 79575
Dividends 14742
1st Contribution minus MM Fees -503
End of Year Balance 687403
RESULTS OF SELECTED CASES (1% MANAGEMENT FEE….25% EXIT TAX)
AVERAGE WAGE EARNER FOR 40 YEARS GROSS AMOUNT AFTER 40 YEARS NET AMOUNT AFTER 40 YEARS
687,403 533,216
SUPER POOR---ONLY EARNED ½ AVG IN MIDDLE YEARS GROSS AMOUNT AFTER 40 YEARS NET AMOUNT AFTER 40 YEARS
92,227 72,251
MAXIMUM EARNER FOR ALL 40 YEARS GROSS AMOUNT AFTER 40 YEARS NET AMOUNT AFTER 40 YEARS
1,464,086 1,140,468
ADDITIONAL ANALYSIS RUNS
5 YR 1-13 1973-1985
x
5 YR 14-27 1986-1999
x
5
= 125
YR 28-40 2000-2012
0= 0 EARNINGS 1=50% OF AVERAGE 2= AVERAGE 3= 150% OF AVERAGE 4= MAXIMUM SOCIAL SECURITY EARNINGS
ADDITIONAL RUNS (1% MANAGEMENT FEE…25% EXIT FEE)
0–4–4
548,201
2–2–2
533,216
4–4–4
1,140,468
GROWTH OF PAYROLL TAX INVESTMENT PAYROLL TAX + 1973
EARNINGS $1
INVESTMENT GROWTH 2.87
1983
$1
1.37
1993
$1
0.44
2003
$1
0.18
HOUSEHOLD NET WORTH ** TOP 1%
$6,816,200
TOP 5%
$1,863,800
TOP 10%
$952,200
TOP 20%
$415,700
** PER WSJ DEC 29-30, 2012….PG B7 (2010 DATA)
LIMITATIONS AND FUTURE RESEARCH INVESTING ALL MONEY IN STOCK MARKET --- NOT REALISTIC? DIFFICULT TO APPLY TO FUTURE ---- LAST 40 YEARS WERE GOOD IN STOCK MARKET HARD TO PREDICT IMPACT ON STOCK MARKET IF MASSIVE INFLOWS AND OUTFLOWS
FUTURE RESEARCH: LOOK AT DIVERSIFIED PORTFOLIO OF INVESTMENTS (STOCKS, BONDS, GOLD?, OTHERS?) ANALYSIS OF HOW MUCH MANAGEMENT FEES CAN REDUCE THE ACCOUNT BALANCE
STRENGTH RESEARCH: A HISTORICAL PAPER…CAN BE DUPLICATED EASY TO UNDERSTAND ---NO FANCY ASSUMPTIONS, USE OF DISCOUNT RATES, ETC HOPEFULLY CAN CONTRIBUTE TO THE DISCUSSION OF “ENTITLEMENTS”
CONCLUSION PERSON IN SOCIAL SECURITY/MEDICARE SYSTEM HAVE SACRIFICED A GREAT DEAL AS A RESULT OF CONTRIBUTIONS TO THE SYSTEM WEALTH LOST BY A FAMILY IN MANY CASES EXCEEDS $1 MILLION RECIPIENTS OF SOCIAL SECURITY AND MEDICARE BENEFITS HAVE EARNED THOSE BENEFITS
Enhancing Student Participation through Technology and Collaborative Learning Barbara Lamberton Associated Professor of Accounting University of Hartford
Agenda • • • • • • •
Purpose What is Storyboarding Process Involved Research Design Participants Results Conclusion
Purpose Issue: Not Enough Engagement and Participation • Some student dominate • Team communication • Stimulate creativity • Consensus building
Purpose Storyboarding- What is it? • Adapted from Creative Arts • Used in Business • Helpful for: • • •
Creativity Idea Generation Participation.
Research Questions • Would Storyboarding Enhance learning Help build consensus Increase participation Increase mastery of specific learning objectives
• Would Technology help?
One Definition “Storyboarding is like taking your thoughts along with the thoughts of others and spreading them out on a wall as you work on a project or solve a problem. When you put ideas up on storyboards you begin to see interconnections, how one idea relates to another, and how all the pieces comes together. Once the ideas start flowing people “hitchhike” onto other ideas. (p.179)” Source: Creative Problem Solving for Mangers, Proctor (1999)
1.Topic Selection One Learning Objective – Balanced Scorecard – Activity based costing – Strategic Planning
2. Idea Generation â&#x20AC;˘ Each student writes down all the possible solutions/ideas with each solution/idea being written on a separate index card.
â&#x20AC;˘ The cards are collected and posted on a wall or bulletin board.
3. Group Discussion â&#x20AC;˘ Each solution/idea is reviewed with each student explaining his or her solutions/ideas.
4. Sorting â&#x20AC;˘ The group looks for clusters of similar concepts. Each clustering is given a separate title.
5. Group Review â&#x20AC;˘ Reviews clustering & decides to do further sorting, adding or deleting cards as needed.
6. Par down solutions â&#x20AC;˘ Selects the top three to five solutions
Technology Enabled â&#x20AC;˘ PowerPoint to replace the manual process of relying on numerous index cards and other materials.
â&#x20AC;˘ Blackboard group capability to facilitate and document group discussion.
Research Design Student Performance on three tasks of similar difficulty: • Balanced Scorecard - manual • Activity Based Costing –technology • Strategic Planning -control All three involved enumeration and classification
Research Design â&#x20AC;˘ Score for each student was the sum of correct responses. â&#x20AC;˘ Survey Students Self reports Based on Issues in Accounting Education format for assessment
Participants • Student volunteers • Management Accounting Class • Coverage included – Balanced Scorecard – Activity Based Costing – Strategic Planning
Tasks Balanced Scorecard • Review of business context • Need for new metrics • Four perspectives
Activity Based Costing • Review of business context • Need for new system • Four generic types of drivers
Strategic Planning Task • Review of business context • Need for new strategic direction • Four strategic directions
Results Comparison of Number of Correct Responses ABC (Storyboarding with Technology) BSC (Storyboarding without Technology) Strategic Planning (No Storyboarding, Control Task)
ABC Task BSC Task Control Task
N
Mean
39 39 39
16.08 7.77 5.23
Std. Deviation 7.40 4.07 2.25
Std. Error Mean 1.18 .65 .36
Key Survey Responses 1 I am usually reluctant to speak up in class. Strongly Agree 0
Agree 4
Neutral Disagree Strongly Disagree 12 16 8
2. Sometimes, I dominate group discussions. Strongly Agree Agree Neutral Disagree Strongly Disagree 4 14 8 12 2 3. Storyboarding helps to establish an atmosphere that encourages everyone to participate in problem solving. Strongly Agree 8
Agree 20
Neutral Disagree Strongly Disagree 10 2 0
Key Survey Responses 4. I have increased my mastery of PowerPoint as an authoring tool. Strongly Agree 6
Agree 22
Neutral Disagree Strongly Disagree 10 2 0
5. I have increased my mastery of accounting learning objectives. Strongly Agree 10
Agree 20
Neutral Disagree Strongly Disagree 6 4 0
6. Storyboarding combined with Blackboard is better than Storyboarding using "pen and paper". Strongly Agree 10
Agree 14
Neutral Disagree Strongly Disagree 16 0 0
7. Storyboarding help me generate a lot of potential solutions to problems. Strongly Agree 8
Agree 20
Neutral Disagree Strongly Disagree 12 0 0
8. Storyboarding can make a boring topic more interesting. Strongly Agree 12
Agree 18
Neutral Disagree Strongly Disagree 10 0 0
9. Storyboarding gives me a chance to participate more fully in group assignments. Strongly Agree 10
Agree 14
Neutral Disagree Strongly Disagree 16 0 0
10. Storyboarding combined with PowerPoint is better than Storyboarding using "pen and paper". Strongly Agree 12
Agree 8
Neutral Disagree Strongly Disagree 14 6 0
Concluding Comments • Storyboard with and without technology – seems to enhance collaborative learning – Helped mastery of specific learning objectives
• Small sample size • Individual difference factors not evaluated • Prior familiarity with technology.