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Management as a University Discipline: Education and Research Shyam Sunder Yale School of Management NTU Management Review Conference on Management Theory and Practice Fudan University, and National Taiwan University Shanghai, China, November 11-12, 2011
Overview • University disciplines • Three functions: teaching, research and service • How are we doing in education and research? • Teaching: content, audience, consequences • Research: paradigms, methods, purpose, instruction, public policy, dissemination • Health of academic cultures • Corporate social responsibility vs. academic social responsibility
University Disciplines • Management is a relative newcomer to universities • Among humanities, social, natural, and formal sciences and professions, it is in professional category • University disciplines share some common characteristics: depth, importance, breadth of interest, human thought • Management’s claim to be in university: a good case, but must watch the pressure towards the vocational trap • We should not take our status in university for granted
Bases of Management’s Claim to be in University • Importance to modern society: e.g., state, cities, technology, transportation, communication, food, all need organization • Depth: multiple perspectives on good management, no formula, common elements, yet all situations are unique • Breadth of interest: large and fast growing demand • Human thought: good management cannot be separated from active thought that only humans are capable of • Active scholarship: live developing discipline that builds new ideas, perspectives, insights, and techniques
Three Functions • Teaching, research and service • Instruction: perspective, independent thinking, self-reflection, analysis, creativity, imagination, engaging environment and people, and society, information, communication • Research: understanding, explanation, discovery, invention, policy analysis, discourse, dissemination • Service: administration, assessment, advice, commentary (important, but will not have the time to go into it today)
Self-Evaluation • Management is thriving as a part of universities in most parts of the world • Rapid expansion of programs, enrollments, revenues, faculty, journals • Quality of instruction, research, and service • How much value do we deliver for the resources spent by society?
Teaching: Content • What we teach • Basics: economics, psychology, accounting, quantitative and communication skills • Management functions (marketing, finance & control, investments, OB & HR) • Strategy, society, law, governance • Industry (insurance, health, real estate, etc.) • What we don’t teach • History, demography, politics, sociology, literature • Balancing “day-1 skills” vs. thinking and analysis
Teaching: Audience • Mostly business: but 40% or more economy is in government and not-for-profit organizations and needs managerial skills just much, or more • Higher income: most business education is expensive, not accessible to lower income people • Urban: mostly • Value creation or distribution: proportion of teaching devoted to redistribution relative to creation of value • Student as “Customer”: or student as product, with parents-alumni-society as customers • Were Confucius’ students his customers?
Teaching: Consequences • Course evaluation: weakening of rigor and course content • Vocational training: emphasis on teaching Day-one skills under guise of relevance (vs. education) • Job search: time and priority accorded by students to job search • International: broader class room experience and understanding of the world
Research: Paradigms • Social science: is the dominant paradigm of management research (plus some mathematics) • Description: primary focus on how things are (or were) • Prediction: Unless the future can be assumed to be like the past, it is difficult to predict from past • Prescription: social science itself does not easily help us make prescriptions about what to do • Need objectives/goals/values
Research: Methods • Great expansion: skills in research methods have been greatly expanded over six decades • Mathematical and simulation models, and optimization • Theory and concepts • Field, laboratory, survey, and interview data • Econometric, statistical, content analyses • Recent additions: internet data, neuroscience, etc.
Research: Purposes • Explanation and understanding of events, organizations, and management (e.g., financial crisis, economic growth of China, etc.) • Instruction to students of management (DCF) • Advice to managers • Advice to policy makers • Advocacy of chosen causes • Satisfying our curiosity and meeting intellectual challenges • Faculty selection, evaluation and promotion
Research: Challenges • Science, humanities, and social science • Mapping from description to prescription • Instruction and advice • Ethical dimensions of management research
Science, Humanities, and Social Science • Science: identifying eternal laws of nature (physics, chemistry, biology, geology, etc.) • Objects of study are inanimate • Great prestige derived from predictive power • Humanities: study of eternal truths of human nature (love, hate, courage, fear, bravery, cowardice, etc.) • but each person is unique, no predictive content • Literature, history, music, religion
What are social sciences? • “Social” recognizes that the subject of study are sentient beings (with free will that humanists recognize), not marbles or atoms without will (that scientists study) • “Science” seeks the prestige associated with the search for eternal laws with predictive ability • Neither sciences nor humanities allow much room for laws of human behavior we seek in social sciences • Free will and laws of behavior do not sit well together; we want but can’t have it both ways • How far can the study of human behavior be a science?
To serve as a basis for social policy, the “laws” of social sciences must have stability (i.e., be robust to their own discovery) Since humans learn and adapt, social science findings can alter behavior in ways that tend to invalidate the findings themselves Findings which are robust to such adaptation can be called “laws” of social sciences, and may serve as the basis for social policy Other findings are like finding a $20 dollar bill on the sidewalk, because the finding will soon render itself invalid Laws of Social Sciences
Independent of the method of research we use, robustness of findings (to their own discovery) is a pre-requisite for their use as basis for policy Like unclaimed dollar bills on side-walk, many findings (e.g., small firm and Monday effects) disappear upon being reported There are other findings (e.g., determination of price by intersection of demand and supply) are robust in this sense (not merely statistically) So the first pre-requisite for usefulness of any research findings for policy is this stability Most such laws are properties of institutions, not behavior [Gode and Sunder, JPE 1993] How Robust Are Our Findings?
Policy makers want to know if the manipulation of the policy variable under consideration has a directional (causal) link to the desired objective. Correlation does not help them Yet, the problem of establishing a causal link between two variables on the basis of field data remains largely unresolved due to endogeneity Experimental methods have been presented as an alternative to address this problem, but they, too, have limitations of their own Consider both approaches briefly Causal Link
Labeling of correlation as cause is more of a rule than an exception in management research journals When correctly labeled as correlation, what can the policy maker do with the finding? To claim that the finding is “consistent” with Hypothesis X fails to point out that It is also consistent with innumerable other hypotheses not mentioned in the report, and No hypothesis has been rejected (violating the essence of Fisher-Neyman-Pearson framework) Problems of Inference from Field Data: Causal Direction
Philosophical difference between accepting the null hypothesis and simply failing to reject it The "fail to reject" terminology highlights the fact that the null hypothesis is assumed to be true from the start of the test; if there is a lack of evidence against it, it simply continues to be assumed true “Accept the null hypothesis" may suggest it has been proved simply because it has not been disproved, a logical fallacy Argument from ignorance. Unless a test with particularly high power is used, the idea of "accepting" the null hypothesis is dangerous. Accepting or Failing to Reject the Null Hypothesis
Researcher’s qualified statement "we found no statistically significant difference,“ Misquoted by others: "they found that there was no difference." Statistics cannot be used to prove that there is zero difference between two populations Failing to find evidence that there is a difference does not constitute evidence that there is no difference Maxim "Absence of evidence is not evidence of absence." “Absence of evidence is not evidence of absence”
Attempts to avoid pitfalls of using statistical significance have had little success Papers: "Significance Tests Harm Progress in Forecasting," and "Statistical Significance Tests are Unnecessary Even When Properly Done” Armstrong: even when done properly, statistical significance tests are of no value. A number of attempts failed to find empirical evidence supporting the use of significance tests Tests of statistical significance are harmful to the development of scientific knowledge because they distract researchers from the use of proper methods Armstrong: instead, report on effect sizes, confidence intervals, replications/extensions, and meta-analyses J. Scott Armstrong Statistical Significance
In practice a difference can almost always be found given a large enough sample More relevant goal of science is a determination of the size and nature of causal effect Hypothesis testing controversial when the alternative hypothesis is suspected to be true at the outset of the experiment, making the null hypothesis the reverse of what the experimenter actually believes Null put forward as a straw man only to allow the data to reject it Rejecting the null hypothesis says nothing or very little about the likelihood that the null is true Choosing the null: Ex ante, or after looking at the data?
Our attempt to produce policy-relevant research in accounting often take the following form: Info. Sys. 1 Price system 1 Info. Sys. 2 Price system 2 Suppose information system 1 and price function 1 are the status quo and the policy maker wants to know the consequences of changing the information system from 1 to 2 Problems of Inference from Field Data: Hypotheticals and Efficiency
We can gather data on accounting numbers and prices under status quo and estimate their statistical relationship, R(1) In many situations, we can also calculate (or reasonably estimate) what the accounting numbers would have been under the policy alternative (the hypothetical) If we could observe prices that would be generated under the policy alternative, we could also estimate the statistical relationship R(2) Does a comparison of R(1) and R(2) help the policy makers? If stronger R(2) implies preference for the policy alternative, it is trivially simple to push R(2) to the upper limit by simply using market prices for accounting Problems of Inference from Field Data: Hypotheticals and Efficiency
Of course, we are rarely so lucky as to be able to observe P(2) An oft-used practice is to estimate the statistical relationship between the hypothetical I(2) and actually observed P(1), and then compare this R(2)* with R(1) and suggest that a stronger R(2)* implies the alternative to be preferred policy Info. Sys. 1 Price system 1 Info. Sys. 2 Price system 1 Problems of Inference from Field Data: Hypotheticals and Efficiency
Logic of Inference Reporting System 1 Price System 1 Reporting System 2 Price System 2
Logic of Inference Reporting System 1 Price System 1 Reporting System 2 Price System 2
This type of inference from field data does not help the policy makers We like them to give us a nod to acknowledge our work, and perhaps even support it But the logical foundations of such inference, and its implications for policy remain to be worked out Logic of Inference and Policy
Lab methods allow us to address the causality problem with greater confidence But they also raise new challenges for use of findings in making of accounting policy Accounting is highly institutionalized (complex interactions, expectations), like engineering Experimental methods were developed for social sciences where a single simple example can support a general proposition about existence, or otherwise Simpler experiments suffice for basic disciplines such as economics, psychology and physics, but not for accounting policy or bridge design Time scale problem: choosing points vs. function What about Lab Experiments?
Ethical Dimensions of Management Research • I do not mean just human subject approvals (though that is also important) • If I discover a way of selling more (marketing, finance, accounting, HR examples) • What do we do with the finding? Teach? • What should we tell the students to do with the finding? • What should be the criteria for selection of our research projects? Money, novelty, curiosity, or social welfare? • Who should decide and how?
Health of Academic Culture • Research and academic performance • Focus on “international journals” • Addressing problems of societies that pay for reseach • Building a research culture at home (e.g., NTU Management Review and other journals in Asia) • Developing an environment in which scholarship can be carefully evaluated and screened without conflict • Focus on international journals weakens development of research culture at home
Journals as Hubs of Academic Cultures • Live intellectual discourse and debate of ideas, methods, and problems • Threats to the effectiveness in serving this function • Fragmentation: balance of management and its functions • Balance of solving problems vs. proposing/applying methods • Balance of curiosity/challenge and promotion and tenure • Balance of passion behind ideas and efficient execution • Balance of internal and external motivations • Balance of short term and longer term perspectives • Actual readership vs. citation count maximization strategies
Social Responsibility • What are social responsibilities of management faculty? • Teach well: prepare thinking, informed, liberal, inquisitive young minds • Scholarship: curiosity, importance, novelty, usefulness, to make a better world • Service: use our time to serve our universities, society, and humankind in the best way possible
As indicated in the title of this conference, if research does not (at least ultimately) lead to better understanding, practice or policy, it will be ignored The methods we choose should take us there Not stick to a method just because that is what gets published Choose methods and topics that will enlighten us Do our very best to prepare thinking students Do we have an honest assessment of our work? History suggests that evaluating and changing ourselves is not an easy task But we should try And, don’t forget passion! To Summarize