Eric W. K. Tsang

Inter-Organizational Knowledge Transfer: Current Themes and Future Prospects

Many papers have been published recently in the fields of strategy and international business research incorporating the role of organizational knowledge as a basis of firm competitive advantage. While such knowledge is normally developed within the firm, it is important that firms possess the ability to learn from others in order to meet the increasing pace of competition. Knowledge transfer, defined here as an event through which one organization learns from the experience of another, has thus become an important research area within the broader domain of organizational learning and knowledge management. This paper presents a theoretical framework, identifies key themes covered by the six articles included in the Special Issue on Inter-Organizational Knowledge Transfer, and then discusses priorities for future research.

10 Learning and Strategic Alliances

Abstract Various researchers have suggested that an important explanatory factor for the growth in strategic alliances is that alliances provide a platform for organizational learning, giving firms access to the knowledge of their partners. The notion that alliances are a vehicle for learning is the basis for an important and cross-disciplinary stream of research. This chapter examines theoretical and empirical research in the alliance learning area. We have two central objectives. The first is to integrate a large body of research by examining the key research questions addressed. The second objective is to critically examine the existing research as the basis for establishing a research agenda. Although the alliance learning area has generated a substantial amount of research interest and spawned wide-ranging types of inquiry, many important and substantive managerial issues remain underexplored.

Case studies and generalization in information systems research: A critical realist perspective

Abstract The status of case study research as a legitimate scientific method in IS research is often challenged by the view that case findings are not readily generalizable. Positivist and interpretivist perspectives have typically dominated discussions of this important methodological issue. I provide an alternative perspective by presenting a critical realist view of generalizing from case findings. I show that critical realism represents a very different view than either positivism or interpretivism. Critical realism recognizes the role of case study research in empirical generalization, theoretical generalization, and theory testing. In contrast, the role of case study research in empirical generalization and theory testing is either ignored or neglected by interpretivism and positivism. Embracing critical realism would therefore enable researchers to more fully explore the potential for case finding generalization.

Interpreting dummy variables and their interaction effects in strategy research

Dummy variables have been employed frequently in strategy research to capture the influence of categorical variables. However, misinterpretation of results may arise, especially when interaction effects between dummy variables and other explanatory variables are involved in a regression. We discuss two approaches of entering dummy variables into a regression and their associated interpretations. We discuss some common mistakes of interpretation and hypothesis testing found in two recently published strategy papers, and highlight the advantages of our recommended approach over the approach usually adopted by management researchers.

Competition, agglomeration, and performance of Beijing hotels

Agglomeration theory argues that locating close to competitors can be beneficial in terms of gaining from heightened demand – more frequent consumer visits and subsequent purchases through reducing consumer search costs. This paper examines the trade-off between competition and the agglomeration effects of physical proximity in the Beijing hotel industry. It seeks to answer two questions: (1) What types of hotels contribute more to agglomeration? (2) What types of hotels benefit more from agglomeration? The results suggest that only high star-ranking joint venture hotels contribute to heightened demand while hotels of all star rankings benefit similarly from agglomeration.

Commentary---Assumptions, Explanation, and Prediction in Marketing Science: “It's the Findings, Stupid, Not the Assumptions”

In his July--August 2007 editorial of Marketing Science, Steven Shugan argues that the realism of assumptions does not matter as long as a theory or model produces satisfactory predictions and claims further that unrealistic assumptions breed good theories. This commentary discusses the problems of his argument and presents a very different view about the realism of assumptions. Assumptions need not be realistic if the only goal of science is prediction. However, a major function of theory is also to explain and not just to predict. The role of explanation is more important in the social sciences because it is far more difficult to produce accurate predictions in the social than the natural sciences. Assumptions, especially core assumptions, often constitute the foundation of the mechanismic explanations provided by a theory. Unrealistic assumptions may lead to faulty explanations and false predictions. Contrary to Shugans view, the realism of an assumption cannot be assessed just based on the output of a theory. It has to be tested independently of or in conjunction with the hypotheses of the theory. Also, contrary to Shugans claim, more realistic assumptions result in better theories. As theory development advances, efforts should be directed toward making assumptions more realistic.

Is This Referee Really My Peer? A Challenge to the Peer-Review Process

A basic assumption of the peer-review process adopted by journals is that referees and authors are similarly knowledgeable about the topics concerned. Given the difficulty of finding qualified referees, this assumption is likely to be violated if the author is a top scholar of the topic and thus has few peers. The peer-review process then breaks down and fails to provide quality comments. Worse still, the submission may be erroneously rejected. Drawing on my experience of submitting philosophy-based papers to business journals, I propose a special review process for improvement.

Count-Based Research in Management Suggestions for Improvement

We review 11 years (2001-2011) of management research using count-based dependent variables in 10 leading management journals. We find that approximately one out of four papers use the most basic Poisson regression model in their studies. However, due to potential concerns of overdispersion, alternative regression models may have been more appropriate. Furthermore, in many of these papers the overdispersion may have been caused by excess zeros in the data, suggesting that an alternative zero-inflated model may have been a better fit for the data. To illustrate the potential differences among the model specifications, we provide a comparison of the different models using previously published data. Additionally, we simulate data using different parameters. Finally, we offer a simplified decision tree guideline to improve future count-based research.

Classifying Generalization: Paradigm War or Abuse of Terminology?

Lee and Baskerville (2003) attempted to clarify the concept of generalization and classify it into four types. In Tsang and Williams (2012) we objected to their account of generalization as well as their classification and offered repairs. Then we proposed a classification of induction, within which we distinguished five types of generalization. In their (2012) rejoinder, they argue that their classification is compatible with ours, claiming that theirs offers a ‘new language.’ Insofar as we resist this ‘new language’ and insofar as they think that our position commits us to positivism and the rejection of interpretivism, they conclude both that our classification is more restrictive than theirs and also that we embrace ‘paradigmatic domination.’ Lee and Baskerville’s classification of generalization is based on a distinction between theoretical and empirical statements. Accordingly we will first clarify the terms ‘theoretical statement’ and ‘empirical statement.’ We note that they find no fault with our classification of induction, we re-state our main objections to their classification that remain unanswered and we show that their classification of generalizing is in fact incompatible with ours. We argue that their account of generalization retains fatal flaws, which means it should not be relied upon. We demonstrate that our classification is not committed to any paradigm and so we do not embrace ‘paradigmatic domination.’

Rejoinder---Robust Prediction and Unrealistic Assumptions

In the response to my commentary on his 2007 editorial entitled “Its the Findings, Stupid, Not the Assumptions,” Steven Shugan raises a number of thought-provoking ideas. In this rejoinder, I focus on three issues that Shugan and I hold the most divergent views. First, while Shugan uses the terms “realistic” and “realism” in several different meanings, I define the realism of an assumption as the extent to which it corresponds with the real world. Second, Shugan makes a strong claim that predictions can be objectively evaluated whereas assumptions cannot. I refute his claim by arguing that testing predictions and testing assumptions follow the same research process of checking whether the proposition concerned corresponds with reality. Third, Shugan maintains that given predictive accuracy, assumptions need not be realistic. I hold an opposite view for the obvious reason that the same prediction may be generated by completely different mechanisms, which in turn are based on different assumptions. Last but not least, the example of socialist economic planning shows that unrealistic assumptions can generate dangerous theories.