سیکل نظریه سازی در پژوهشهای مدیریتی The Cycles of Theory Building in Management Research

Some scholars of organization and strategy expend significant energy disparaging and defending various research methods. Debates about deductive versus inductive theory-building and the objectivity of information from field observation versus that of large-sample numerical data are dichotomies that surface frequently in our lives and those of our students. Despite this focus, some of the most respected members of our research profession (i.e., Simon (1976), Solow (1985), Hambrick (1994), Staw and Sutton (1995), and Hayes (2002)) have continued to express concerns that the collective efforts of business academics have produced a paucity of theory that is intellectually rigorous, practically useful, and able to stand the tests of time and changing circumstances. The purpose of this paper is to outline a process of theory building that links questions about data, methods and theory. We hope that this model can provide a common language about the research process that helps scholars of management better understand the roles of different types of data and research, and thereby to build more effectively on each other’s work. Our unit of analysis is at two levels: the individual research project and the iterative cycles of theory building in which a researchers attempt to build upon each other’s work. The model synthesizes and augments other studies of how communities of scholars cumulatively build valid and reliable theory.1 It has normative and pedagogical implications for how we conduct research, evaluate the work of others, train our doctoral students, and design our courses. While many feel comfortable in their understanding of these perspectives, it has been our observation that those who have written about the research process and those who think they understand and practice it proficiently do not yet share even a common language. The same words are applied to very different phenomena and processes, and the same phenomena can be called by many different words. Papers published in reputable journals often violate rudimentary rules for generating cumulatively improving, reliable and valid theory. While recognizing that research progress is hard to achieve at a collective level, we assert here that if scholars and practitioners of management shared and utilized a sound understanding of the process by which theory is built, we could be much more productive in doing research that doesn’t just get published, but meets the standards of rigorous scholarship and helps managers know what actions will lead to the results they seek, given the circumstances in which they find themselves. Our purpose in this paper is not to praise or criticize other scholars’ work as good theory or bad theory: almost every published piece of research has its unique strengths and shortcomings. We will cite examples of other scholars’ research in this paper, but we do so only to illustrate how the theory-building process works. We hope that the model described here might constitute a template and a common language that other scholars might use to reconstruct using how bodies of understanding have accumulated in their own fields. 2 In the first of the four sections of this paper, we describe a three-step process by which researchers build theory that is at first descriptive, and ultimately normative. Second, we discuss the role that discoveries of anomalies play in the building of better theory, and third, we describe how those who build, evaluate and utilize theories can tell whether they can trust a theory – whether it is valid and applies to the situation in which they find themselves. Finally, we suggest how scholars can engage in course research – to design student courses in ways that help faculty researchers build better theory. I. The Theory Building Process The building of theory occurs in two major stages – the descriptive stage and the normative stage. Within each of these stages, theory builders proceed through three steps. The the theorybuilding process iterates through these three steps again and again. In the past, management researchers have quite carelessly applied the term theory to research activities that pertain to only one of these steps. Terms such “utility theory” in economics, and “contingency theory” in organization design, for example, actually refer only to an individual step in the theory-building process in their respective fields. We propose that it is more useful to think of the term “theory” as a body of understanding that researchers build cumulatively as they work through each of the three steps in the descriptive and normative stages. In many ways, the term “theory” might better be framed as a verb, as much as it is a noun – because the body of understanding is continuously changing as scholars who follow this process work to improve it. The Building of Descriptive Theory The descriptive stage of theory building is a preliminary stage because researchers generally must pass through it in order to develop more advanced normative theory. The three steps that researchers who are building descriptive theory utilize are observation, categorization, and association. Step 1: Observation In the first step researchers observe phenomena and carefully describe and measure what they see. Careful observation, documentation and measurement of the phenomena in words and numbers is important at this stage because if subsequent researchers cannot agree upon the descriptions of phenomena, then improving theory will prove difficult. Early management studies such as The Functions of the Executive (Barnard, 1939) and Harvard Business School cases written in the 1940s and 50s were primarily descriptive work of this genre – and was very valuable. This stage of research is depicted in Figure 1 as the base of a pyramid because it is a necessary foundation for the work that follows. The phenomena being explored in this stage include not just things such as people, organizations and technologies, but processes as well. These observations can be done anywhere along the continuum from analysis of huge databases on the one end, to field-based, ethnographic observation on the other. Without insightful description to subsequently build upon, researchers can find themselves optimizing misleading concepts. As an example: For years, many scholars of inventory policy and supply chain systems used the tools of operations research to derive ever-more-sophisticated optimization algorithms for inventory replenishment. Most were based on an assumption that 3 managers know what their levels of inventory are. Ananth Raman’s pathbreaking research of the phenomena, however, obviated much of this work when he showed that most firms’ computerized inventory records were broadly inaccurate – even when they used state-of-the-art automated tracking systems (Raman 199X). He and his colleagues have carefully described how inventory replenishment systems work, and what variables affect the accuracy of those processes. Having laid this foundation, supply chain scholars have now begun to build a body of theories and policies that reflect the real and different situations that managers and companies face. Researchers in this step often develop what we term constructs. Constructs are abstractions that help us rise above the messy detail to understand the essence of what the phenomena are and how they operate. Joseph Bower’s Managing the Resource Allocation Process (1970) is an example of this. His constructs of impetus and context, explaining how momentum builds behind certain investment proposals and fails to coalesce behind others, have helped a generation of policy and strategy researchers understand how strategic investment decisions get made. Economists’ concepts of “utility” and “transactions costs” are constructs – abstractions developed to help us understand a class of phenomena they have observed. We would not label the constructs of utility and transactions cost as theories, however. They are part of theories – building blocks upon which bodies of understanding about consumer behavior and organizational interaction have been built. Step 2: Classification With the phenomena observed and described, researchers in the second stage then classify the phenomena into categories. In the descriptive stage of theory building, the classification schemes that scholars propose typically are defined by the attributes of the phenomena. Diversified vs. focused firms, and vertically integrated vs. specialist firms are categorization examples from the study of strategy. Publicly traded vs. privately held companies is a categorization scheme often used in research on financial performance. Such categorization schemes attempt to simplify and organize the world in ways that highlight possibly consequential relationships between the phenomena and the outcomes of interest. Management researchers often refer to these descriptive categorization schemes as frameworks or typologies. Burgelman & Sayles (1986), for example, built upon Bower’s (1970) construct of context by identifying two different types of context – organizational and strategic. Step 3: Defining Relationships In the third step, researchers explore the association between the category-defining attributes and the outcomes observed. In the stage of descriptive theory building, researchers recognize and make explicit what differences in attributes, and differences in the magnitude of those attributes, correlate most strongly with the patterns in the outcomes of interest. Techniques such as regression analysis typically are useful in defining these correlations. Often we refer to the output of studies at this step as models. Descriptive theory that quantifies the degree of correlation between the category-defining attributes of the phenomena and the outcomes of interest are generally able to make probabilistic statements of association representing average tendencies. For example, Hutton, Miller and 4 Skinner (2000) have examined how stock prices respond to earnings announcements. They coded types of words and phrases in the statements as explanatory variables in a regression equation, with the ensuing change in equity price as the dependent variable. This analysis enabled the researchers then to assert that, on average across the entire sample of companies and announcements, delivering earnings announcements in a particular way would lead to the most favorable (or least unfavorable) reaction in stock price. Research such as this is important descriptive theory. However, at this point it can only assert on average what attributes are associated with the best results. A specific manager of a specific company cannot yet know whether following that average formula will lead to the hoped-for outcome in her specific situation. The ability to know what actions will lead to desired results for a specific company in a specific situation awaits the development of normative theory in this field, as we will show below. How Theory is Improved within the Descriptive Stage When researchers move from the bottom to the top of the pyramid in these three steps – observation, categorization and association, and in so doing give us constructs, frameworks and models – they have followed the inductive portion of the theory building process. Researchers can then get busy improving these theories by cycling from the top down to the bottom of this pyramid in the deductive portion of the cycle – seeking to “test” the hypotheses that had been inductively formulated. This most often is done by exploring whether the same correlations exist between attributes and outcomes in a different set of data than the data from which the hypothesized relationships were induced. When scholars test a theory on a new data set (whether the data are numbers in a computer, or are field observations taken in a new context), they sometimes find that the attributes of the phenomena in the new data do indeed correlate with the outcomes as predicted. When this happens, this “test” confirms that the theory is of use under the conditions or circumstances observed.2 However, researchers who stop at this point simply return the model to its place atop the pyramid tested but unimproved. It is only when an anomaly is identified – an outcome for which the theory can’t account – that an opportunity to improve theory occurs. As Figure 1 suggests, discovery of an anomaly gives researchers the opportunity to revisit the foundation layers in the theory pyramid – to define and measure the phenomena more precisely and less ambiguously, or to cut it into alternative categories – so that the anomaly and the prior associations of attributes and outcomes can all be explained. In the study of how technological innovation affects the fortunes of leading firms, for example, an early attribute-based categorization scheme was radical vs. incremental innovation. The statements of association that were built upon it concluded that the leading established firms on average do well when faced with incremental innovation, but they stumble in the face of radical change. But there were anomalies to this generalization – established firms that successfully implemented radical technology change. To account for these anomalies, Tushman & Anderson (1986) offered a different categorization scheme, competency-enhancing vs. competency-destroying technological changes. This scheme resolved many of the anomalies to the prior scheme, but subsequent researchers uncovered new ones for which the TushmanAnderson scheme could not account. Henderson & Clark’s (1990) categories of modular vs. architectural innovations; Christensen’s (1997) categories of sustaining vs. disruptive technologies; and Gilbert’s (2001) threat-vs.-opportunity framing each uncovered and resolved anomalies for which the work of prior scholars could not account. This body of understanding has improved and become remarkably useful to practitioners and subsequent scholars (Adner, 5 2003; Daneels, 2005) because these scholars followed the process in a disciplined way. They articulated theories that could be falsified – that could yield anomalies. Subsequent scholars then uncovered what these anomalies were, and resolved them by slicing the phenomena in different ways and articulating new associations between the category-defining attributes and the outcome of interest.