The Efficacy of Little t Theories

Transcription

1 The Efficacy of Little t Theories Scott L. Schneberger Hugh J. Watson Carol E. Pollard Appalachian State University University of Georgia Appalachian State University schnebergers@appstate.edu hwatson@terry.uga.edu pollardce@appstate.edu Abstract While debates continue about the rigor and relevance of IS research, theories are still widely accepted as the bedrock of scientific research. This paper addresses the issues surrounding academic research rigor and relevance by discussing the rigor and relevance of research theories in particular, big T and little t theories. Big T theories are usually highly recognized, refined, validated, and generalizable theories usually associated with rigorous research and publication. Little t theories may be relatively immature, little known, narrowly focused, simple, and highly relevant theories more generally appreciated by business practitioners. Little t theories have special characteristics that can be very useful to researchers and teachers, and highly relevant to business practitioners. Moreover, academics can take clear steps to build, publish, teach, and apply little t theories. 1. Introduction More than sixty years after Lewin [10] noted nothing is so practical as a good theory, information systems researchers and authors continue to discuss the implications of the use of theory on the practicality of research. Most of the published articles on the topic have been critical. Robey and Markus [13] disparaged IS research relevance and framed the issue as one of scientific rigor versus practical relevance. Among their many reasons for irrelevance including arcane explanations, advanced statistical analyses, and excessive references to other published work was theoretical abstractions. Theory is a well established part of scientific rigor, but practitioners do not usually equate theory with practical relevance. Robey and Markus argued there is no inherent conflict between rigor and relevance, and they provided four strategies for optimizing practical relevance with rigorous, theoretical research: cultivating practitioner sponsorship, adopting new research models, producing consumable research reports, and supporting nontraditional research outlets (pg. 7). Benbasat and Zmud [2] set off a series of articles and responses on IS academic research rigor and business practitioner relevance. They highlighted some scathing criticisms about research by business leaders and authors, explained why irrelevance occurs, and gave their recommendations for making research efforts and articles more relevant. Davenport and Markus [4] agreed with them about IS research irrelevance and made recommendations that went even further. Lyytinen [11] added his European perspective, and noted how important teaching is to bringing IS research relevancy to business. During the same period, however, there were calls for more pure theory articles (e.g., Zmud, [25]) and more theory building than theory testing (e.g., Weber, [21]) in a leading IS journal. Similar streams of articles and issues surrounding good theory, theory building, and practical relevance have surfaced in management (e.g., Van de Ven, [17]) and administrative science (e.g., Weick, [22]). Most follow the same line that good theory and scientific rigor are absolutely required for good research, and that relevance to practical problems is absolutely required to justify research. Few articles, however, have directly addressed the practical relevancy of the theories themselves. This paper extends the concept first put forward by Dennis and Valacich [5] of big T and little t theories, and explains how little t theories in particular can be useful in improving IS research, teaching, and business relevance. After providing the background on theories in general and big T/little t theories specifically, we discuss how little t theories can be especially useful in research, teaching, and practice, and finally prescribe how to effectively use little t theories /07 $ IEEE 1

2 2. Background In 1989, six articles in The Academy of Management Review focused on the criteria and methods for building good theory. It was noted [17] that although researchers can usually name a good theory, few can explain precisely why a theory is good. Another author [24] pointed out that even the excellent treatises on what constitutes a good theoretical contribution used obtuse terms and concepts that tended to complicate rather than clarify. Some authors (such as [16]) took a reverse tack and described what good theory is not. A scan of these and other articles on good or strong theories, moreover, suggests that the word theory may itself be nebulous since there are so many diverse definitions of it from speculations to axiomatic laws governing the universe. But all agreed that theory is the bedrock of scientific research Theory A theory is a statement of relations among concepts within a set of boundary assumptions and constraints [1]. Its purpose is to organize or simplify a complex world, and to explain that world to others. The primary goal of theory is to answer the questions of how, when, and why (p. 498). These answers come primarily by indirectly testing the relationships (called propositions) among the concepts or constructs by empirically testing the relationships (called hypotheses) among variables representing the constructs. Since researchers must operate under some constraints, a theory must be bounded by assumptions about implicit values, time, and space. The range of those boundaries define the generalizability of the theory; those with few or wide boundaries are more generalizable than those with many constraints. These theoretical concepts are shown in Figure 1. A good construct, while not usually directly observable (like self-worth), should be valid that is, it should be clear and simple so that its associated variables share variance (convergent validity) and are distinguishable (discriminant validity). Good variables should be measurable and coherent that is, valid, noncontinuous, and reliable so that any actual variance will be observable within time and space bounds. Relationships (propositions and hypotheses) should be nontautological and clearly identified as antecedent or consequent. Moreover, relationships should be able to explain and predict the purposes of a theory. Given the wide breadth of theory definitions, theorists also like to point out what a good theory is not constructs variables propositions hypotheses constructs variables Figure 1. Theory components (from [1]) (such as [16]). A good theory is not, they say, a list of references, a list of variables or constructs, data, a diagram, or a hypothesis. Good theory explains why particular events happen and which events happen before other events, appears axiomatic, and even delights. Lists, data, and diagrams in particular do not explicitly explain and they do not clearly predict. In short, if a theory is not testable or falsifiable and does not predict, it s not a theory. But other theorists (such as [22]) maintain that references, lists, data, diagrams and hypotheses may only be strong theories in their early development stages. Since every construct and relationship has its own degree of abstraction and generality, then theories themselves form a continuum [14] of weak to strong theories based on the strength of components. Moreover, some would argue, no theory captures all the information measured in data and cannot predict with absolute certainty. So when is a theory really a theory, after all? The consensus appears to fall around the less parsimonious but more practical notion of a theory continuum. While one might label the two ends of the theoretical continuum as complete theory and incomplete theory, or mature and immature, we choose to label them, respectively, big T theory and little t theory. Since each theory component (i.e, constructs, relationships, and boundaries) can have its own degree of maturity or development, a theory can be located anywhere along the continuum. A big T theory would be more highly developed; a little t less so. In this sense, the wide range of theory definitions from suppositions to laws can also be accommodated along the continuum. 2

3 A theory s placement on the continuum can be based on its characteristics. Examining theory components and their purposes, big T and little t theories can be generally characterized based on their Big T Little t Theory of Reasoned Action Technology Acceptance Model Strategic Grid Organizational Transformation Figure 2. Big T/Little t Theory Continuum intrinsic, application, and perceptive characteristics. See Appendix 1 for a listing of big T and little t theory characteristics. While not in priority or weighted order, the characteristics can be used to more closely examine big T and little t theories and relatively determine where a theory should be located on the theory continuum. See Figure 2 for some examples of big T and little t theories on an illustrative continuum Big T theories Dennis and Valacich [5] first described a big T theory as one that is overarching, widely recognized and used, and has a formal name. Some examples could be Theory of Reasoned Action [7], Contingency Theory [6], and Media Richness Theory [3]. Most, if not all, fit the description of a good or strong theory with all the essential parts theorists want to see. In the view of a theory continuum, they are the mature theories that have been tested, honed, and re-tested. They have strong, valid constructs and variables (and usually artifacts), they have well-tested and statistically significant relationships, they have clearly stated boundaries, and they are excellent at explaining why things occur and predicting when they will occur. They are formed from induction and deduction, and then tested empirically. In short, they are models of what a good theory should be, are the theories almost everyone knows of, and are recognized as the foundation of much of IS published research. There are also other, perhaps less obvious characteristics of big T theories. Given their maturity, they tend to have a broad focus and cover a wide range of domains; they can be used in many settings even across disciplines and have very few constraining boundaries. Because of this broad applicability, they also tend to have a high degree of generality but still a high degree of definition, often making them appear complex. Since they have wide applicability and established validity and credibility, they tend to be recognized and used more often by researchers and authors 1. In research, they are most often used for theory testing since they are already established and mostly complete. They tend to transform over the years as they are applied to newer settings, and the focus tends to be on items or events rather than on processes. They tend to look backward more than forward since they minutely explain past empirical events and so have low time sensitivity. New variables are mostly chosen on the basis of measurability within newer settings, and there are fewer efforts to disprove the theory outright. To further support the concept of big T/little t theories and a continuum of theory assessment vis-àvis each other, the two big T theories shown in Figure 2 can be described and evaluated using an illustrative sample of the characteristics listed in Appendix A. The Theory of Reasoned Action (TRA), for example, is one of the most fundamental and influential theories of human behavior and is grounded in social psychology. Since its initial development over 30 years ago [7], it has been used to predict a diverse range of behaviors in a wide range of domains. In their meta-analyses of research using TRA, Sheppard, et al. [15] documented its use in organizational behavior, marketing, social psychology, and experimental psychology. Its application within the IS field has also been documented [18]. Thus the 1 Twelve months of the leading IS journal MISQ contained 31 articles with 27 named theories. The same twelve months of MISQ Executive (directed more at practitioners) contained 16 articles in which there were 2 named theories. 3

4 TRA has a broad focus, with few assumptive constraints and exhibits high generality. It also rates highly on its definitive terminology, logical foundation, and low time sensitivity. The Technology Acceptance Model (TAM), on the other hand, was designed solely to predict acceptance and usage of information technologies. The TAM is intentionally more parsimonious than the TRA, in that it excludes the attitude construct in predicting intention. While the TAM is widely used to study a diverse set of technologies and users, its use has been primarily in the IS field. The TAM qualifies as a big T theory as it has been widely used and has widespread applicability to many different types of IT. While its predictive accuracy is somewhat limited [18], it is well recognized as a useful instrument to measure technology acceptance and is well validated. It also has high relevance to practice because of its parsimony. Thus, while qualifying as a Big T theory, the TAM is appropriately placed to the right of the TRA which has greater generality, extensive acceptance, is highly credible, exhibits strong validity, and can be applied to a much wider range of domains than the TAM Little t theories A little t theory, on the other hand, can be thought of as a simple theory that provides value on its own or as an immature but developing theory. A little t theory may or may not be widely recognized or used, and may not even have the word theory in its title (or even have a title). It may, indeed, have many of the characteristics of what pure theorists ascribe as not theories. It may be as little as a framework of relationships or a 2x2 matrix with four related cells in two dimensions [20], such as the Strategic Grid (see Figure 3). Strategic Impact on Existing Operating Systems High Factory Strategic Low Support Turnaround Low High Strategic Impact of Application Development Portfolio Figure 3. The Strategic Grid [ mcfarlin] The Strategic Grid has proven useful as a way of looking at a company s current and future applications portfolio as a competitive weapon. For example, firms in the strategic cell are heavily dependent on both current and future applications in order to compete in the market place. Little t theories have often not been thoroughly tested, honed, and re-tested. They might not have strong, valid constructs and variables (let alone validated artifacts), and may not have tested, statistically significant relationships. Because they are usually developing, they often do not have clearly delineated boundaries, and therefore are not broadly generalizable but more limited in domain and scope. Rather than being formed from induction and deduction, they are likely shaped from simple observation. Given that basis, they are usually not excellent at explaining why things occur or are likely to occur beyond a very limited situation. They are not models of what a good, developed theory should be to pure theorists, may not be well recognized (although some are), and they are often not the foundation of strong research programs. Little t theories, like the Strategic Grid, are sometimes associated with work done by consultants and are not considered rigorous theories. They do not provide rich insights into casual relationships, may be based more on observation that empirical studies, and are limited in their applicability. Little t theories carry many implications for research, teaching, and practice. Given their characteristics (see Appendix 1), they tend to have a more narrow focus and few domains; they can t be used in many settings let alone across many disciplines. Because of this narrow applicability, they also tend to be poorly defined, although appearing simple and relatively easy to understand. Since they may appear simplistic, do not have wide applicability, and have very little validity and credibility, they might not be recognized and used by researchers and authors. Leading IS journals appear reluctant to accept papers based solely on little t theories, perhaps because they appear to imply little scientific rigor or because they re simply not thought of as theories. In research, they are most often used for theory building than theory testing since they are not established and are mostly incomplete. Rather than just transforming over the years as mature theories do, little t theories might be more connective, linking big T theories or filling gaps in explaining real events (some little t theories, however, do not evolve but remain basic little t theories). Little t theories are much more focused on observed processes than products or events. Their terminology is not very definitive, and they tend to look forward rather than backward since they are not based on minutely measured events. New 4

5 variables are mostly chosen on the basis of observed usage or anecdotal evidence. Because of this tenuousness, there are many opportunities to disprove the theory outright. But there are upsides to little t theories. They tend to have a shorter cycle time for theory development and do not require strong rigor in the process. Given the speed of technology development and the short cycle time of developing theories, they are much less likely to trail technology. For that and many of the reasons stated above, business practitioners are inclined to view them as more real world, simpler, potentially current, and much more relevant to their everyday problems. Moreover, teachers may find them easier to use in a classroom for the same reasons. To many, their relaxed rigor makes them appear more relevant to busy, practical-minded business executives and students. As suggested above, theories can be placed on a continuum of theory characteristics and even with little t theories, there are differences. Consider, for example, Kotter s organizational transformation model [9], which recommends an eight-step process that organizations should follow in order to successfully bring about significant organizational change: 1. Establish a sense of urgency 2. Form a powerful guiding coalition 3. Create a vision 4. Communicate the vision 5. Empower others to act on the vision 6. Plan for and create short-term wins 7. Consolidate improvements and produce more change 8. Institutionalize the new approaches Though Kotter s model is simple (and little t), it is more rigorous (further to the left on the continuum) than the Strategic Grid. It is more prescriptive, predictive, broader, theory based, and actionable than the Strategic Grid. Models like Kotter s are obviously useful in working with executives and in the classroom, but they can also be valuable in research. To illustrate, several years ago one of the authors had studied a large number of data warehouse implementations. While all of them were successful, some were more so than others. Why was this the case? The answer was evident in Kotter s model. All of the most successful implementations had followed Kotter s model for successful organizational transformation, even though management did not appear to be explicitly aware of it. A closer look at Kotter s model reveals several interesting things. It clearly helped to understand an important phenomenon through its insights about how to successfully bring about organizational transformation. It played an important role in interpreting and developing the research findings about data warehouse implementations. Even though Kotter s model was not developed for data warehouses, it proved useful in this domain. This suggests that its use is potentially broad. And finally, the model is related to other theories. For example, Ginzberg s [8] model for bringing about change with its unfreezing, moving, and refreezing steps maps well onto Kotter s model. 3. Discussion The key point in most of our cited articles on academic rigor and relevance is that the terms need not be exclusive; academic researchers can perform and publish research that is scientifically rigorous while concurrently relevant to business students and practitioners. We similarly maintain that academic researchers can use theories that are strong and practical. Given the characteristics of big T and little t theories (see Appendix 1), big T theories might appear to be best suited for rigorous research and publishing while little t theories are better for teaching and business relevance. We claim that little t theories can be highly effective in all three areas. 3.1 Little t theories in research Even though little t theories are considered less mature or developed than big T theories, a little t theory still has at least two constructs and one relationship which can be rigorously tested through appropriate variables and hypotheses using sound methodology and analysis. The key is in choosing appropriate variables to represent the little t theory constructs, and sound hypotheses to represent the little t theory proposition(s). Those variables and hypotheses can be as valid, noncontinuous, and reliable as those of big T theories so that any actual variance will be observable within stated value, time, and space boundaries. Little t relationships (propositions and hypotheses) can be nontautological and clearly identified as antecedent or consequent. Little t research artifacts could be as well tested and validated as big T artifacts. Moreover, little t relationships can be used to explain and predict to some degree the very purpose of a theory. A little t theory may not presently explain and predict well, but its ability to do so can be scientifically demonstrated and improved. 5

6 Indeed, lesser developed little t theories offer bountiful opportunities for theory testing and building. A little t researcher can be more productive than a big T researcher, advancing scientific knowledge in leaps and bounds rather than in small, incremental improvements. And for those researchers with destiny in mind, a little t theory may someday be a big T theory; today s little t theory builder may be tomorrow s widely known big T theory founder possibly with naming rights. To be sure, the number and range of big T theories will increase and likely get their start as little t theories. The IS field needn t restrict itself to borrowing big T theories from other disciplines; IS little t theories can be honed into IS big T theories. Little t theories can be used to explain or discuss the results of big T theory empirical results. Assuming the research addresses a practical problem, a little t theory built from observed practice may offer relevant explanations. Little t theories may provide foresight for applying the results of big T theory empirical findings. Finally, little t theories can be used to recommend further or related big T theory-based research. Even though little t theories usually do not have clearly delineated boundaries doesn t mean there aren t many opportunities to test and expand boundaries, thereby making little t theories more generalizable and useful across domains (such as the Organizational Transformation Model). And while little t theories likely formed from simple observations and conjecture, scientific induction and deduction can be applied to strengthen them. We maintain that although little t theories are not ideal models of what a good, strong theory should be to strict theorists, little t theories can still be useful and support strong research programs. Besides being useful in building big T theory, little t theories can be used to connect or link big T theories. With few firmly established constructs or relationships, little t theories can be tested with variables and propositions common among established, big T theories. Little t theories are much more focused on observed processes than products or events; using little t theories in research can add a stronger process focus to big T theories. Moreover, there are many opportunities to disprove little t theories outright and make a decisive and strong contribution to the IS field. Finally, the short cycle time of developing little t theories means a researcher can stay closer to the cutting edge of technology where there is likely the greatest potential opportunity for return. This ability to keep research more current can make research results appear more relevant and constructive to teachers and busy, practical-minded students. 3.2 Little t theories in teaching While teaching and learning principles are beyond the scope of this paper, we claim that little t theories can be especially useful in undergraduate and graduate courses including business IS courses. Little t theories are generally simple and easy to understand making teaching and learning them less difficult. Students themselves can be considered as under development ; little t theories under development can resonate with students exploring new concepts and applications. A little t theory not widely recognized or used can still challenge a student to consider constructs and relationships albeit at a simpler level. While a little t theory might not show a student what theorists describe as strong or pure theory, more simple and direct little t frameworks or 2x2 matrices can effectively show simple concepts and relationships. After all, simple little t theories were mostly what the IS field had to teach in the field s early days. They can still be useful and instructive today. Little t theories do not have to be strong in explaining or predicting phenomena for teaching constructs and relationships. Moreover, they are more likely to be remembered given their simplicity and ease of understanding. They may not lend credibility within academic research circles, but may within practitioner circles as simple guidelines to apply under many conditions. This particular feature makes little t theories especially useful to the case method of teaching or collaborative learning. Little t theories are usually based on practical experience and observation, the foundation for good case studies. Teaching case studies are usually based on an actual organization dealing with an actual problem within a very narrow domain. The case assumptive constraints are many (and fodder for active classroom discussion), they are not exhaustively laid out (leaving room for classroom speculation), and they are more focused on a business process than a business product (making them useful for a wide range of practical problems). These business teaching case characteristics are the characteristics of little t theories; using little t theories to guide, summarize, or teach new concepts from business teaching cases is natural and can be highly effective. Although little t theories by themselves are relatively incomplete with a narrow focus, they can be used as building blocks to explain or highlight more mature, strong, and widely recognized big T theories. Little t theories tend to look forward more than 6

7 backward; students looking forward to their own careers may value them more highly than finely honed big T theories based on past empirical evidence. Indeed, their shorter cycle time for theory development can make them appear more much more up to date on newer information technology and applications. Students are likely to view them as more real world and relevant to their future careers. For these reasons, little t theories can also be especially useful in executive education. Little t theories can strongly resonate with experienced executive students. 3.3 Little t theories for business relevance Business relevance is the greatest strength of and may hold the greatest potential for little t theories. As Weick [23] states, some theories seem to matter more than others theories that move us and are more likely to be applied to the future with foresight than used to explain the past in hindsight. His theories that matter most are the ones that are simple, emotive, sensitive to context, based more on use than logic, derived from observed activity, and time sensitive. He could have called them little t theories, and they tend to matter most to business practitioners making decisions about the future. As Robey and Markus [13] point out, the domain of IS research is in the realm of practice and it should be consumable by practitioners. A little t theory may not be widely recognized or used in academic research, but it was likely born in observing practical activities and is likely to be most used in practical activities. It may not explain and predict accurately, but it can serve to guide generally. Practitioners are unlikely to care that little t theories have not been thoroughly tested, honed, and re-tested that they might not have strong, valid constructs and variables (let alone validated artifacts) or statistically significant relationships. Practitioners are neither apt to know nor care that little t theories are not considered by theorists to be good, strong, highly developed theories. Their focus is on relevance. Consultants seem to understand this. They provide business decision makers with simple frameworks, process diagrams, lists, and action recipes. While often considered self-serving, egregious, and less rigorous by academics, their work is usually compiled and published on new IS topics before academics even get started with rigorous research efforts [13]. Moreover, academics regularly use consultant material in their classrooms. Consulting results are more like little t theories than big T theories, and this suggests the significant potential practical relevance of little t theories. Giving little t theories to practitioners may be giving them potentially simplistic explanations and predictors. But practitioners are apt to value the timeliness of even immature little t theories more than the historical accuracy of big T theories. Decision makers make decisions about the future, not the past; the closer the theory to the future, the better in their eyes. Moreover, in a fluid business environment, decision makers are more concerned with processes than products as are little t theories. 4. Prescriptions We believe little t theories should be widely developed, tested, and published. Instead of being shunned due to their immaturity or imperfections, academics should embrace and exploit the strengths of little t theories. Academic researchers should more closely observe actual business practices and seek to explain them with little t theories and then empirically, and rigorously, test and improve them. Close working relationships with practitioners (recommended in [20], [13], [2], and [4]) are needed to foster, support, and validate little t theory research. Formal little t theory research programs and research centers can help ensure academic research timeliness and relevance. Leading IS journals should seek and accept papers based solely or mainly on little t theories. Even relatively immature or developing theory can make a valuable contribution to the field; theory building should be sought and recognized as much as theory testing [21]. Moreover, timely little t theory application should also be recognized by journals to spur further research and provide material for classroom teaching. Little t theories should be sought and accepted at leading IS conferences even with specialized tracks on little t theories to support little t theory creation and development. Little t theories should be adapted to and taught in classrooms undergraduate through Ph.D. levels. Business students should understand what a little t theory is, and learn the most timely and useful little t theories. The teaching notes for teaching cases should include little t theories applicable to the case, and teachers should explain their significance to the case. Ph.D. students should thoroughly understand the big T/little t theory continuum and the process of creating a little t theory, developing and refining it, and evolving it into a big T theory. Finally, little t theories should be extensively published in practitioner media outlets, presented at 7

8 trade conferences, and presented to IS and IT trade organizations. The advantages of academic objectivity, scientific training, and academic resources should be promoted to business practitioners. Academics and little t theories should be synergistically useful and used. 5. Summary This paper addresses the issues surrounding academic research rigor and relevance by discussing the rigor and relevance of research theories in terms of big T and little t theories. Big T theories are highly recognized, refined, validated, and generalizable theories usually associated with rigorous research and publication. Little t theories are relatively immature, little known, narrowly focused, simple, and highly relevant theories generally appreciated by business practitioners. Little t theories, however, have special characteristics that can be very useful to researchers and teachers, and highly relevant to business practitioners. Moreover, academics can take clear steps in many areas to build, publish, teach, and apply little t theories References [1] S. Bacharach, Organizational Theories: Some Criteria for Evaluation, The Academy of Management Review, vol. 14, no. 4, 1989, pp [2] I. Benbasat and R. Zmud, Empirical Research in Information Systems: The Practice of Relevance, MIS Quarterly, vol. 23, no. 1, Mar 1999, pp [3] R. Daft and R. Lengl, "Organizational information requirements, media richness and structural design," Management Science, 32, pp ,. [4] T. Davenport and L. Markus, Rigor vs. Relevance Revisited: Response to Benbasat and Zmud, MIS Quarterly, vol. 23, no. 1, Mar 1999, pp [5] A. Dennis and J. Valacich, Conducting Research in Information Systems, Communications of the AIS, vol. 7, art. 5, July [6] F. Fiedler, A Theory of Leadership Effectiveness, New York: McGraw-Hill The reader might conclude that this paper itself presents a little t theory about research theories. [7] M, Fishbein, and I. Ajzen. Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, Addison-Wesley, Reading, MA, [8] M. Ginzberg, Steps Towards More Effective Implementation of MS and MIS, Interfaces, May 1978, pp [9] J. Kotter, Leading Change: Why Transformation Efforts Fail, Harvard Business Review, vol. 73, no. 2, [10] K. Lewin, The Research Center for Group Dynamics at Massachusetts Institute of Technology, Sociometry, vol. 8, 1945, pp [11] K. Lyytinen, Empirical Research in Information Systems: On the Relevance of Practice in Thinking of IS Research, MIS Quarterly, vol. 23, no. 1, Mar 1999, pp [12] W. McFarlan and J.L. McKenny, Corporate Information Systems, Irwin, Homewood, IL, 1983., [13] D. Robey and L. Markus, Beyond Rigor and Relevance: Producing Consumable Research about Information Systems, Information Resources Management Journal, vol. 11, no. 1, Winter 1998, pp [14] P. Runkel and M. Runkel, A Guide to Usage for Writers and Students in the Social Sciences, Rowman and Allanheld, Totowa NJ, [15] B. Sheppard, J. Hartwick, and P. Warsaw, The theory of reasoned action: A meta-analysis of past research with recommendation for modifications and future research, Journal of Consumer Research, vol. 15, pp [16] R. Sutton and B. Staw, What Theory is Not, Administrative Science Quarterly, vol. 40, no. 3, Sep 1995, pp [17] A. Van de Ven, Nothing Is Quite So Practical as a Good Theory, Academy of Management Review, vol. 14, no. 4, Oct 1989, pp [18] V. Venkatesh, M.G. Morris, G. B. Davis and F. D. Davis. User Acceptance of Information Technology: Toward a Unified View MIS Quarterly, vol. 27 (3), September 2003, pp [19] H. Watson, H.J., D.L. Goodhue, and B.J. Wixom, The Benefits of Data Warehousing: Why Some Companies Realize Exceptional Payoffs, Information and Management, vol. 39, May, 2002, pp [20] H. Watson, Conducting Research that Is Theory Based and Relevant, an unpublished talk given at Appalachian State University, May 4,

9 [21] R. Weber, Theoretically Speaking, MIS Quarterly, vol. 27, no. 3, Sep 2003, pp. iii-xii. [22] K. Weick, What Theory is Not, Theorizing Is, Administrative Science Quarterly, vol. 40, no. 3, Sep 1995, pp [23] K. Weick, That s Moving: Theories That Matter, Journal of Management Inquiry, vol. 8, no. 2, 1999, pp [24] D. Whetten, What Constitutes a Theoretical Contribution? Academy of Management Review, vol. 14, no. 4, 1989, pp [25] R. Zmud, Editor s Comments, MIS Quarterly, vol. 22, no. 2, Jun

10 Appendix 1 Big T/Little t Theory Characteristics Big T Theories Characteristics Little t Theories Intrinsic Broad Focus Narrow Very few Assumptive constraints Many High Generality Low Quantitative Basis Qualitative High Complexity Low Long Cycle time Short Low Ease of development High High Completeness Low Mostly low Currency Mostly high Products Primary area of interest Processes Definitive Terminology Abstract Backward Primary perspective Forward Measurability Variable basis Usage Logic Foundation Activity Low Time sensitivity High Application High Usage Low Multiple disciplines Domain Single discipline Wide spread Applicability Limited High Use by other theories Low Often low Teaching ease Usually high Theory testing Research use Theory building High Prediction accuracy Low Transformational Theory application Connective Perception High Recognition Low Often difficult Ease of understanding Easy Extensive Acceptance Limited High Credibility Low Established Validity Tenuous Varied Direct relevance to practice High 10