Toward exemplary research in the management of technology—an introductory essay

Journal of Engineering and Technology Management, 10 (1993) 7-22 Elsevier

Toward exemplary research in the management of technology-An introductory essay Philip Anderson Johnson Graduate School of Management, Cornell University, Ithaca, NY 14853, USA

Abstract This article examines the current status of management of technology (MOT) as an academic field of study, and suggests what scholars in the area must do to foster its intellectual development. Attacks on the fuzzy definition of innovation and the lack of a paradigm governing the field do not identify what must be done to realize progress. The field requires widely accepted exemplars that permit a transition to puzzle-solving. Without exemplars we cannot identify anomalies, borrow by analogy from other fields, or form a vibrant research community. Some rules of thumb for generating exemplary research are proposed. The papers in this special issue are briefly profiled to highlight the signposts they provide toward exemplary research in MOT. Keywords.

Management of technology; Paradigms; Research communities; Theory development

1. Introduction Academics, like most entrepreneurs, exploit gaps that reflect the tenor of their times. When stagflation dominated the economic landscape in the late 197Os, a spate of papers appeared to explain how this could be so. When areas such as Silicon Valley and the Route 128 area of Massachusetts experienced enormous economic growth, academics flocked to topics such as regional science-based development, university-industry partnership, and entrepreneurial networks. The frenetic takeover and leveraged buyout markets of the 1980s may be partially responsible for the upsurge of publications using agency theory to examine managerial incentives. Similarly, recent dramatic growth in the number of scholars who profess interest in the management of technology (MOT) reflects the spirit of the age. This is an area of interest which has emerged principally from the practical needs of industry for trained personnel, not from a breakthrough discovery which has signalled to academics that a fertile field of exploration has become Correspondence to: Professor Philip Anderson, Johnson Graduate School of Management, Malott Hall, Cornell University, Ithaca, NY 14853, USA.

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more accessible or tractable. An interest in technology is traceable to the roots of most social scientific disciplines. As Morgan (1980) notes, humans organize thought via metaphors, and it is difficult to employ a mechanistic conception of anything without viewing technology as a key variable. Yet the emergence of technology in relatively recent times as the primary lever of competitive economic advantage has created a demand for personnel who can help enterprises take advantage of technological innovation. That demand more than anything else is responsible for the growth of technology management as an academic specialty. The burgeoning of interest in this area is no guarantee that it will come to constitute a vibrant intellectual field that contributes to social scientific knowledge over the long term. Crane (1972) suggests that a growth phase is characteristic of most scientific domains, though its usual impetus is a specific discovery or breakthrough. As a result of rapid growth, specialization arises. Once an area is well-defined, its leaders tend to defend their own ideas and resist those of newcomers, making it difficult to recruit new members. Without an influx of new perspectives, the field stagnates, because as older problems are solved, specialists are unable to define interesting new ones, due to their restricted intellectual range. Anomalies arise, and scientists abandon fields that cannot produce new approaches to deal with them. In the evolutionary view of scholars such as Toulmin (1966 ), specialties seldom adapt via Kuhnian crisis-and-response; rather, science progresses through the growth and decline of hundreds of lines of inquiry, or research programmes (Lakatos, 1970). The cautionary implication is that MOT, like many topics before it, may prove to be a fad rather than an enduring feature of the intellectual landscape. The rooting of this field’s growth in practical demands for trained personnel, rather than in a scholarly breakthrough, creates particular hazards for academic inquiry. As Brief and Dukerich (1991) note, exhorting people to construct useful theories may impede theory development. Practitioners require models that predict what will be; often, predictive power can be obtained only by taking the particulars of a case into account, sacrificing generalizability and leading to an infinite regress of contingencies. Scholarship advances when it is aimed at theoretically interesting anomalies, which may almost definitionally be uninteresting to managers (Davis, 1971). Cautions Van Strien ( 1978), practical paradigms have a way of becoming caught up in efforts to improve process consultation knowhow, leading scholarly progress to peter out. What is the current status of MOT? What should scholars in this area do to foster its intellectual development and protect it from stagnation? How do the papers in this special issue lead us toward exemplary research in the management of technology? These three questions are the subject of this introductory essay.

2. Current problems of the field Research in the management of innovation and technological change has been reviewed repeatedly in recent years (e.g. Adler, 1990; Bamberger, 1991; Van de Ven, 1986). Therefore, there is no need for another mapping of the field, nor does the present venue provide space for an extensive literature review. What is needed is an analysis of the main obstacles that stand in the way of scientific progress in this area of scholarship. For criticism of its development has focused on two barriers to further advance, neither of which correctly identifies what must be done to realize progress. The first line of attack is represented by Bamberger (1991), who highlights definitional dissensus as the key problem of innovation research. Scholars cannot agree how to define an innovation, how technical and administrative innovations differ, or how to generate a list of innovations. Furthermore, the measures used in innovation studies lack demonstrated reliability and validity. Until consensus over such basic issues is reached, Bamberger argues, innovation research will not advance. Disagreement over terminology and fundamental principles certainly hinders the growth of research, but it is hardly fatal. Kuhn (1970a) notes that basic terms in science (e.g. “cell”, “ mass”, and “element”) have changed in meaning over time. Bryant (1975) cites a long list of terms in sociology whose meaning is the subject of sharp debates. Not the least of these is the term “sociology” itself. As Kuhn (1970a) notes, 19th century chemists were able to share fundamental tools such as constant proportion despite disagreeing about such fundamental concepts as the nature of matter. Ultimately, definitional controversies are not resolved by scholastic debate but by the introduction of powerful theories which favor one set of definitions over another. It is unlikely that progress will stem from further criticism and debate aimed at achieving definitional consensus among MOT scholars. The second line of attack focuses on the lack of a paradigm characterizing the management of technology. The lack of a paradigm is a sign of an immature discipline (Lakatos, 1970). Pragmatically, the more paradigmatic a discipline is, the more power it wields within the university and overall scientific communities (Pfeffer and Moore, 1980). Hence, calls for paradigm development characterize virtually all young interdisciplinary fields of inquiry, for instance entrepreneurship (MacMillan and Katz, 1992) and information systems (Van Gigch and Le Moigne, 1989). The central question is whether a specialty such as MOT can advance theoretically in the absence of a paradigm. Kuhn (1970a) suggests that in the natural sciences at least, scientific advance cannot take place without a paradigm. However, the term “paradigm” has been used so loosely by so many scholars that it is difficult to pin down precisely what the lack of one means, what kind of obstacle is created and what must be done to make intellectual headway.

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Some sociologists and organization theorists argue that for these disciplines, convergence on a paradigm would be dysfunctional. Daft and Lewin (1990)) for instance, criticize organization theory for closing too rapidly on a paradigm (presumably open-systems contingency theory) and fostering normal science before the discipline has matured. Pre-paradigmatic thinking, they argue, produces scholars without prejudice, alert to novel phenomena. Other scholars (e.g. Pondy and Boje, 1981) suggest that the coexistence of several paradigms is to be encouraged in organization theory. Morgan (1980) argues that since no one metaphor can capture the totality of organizational life, theorizing is simply a subjective enterprise concerned with generating one-sided views of organizational life. Hence, it is healthy for many metaphors to co-exist, and danger lies in accepting any one paradigm as a more concrete representation of organizations than another. Is the lack of a paradigm characterizing MOT a virtue or a barrier to progress? To understand why paradigms are crucial to the health of the field, we must distinguish what a paradigm is and is not. As Harvey (1982) has noted, too often the word paradigm is used loosely as a way of constructing arbitrary pigeon-holing schemes for sets of ideas. The crucial point is that a paradigm is not simply a theoretical perspective or way of analyzing the world. For instance, Marxism is not a paradigm; neither is structural-functionalism. Such theoretical orientations are better understood as themata (Holton, 1975 ), fundamental presuppositions or ways of viewing the world. Unlike perspectives, paradigms never characterize an entire scientific discipline (Eckberg and Hill, 1979). Building on Masterman (1970)) Eckberg and Hill (1979) distinguish three types of beliefs referred to by the term “paradigm”. First, meta-paradigms are epistemological viewpoints which act as unquestioned presuppositions shared by members of a discipline. For instance, logical positivism is the meta-paradigm that characterizes most work in organization theory and the management of technology. Second, a disciplinary matrix (Kuhn, 1970a) encompasses the shared generalizations of a specialized community within a discipline. This includes symbolic generalizations (e.g. “action equals reaction”), models which supply the group with preferred analogies/metaphors, and shared values. MOT lacks a disciplinary matrix, which is why it is not yet a discipline. Third, and most important, is the exemplar, which is the most central meaning of a Kuhnian paradigm. An exemplar is a concrete problem-solution that shows scientists by example how their job is to be done. However, Masterman (1970) suggests that it is less important to understand what an exemplar is than what it does. It allows the members of a research community to solve puzzles by using a picture of one thing to represent another. By doing exemplary problems the student acquires the ability to see several different situations as being like each other. Kuhn employs the example of Newton’s second law, which states that force

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equals mass times acceleration (F= ma). The student of physics comes to see that such apparently distinct problems as the rate of fall of a body or the height of a swinging pendulum are all solved the same way, using the second law. What one acquires by working through exemplary problems is a facility for analogy, in this case a way of seeing that a problem can be solved by applying the second law. The student acquires this facility by being shown situations in which other scientists solved problems by drawing an analogy to an exemplary solution (Kuhn, 1970a). Thus to say that MOT lacks a paradigm is to say, most importantly, that it lacks widely accepted exemplars. Without exemplars, a field of inquiry can confront problems but it cannot generate and solve puzzles. Puzzle-solving is the unique activity that characterizes normal science. Without puzzle-solving there is no normal science, and without normal science, there is no scientific progress (Kuhn, 1970a). The key distinction between puzzle-solving and problem-solving is that in the former case, it is known that a solution exists. The details of working out the solution require cleverness and ingenuity, but the paradigm (in the form of an exemplar) provides not only rules specifying an acceptable solution but also a tool for solving the puzzle plus a description of how the tool is to be applied (Masterman, 1970). Masterman suggests that the exemplar interpretation of “paradigm” answers the question that Popperian models of scientific development cannothow do new areas of inquiry emerge? For a pre-paradigmatic specialty such as MOT, this is a crucial question. Masterman argues that in the beginning, a field is characterized by a “trick”, an embryonic technique plus an insight applicable to the field. Later, through mathematization and the development of experimental procedures, this beginning is elaborated into a set of habits that characterize a community of scholars. But in the beginning a combination of insight and technique allows puzzle-solving by applying the “trick” in a number of settings. Eckberg and Hill (1979) cite as an illustration the cognitive dissonance research program. Once the insight was understood, similar experimental techniques were applied to dozens of situations in which one might expect dissonance, generating and solving a large number of puzzles. To this illustration we might add the application of game theory in economics. The insight is that dynamic problems with asymmetric information can be modeled while retaining strict individual-level rationality assumptions. The “trick” is using backward induction to locate a Nash equilibrium. It has been employed again and again in extraordinarily clever ways to solve puzzles, economic situations which otherwise appear intractable. Similarly the insight that characterizes population ecology in organization theory is that the entry and exit of firms, rather than the adaptation of firms, may drive transformations in populations of organizations. The “trick” is to analyze exit rates with event-history techniques that employ “spell splitting” to examine the effect of time-varying covariates.

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It has been used to study a wide variety of influences on organizational exit rates. These instances illustrate the key role of exemplars in getting a field off the ground by making puzzle-solving possible. As Eckberg and Hill (1979) emphasize, a paradigm is not simply a theoretical perspective. Ongoing puzzlesolving occurs only when a group exists which shares a consistent body of belief. Through long-term tutelage via exemplar, students learn where to look for puzzles. In the above examples, puzzles are situations where the insight-andtechnique of cognitive dissonance, game theory, or organizational ecology directs scholars to seek application of the exemplar, secure in the knowledge that a solution probably exists if the researcher applies the tool in a clever enough way. The exemplar directs attention to a set of puzzles that it is well-suited to attack, permitting cumulation and refinement of the paradigm through repeated application in different settings. This is why paradigms never characterize entire disciplines, because extended puzzle-solving is not possible at so broad a level. The core argument of this paper is that the lack of exemplary research in MOT creates three specific obstacles to scholarly progress which must be addressed. First, without exemplars we cannot detect anomalies. Second, without exemplars it is difficult to progress via analogy and to provide analogies that would cause other fields to draw upon this one. Third, without exemplary research, it is impossible for a genuine invisible college, a research community, to coalesce around this area of inquiry. The suggestion that MOT needs a paradigm does not tell us what to do. As Kuhn (1970b) notes, social scientists cannot improve the status of their field by legislating agreement on fundamentals and then turning to normal science. Following Masterman (1970), it is more important for scholars in this area to grasp what an exemplar should do than to debate what it should be. One of the most important things that normal science puzzle-solving achieves is to make it possible for scholars to detect anomalies. It is the recognition of anomalies that is the cornerstone of genuine scientific progress. An anomaly is more than simply a result we did not expect. All sciences, even the most paradigmatic, contain inconsistencies and are unable to explain awkward facts (Masterman, 1970). These are difficulties that scientists in paradigmatic fields expect to clear up eventually. An anomaly poses a more serious challenge-no dismissal or post-hoc explanation suffices to restore the overall theory to plausibility. The only ways to explain it within the confines of existing theory would destroy the elegance of these theories. When the logical extension of the existing paradigm leads to paradoxical results, the theory collapses, and only in this way do better theories make their appearance. Why is normal science puzzle-solving based on exemplars a prerequisite for detecting anomalies? The reason anomalies are observed is that normal science has pushed the exactitude and scope of observation forward to such an

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extent that one knows with precision that the results one expected did not occur. If one does not know with precision what to expect, it is easy to attribute (post hoc) surprising results to an overlooked contingency, dismiss the unexpected finding as an artifact of inexactly operationalizing a construct, or account for the discrepancy as measurement error. This is precisely the state of MOT (and many social science specialties) today. Truly counter-intuitive findings are difficult to imagine, because there is no body of work that applies the same insight to many different areas. Cognitive dissonance theory, for example, has worked so well in so many situations that were it clearly shown not to hold in a setting where it ought to, we would know we were onto an important trail. This cannot happen when an unexpected finding merely runs counter to an observation that has been shown to hold in one or two instances. Without a paradigm, there can be no anomaly. Furthermore, the history of science suggests that anomalies are usually observed before they are recognized. In paradigmatic fields, scientists wrongly dismiss puzzling findings because they are unable to shed a certain world view. Eventually, the persistence of these findings causes someone to reconstruct the paradigm in order to explain them; when an anomaly is explained by a new theory, it is the basis for a paradigm shift. However, in pre-paradigmatic fields, the anomalous observation is not even recognized as posing a threat to a theory system. It is entirely possible that MOT scholars have already produced some of the most important observations of the next twenty years, and have dismissed them or failed to recognize their significance because there was no exemplar to indicate that they posed critical problems. The second obstacle thrown up by the lack of exemplars is the inability to progress via analogical borrowing. A paradigm is more than just a tool. It is also a way of seeing, a concrete picture of a thing (A) used to describe another thing (B) (Masterman, 1970). For example, much progress in deciphering the genetic code depended on seeing the code as a language, and thus drawing upon linguistic concepts such as grammar and syntax. Similarly, organizations are clearly not biological organisms, but biological analogies have allowed population ecologists to transport highly useful methods and concepts (e.g. mutualism, density-dependent selection) from one arena to the other. Pickering (1980) suggests that an exemplar is the concrete embodiment of an analogy. It is a paper or collection of papers that makes a connection between an established body of knowledge and a problem on the frontier of knowledge. It pushes science forward by bringing to bear conceptual and methodological apparatus inherited from a different specialty. Pickering cites the example of a new area of physics which languished for five years until it was demonstrated that well-known methods from quantum electrodynamics could be applied to elaborate the novel theory. It is the ability of an exemplar to help us draw connections between what we know and what we wish to know that helps normal science progress rapidly.

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Conversely, one sign of a vibrant field is that its exemplars serve as analogies upon which other specialties draw. The success of neural science has contributed heavily to the development of neural network concepts in computer science. The insights provided by punctuated equilibrium models of paleontology have led both organization theorists and political scientists to propose analogous models in their fields. The emerging science of chaos is providing a rich series of models which other disciplines are exploring as ways to explain anomalous findings. The aim of those who would make MOT a discipline should be to develop exemplars that not only explain how technology is made manageable but which also prove useful for other specialties to borrow as tools for framing and solving a different set of puzzles. The third obstacle to progress posed by the lack of exemplary research in this field is that without exemplars, a research community cannot coalesce around a problem. Kuhn (1970a) makes the pivotal point that a paradigm governs’not a subject matter but a group of scholars. There can be no paradigm characterizing the topic of technology management. There can only be exemplars that provide shared understanding to a group of scholars interested in the subject. Asking where the field is and where it is going is simply a shorthand way of asking how the community of scholars who share this specialty is evolving. Every research area contains two types of subgroups (Crane, 1980). The first is groups of collaborators, who interact with each other face to face and often co-author papers with one another. In MOT, the Minnesota Innovation Research Program (Van de Ven and Associates, 1988) has spawned such a group. The second subgroup is the communication network that links groups of collaborators. After Price and Beaver (1966), this is termed an invisible college, since its members are tied together by reading each other’s papers, not necessarily through personal contact. Such invisible colleges are networks whose members are familiar with each other’s work and with a core stream of previous works that have influenced the definition of the problem class and its boundaries. The confines and dynamics of research communities have been studied through co-citation analysis (Garfield, 1979); Klavans (1991) provides an interesting overview of this literature and of the research communities most central to scholars who study technology management. Research communities typically coalesce around a set of journals, one of which is the most central in the co-citation network. Some research communities become ingrown and isolated: a small group of researchers ends up talking to each other but not to a broader community. Most of the citations to articles in their central journal are from other articles in the same journal, which have little impact elsewhere. The consequence-regardless whether they agree upon a paradigm-is typically stagnation and decline. In contrast, vibrant fields are densely connected to other research communities. They em-

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ploy cutting-edge developments in related invisible colleges while producing novel facts and explanations that in turn provide grist for the mill of other research programs. Klavans’ analysis suggests that to date, research communities focusing solely on issues related to technology management (e.g. the management of research and development) have been isolates. No identifiable invisible college has yet coalesced around the problem class of technology management; instead, scholars in this area have published their work in journals which are part of other networks, principally devoted to other domains. The primary reason for this state of affairs is the lack of widely accepted exemplars. There is no invisible college in MOT because there is no set of papers that provides a model or guidepost for other research. What must emerge for the field to progress is a body of knowledge which members of a community master, refer to, and build upon. A research community cannot arise through the establishment of social ties, the founding of a professional society, or the establishment of a journal. What is required is widespread agreement that a particular exemplar-an insight combined with a technique that defines puzzles and suggests potentially profitable analogical borrowings-is a model for research worth doing in a variety of settings. A journal or professional organization or social network can facilitate the emergence of that consensus. However, the present situation in which MOT articles seldom cite each other or share common references will continue until there appears some common body of knowledge which one must master to carry out leading-edge research in technology management. 3. How can the field progress? Intellectual progress in the management of technology depends primarily on our ability to generate exemplary research. Simple continued cumulation of existing lines of research will not suffice. We need exemplars to help us identify anomalies, develop useful analogies that permit us to draw upon existing bodies of knowledge, and provide a focus for the emergence of an invisible college which builds upon a core stream of articles while remaining densely connected to other research communities. Such exemplars are problem-solutions that serve as models which can be applied to a wide variety of domains. How and where can such seminal problem-solutions arise? There is no formula by which we can produce them; puzzle-solving models are not themselves the solutions to higher-order puzzles. One possibility is simply to borrow useful exemplars from other areas. Hence, one could ask what would happen if technology management were merely a repeated game, or if innovation projects were analogous to genes, or if technology could be thought of as a cultural symbol system to be deconstructed. The general problem with this strategy is that the more powerful the analogy is, the less it can support a distinctive

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academic specialty. Suppose for example that all problems in technology management could be modeled game-theoretically as economic choices. MOT would simply become a topic area within economics. Genuine academic specialties can seek inspiration from exemplars in other areas, but cannot borrow them wholesale. For guidance on how to do exemplary research, we may turn to recent works examining the psychology and sociology of discovering. This line of research has been pioneered principally by historians and philosophers of science, who have asked whether there is a pattern to insightful discoveries across scientific disciplines and problem areas. No clear picture has emerged that ties together a large number of diverse discoveries, but some interesting rules of thumb have been proposed. Oliver (1991) provides a rich illustration of one of the great paradigm shifts of this century, the emergence of plate tectonics as the dominant way of viewing geology. From his experience as a geologist, he emphasizes the importance of original observation. First, he suggests that new observations of important phenomena almost always produce surprise and discovery. For example, the first geologists to explore the sea bed were almost assured of finding something interesting (and in fact anomalies discovered on the ocean floor led to plate tectonics). Second, he argues that the most consistently successful way to make discoveries is to bring instruments and measurement techniques from one branch of science into a different branch for the first time. Again he emphasizes that the ability to observe phenomena no one else has ever seen is the most likely way to produce an exemplary discovery. Third, he urges scholars to learn what the majority of scholars in an area are doing, not to criticize or emulate the mainstream but to ask what is being overlooked. The discoverers of plate tectonics focused on the sea bed while most other geologists concentrated exclusively on the land. Root-Bernstein (1989) articulates no fewer than 43 principles, or rules of thumb, for maximizing discoveries. Of these, six seem to be most useful for MOT scholars. First, he suggests that discoveries are most likely in areas of greatest ignorance. Second, areas undergoing rapid change are most likely to produce breakthroughs. Third, previously well-plowed fields that have been abandoned can be fertile sources of discovery later, when new techniques and insights allow us to view them in fresh ways. Fourth, seek areas where theory and data appear to contradict, especially where the contradiction has been smoothed over in an ad hoc way. Fifth, look for what is said to be impossible. Sixth, look for the intersection of different lines of inquiry. Applying these heuristics to MOT requires us to specify roughly what the domain of the field is, what are its boundaries. Without an idea what the field contains, we cannot know what observations have been made, what the mainstream is doing, within what areas ignorance is maximal or change has been most rapid, where different lines of inquiry might intersect. Any mapping of a

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field is bound to be incomplete and partially a matter of taste; as with most mapping exercises, the chief benefit is the process, not the resulting domain definition. Certainly no claim is made that the picture presented here is objective or comprehensive. Narrowly, technology is any means of accomplishing a task; shoveling dirt is a technology. By incorporating engineering and technology management, we restrict our domain to technologies embodied in products or processes that require some engineering/scientific knowhow to comprehend. We further restrict our domain to cases where some uncertainty and choice are involved, technologies need to be managed when there are alternative ways of effecting a product or process and technical exploration is required to understand the nature and limits of the alternatives. In such cases, MOT principally addresses three levels of analysis: who carries out technical exploration, how it is carried out, and what its impact is on the organization and its environment. At the first level of analysis, MOT focuses on individual actors and their decision patterns. The principal question is what practices optimize the innovative capability of individuals involved in technical exploration; illustrative topics include managing technical careers, motivating technical professionals, and renewing capabilities and competences. At the second level of analysis, MOT concentrates on ways of organizing technical exploration routines. Illustrative topics include managing a cross-functional technology development team, organizing an R&D laboratory, designing products for manufacturability, or transferring knowhow from one area to another. At the third level of analysis, the field is concerned with strategic management, how technology affects the organization-environment ‘fit. Illustrative topics include how technology evolves, how standards emerge, how different technologies interact synergistically, how technological substitution takes place, and how technological change alters market and competitor relationships. In which of these areas is exemplary research most likely to appear, and how may we improve the odds of doing exemplary research? A useful heuristic might be to apply rules of thumb for . generating discoveries to the general areas of study outlined above: l Make new observations of important phenomena, perhaps by using instruments imported from other branches of science. At the individual level, cognitive mapping techniques developed in social psychology might permit genuinely original observations. At the organizational level, efforts to track the explicit timing and sequence of events (e.g. Van de Ven and Associates, 1988) have produced new data that seem quite promising. At the strategic level, a long-standing model suggests that following the emergence of a dominant design, process innovation should exceed product innovation, yet no one has created a metric by which this assertion could be tested. l Seek that which is being overlooked, explore the sea bed when others are exploring the land. At the organizational level, for example, most attention is

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focused on innovation project teams. An opportunity exists to examine technical exploration via other avenues. At the strategic level, breakthrough innovations have received considerable attention at the expense of innovations which might have been discontinuous advances, but which failed to substitute for the technologies they were intended to supplant. l Search in the areas where ignorance is greatest. As Bamberger (1991) suggests, we know very little about how product and process innovations differ or how technological advances depend on complementary breakthroughs which may be administrative innovations. Examining these fundamental sources of ignorance that strike at the heart of what innovation is may be an important avenue toward producing exemplary research. l Search in areas characterized by rapid change. Due to game-theoretic analysis, models incorporating network externalities and the development of standards have far outrun empirical observation testing their implications. Similarly, cognitive processing models based on neural science have developed rapidly without causing MOT scholars to re-think how individual technical professionals conceive of innovations. l Re-examine areas that were abandoned years ago as well-plowed. An interesting example here is Jelinek and Schoonhoven’s (1990) return to the original Burns and Stalker (1961) suggestion that organic organization is required in an innovative milieu. Through field studies in Silicon Valley, they found that innovative firms deviate significantly from the organic model, leading to intriguing insights about organizing for innovation. l Seek areas where theory contradicts data or where data are contradictory. For example, theory suggests that older technologies approach performance limits with maturity; data suggest that these limits are demolished when a new technology appears as a competitive threat. Theory suggests that incremental progress accounts for the majority of technical progress, but data suggest this is often not the case. Contradictory data seem to imply that pioneering technologies sometimes confer first-mover advantages and sometimes do not. Contradictions suggest the possibility of producing an exemplary solution which becomes widely accepted because of its ability to bridge an apparent paradox. l Examine that which is thought impossible. It has been suggested that behind every innovation stands a product or process champion, that innovations cannot proceed without champions. Diffusion theory suggests that the rate of diffusion can never become negative. Economic theory suggests that it is impossible for incompatible technologies to achieve significant market penetration in the presence of strong network externalities. Examining such impossibilities may be a route to producing powerful discoveries. l Examine areas where lines of inquiry intersect. For example, both microlevel and organization-level theorists are interested in how skills and competences are built up and transferred via individual or organizational learning.

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In such cases, an opportunity for discovery arises when one individual is broad enough to bridge the gap between differing perspectives or units of analysis. In summary, no analysis can predict the direction in which MOT will evolve. Rather, it is hoped that by sensitizing a potential community to the need for exemplary research and suggesting how it might be generated, the development of model problem-solutions can be encouraged and readers might be stimulated to think about why the problems that interest them might lead to such breakthroughs. 4. Research issues in the management of technology: Perspectives and models In this first special issue of the Journal of Engineering and Technology Management, we sought papers that point toward ways in which exemplary research might develop. This set of articles is also meant to provide some insight into directions in which the invisible college of technology management scholars is likely to evolve. The works in this issue collectively stimulate thought about what types of issues will occupy the technology management research community, what types of methods its members will employ, and what other research programs its members will rely on for inspiration and will in turn provoke with novel findings and interpretations. Andrew Van de Ven encourages members of this invisible college to focus on how novel technologies arise from a social infrastructure that incorporates not only the structure of an emerging industry but also basic societal endowments and institutional arrangements. This research illustrates the power of specifying what is being overlooked, and how MOT might benefit from exploiting intersecting lines of inquiry. His work suggests that technology management scholars must be conversant with and will contribute to sociological research programs stressing how interorganizational ties are formed and, subsequently, constrain the life chances of individual enterprises. Leonard Lynn, Henry Piehler, and Mark Kieler’s article illustrates the potential of large-scale, cross-cultural survey research. This work suggests the power of being the first to make new observations of important phenomena. Examining career patterns and information flows, they uncover a novel fact and suggest an original interpretation of Japanese engineering careers: what would be the consequences of developing a cadre of older professional gatekeepers? And what are the degrees of difference in practices between the U.S. and Japan? This paper demonstrates that scholarship in careers and communication networks will continue to stimulate investigations into technology management. Stephan Schrader, William Riggs, and Robert Smith bring the heritage of research in problem framing and solving to bear upon the way in which technical professionals approach technical puzzles. Their paper proposes new linkages between technology research and the literature on decision-making, prob-

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lem-solving, and learning, keying on the way in which technical problem solvers think about the degree of uncertainty and ambiguity inherent in the tasks they undertake. The thrust of this work is to suggest a way to bring measurements from another branch of science to bear on MOT; those who build on this research are almost assured of making novel observations. Nancy DiTomaso, George Farris, and Rene Corder0 return to a classic work on scientists in organizations to ask whether its characterization of productive research climates holds in an era where American males constitute an evershrinking fraction of the scientific work force. The thrust of their work is to heighten our sensitivity into the ways in which changing work force demographics might alter long-held assumptions in our field. It conveys a sense of the insights that arise from returning to fields that were thought to be wellplowed with fresh insights and original questions. It also suggests that technology management may in the future more strongly draw from and contribute to the research program of social demography. Frank Dubinskas employs ethno-methodological techniques to generate novel observations in an area of relative ignorance, how development projects actually evolve. He generates two powerful metaphors, the vat and the funnel, which in turn provide analogies for other specialties seeking to borrow ideas from MOT. This work illustrates the way in which personally examining phenomena that are usually reported at arm’s length can lead to intriguing and original insights. Michael Hitt, Robert Hoskisson, and Robert Nixon build upon one of the most important emerging themes in strategic management, how organizational capabilities create value. This is an area experiencing rapid advance in a related field where the interests of researchers at all three levels of technology management intersect. The authors propose a novel point of view on the value of cross-functional integration, arguing that its benefits may be partially indirect and contingent. Their work suggests that the tremendous growth of research in strategy and in technology management is likely to proceed hand-inhand, since so many issues central to both intertwine. Collectively, these papers provide a sense of where the technology management research community stands and how it is evolving. Each paper illustrates a way of doing research in MOT that can lead to important discoveries and exemplary research. They provide interesting templates for those who would reach toward the types of puzzle-solutions the field requires at this stage of its development. They also suggest which other fields are likely to be most closely linked to the invisible college of technology management. For MOT to become a giving specialty, an area from which these linked fields draw inspiration and whose work they cite, we must develop exemplars which by analogy illuminate a variety of problems and show how to solve them as puzzles. The articles collected here serve as prototypes and guideposts for those whose research would provide the foundation for such an achievement.

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