The social sciences are different because those fields seek to understand different aspects of human society, which is ultimately an emergent macro-phenomenon based on human interactions. The object of their study suffers greatly from being dependent on (and the result of) human decision-making, which is motivated by what people value, and therefore the nature of the phenomenon studied includes and depends on a large degree of unobservable, subjective, and chosen factors.

Because of the unobservability and subjectivity, and thus the complexity and evolving content and structure of orders and processes in the social realm, the social sciences can, should, and probably must utilize a different set of methods than the natural sciences. Simple observation is not sufficient to study the unobservable. Objective measurements do not properly capture the nature of subjective decision-making.

This has led to the commonly used distinction between the “hard” (natural) and “soft” (social) sciences, sometimes even leading to the outright dismissal of the latter as “unscientific.” But from the perspective of the nature of what they study, it would be just as accurate to call the former “simple” and the latter “complex.”

Unfortunately, the social science disciplines have largely followed economics’ physics envy in seeking to adopt the methods of the natural sciences in a vain attempt to generate results that are as reliable (reproducible). This outcome has not materialized, as shown by analyses of social-science research’s reproducibility (e.g., OSF’s assessment of psychology). Given the nature of what the social sciences study, the lack of reproducibility should be expected. And it is not a flaw if one properly understands what empirical research in the social sciences can do.

The Delusion of Wannabe Physicists

Unfortunately, social scientists in general (and economists in particular) have let their physics envy and adoption of natural-science methodology affect their understanding of the object for their scholarship. It is not uncommon that peers in our field of study, serving as reviewers, demand that a study complies with standards that are appropriate for the natural sciences but that are alien to the realm of a social science.

For example, consider this from a review report that a friend recently shared with me:

If you measure X, the paper should also claim X, define X, and build its theory on X.

The 1:1 correspondence of measure, claim, and theory is a perfectly valid request were it not directed at a paper studying some aspect of the social realm. Because in the social sciences, practically every empirical measure, regardless of how advanced, is a proxy of the concept that explains the actions that generate the phenomenon. To use a simple example, economists have long known that a person’s ability strongly correlates with their job performance and therefore salary. How do we measure ability? The answer is that we do not. Typically, we use educational level to capture most of it. But it is a proxy, an indirect measure of the concept we are trying to capture.

Imagine demanding that a theory that seeks to explain someone’s salary level must be formulated as 1:1 correspondence with educational level rather than ability. I think everyone can understand that this demand is unreasonable and makes no sense. And of course it doesn’t: the theory is that ability and not educational level determines someone’s salary level. When we use proxies (unless we HARK), the theory is typically better than the measures - because the theory is the real explanation and the measure is an attempt to corroborate by way of indirect and approximate measures.

This should be rather obvious to social science scholars, because certainly we know that the phenomena we study are not subject to the simple causalities applicable in the natural sciences? Unfortunately, it is difficult to honestly answer “yes” to this question. In part because we do not have the important discussions on methodology (although some try) that we probably need to have, and in part because many of us choose to be deceived by the effectiveness and reliability of physics and other social sciences.

The Role of Empirics in Social Science

None of this means that empirical research is therefore worthless and should be as expunged from the social sciences as the entrepreneur has been from mainstream economics, however. There are certainly reasons to study social phenomena using data. But we must be aware of and guided by what we study in our choices of methods and how we assess those methods. It is not defensible to borrow methods, approaches, and metrics wholesale from fields that study very different things.

The physics envy of first economists and then the rest of the social sciences sought to make our fields of study as reliable and reproducible - scientific - as the natural sciences. This has not materialized. On the contrary, we now wrestle with an ever-growing problem of lacking reproducibility, a scholarly literature that does not recognize the limitations of its (empirical) findings and therefore risks the reputation and status of all social sciences. Unfortunately, this - and the problems ahead - was entirely avoidable if we had recognized the nature of the social realm that is the object of our study.

Human beings are not rocks and what emerges from humans interacting with each other is much more than merely a heap of rocks. The social sciences study those phenomena that are either designed by or emerge from the (inter)actions of people. Neither can be understood by simple observation or objective measurement alone.

While a university has a “global” culture, the units (including various colleges—e.g., Arts and Sciences, Engineering, and Business) within that university have specific variations of that culture; we can think of these as sub-cultures. Within a unit such as a business school culture, there are additional subcultures that we recognize commonly as departments dealing with business functions such as marketing, accounting, finance, information systems, and management. Finally, within each department, subcultures may form around academic domains that sum to the total of a business function. In a management department, for example, subcultures may form around the domains of organizational behavior, strategic management, human resource management, and entrepreneurship. The likelihood of subcultures developing within individual departments increases with faculty size and areas of academic interest.

The culture within the management department where I began my career almost 50 years ago was quite strong and influential. We were a small group, meaning that we did not have subcultures beyond that of being a management department. Part of that culture found me, as a first-year assistant professor, participating in conversations with senior faculty during which we discussed performance-related expectations and paths through which they could be satisfied. Perhaps not surprisingly, excellent research productivity, highly effective teaching performance, and value-creating service outcomes were highlighted. Importantly, departmental faculty supported one another when pursuing desired outcomes. In this sense, no faculty member operated in a vacuum; rather, all faculty committed to mutual respect and collegial interactions. Indeed, this department’s culture called for developmental interactions among faculty, but particularly between senior- and junior-level faculty members. Supporting this cultural expectation were frequent discussions about research ideas, teaching pedagogies, and ways to serve the department, the school, the university, and the broader professional community.

Serving as a reviewer for academic journals was another performance expectation that was a part of the culture that I joined as an assistant professor. We talked frequently about the importance of reviewing given that published scholarship was and is the source of researchers being able to present their findings to researchers and practitioners. In that culture, we valued opportunities to serve the profession by reviewing other scholars’ work to determine its ability to contribute to on-going dialogues about interesting research questions. We talked about journals to which we desired to contribute as reviewers and how we could develop/hone our reviewing skills to the point that premiere academic journals would desire to receive reviews from us—first as ad hoc reviewers and then hopefully as a member of an editorial board. Senior-faculty gave us papers to “review” as a path to providing feedback to us. Typically, the papers we reviewed were ones with which our senior-faculty members were involved.

The feedback we received in response to our review was the foundation on which we improved our reviewing skills. Our culture placed very high value on receiving positive recognition internally by the dean’s office and externally from journals for the quality of our reviews. For us as assistant professors, being involved with reviewing processes found us gaining exposure to some of the latest theoretical developments and newest research methodologies that in turn served as intriguing opportunities for us to discuss what we learned through reviewing with our colleagues to identify research questions that we might wish to examine.

I believe that today’s entrepreneurship scholars can benefit too from a culture that nurtures the academic reviewing process. For senior faculty members, the call is to develop and nurture a culture that validates the importance of the reviewing process; for junior-level faculty members, the call is to accept senior-level scholars’ counsel about the reviewing process and to develop skills that will allow them to contribute to that process substantially.

From my initial academic appointment, I remained commitment to my belief about the importance of academic reviewing; and I was honored to serve as editor of a major journal and as a consulting editor and associate editor for two other journals during my career. These opportunities are in addition to serving as a member of many editorial review boards. Across these experiences, I engaged with many excellent associate editors, reviewers, and editorial review board members. I remain thankful for their commitment to service to the profession through their editorial work and for opportunities to collaborate with them.

But these were “yesterday’s” experiences which are quite different from those I am encountering today. Currently, I am involved with a special issue of an academic journal; an aspect of entrepreneurship is the focus of this special issue. Disappointingly, I can report that I contacted 11 people to secure three reviewer commitments for one paper and contacted nine people to receive three commitments for another paper. These episodes, and others that I could cite too, suggest strongly to me that the reviewing process is less valued today compared to its historical value. In addition to having difficulty receiving reviewing commitments, the number of reviewers who are “late”—and often very late--in completing their agreed-upon task is too large. All of this is quite disappointing for a more senior member of the academic community with a strong interest in entrepreneurship.

So, what should we as entrepreneurship scholars do about this? My thought is that we need to include the importance of cosmopolitan service—in the form of review for academic journals—as part of an entrepreneurship faculty’s culture. Local service to one’s department, school, and university is critical, of course. However, serving the broader collective body of scholars throughout the world is certainly important and perhaps equally so. Our academic enterprise cannot disseminate its findings with confidence in their validity and reliability without having access to a strong reviewing process.

In closing, let me encourage all entrepreneurship faculty members to commit to developing and supporting a culture for their unit that values high-quality academic reviewing. The outcomes to be gained from doing so are foundational to our efforts to enhance science and managerial practice about entrepreneurship through our published work. On a personal note, I can say that I intend to submit a manuscript to an entrepreneurship journal next week for publication consideration. May I respectfully ask that you accept a request to review that paper if one is sent to you?

Of course, any consternation with the state of entrepreneurship theory can be easily attributed to the fact that I was just beginning my Ph.D. program and didn’t know the ropes. The study of entrepreneurship is obviously united by its titular phenomenon. Any mature scholar in the field clearly knows what “entrepreneurship” is, its connotation, ethicality, relation to society, relation to other fields of study, methodology, and basically any other characteristic, unlike the legion of other disagreeing scholars; and one day, upon my successful development through the Ph.D. program, I hope to join them.

We can call the current state of the field “theoretical eclecticism.” Currently, there are no “schools” of thought that have developed a full system of entrepreneurship theory, and similarly there are no universally accepted descriptions of the entrepreneur or entrepreneurship. As a result, an ecosystem of differing theories (definitions, analyses, explanations, and ethical judgments) has developed such that scholars and students can, and do, pick and choose whatever theory is most congruent with their personal beliefs, most convenient for publishing their next paper, or most plausible sounding without further investigation.

While theoretical eclecticism does not necessarily imply contradiction between theories, those theories are set to develop in conflicting directions. Presently, an ecumenical scholar or student can say that all these views of entrepreneurship, even the seemingly opposite views, just describe different aspects of entrepreneurship and therefore can be reconciled. But this attempt at a theoretical peace, by my estimation, just kicks the can down the road, and I think many students and scholars see this too.

Indeed, I—as a student, much less as a future academic—should not be able to review a set of mostly disconnected theories on a seemingly undefinable subject and combine the ones most likely to get me published, as if ordering food off a menu, calling it science all the while. This is why Ph.D. students struggle so much with theorizing well. The theories lumped in together under the field called “entrepreneurship” do not agree with each other on what they are studying, why, or even how. For every claim that some part of entrepreneurship is essential, such that without it we are no longer studying entrepreneurship, there are dozens of articles disputing said essentiality or outright ignoring it while claiming to study entrepreneurship.

As I mentioned already, I am a Ph.D. student; I am not going to attempt to offer some intricate, in-depth, highly sophisticated theory exhaustively explaining why entrepreneurship finds itself in its current state and where it can go from here. Rather, I would like to briefly offer my intuition as to the most immediate incentives causing entrepreneurship’s theoretical eclecticism, hopefully to direct the conversation towards doing something to fix these obviously destructive incentives.

First, there is an incentive for researchers to hide any ideological beliefs underneath the language of entrepreneurial theory while arguing on subjects that fundamentally require an ideological position. Arguing politics in a professional environment is, of course, currently seen as deeply unprofessional, but suppressing those arguments, especially as entrepreneurship begins its foray into the ethics of entrepreneurship, does not magically cause those arguments to disappear. Rather, suppressing ideologically motivated arguments only incentivizes those researchers to search the field for theories or combinations of theories which lean in their direction and contrive a positive analysis which really should have been framed as a normative analysis. If, as a hypothetical, I am a student entering the field for the first time and am also a socialist, I cannot simply write a socialist ethical analysis of entrepreneurship, since that would be for the field of ethics and not entrepreneurship. Instead, I have to look for theories which align with what I think about entrepreneurship given my socialist beliefs. I could draw from the “dark side” and moral hazard, anti-heroism, and destructive entrepreneurship literatures to develop a counterintuitive, novel, maybe even interesting theory that entrepreneurs are actually oppressors by default. I could then follow this up by deriving from this grab bag of theories that only social entrepreneurs alleviate oppression, due to their forsaking of profit for some other social end. This could just as well work for a student or academic of any other ideology. In all cases, the suppression of normative discussion causes researchers to instead express themselves through ideologically “coded” theories that are otherwise disconnected; so even though the theories are connected by a privately held ideology, the use of theories is fundamentally eclectic. On the macro level, suppressing ideological discussion prevents clear schools of thought from emerging and integrating our extant theories into larger systems of thought on entrepreneurship. In other fields that developed in times where academics could argue ideology, say economics or sociology, schools of thought developed and the fields flourished.

Second, there is a strong pressure to develop something novel and interesting. This has pushed many students and academics to develop or adopt theories which are counterintuitive and contrarian or apply them in such ways simply because doing so is against the grain and therefore interesting. If I can speculate, I think the drive for novelty and interest is primarily responsible for keeping entrepreneurship undefinable. Simply holding to a stationary definition of “entrepreneur” is, after all, boring and practically invites redefinition. But playing the contrarian and contriving fringe definitions and applications and theories of entrepreneurship is novel, is interesting, and would, over time, destroy the concept’s meaning by so greatly diluting it.

Third, there is no outlet for direct scholarly argument and counterargument on theory. That is, once some theory is published, the incentives align such that a pure refutation of said theory is either too costly to publish or unpublishable in the first place. This means that the field is always moving (and fracturing). But what if a prior iteration of a theory or a prior paradigm was closer to reality than that which came after it? What if the theory is flawed and therefore merits a full refutation? One stroll down the history of thought will show that direct arguments used to be the normal, accepted method of scholarly disagreement, so there is precedent for this. In our present context, the lack of a full-length, popular, scholarly “pushback” mechanism means that there is no way to fully roll back a theoretical “contribution” once it has been published. Had such a mechanism existed, entrepreneurial theories could have been ironed out or outright rejected, preventing the field from growing apart from itself to the extent that we have witnessed.

While I don’t expect this entry to directly change anything, I hope that the addition of an entrepreneurship student’s non-institutionalized perspective will somehow provide something useful and eventually contribute to a coherent field of entrepreneurship research. Finally, I hope this (very) general narrative of the field illuminates why students sometimes struggle with theorizing in their classes and research.

Considering the preference for one system over another in a somewhat more granular way, we note that today, estimates from surveys show that for 18–29-year-old people, those stating a preference for socialism range from 35 to 60 percent. Results from a Rasmussen survey highlight operational specifics of this preference in that 76 percent of under-40 voters agreed with the statement that “major industries like health care, energy, and big tech should be nationalized to give more control and equity to the people.” Other reasons for the preference of socialism among parts of the US population today include perceptions or beliefs that corporations and wealthy citizens pay too little in taxes, housing costs are too high, cronyism accurately describes capitalism in today’s markets, and income inequality creates too many disadvantages for lower-income households compared to their higher-end counterparts. On the other end of the potential reasons for this situation are people suggesting that the country’s educational system is failing to effectively differentiate socialism’s principles from those of a capitalist system. The degree to which this is an accurate conviction and what to do if it is will play out across local school systems in the years to come.

An argument could be made that a strong commitment to ethical business practices and ethical entrepreneurial practices could create positive visibility for capitalism and a concomitant reduction in an appreciation for socialism. This is not to say that entrepreneurs and those practicing entrepreneurship within established organizations operate unethically; rather, this is to say that a renewed and more robust commitment to ethical practices in general and ethical entrepreneurial actions in particular could highlight capitalism’s ability to provide opportunities to all and to remediate society’s problems.

Here we envision business ethics as a set of moral principles that guide/influence how companies interact with customers, suppliers, communities, and society in general when engaging in business transactions. As fundamental truths, principles inform individuals’ evaluations of situations and their selection of actions to take to deal with them. Compassion, dignity, justice, fairness, truth, honesty, and accountability are examples of principles. Ethics extends beyond satisfying legal requirements and obligations; as such, it involves individuals’ discretionary decision-making behaviors that flow from and are consistent with their principles.

Entrepreneurship, in terms of venture creation, opportunity identification, and the pursuit of those opportunities, is deeply embedded with questions of ethics. Researchers study the intersection between entrepreneurship and ethics. Highlighting the importance of this scholarship is a recently announced special issue of the Journal of Business Ethics (called “Ethics and Entrepreneurship: Foundations and Philosophical Perspectives”). The guest editors for this special issue encourages scholars to “reimagine entrepreneurship as an inherently ethical practice.” The guest editors also note that in their view, “the intersection of ethics and entrepreneurship presents a unique opportunity to rethink and shape new ethical paradigms and foster innovation approaches to the persistent questions of responsibility, justice, and human flourishing.” Start-up entrepreneurs and corporate entrepreneurs who are willing to explicitly integrate business ethics into how they engage in entrepreneurial actions when competing as capitalists have opportunities to satisfy the needs of multiple stakeholders. With ethical practices at the core of entrepreneurial actions, start-ups and established firms can demonstrate the “best” that capitalism has to offer while demonstrating its superiority to state-influenced and/or state-controlled entrepreneurial actions.

There are many examples of firms that integrate ethics and entrepreneurship when operating their business; Big Spoon Roasters is such a firm. Believing that “food should be good for people who eat it, good for the people who grow it, and good for the planet,” this entrepreneurial venture produces a variety of nut butters and bars. The firm’s mission is to “build a healthier relationship with our planet through delicious, nutritious foods made without compromise.” The principles guiding the venture’s actions include striving for environmental, social, and fiscal sustainability, integrity, and social responsibility. Big Spoon says that the venture is “selective about its ingredients, prioritizes an ethical and environmentally conscious supply chain, strives for zero waste, makes its nut butters and bars in-house, pays certified living wages, and offers robust employee benefits, including subsidized health insurance and a monthly wellness stipend.”

Entrepreneurs and entrepreneurial ventures operating as does Big Spoon Roasters have opportunities to earn profits and serve society while engaging in ethical practices. Outcomes achieved by this type of capitalism can serve as a model for ventures and established firms. Moreover, firms can achieve these outcomes without experiencing the constraints of a state controlled/state influenced economic system. Stated somewhat differently, these outcomes demonstrate capitalism “at its best” while simultaneously highlighting socialism’s deficiencies. An increasing number of entrepreneurial ventures and organizations pursuing and achieving these types of positive outcomes has the potential to diminish the perceived attractiveness of socialism among US citizens.

In both entrepreneurship theory and practice, the term value proposition captures this principle by stressing both that it is the value of what is offered that is important and that the entrepreneur is limited to making a proposition of value. It is then the customer who decides (for themselves) whether the proposition is good enough to pay the asking price. Value, in other words, is subjective and ex post.

But value is also more than just the product offered for sale. As Kirzner (1973) argued, every cost that an entrepreneur takes on in the enterprise is by definition a production cost - including advertising, which informs customers of the product and its uses (and usefulness, i.e., potential value). Those costs are chosen (meaning: accepted) because the entrepreneur expects them to contribute in some way to the value offering. In fact, the asking price itself - whether it is high or low, higher or lower than competitors - is part of the entrepreneur’s value proposition because of what it communicates. The same is true for the entrepreneur/seller’s reputation, appearance, etc.

In other words, market logic requires that an actor has legitimacy in the eyes of those choosing to deal with them. No entrepreneur can make a proper sale of a good to some customer who considers them (or the enterprise) illegitimate. And no entrepreneur can sign contracts with suppliers or employees unless they, the suppliers and employees, considers the entrepreneur legitimate.

Legitimacy is thus an aspect of market action. It is not a distinct phenomenon (reputation is, but legitimacy itself is not) and is in both fact and theory inseparable from regular market actions. Legitimacy is necessary for market action; illegitimacy prohibits market action. But legitimacy is also, as is value generally, in the eyes of the beholder. Legitimacy is part of the value an entrepreneur offers or, depending on how one slices the concepts, a (necessary) part of the proposition.

Many entrepreneurship scholars seem unaware of how legitimacy plays into and, in fact, is a necessary aspect of market action. It is particularly important for entrepreneurs. This latter point has been made in the literature, but primarily as a justification for importing theories on legitimacy or legitimation from sociology. The conclusion is then that entrepreneurs, who do not yet have an established reputation “in the market” (among potential customers), “struggle” with gaining legitimacy and are fighting an uphill battle of sorts against incumbents and, perhaps, primarily big businesses.

But this misses the fact that legitimacy is not a separate explanans in business practice (and also not in market theory). It is an aspect of specific business situations, an outcome of a multitude other factors such as market actions, reputation, social and economic status, communication ability, haggling skill, etc. Economists such as Hutt (but also many others, today primarily represented by the “Austrians”) recognized this and also properly embedded legitimacy of market actors within the overall market logic.

In contrast, sociologists study legitimacy in the abstract - as a means of social influence or power that is devoid of the market setting. Legitimacy may here have explanatory value when studying power in terms of social and societal structures, which is the avenue that sociologists typically tread. But this is a different concept than legitimacy in markets, which is subject to consumer sovereignty and ex post value determination. Entrepreneurship as a market phenomenon is squarely positioned within the latter. One can perhaps argue that some applications of abstract entrepreneurship are social rather than economic phenomena, as many of our peers do, but the sociologists’ take on legitimacy is necessarily the wrong frame if one studies, whether in theory or empirically, the legitimacy of entrepreneurship as startups or small business or innovation. Those are decidedly market phenomena and embedded in a market setting, in which legitimacy is part of the market logic - not separate sociological social power structures.

It is easy to imagine the pushback from legitimacy scholars to what is stated above. The problem is that most, if not all, of their possible counterarguments would rest on one (or both) of two unfounded assertions: either that entrepreneurship is not (primarily) a market phenomenon, which rejects the history of the term as well as the study of entrepreneurship in both the present and recent past; or, worse, that there is no market logic, which is simply economics denialism. Sociologists, many of whom suffer from an inferiority complex with respect to economists (much akin to economists’ “physics envy”), would certainly make this type of claims.

Entrepreneurship scholars, however, who conduct research within the business school and study primarily business phenomena, have neither the luxury nor the degrees of freedom to make such claims. For us, the core of the entrepreneurship phenomenon cannot easily be separated from the market logic. If at all.

There is nothing wrong with breadth in scholarship or the pursuit of explanations that do not squarely fit within the core of one’s discipline. In fact, it has long been claimed that the interesting questions lie in interdisciplinary research rather than within the traditional disciplinary silos. So broadening our quest for explanations may not be a bad thing. It could rather allow us to better understand business and the economy and as a result help improve our core theories.

Yet breadth has its limits; it is also possible to get spread too thin or to even lose track of the discipline’s core as one pursues adjacent explanations or deeper conceptualizations. Or, as has also been the case, to wander into the territory of other, more established disciplines where we neither belong nor can contribute much. I’m thinking in particular about philosophy, but the same can be argued for other disciplines as well.

Philosophy is a discipline with ancient origins. Needless to say, it has addressed a lot of issues, plenty of aspects and nuances of those issues, settled disagreements and come up with solutions. (There are of course also issues that have not been settled.) Philosophers are trained not only in how to think properly but also in what has been thought through the centuries and millennia - the history of the discipline. It would be difficult to think entrepreneurship scholars (or anyone, really) could teach philosophers in their own discipline. Not impossible, but quite unlikely.

Yet this is what many recent journal articles attempt to do. We should naturally draw from and use tools and frameworks and theories and conclusions from philosophy, and apply them to better understand entrepreneurship. But contributions to the entrepreneurship literature increasingly attempt to figure out the nature of abstract concepts that are of varying relevance to entrepreneurship and, in the process, attempt to take the place of philosophy. To put it differently, some of these articles presume to produce an entrepreneurship ersatz to philosophy’s conceptualizations and answers.

A case in point is the concept uncertainty, which certainly (pun intended) has become core to entrepreneurship theory. But it is relevant to entrepreneurs and our understanding of entrepreneurship in specific ways. It will unlikely improve our understanding of entrepreneurship to spend articles philosophizing on the concept of uncertainty itself, what it means, what is its nature, its boundaries, its implications on the world. This has already been addressed by philosophers in general terms and economists with respect to the economy. What can entrepreneurship scholars contribute by asking the same questions and formulate (the same) answers? (Other than publications to put on one’s CV, of course.)

Philosophical treatments of uncertainty explore its ontological nature; entrepreneurship research should instead examine how uncertainty shapes entrepreneurial decision-making.

The fact is that philosophizing on uncertainty is not only an exercise with dubious value to entrepreneurship, but few entrepreneurship scholars are adequately trained to contribute meaningfully to such philosophical debates. It would better serve us as a discipline and the understanding that we generate to instead import the theories, frameworks, and concepts that already exist and have been well worked out in other disciplines. They are frankly in much better shape than what we can expect to produce ourselves. And it would be a much more effective use of our time and effort (and journal “space”) to apply existing general theories to shed light on specific phenomena than to reinventing wheels.

All that has been written attempting to “figure out” uncertainty in the entrepreneurship journals is not bad, of course, but the point I am making is that some (much?) of it is - it has been done better elsewhere. And even that which is not bad is still wasted effort - because it has been done elsewhere.

The entrepreneurship discipline still lacks a hard core of theories, ideas, concepts, and frameworks - we even lack a generally accepted definition of entrepreneurship. So before we attempt to redo, correct, and replace what other disciplines have already produced, perhaps we should take care of our own house first. And revisit ideas and theories that may have been prematurely discarded.

Taking a step back and strengthening (by reinvestigating, reconceptualizing) the core is often a good idea. It is especially a good idea in a discipline that is as young and, still, immature and underdeveloped as ours. (Even more so, I might add, since we collectively have turned our backs on the long history of theorizing on entrepreneurship, going back at least to the 1730s and Richard Cantillon. But this is a separate issue.)

If we are honest with ourselves, our discipline largely lacks its own theories and framework. We have imported a lot from other disciplines - primarily management, economics, sociology, and psychology - but have not created that theoretical and conceptual hard core that makes a discipline. Our priority should be to fortify the theoretical foundations of entrepreneurship before venturing into other domains.

In many cases, fuzzy logic allows us to define (and measure) concepts with greater precision than does the crisp logic that a majority of scientists (physical and social) rely on. Granted, there is some irony that “fuzzy” logic, or fuzzy anything would provide precision. But despite the label, it provides a structured, formal system for representing gradient boundaries and partial category membership. Instead of forcing concepts into binary categories, fuzzy logic allows us to specify degrees of membership in a category. In other words, it gives us a mathematically precise way to represent ambiguity without pretending ambiguity does not exist.

This is exactly the problem Per identifies when discussing categories such as “old/young” or “entrepreneurial opportunity.” A crisp definition requires a dividing line: perhaps an opportunity is entrepreneurial or it’s not. But where exactly is that line? If launching a radically innovative new venture clearly counts as entrepreneurial, while simply purchasing an already existing franchise doesn’t, what about the countless cases in between? Crisp logic forces us to treat very similar cases as categorically different merely because they happen to fall on opposite sides of an arbitrary threshold.

My recent article with Nobre and Packard argues that this problem is pervasive throughout entrepreneurship theory. We note that “the various constructs foundational to entrepreneurship theory—for example, resource, opportunity, and uncertainty—are all better understood as fuzzy sets.” Rather than treating concepts as either fully “in” or fully “out” of a category, fuzzy logic allows them to possess partial membership. Some ventures could be highly entrepreneurial, others moderately entrepreneurial, and others only weakly entrepreneurial.

Importantly, this does not reduce rigor. It increases it.

The solution is pretty easy, albeit uncomfortable to some of our sensibilities. Fuzzy logic and Fuzzy Set Theory have been right there in front of us for a generation, but we either don’t realize quite what they are, or we don’t have the comfort level to bail on crisp logic and probability theory even when it’s not the best tool (yes, there are other explanations for non-adoption). And, I’ll raise my hand on this one and admit guilt. As a statistician and actuary in my first career, fuzzy logic was miles away from my training, and reputationally, miles away from what I was interested in considering.

Backing up just a bit here might be helpful for some. Lotfi Zadeh, the originator of fuzzy set theory, recognized decades ago that many real-world concepts do not possess precisely defined criteria of membership. The world itself often does not cooperate with crisp categorization. Human cognition naturally handles this through gradient reasoning and natural language: somewhat risky, highly uncertain, moderately innovative, probably feasible, barely profitable. I suspect that a strong majority of us agree in principle with the essence of what Zadeh is getting at here and with his characterization of human cognition and language as they pertain to gradient logics. Indeed, entrepreneurship scholars already use these kinds of gradient descriptions constantly in their theorizing. We routinely talk about high uncertainty, moderate novelty, strong opportunities, or weak signals. Yet we often abandon that realism once we define and operationalize concepts empirically, replacing nuanced gradients with binary cutoffs and artificial thresholds (or just struggle to provide any sort of real precision).

Consider uncertainty itself. Traditional approaches often treat risk and uncertainty as discrete categories (including my own work). But in practice, uncertainty is experienced as a gradient. Entrepreneurs rarely encounter situations that are either fully certain or utterly unknowable. Instead, they navigate varying degrees of uncertainty. As we wrote in the article, “an uncertainty admission is not all-or-nothing but greater or lesser, a fuzzy boundary between completely certain and utterly uncertain.”

That observation matters theoretically because crisp categorizations can create misleading conclusions. Suppose two entrepreneurs evaluate nearly identical situations. One researcher-defined threshold categorizes the first situation as “risk” and the second as “uncertainty,” even though the underlying differences are trivial (although qualitative/categorical differences between risk and uncertainty can be important). The result is conceptual distortion masquerading as precision. Fuzzy logic avoids this problem by allowing graded membership rather than abrupt discontinuities.

The same applies to opportunity. Entrepreneurship scholars have debated for years whether opportunities are discovered, created, subjective, objective, real, imagined, or socially constructed. Part of the reason this debate persists is that “opportunityness” itself behaves like a gradient concept. Some opportunities appear overwhelmingly obvious to nearly everyone. Others are seen only by a few individuals willing to challenge prevailing assumptions. Many reside somewhere in between.

Airbnb is a useful example. Today, it is difficult to imagine modern travel without it. But early on, many people perceived the idea as absurd or unsafe. Was Airbnb an opportunity at that moment? Under crisp logic, we are pressured to answer yes or no. Under fuzzy logic, we can say something far more realistic: the idea possessed varying degrees of perceived “opportunityness” depending on the observer, context, and evolving market understanding.

That realism is what fuzzy logic contributes to theory construction. It allows us to acknowledge that boundaries are often contextual, and they are often gradual rather than abrupt, while still retaining formal rigor.

I also have an ongoing suspicion that part of the reluctance to take fuzzy logic seriously stems from the term itself. Zadeh perhaps did not fully appreciate how unscientific the word “fuzzy” would sound to English-speaking audiences trained to associate scientific rigor with sharp precision. “Gradient logic” or “gradient set theory” might have conveyed the core insight more effectively to modern scholars. After all, the fundamental point is not vagueness, but precision through graded membership. Many phenomena exist on continua rather than within binary boxes.

In fact, Zadeh himself warned against excessive precision in complex systems. He proposed what he called the principle of incompatibility: “As the complexity of a system increases, our ability to make precise and yet significant statements about its behavior diminishes.” This insight even mirrors the paradox raised implicitly in Per’s essay. Precision achieved through unrealistic simplification might actually reduce theoretical significance.

So, I invite a real consideration of what exactly fuzzy logic offers and a thoughtful consideration of how it addresses the “paradox”. Rather than abandoning rigor, it redirects rigor toward modeling reality more faithfully. Instead of pretending that social phenomena possess naturally sharp edges, fuzzy logic allows us to specify realistic gradients and partial membership structures. In doing so, it helps resolve several long-standing problems simultaneously: arbitrary thresholds, weak construct validity, artificial demarcations, and theories that become detached from lived entrepreneurial reality.

Perhaps most importantly, fuzzy logic aligns theory with how entrepreneurs themselves actually think and act. Entrepreneurs rarely operate through rigid binaries. They navigate ambiguity through approximations, intuitive gradations, evolving interpretations, and judgments about what is more or less possible, valuable, innovative, or uncertain. If entrepreneurship theory hopes to accurately describe entrepreneurial cognition and action, our conceptual tools should reflect that reality.

Per is onto something regarding the shortcomings of our definitions. But the next step might be recognizing that the strongest definitions are not always the sharpest ones. Sometimes the most scientifically meaningful boundary is not a hard line at all, but a carefully theorized gradient.

As scholars, we typically define the terms we use for the purpose of not inviting unintended interpretations and, especially, misunderstandings of the argument made. But it goes well beyond providing a selective dictionary to shield us from innocent (or intended) misreadings and misrepresentations. It is not merely that the terms used need to be explained, but that many of the terms we use refer to concepts that are building blocks for theorizing and, therefore, understanding. Unless they are properly and clearly defined, the theorizing based on them fails.

The meaning of definition in this context is often forgotten. When peer reviewers ask authors to define their “core terms,” the issue is not that, for example, the peer reviewer doesn’t understand what is meant by opportunity or startup or entrepreneur. Those terms are easily understood in general, but scholarship is about precision.

For example, we may use a term as “old” to refer to someone who is 80 years of age. Nobody will misunderstand this. The same is true for people who are 85 or 78, they too can be easily and without confusion categorized as also “old.” The contrast to “young,” which we might use to refer to someone who is 18, 22, or 25 years, is also clear. No one would mistake a 25-year-old for “old” or an 80-year-old for “young.”

This is sufficient for everyday conversations. In fact, non-scholars often use examples of obvious cases in lieu of definitions of the terms. Asking someone to define what “old” means, they might say “someone who is like 80 years old or more.” But this is not a definition and it is entirely unsuitable for scholarship. Why is an 80-year-old “old” whereas a 79-year-old is not?

And why is it that a person at 80 years is old whereas a church or findings in an archeological dig are not? It is also not sufficient to say that it is “relative,” because that only raises the question relative to what? An 80-year-old is indeed old compared to a 22-year-old, but so is a 50-year-old. But a 50-year-old is young compared to an 80-year-old. And a 22-year-old might be young also compared to a 25-year-old.

What matters for definitions, which must clarify the meaning, is not what falls within or without - but what constitutes the boundary. If we were to categorize people into old vs. young, where does the 50-year-old fall - in the “old” category together with 80-year-olds or in the “young” category with 22-year-olds? We have to draw the line somewhere, but that line cannot be drawn arbitrarily. In fact, where we draw the line is what makes the definition useful or not.

Definitions of concepts are not about the contrast between what-is or what-is-not, but about what determines the distinction, the basis for making it, and what it divulges in terms of meaning. It is the demarcation that makes the definition, not what cases end up falling on either side of the dividing line.

Getting definitions right is of extreme importance when conceptualizing in social science because the concepts that matter in people’s behavior are rarely physical entities with what appears to be “obvious” boundaries. An astronomer typically does not need to spend much time on explaining what demarcates a star from its surrounding space. Similarly, a biologist might not need to elaborate much on where a virus or body begins or ends.

In social science, however, most concepts do not have limits that appear obvious. There is no physical reality to institutions, behavior, values, etc. and therefore also no obvious way to measure where they begin or end. What is an entrepreneurial opportunity and what is not? It is easy to point to Ford’s Model T or Apple’s iPhone as taking advantage of entrepreneurial opportunities. It is similarly easy to recognize that Elon Musk is an entrepreneur. But where does one draw the line?

Is it an entrepreneurial opportunity to start a new type of restaurant? Maybe. Is it an entrepreneurial opportunity to start a Chinese restaurant to compete with the existing one(s)? Is it an entrepreneurial opportunity to start another Subway or MacDonald’s franchise in a town that already has several? Is it an entrepreneurial opportunity to take over an already existing franchise? What examples fall inside vs. outside of the definition? And, more importantly, what knowledge does the demarcation produce?

The point of a definition is not to state a difference that is obvious, but an exercise in figuring out dividing lines. The essence is the dividing line, the demarcation of what-is from what-is-not and, specifically, what we can learn from drawing this line. But it is not a matter of drawing a line at random to see what, if anything, can be learned from it. The difficulty of definitions is to work out an argument for why drawing the line exactly where it is drawn is not only appropriate but also useful and produces new insights.

In other words, definitions in scholarship are not primarily about defending the argument from misunderstandings or misrepresentations. They are tools in theorizing, building blocks for creating new insights that help us understand the world better. Definitions must therefore not only be worked out clearly but be carefully crafted. Defining is in itself an act of scholarship. It should be taken much more seriously.

What follows is by no means an exhaustive list, but I find these three trends extra troublesome because they all indicate a general change in the ethics of the scholarly enterprise. That is, what used to be the prevailing ethics is changing. These trends, which will have disastrous consequences unless they are properly addressed, suggest a paradigm shift that is not due to a crisis in our theoretical understanding of the subject matter, as is the case with a Kuhnian paradigm, but a changing conception of what the scientific enterprise is about.

First, AI has entered with a vengeance. Whereas we may complain that students are “cheating” by letting AI generate their homework and term papers, the same complaint should increasingly apply to our peers. Sadly, there are quite a few scholars who have no problem - or shame - with submitting fully AI-generated slop for peer review at journals. Much of this is, at least in my experience, from China and other countries that do not have developed scholarly traditions. But this is not limited to them - it’s a general problem. And it is increasing fast.

The problem here is that we can no longer trust that what is submitted under a scholar’s name is also produced by that scholar. As a result, we can also not trust the contents of the articles: that the questions asked are not already answered, that the data supposedly analyzed exist, that the results generated are reliable, or even that the implications stated are relevant.

Second, the service component to academic life is obviously not working anymore. It used to be an unstated rule, if not duty, to “pitch in” to for the good of the scholarly community by reviewing, editing, serving on committees etc. Without payment or direct benefits. But by serving our peers and the community in different roles, putting in the hours and sharing our expertise and experiences, we signaled that we are team players, good colleagues, and place the scholarly enterprise first. Not anymore. Even invitations to review for top-ranked journals are habitually turned down. The organizations have a difficult time finding peers to serve on their boards and in leadership roles. As an increasing number of us do not pitch in to support the community, the burden on everybody else is increased.

The “old” model was to trust scholars to pitch in when and where they can. It was based on a sense of honor and duty among scholars to support the institutions and mentor new generations of scholars. The new model, as far as I can tell, is one of free-riding in which there is no shame in taking out of the system without first adding to it. The result will be that we all suffer from a “tragedy of the commons” (Hardin) or “collective action problem” (Olson).

Third, there’s a sad political turn in our scholarship that is evident in journal submissions, published articles, and calls for special issues. Whereas both sides are guilty of committing such anti-scholarly sins, the major push appears to come from the left. I have seen an increasing frequency in how scholars skeptical of markets and market phenomena for ideological reasons make up “research” or scholarly-sounding arguments that only serve to confirm their views. This brand of “research” hides behind numerous labels but has recently started positioning itself as a critique of an apparent a bias in research that they call “entrepreneurialism” or “the entrepreneur as hero.”

This appears to be merely a new name for the opposition of “neoliberalism,” a term referring to a conceptualization that is so vague and varied - not to mention ideologically loaded - that it is useless. Per Wikipedia,

The term has multiple, competing definitions, and is most often used pejoratively. In scholarly use, the term is often left undefined or used to describe a multitude of phenomena.

Unfortunately, this subpar scholarship, to put it mildly, mysteriously finds reviewers and editors to sign off on the slanted research (presumably for ideological reasons). There is certainly reason to be skeptical of our assumptions, concepts, and constructions, but much of what I have seen has merely asserted the political view and then built a case around it, whether an article contribution or call for papers.

This approach is the very opposite of real scholarship, but like the previous two issues the scholars who commit these offenses seem to not mind. Perhaps they consider their political points to have priority over their scholarly contributions. I will not psychologize the individuals who produce this gibberish and manage to get it published. I certainly recognize that there is (and should be) a place for political rhetoric, including politically motivated research agendas, but it is not within the scholarly enterprise.

These three trends are different but are based in the same core issue: the changing ethics within the scholarly community. Part of it may be due to the publish-or-perish paradigm we are in, which is as subject to (and suffers from) Goodhart’s Law as anything else. But it doesn’t matter where this change comes from. The fact is that it is here and we must deal with it. If we, as a discipline and scholarly community, allow our peers to submit and publish AI slop, shy away from pitching in to support our community, and push politically slanted scholarship through the peer-review process, then we will soon be hopelessly irrelevant. Because we are not actually contributing anything to our understanding of the subject matter.

These three trends, although not exhaustive of the problems we face, are strong indications of a shift that is already underway. They contribute to scholars killing off scholarship. What will we do about it? And what can we do about it?

Almost 30 years have passed since then and much has happened. Per common storytelling about our discipline, we first saw a consolidation around the entrepreneurial opportunity as a construct, then critiques that emanated in the “creation” opportunity of Alvarez and Barney (2007), which started the “opportunity wars” that lasted some two decades. Today researchers have all but abandoned the opportunity construct and moved on to other approaches, theories, and measurements.

This raises the question: what is our scholarly field about? What is the distinctive domain of entrepreneurship as a field of research that has, so to speak, given up on the opportunity?

The entrepreneurship discipline is much more advanced and sophisticated today than it was 30 years ago. We have well-regarded journals and institutional support; we have separate entrepreneurship departments and faculty lines dedicated to entrepreneurship research and teaching; we have undergraduate majors and PhD programs; and we have the attention of donors (and therefore deans) for funding lasting expansions of our programs. But to what end? In terms of the domain of entrepreneurship research, we are untethered – just as scattered, unstructured, and unfocused as a research discipline as we were 30 years ago.

In some ways, entrepreneurship today is in more dire straits than it was back then. The reason is in part that the field is broader and thus encapsulates a greater scope and, therefore, a greater variety of concepts, theories, phenomena, and metrics. There is simply less agreement about what a core to our discipline could be. In part, it is also because we have become even more ignorant of entrepreneurship theorizing in the past 300 decades. Other than largely habitual citations to Cantillon whenever the “uncertainty bearing” aspect of entrepreneurship is mentioned, or Schumpeter for “innovation,” or Kirzner for “alertness,” entrepreneurship scholars suffer from significant historical dementia. Relatedly, we have increasingly turned away from – nay, ignored and even rejected – the history of entrepreneurship research.

Instead of drawing from 300 years of scholarship on entrepreneurship, whether or not we agree on it as a the “driving force” of change in the market economy (as it was often construed), we choose to not even recognize it. But Cantillon built his economics treatise (the world’s first) on the core role of the entrepreneur. Jean-Baptiste Say recognized the entrepreneur as “the principal agent of production” in 1803. Those are but two examples of very early scholars who recognized how dynamic the economy is – and that entrepreneurship is at the very core of explaining the nature, significance, and unfolding of the economy and society.

Entrepreneurship scholars – and our discipline overall – choose to remain ignorant of this impressive 300-year legacy of scholarship, which we can leverage, build on, critique, etc. In fact, judging from contemporary entrepreneurship research, our interests appear to be mostly about psychologizing about specific behaviors that in some way can justifiably be called “entrepreneurial.” I do not intend to say that this is uninteresting or unimportant. Au contraire, it is very interesting and important. But not in a vacuum. And it undermines our raison d’être as a scholarly discipline to do a subset of what psychologists do but slapping the label “entrepreneurship” on it to make it appear more interesting. If entrepreneurship is a label that has no substantive meaning, which I fear could arguably be the case for most articles in entrepreneurship journals, the contributions also mean nothing.

We probably should not engage in a renewed “opportunity wars” situation, albeit it generated important scholarly output. But is it not time to recognize the history of the study of entrepreneurship and claim our legacy?

But if theory is king, the king needs a queen. In our field, that queen is rigorous empirical analysis.

I have heard leading scholars in our field publicly dismiss empirical analysis as easy, suggesting that anyone can do it. While I have great respect for scholars who consistently push the theoretical boundaries of entrepreneurship research, I strongly disagree with that assessment. It understates both the difficulty and the value of serious empirical work.

Sure, many scholars can run a regression in statistical software when they have a clean dataset already assembled for them. But that is not the same as doing rigorous empirical research. Careful empirical analysis requires far more than typing a few commands. It requires thoughtful research design, informed choices about measurement, and sound judgment about model selection and identification. It requires attention to the structure and limitations of the data, to likely sources of bias, and to the assumptions built into the methods being used. It often requires substantial time cleaning, organizing, documenting, and understanding data that are far messier than they appear in published articles.

More than that, good empirical work requires a real base of knowledge. It requires familiarity with statistical theory and counterfactual thinking. It requires understanding when a method is appropriate, what assumptions it relies on, and what kinds of conclusions it can and cannot support. It requires an ability to think carefully about sampling, operationalization, endogeneity, missing data, measurement error, and threats to validity. It also requires substantive knowledge of the context being studied, because empirical choices cannot be made well in the abstract. They have to be made in light of the phenomenon and the data at hand.

As the students in my doctoral seminar in quantitative research methods at the University of Louisville quickly discover, even replicating a published study using publicly available secondary data is in practice far more difficult than it sounds. Real world data are messy. Key operational decisions are often unclear. Variable construction is not always fully documented. Details that seem minor in print often turn out to matter a great deal once

one tries to reconstruct the dataset and analysis from the ground up. In many cases, the rationale for selecting a particular empirical strategy is thinner than it should be. Good empirical work demands a strong understanding of one’s data, careful attention to detail, and methodological competence that goes well beyond software fluency.

That is one reason replication studies deserve more respect in entrepreneurship research than they have often received. Replication is not a lesser form of scholarship. It is one of the clearest ways a field assesses whether its knowledge base is reliable. Careful replication can show whether widely cited findings are robust, whether they depend heavily on particular modeling choices, and whether they hold across different samples or contexts. If we care about cumulative knowledge, replication should be viewed as a meaningful contribution rather than a secondary exercise, and it should be a regular and respected part of our field.

Encouragingly, some journals in our field, including Small Business Economics and Entrepreneurship Theory and Practice, have begun to welcome replication-oriented work and related practices that strengthen transparency and credibility. That is a positive development. Entrepreneurship research will be stronger and more credible if important findings can be reproduced, extended, and scrutinized more carefully.

More broadly, our field would benefit from taking transparency more seriously. Scholars should be clear about how samples are constructed, how variables are operationalized, and why particular empirical strategies are chosen. Readers should be able to understand the reasoning behind the modeling decisions that shape the results and the limitations of these choices. When possible, data and code should be shared. When that is not possible, authors should still provide enough detail for others to evaluate the analysis in a meaningful way.

We should also place greater emphasis on practical significance. Too often, empirical findings are discussed mainly in terms of statistical significance. Yet a coefficient that crosses a conventional threshold does not necessarily represent a meaningful effect. Entrepreneurship research often addresses questions with real economic and social implications. Interpreting the magnitude of effects should therefore be a routine part of empirical reporting.

None of this is meant to diminish the importance of theory. Strong theory remains essential to the progress of entrepreneurship research. Theory gives us the questions, the concepts, and the explanations that guide inquiry. But theory alone is not enough. Empirical analysis is needed to test theory, challenge it, disconfirm it, refine it, and extend it. It is the part of the scientific process that forces our ideas to confront evidence.

That is why empirical work deserves more respect than it often receives in our field. Not because theory matters less, but because a field cannot build cumulative and durable knowledge if evidence is treated as secondary. If theory is king, rigorous empirical analysis is the queen that helps keep the kingdom grounded in reality.

Entrepreneurship scholars certainly do not make this error: we study this very phenomenon that economists leave out. However, entrepreneurship research has over the past couple of decades increasingly adopted an approach that makes another error: studying the Danish prince without reference to the play. Perhaps a better analogy is that we study the queen in chess, the most powerful of pieces, and attempt to understand her moves and motivations without considering the rest of the board or the game itself.

No common denominator?

What I mean by this is that entrepreneurship studied in a vacuum doesn’t mean anything. As a result of studying entrepreneurship without its context, we even struggle to define the phenomenon or identify who is an entrepreneur. We can perhaps agree that Henry Ford and Elon Musk are entrepreneurs, but what about Andrew Carnegie or John D. Rockefeller? And how “entrepreneurial” is the person starting another independent dry-cleaner or Chinese food restaurant? What about a MacDonald’s franchisee? Is a handyman more entrepreneurial offering their services through an LLC than as a sole proprietor? How about an Uber or Lyft driver?

Entrepreneurship as a discipline has wandered into studying specific aspects of “the entrepreneur” - their traits, backgrounds, motivations, and perception - and “entrepreneurship” - social, cultural, institutional, political… But these studies tend to rely on vague definitions or do not offer one at all. So what are we studying? And without a definition of what makes someone an entrepreneur, what does studying “entrepreneurs’” motivations tell us?

A definition is never better than its demarcation criteria, what lies within the definition and what does not.

It is the lack of good definition(s) that has led us to the mess that is entrepreneurship research, where practically any rationalization of what could be conceived as “entrepreneurship” is accepted as sufficient basis for both theorizing and empirical analysis. This, again, raises the question of what our findings mean. If studies define entrepreneurship differently, their conclusions are about different phenomena that are all confusingly referred to as “entrepreneurship.”

What is it that political entrepreneurs, social entrepreneurs, cultural entrepreneurs, innovator-entrepreneurs, investor-entrepreneurs, and businessman-entrepreneurs have in common? Probably very little. The main thing might be that they are all studied by scholars in the entrepreneurship discipline!

The problem is that focusing on only the queen, and with it anything that seems to us queen-like, without considering what makes a queen, the context and framework for its behavior, what can we possibly learn about “her”?

A proud history - if we claim it

Entrepreneurship theorizing has a long and proud history. Going back through the history of thought, we find that entrepreneurship and the study of the economy were one and the same. Already Richard Cantillon, writing his Essai around 1730, famously identified entrepreneurship as the uncertainty-bearing function: entrepreneurs are defined by actions for which there are known costs but unknown outcomes. Whereas Adam Smith did not spend much time on the entrepreneur (or “undertaker,” in his words), entrepreneurship was core to understanding the dynamic, evolution, and mechanisms of the economy to many others. For example, the French economist Jean-Baptiste Say noted that entrepreneurs are those who undertake reallocating resources from valued uses to more valued uses.

In fact, the entrepreneur takes center stage in classical economics (economics before the 1870s). The Danish prince had its proper role in Hamlet; the queen was studied as one of (and the most important) pieces on the chess board.

But even after the marginalist revolution in economics (the 1870s discovery of subjective value and marginal analysis), the entrepreneur played a core role in how many highly influential economists sought to understand the economy. Schumpeter, writing in 1911, identified in the entrepreneur the force that breaks up the status quo and pushes the economy to a higher level of productivity. Frank Knight’s Risk, Uncertainty, and Profit (1921) attempts to explain why entrepreneurship, which cannot earn a market wage, needs to organize a firm for profit. Israel Kirzner explained how entrepreneurship as “alertness” generates a tendency toward general equilibrium.

We do, as a discipline, pay lip service to this history and habitually cite Schumpeter and Kirzner, whose work gave rise to the opportunity construct as originally discussed by Venkat and Shane. But we do not actually use the centuries of scholarship on our subject matter. Certainly, we do not want to be mistaken for economists. But we do not appear to have that problem regarding psychology, management, sociology. Why is that? If anything, our discipline should be better off if we did the exact opposite: adopt the long and proud tradition of entrepreneurship theorizing, albeit by economists, and only sparingly import theories from other disciplines.

It is not because economists, especially the classical or heterodox varieties, have given entrepreneurship more attention, but because entrepreneurship is an inherently economic phenomenon - whether or not it is for-profit actions in a market setting.

Economics-free entrepreneurship at our own risk

Whether we want to or not, our discipline suffers from not acknowledging that entrepreneurship is an embedded phenomenon. It is embedded in an economic context, which means it is subject to scarcity and thus a matter of trade-offs. This does not make entrepreneurship a free-market or for-profit phenomenon, but one that takes place in specific economic conditions and within an institutional framework. Black-market entrepreneurs are just as much entrepreneurs as those starting “legitimate” businesses.

This does not mean we should adopt economic models of general equilibrium or “perfect competition.” On the contrary, those models are based on assumptions that exclude (or expunge) the entrepreneur. But economics is not limited to those (often derided) models, which have only been around for the past several decades. Cantillon, Say, Schumpeter, Knight, and Kirzner were/are all economists, but they theorized on entrepreneurship as an economically embedded function for change and progress.

We should not adopt their theories just because they exist. But we need a framework for proper conceptualization and for developing good theory to explain what entrepreneurship is, what it does, how it does it, and what it implies for society and the world. At present, entrepreneurship does not have an alternative - we have instead, it seems, opted to study entrepreneurship in a vacuum, contextless and without a proper framework for understanding. As a result, entrepreneurship research appears ad hoc and the state of our science is a jumble of “contributions” that point in different directions, are based on vague and varying definitions, and study what seem to be different things. The field’s theoretical contributions are inconsistent and can at times even appear contradictory.

Certainly, entrepreneurship is a young scholarly discipline. But this does not mean it must also be immature. Given the 300-year history of entrepreneurship theorizing, we could claim to be one of the most mature and best-founded research traditions in the social sciences - and beyond. That we are not is a choice. And it is not a wise one.

He isn’t the first to “Boot Scoot Boogie” across genres, as evidenced by Brooks & Dunn, who broke the country purist mold amid controversy in the 90s, or the Bellamy Brothers, who tried, only to see their streak of number one hits end in 1987 as their song “Country Rap” barely reached No. 31 on country radio. Equally skeptical of this genre-blending were rap artists and fans who viewed country music as the Appalachian antithesis of rap’s urban origins.

How the times have changed. Country–hip-hop fusions have spent 15 or more weeks at Number 1 on the Hot 100 over the last few years. Who would have ever thought that Lil Nas X’s 2019 “Old Town Road (Remix)” featuring Billy Ray Cyrus, renowned for his 90s country hit, “Achy Breaky Heart,” would not only kick down a door, but obliterate a divide that was once considered wider than the aisle between Republicans and Democrats?

Clearly, there can be opportunity and value in blending genres to create new categories, but while times have changed in country music, yesteryear is alive and well in the review process of many entrepreneurship, management, and organizational journals, with editors and reviewers sounding more like country music fans from the 1970s than from today.

Consider, for example, a theory paper I recently had rejected for not looking enough like other articles from that journal. Indeed, almost none of the feedback focused on the paper’s content, with the criticism focused on my structural and stylistic choices, not because they were bad or unjustified, but because they were not the norm. What unforgivable sins had this paper committed? '

1. It used fiction as inspiration,

2. it blended theories to raise questions, and

3. it did so creatively, such that it did not resemble the structure of most papers.

As a result, gatekeepers could not allow it to proceed, despite everyone agreeing that it had achieved its objective of generating highly valuable theoretical insights. Indeed, all three reviewers and the editor acknowledged its contributions, repeating them back to me as if they were their own ideas, while praising the paper’s writing and flow, but then the nitpicking started, questioning my stylistic choices, promoting the replacement of them with their own, and confusing their own more conservative preferences for objective standards of quality.

I realize that my angst can be dismissed as sour grapes, but before doing so, consider one comment that captured the sentiment of almost all the others. It still haunts me because it may foreshadow a bleak future for scholars like me who like to challenge structural norms. It focused on the length of my introduction, which numerous scholars had vetted for me before submission.

The problem? The introduction was more than three paragraphs long. Mind you, even Jay Barney, the champion of the three-paragraph introduction, explicitly states - more than once - in his 2018 AMR editorial that his advice is not meant to be an algorithm and that authors should use their judgment. However, with the advent of Large Language Models (LLMs), Barney’s advice is rapidly becoming dogma, enforced by self-righteous reviewers. What was intended as a guideline is applied by many until it becomes an LLM algorithm, enforced by gatekeepers. That algorithmic rule then becomes a norm used to communicate the legitimacy of new research, such that ensuring compliance with that norm becomes more important to gatekeepers than actual evidence of quality scholarship, clearly presented.

Just imagine this dialogue:

Editor and reviewers: This is an amazing song that draws on both country and rap, but we’re only interested in a good country song.

Author: The song is great because it deviates from the structure of the typical country song. It draws heavily on the structure of a rap song while retaining the melodies characteristic of a country song, creating a new category: country rap.

Editor and reviewers: We like the new category and agree that the song is different and great because of it. However, this does not sound like a country song. You might want to use Trace Adkins’s “Honky Tonk Badonkadonk” as an exemplar and rewrite the song. Also, everyone knows that a country song starts with four verses before the chorus. Yours started with the chorus. That was great, but you need to fix it. A country song should start with four verses.

Author: Ugh! Seriously?! “Honky Tonk Badonkadonk” is an assault on my eardrums. “Come on, Billy Ray, maybe the Rap industry wants a piece of this new genre? Lil’ Nas X out!”

It is no wonder our papers still sound more like the simple song structure of a George Jones hit from the 1970s than the innovative, diverse hits of today’s country music. To paraphrase another old country song, “Take this [3 paragraph intro] and shove it!”

There are different ways of creating theory, of course. In the numerous editorials in our most highly regarded journals, senior scholars and editors share their view of what makes a theory and, importantly, a contribution to theory. Typically, this is done through applying “the scientific method” to formulate hypotheses that are then tested empirically on specifically collected data suitable for the task. But this is a practice that we borrowed from the natural sciences, arguably following economists’ “physics envy,” to make our discipline appear more “scientific.”

But the social sciences have always relied on a very different method for uncovering truths about the social world, which is arguably better suited for its dynamic, value-oriented, and behavioral, if not interpretive, subject matters: rationalism. We need theory to demarcate concepts and phenomena, untangle them, and produce explanations.

I mention this difference not as an argument for one or the other, only to illustrate that our theories differ not only in what they study and how they deal with observations. There are more fundamental (and potentially incompatible) differences too.

Add to this that the publishing requirement in our profession (read: “publish or perish”) pushes every one of us to produce numerous “contributions to theory” and to do so regularly. There is nothing wrong with the requirement, considering what theory is and does, but its institutionalization as a prerequisite for a position and currency in the career has made publishing - and thereby theory contributions - subject to Goodhart’s Law: “When a measure becomes a target, it ceases to be a good measure.”

The truth of Goodhart’s Law is behavioral: we measure something to gather knowledge about the state of the world. But when we use that metric as a goal or aim, we produce individual and collective incentives to meet the target. GDP is a good example. It was originally created by the economist Simon Kuznets as a means to approximate an economy’s ability to produce valuable goods, which itself approximates a population’s standard of living. But as GDP became the universal metric of national prosperity, it became a political goal. Consequently, what mattered was to effect higher GDP (the statistic) rather than what it intended to measure (prosperity). As a result, politics has a strong incentive, and thus finds ways, to improve the statistic whether or not it improves people’s standard of living. China’s GDP targets on every political level exemplify this. But it applies to most countries.

Goodhart’s Law in publishing has led to a greatly expanded view of what constitutes a theoretical contribution. To be honest, how many scholars today can explain the difference between a theory, a model, and a hypothesis? Or, to put it differently, what is the difference between theory and the “arrows” between the boxes in one’s paper? (Hint: they’re not the same.)

This theory “inflation” would not be a problem were it not for the fact that we, as a discipline, have also adopted the view that a theory contribution must add to theory. Clearly, this is not true. Einstein did not “add to” Newton. Marginalism in economics did not merely “add to” classical economics. These noteworthy contributions to theory in physics and economics, respectively (both of which so major that they caused Kuhnian paradigm shifts), constituted progress because they replaced prior theory.

In entrepreneurship research, however, we have adopted a faux humility that demands of us that we “build on” and “add to” the theories and explanations that we have accumulated over the decades. Do not point out flaws, challenge, or attempt to replace prior explanations. If you do, not only will your reviewers be the very people whose work you criticize (and therefore will mercilessly shoot it down), but you will not be considered a team player.

Whereas the psychology and institutional incentives are easy to explain, this practice is unsustainable and counterproductive. A discipline is defined by its theory, but what if its theories are inconsistent, build on contradictory assumptions, and are not empirically confirmed? What if physicists were publishing research that draws from whichever parts of Newtonian or Einsteinian physics fit with the thesis without regard for what is demonstrably false? What if economists produced analyses that picked whichever cherries deemed needed to publish from both the classical value theory (debunked in the 1870s) and marginalist theory (the standard since)?

Granted, entrepreneurship has not experienced much of a paradigm shift yet, especially not the type of revolution that both physics and economics have benefitted from (or suffered, depending on one’s perspective). But that is not the point. The issue is that we do not actually have a paradigm. We have a plethora of theories that we use side by side but that we have not bothered (for both good and bad reasons) to assess or evaluate. We certainly have not attempted (or even accept) to liberate our scholarly tradition from theories and speculations that are inconsistent or cannot be confirmed or validated.

I deliberately chose the word liberate to describe the weeding out of “bad” theory, because without the ability in research practice to assess, challenge, criticize, and debunk theories and findings, our body of theory not only retains those errors but builds on and thereby cements and augments them. This is highly problematic, because it raises questions about the reliability and value of the discipline’s entire scholarship. What is our contribution to the world?

A discipline is defined by its theory. Including those errors and misunderstandings that have not been weeded out. We need to find a way to rid our discipline of faulty, “bad,” and insufficiently explanatory theory. And build on and add to the rest.

We should drain our theory swamp, one might say. It would benefit the discipline.

The irony here is quite ‘thick.’ As a phenomenon, entrepreneurship is anything but boring. It involves actors betting on uncertain futures, challenging the status quo, pursuing bold visions that others dismiss as impossible. The entrepreneurs we study embrace creative destruction, pursuing bold bets about future possibilities. And yet, as much as we still enthusiastically explore novel phenomena in our research, field-level risks are growing that our research is becoming boring. By this, I mean that in response to intensifying publish-or-perish pressures, we all face a temptation to ‘extend theory’ rather than challenge, genuflect rather than question, and carefully sand down any hint of genuine debate until only safe, publishable claims remain.

How do we escape this trap? We need to recover the capacity for genuine intellectual debate -- the kind of productive disagreement that might actually advance our understanding of the phenomena we study rather than merely extend our citation counts.

How Our Processes Make Us Boring

To diagnose the causes of field-level ‘boringness,’ let’s first talk about journal submission and review processes. As an author, frequent reviewer, and editor, it is not difficult for me to see situations where editorial and reviewer ‘gatekeeping’ is stifling productive debates. Although we all rightfully claim that ‘rigorous’ reviews are a critical mechanism for quality control, it’s clear that the review process often devolves into ‘rent seeking behaviors’ and paradigm protection. I recognize this is a strong claim, and I want to be careful here. From an editorial perspective, I think most reviewers are acting in good faith, investing enormous amounts of time and energy into unpaid labor to review papers.

The issue here is not that editors and reviewers reject papers -- rejection is essential to enhancing productive debates. The issue is why papers are getting rejected and for what reasons. Manuscripts that challenge dominant theoretical frameworks face steeper odds not always because they are necessarily weaker in methodology or evidence, but because they trigger defensive responses from editors and reviewers deeply invested in the frameworks being challenged. The review process is designed to evaluate the quality of a manuscript’s ‘contribution,’ but quality judgments are inevitably filtered through the reviewer’s own subjective perspectives. When your paper threatens the intellectual real estate of your reviewers, ‘quality concerns’ become remarkably elastic.

As an author, paper rejections are always painful. When you’ve spent hundreds and hundreds of hours working diligently and carefully on research, rejection decisions can ‘feel’ very arbitrary. It’s hard not to take the rejections personal. And while most reviewers are often gracious with their critiques, we have all received ‘valuable and helpful feedback’ from asshole reviewers who tell us to ‘learn how to write’ or even to ‘find a new career.’ Seriously, f*** those people! J

However, petty comments from reviewers are not what I am talking about. My concern here extends more from the perverse incentives that adversarial review processes often create. Scholars learn, often through painful experience, that the path of least resistance involves working within established paradigms rather than questioning them. Yet, even as our own theories teach us that fresh, bold ideas often emerge at the margins -- the kind that might actually advance our understanding – scholars often shy away from pursuing provocative arguments. This is an unfortunate consequence of the adversarial nature of the peer review process.

And yet, expressing bold ideas shouldn’t metastasize into career risks, right? In many recent decision letters – in both R&Rs and rejection letters – I’ve been imploring many authors to be bolder in making important claims with their work. From an editorial perspective, when authors shy away from clearly discussing the implications of a critical argument, contradictory empirical finding, and or even a provocative conclusion, it makes the overall contribution seem weaker and less compelling – perhaps even boring.

How Our Incentives Reward Boring

The problems extend beyond individual review decisions into more systemic patterns. Consider the phenomenon of citation cartels (i.e., networks of scholars who disproportionately cite each other’s work, often regardless of direct relevance, to boost mutual citation metrics). There are a growing number of studies published outside of our field that are documenting the emergence of these patterns across multiple fields. The pressure is real. When citations become currency, some scholars will inevitably game the system.

In entrepreneurship research, the problem manifests in subtler forms. We are a relatively small field with tight networks. The scholar reviewing your paper today may be the colleague you hope to collaborate with tomorrow, or the editor you feel you need to impress for your next submission. These interconnections create powerful, but perhaps perverse incentives for mutual accommodation: cite my work and I’ll cite yours; don’t challenge my framework too aggressively, and I’ll return the favor. The result is not necessarily conscious corruption but rather a gradual drift toward epistemic coziness. Coziness is comfortable. Coziness is also boring.

This coziness feeds what we might call premature epistemic closure -- the tendency to treat open questions as settled before they actually are. In our applied context, premature closure occurs when we stop treating foundational assumptions as open for debate simply because challenging them has become professionally inconvenient. Debates that should remain active go dormant, not because they have been resolved but because people become bored. I think a bit of this happened with the ‘opportunities debate’ that ‘raged’ in the field over the span of about a decade. Others have written more thorough and interesting analyses and post-mortems of this debate, but one question I still keep asking myself is: was it ever really a debate? Apart from several early papers that mentioned the possibility of ‘discovering’ new opportunities, was there really ever a stream of research that carefully developed the discovery perspective? Where was the empirical literature that stress-tested different assumptions between these two ‘competing paradigms?’ Instead, as a field, we developed hundreds of papers ostensibly framed in the creation versus discovery debate, but eventually boredom set in and we moved on to different questions. Fair enough, but perhaps we missed out on an opportunity?

How Our Communication Styles Perform Boring

Even when genuine disagreements do emerge, our discourse practices often undermine and turn people away from productive engagement and debate. I think three patterns are particularly concerning and often turn people away from productive disagreement and debate: the turn to personal attacks, the refuge of excessive hedging, and the paralysis of performative deference.

Challenging bold claims doesn’t mean reviewers should engage in ad hominem attacks –critiques directed at the person rather than their ideas – which represent the most obvious failure mode. In principle, we all recognize this as a logical fallacy. Aristotle catalogued it millennia ago (Side note: RAH – this gratuitous Aristotle reference is for you!). Yet, there are some who can’t seem to help themselves by avoiding personal attacks in journal review processes. Setting aside embarrassingly ridiculous review comments (e.g., telling Ph.D.-trained scholars or Ph.D. students to ‘find a new career’), there is never a good reason to resort to critiquing the person versus the argument during the review process. More insidious, however, is a tendency among some to treat membership in a particular theoretical ‘camp’ as automatically disqualifying. This kind of discourse is not just embarrassing and boring -- it is actively destructive. But it has the same effect: it shuts down the genuine exchange of ideas.

The opposite extreme -- excessive hedging and qualification -- is a more difficult problem to address precisely because it looks like scholarly virtue. Academic writing is prone to cautious phrasing, and some qualification is appropriate. But when every claim comes wrapped in so many caveats that the underlying position becomes indecipherable, we have moved from intellectual humility to intellectual evasion. “It might be suggested that perhaps, under certain conditions and with appropriate caveats, there could potentially be some evidence that possibly supports the tentative conclusion that...” This kind of writing protects the author from criticism by making it impossible to pin down what they actually believe. It is the rhetorical equivalent of hiding behind the furniture. It is also, not coincidentally, profoundly boring to read.

Finally, there is the problem of performative deference -- the tendency to treat established scholars or perspectives with such reverence that genuine engagement and critique become impossible. As an editor and reviewer, I see this so often in literature reviews that merely summarize without critique, in theoretical sections that genuflect before received wisdom of existing theory, and in responses to reviewers that prioritize accommodation over intellectual integrity. Some deference is appropriate, but when deference becomes performative -- when we defer not because we genuinely agree but because we fear the consequences of disagreement – things become weird. I’ve been writing a lot of reviews and editorial letters over the past few years where I have implored authors to ‘push back’ if they don’t agree. Reviewers and editors aren’t co-authors. And unless we want to generate completely boring research, we need to make sure we stay open to new ideas and fresh perspectives.

Why Productive Disagreement Is Good for Business

Having catalogued these problems, let me now turn to the constructive case: how do we build and enhance productive debate and disagreements in entrepreneurship? This is not just a normative question about how science ought to work, but a practical question for how as authors, reviewers, and editors, we can enhance the quality of debate in our field.

Research in the philosophy of science has repeatedly demonstrated that disagreement drives knowledge production. A comprehensive study published in eLife analyzing millions of citation sentences found that disagreement is a common and essential feature of scientific discourse, with rates varying significantly across fields (Lamers et al., 2021). The mechanism is intuitive: when researchers are forced to defend their claims against genuine challenges, they generate better evidence, sharpen their reasoning, and discover flaws that would otherwise remain hidden. Disagreement is not a bug in the scientific process; it is supposed to be a feature.

For entrepreneurship research specifically, it is clear that we need to figure out how to harness productive debate and disagreement to continue to advance our field. We study phenomena characterized by radical uncertainty -- where outcomes are genuinely unknowable and the futures are unpredictable. If any field should be comfortable with intellectual uncertainty and the suspension of premature judgment, surely we can tolerate theoretical uncertainty in our scholarly work. Embracing genuine debate would not only improve our science – I think it would also make us more credible to the practitioners we claim to inform.

Toward a Less Boring Future

What might more productive debates and practices look like? Let me offer a few principles, presented not as a complete solution but as starting points for further discussion.

First, we should continue to develop field-level outlets that protect and reward intellectual risk-taking. On this point, I really like what Rob Pidduck and Dan Clark are doing at JBVi to create a journal-level forum for debates on key topics. We need more of these efforts and initiatives where researchers with opposing views can engage in a public dialogue to surface critical points of theoretical disagreement. Perhaps we should also experiment more with adversarial co-authoring where scholars from ‘competing schools’ work together on papers to surface important areas of disagreement? Surfacing critical points of disagreement is profoundly important work. We would all benefit from more of this type of work, and, I think, be less bored.

Second, as editors and reviewers we need to distinguish between productive and destructive debates and disagreement. As an editor and/or reviewer, it is often quite strange to handle papers that are directly critical of my own work. For all of us, it’s easy to dismiss critical arguments of your research by claiming that the authors are engaging in a strawman fallacy. At the same time, anyone who builds on your work always has to summarize and interpret your 40-page paper and create a much shorter set of arguments. No one will ever fully capture the nuance of your original arguments. Rather than becoming too defensive by dismissing such ‘simple-minded critiques,’ I think it is wise to remember that productive debates are good for business. Motivating research questions effectively can be very difficult, but if there are truly important conflicts between opposing theoretical views or differences in the interpretation of empirical data, these conflicts provide a compelling basis for follow on work.

Rather than constructing a strawmen or questioning the motives of authors, reviewers, and/or editors, productive debates engage with the strongest version of an opponent’s argument. Productive disagreement acknowledges uncertainty and the possibility of being wrong while destructive disagreement claims certainty it does not possess. Productive debate and disagreement treat opponents as colleagues pursuing shared goals through different means while destructive disagreement treats them as enemies to be vanquished. These distinctions are not always easy to maintain in practice, but they are worth striving for.

Third, we should practice what we preach about uncertainty. If we genuinely believe that entrepreneurship involves navigating conditions where probabilities cannot be calculated and futures are genuinely open, we should bring that same epistemic humility to our theoretical commitments. Our frameworks are tools, not monuments. They can and should be tested, challenged, and replaced when better alternatives emerge. The goal is not to be right but to be less wrong over time. In this regard, productive disagreement and debates should continue to acknowledge uncertainty and the possibility of being wrong.

Let me give you a clear example of what I mean here. In a recent article, my co-authors and I argue that the problems of Knightian uncertainty create boundary conditions on the use of artificial intelligence in entrepreneurship. Since we have published the paper, I have reviewed and served as an editor for a growing number of papers that both extend and/or critique some of our arguments. Some of the critiques have been quite helpful and illuminating (e.g., Ramoglou et al., 2025). Others less so.

In my own work as a reviewer and editor, I’ve written strong critiques of some of the interpretations of our work, but at a fundamental level, I welcome these strong critiques. If we are wrong, I want to know why and how we are wrong. A truly productive and adversarial review process should surface weak assumptions, critical insights, empirical puzzles and counterfactuals, and clear insights.

For example, maybe, as some have argued, we need to abandon our preoccupation with Knightian uncertainty as a field? I’m open to these critiques, but what I haven’t seen yet is a credible replacement for Knightian uncertainty. Bayesian frameworks are a very interesting alternative, but what is not clear to me is how Bayesian approaches address the problem of induction when the future is diverging from the present and past due in no small part to entrepreneurial innovation. Saras (2009) builds on the logic Knightian uncertainty to surfaces a related issue in her work on effectuation (i.e., the problem of isotropy). A productive debate between Knightian and Bayesian approaches should surface these differences into analytically rigorous arguments and hypotheses that can be evaluated through empirical studies.

In addition, perhaps our arguments regarding the problems of Knightian uncertainty creating boundary conditions for AI are built on a flawed logical premise that will be falsified when superintelligent AI systems demonstrate the ability to perfectly predict future events? While I think this is incredibly unlikely, challenging myself to think about these counterfactuals continues to force me to think deeply and rigorously about AI ‘epistemology.’ In response, my co-authors and I have started stress testing these arguments with both computational models and a growing number of early-stage empirical studies to shed new light on some of these critical problems. Through these efforts, my co-authors and I continue to learn a lot about the capabilities of AI systems and core challenges entrepreneurs face in using these technologies in entrepreneurship.

An Invitation

So: is entrepreneurship research becoming too boring? It is easy to dismiss these claims – especially if you want to challenge the premise that ‘boring’ research is actually a problem. Clearly my goal here is provocation. But I do welcome disagreement. Tell me where I am wrong. Show me the evidence that our field’s epistemic practices are healthier than I have suggested. Demonstrate that the problems I have identified are aberrations rather than patterns. That kind of pushback would be exactly the point.

Because here is the uncomfortable truth: if we cannot disagree productively about how we produce knowledge, we have little hope of producing knowledge worth disagreeing about. And that would be truly boring!

Peer review is the gold standard in academic research, a mark of approval and quality of one’s scholarship. So strong is its ‘brand’ that, at least among non-scholars, if something is peer-reviewed it is thought of as ‘true’. Similarly, and equally unfounded, what is not peer-reviewed, is ‘false’.

This exaggerated belief in peer review is problematic for scholarship since there is no actual correlation between peer review and truth. The reproducibility crisis is a case in point, which shows that surprisingly many results in published studies cannot be reproduced. This fact has been used by many to deride scholarship in general. But what most of these critics are missing is that scientific progress, and knowledge generation more generally, does not, should not, and cannot rely on whether singular studies are ‘true’, but on the aggregate progression of scholarship towards truth that arises from studies that are not.

Simply put, there is a reason scholars refer to and build on ‘the literature’ rather than singular studies. The former approaches truth; the latter are never true. Newtonian physics was not true but gave rise to many important findings and applications. Einstein’s relativity theory is also not true. Both facilitated generation of greater understanding for how the world works.

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The function and decline of peer review

Peer review was never a way of figuring out whether a study was true. It was a way of weeding out obviously flawed studies, improper use of data or methods, and inappropriate theorizing. To not publish what is obviously not up to snuff improves quality and enhances the process as a result. Peer review is, in fact, a fairly effective means toward that end because it utilizes experienced scholars with relevant and deep expertise in the subject matter to shed light on a study’s quality and contribution to the literature.

Or it was effective. The expansion of academia and its adoption of publish-or-perish has subjected peer review to, and ultimately made it a victim of, Goodhart’s Law: “When a measure becomes a target, it ceases to be a good measure.”

The problem here is that no one involved in the peer-review process has a reason to take it seriously. Journal editors are unpaid for the service and must publish their own research, so they often use peer reviews as “votes” on whether to publish a paper rather than as input about the quality of the contribution. This, in turn, means authors are incentivized to appease reviewers rather than pursue scholarly quality. Reviewers, who, like editors, are unpaid and must publish research of their own, have little or no incentive to spend time and effort to identify or help develop a paper’s contribution. In fact, they benefit from not doing so, which explains why many reviewers treat reviewing as a hunt for errors, real or imagined, to build a case for rejection. This saves time in reviewing and minimizes the chances that the paper gets a revise-and-resubmit decision, which means the same reviewer will be asked to assess the revised version.

The scholarly process is supposed and expected to generate knowledge of the world, to then be shared and applied for the betterment of mankind and society. But can its practice deliver? The incentives suggest that the answer is not obviously yes. Scholars are pressured to produce research quantity, not quality. That voluminous work produced is then assessed by peers, whether as reviewers or editors, with strong incentive to find reasons to reject and move on.

There are many problems that arise out of this messy situation of destructive incentives. But what might be a solution?

Saving peer review

Other than starting anew and risk throwing out the baby with the bathwater, is there a way of saving peer review by restoring its function? I think there is.

The problem with peer review is that the incentives are out of whack. More than that, actually. The incentives are destructive. So the question becomes: is there a way of shifting incentives such that scholars are rewarded from conducting high-quality reviews? This would be an important step toward restoring peer review.

My solution is to make peer-reviewing much more like publishing—by making it publishing. Specifically, I propose that journals publish reviews side by side with the accepted article. Publishers would have fiercely resisted this move decades ago when journals were published on paper and shipped to libraries and scholars. The cost would be too high. But today all journals are published primarily online, so space is not an issue.

There are several benefits from publishing peer reviews with articles. Here are a few:

First, reviewers get something for reviewing. While peer reviews will be unlikely to count for as much an article, it should count for something. And the reviewer will have their name on it, it will be in the journal and recognized by peers in the discipline for the service.

In addition, reviews can be cited by others. A good review may increase a scholar’s impact as measured by citation counts. This may help solve (part of) the problem of getting reviewers to accept reviewing, something I struggle with all the time as journal editor.

Second, reviewers will be held accountable because their reports will be published. This should do away with nasty commentary, error-seeking, and unconstructive reviews. They also cannot be petty or self-serving.

Third, it will bring about a shift of peer review focus toward providing constructive and substantial feedback—scholarly feedback, without nitpicking, comments on spelling mistakes, or empty suggestions. As the peer review report is a critical “mini-article” to be published, it should focus more on suggesting to the author a more appropriate lens, a more useful framework, how to refine the contribution, how to improve the methods or data analysis, etc. The reviewer gets a clear incentive to not only do the review, but to collaborate with the author on making the paper shine. So, reviewing (and, as a result, authoring too) is nudged towards the actual contribution and how to make it an effective one.

Fourth, any “second round” of reviewing becomes a matter of 👍 or 👎 rather than producing another list of changes required to meet the bar. If it will be a matter for peer reviewers at all—the editor should be able (and willing) to make the decision upon seeing the revised paper in light of previous-round reviews. This makes reviewing much less of a chore (only one round instead of 4-5), puts pressure on authors to do a good job revising, and speeds up publication times significantly.

Fifth, the scholarly exchange that happens (or should happen) through the peer review process at journals becomes open for all to see and learn from. In my experience as both author and editor, there is sometimes a very useful and constructive conversation between the authors and peer reviewers. They may not agree, but both perspectives (and the arguments for them) are scholarly valuable. Hiding them in the manuscript management system for no one to see is a complete waste of thinking, effort, and scholarship.

Peer review is dying

Whether or not you agree with the above example, it should be clear that peer review is in poor shape and practically on life support. It is a system based on scholarly honesty and curiosity about what other scholars have accomplished. The publish-or-perish system has undermined these virtues and therefore the system. I am not saying scholars are necessarily dishonest—they just don’t have time to do “service,” which is what academic research depends on.

For example, when I invite reviewers to assess papers for the Journal of Business Venturing, I often have to invite 7-8 scholars to get the required three reviewers. And of those three, it is not uncommon that at least one falls through and never produces a review. This is frustrating for me as editor, for authors who cannot get a timely decision, and for the publisher because it takes so long for papers to be published. This tells me the incentives are wrong.

The solution is to change those incentives.

At present, we are seeing great resistance from researchers, journal editors, and publishers alike. They are certainly embracing AI as a topic used to produce more research, edit/publish special issues, and engage in discussions on its possible impact and implications. That’s low-hanging fruit that fits perfectly with the publish-or-perish status quo. But it changes little in any meaningful sense. It is business as usual.

I will not waste space to discuss how generative AI works, which I have done elsewhere (and many others have too), but note that it can already conduct several stages in the academic research process. And it can do so almost flawlessly. For example, it can inductively analyze data, test hypotheses, choose and do whatever robustness checks are needed, and draw appropriate conclusions from the data. It can also write up reasonably good papers based on the findings. And it can do all of this much faster and more effectively than any human can.

This means we have three options for how we as a research community deal with AI: (1) we can choose to see it as a threat and fight it tooth and nail; (2) we can adopt it as a (limited) tool to speed up the research process, much like how we use statistical softwares and online questionnaires; or (3) we can embrace it and use it to strengthen and update the scholarly process (i.e., innovate). I suggest that these options are also, unfortunately, stages: we are currently at (1), when the battle is lost we will attempt (2), and then desperately fight to not get to (3). And this will be disastrous for research and scholarship.

Academic research desperately needs an overhaul. To use an analogy, we are holding on to how we make buggies and buggy whips whereas Henry Ford has already produced the Model T. That’s obviously unsustainable regardless of how proud we are of our trade and “how we do things.” To use another historic analogy, smashing the Spinning Jenny is not a viable strategy; it is here to stay and will change our trade forever.

To be perfectly clear, I am not suggesting that research needs an overhaul *because* AI can do the same things that researchers currently do. In this sense we are in a very different situation than the analogies above. This is not a change that is forced by technology, but a flaw that technology is (or soon will be) uncovering. In fact, AI will both make it very clear what the problem is and, at the same time, offer an opportunity to solve it. We either seize this opportunity or risk the same fate as the buggy whip makers and spinners. Choosing the wrong approach risks the research enterprise, not merely our jobs. It would be disastrous.

The real issue we are facing is not the “AI threat” but the poor quality of research in the social sciences generally and, especially, the business disciplines. Core to the quality issue is that we are generally not very good at producing theory. (To be blunt, we suck.) This is devastating for a field of study, because, as I put it in a recent paper (Bylund & Packard, 2022), “’Theory’ is explanation of reality and how it works - a causal account of observations.” A scholarly field’s body of theory is not merely a set of hypotheses or stack of papers, but the accumulated and amassed explanation and understanding that we have collectively accomplished through careful, systematic study.

No body of theory is perfect, but the alpha and omega of the research enterprise is to advance it. It is not a consistent advance toward truth as mistakes will be made to later be corrected. But the problem here is not the process but that we have, through poor theorizing, riddled the body of theory with errors, contradictions, and unaddressed gaps.

A partial explanation of this issue is that the business disciplines were originally practical and not theoretical fields. Unlike disciplines like economics there is no consistent framework for study and no shared, abundantly tested, and broadly applied assumptions. The business disciplines lack the advanced and long-exercised theory tradition that economics and other older disciplines have and still benefit from. But these are problems to overcome and they are not issues that haunt us if we do research properly. Unfortunately, this is not the direction we have been moving in.

Business scholars tend to have a skepticism toward theory and theorizing. Many of them argue that there is “too much” theorizing going on in the journals, apparently ignorant of the meaning and use of theory in social science. A field that collects and analyzes data but does not generate theory has accomplished nothing (other than a mass of data). As Ronald Coase once put it (about American institutionalists), “Without a theory they had nothing to pass on except a mass of descriptive material waiting for a theory, or a fire.” This applies to business scholarship as much as to any other field.

The problem with such theory skepticism is that it stands in the way of developing and adopting good theorizing practices, feeds resistance to refine and challenge the body of theory, and perpetuates a relative inability to recognize good and sound theory.

This is also what AI will make abundantly clear: that theories in the business discipline are overall not very good, lack a sound foundation in proper assumptions and logic, and are built somewhat ad hoc and therefore are inconsistent and/or contradictory. This has not been obvious because we produce a lot of studies that test a jumble of hypotheses that are typically produced after the results are known (because they must meet the peculiar requirements of being derived from previous theory, yet be surprising, and supported by the data). Analyses, assessments, and comparisons of theories are largely absent in the literature. There is no consistent framework and no fundamental, generally accepted assumptions.

I expect that we as a collective will, after first attempting to resist AI, use it more broadly as a tool - but without changing how we do research. But this does not constitute a move from abacus to calculator or from calculator to statistics software. It is not merely automation of a manual task. AI adds (granted, imperfect) intelligence that can replace human scholars in most of the research practice. AI can, or will soon be able to, do a better job than human researchers in most or all types of data collection, statistical analysis, and hypothesis testing. We should let it.

What AI cannot do is what we in the business disciplines have largely refrained from doing: construct theory by developing explanations for the mechanisms and causalities that to some extent may be revealed in empirical data. This is the core of scholarship and it is a creative, imaginative, yet deeply insightful enterprise. In an ideal world, one in which the value of what we accomplish trumps all other concerns, we would embrace AI as a means of reengineering the research process such that we can fully take advantage of its power to release us from the tasks that can be carried out by AI.

But this would mean that we also embrace the fact that we and our scholarship are never better than the theories we have generated. And, consequently, that we as scholars must focus on true scholarship: creating explanations and deeper understanding for the world, which can then be communicated and applied to improve people’s lives. Herein lies the promise and threat of AI. It is a promise, because the opportunity is one barely without end - the value of good, relevant theory is nothing short of enormous. It is a threat, because most of us have focused not on theory development and theorizing but on developing skills in manipulating and analyzing data. The latter will in the age of AI be as valuable a skill as hand-spinning in the age of the Spinning Jenny.

To be sure, a scholar must know and understand data analysis and statistics. I am not suggesting that we abandon such knowledge. On the contrary, it is necessary that we have deep knowledge in order to oversee and direct AI to properly conduct those tasks. The problem arises if we expect to continue to be paid to carry out the tasks that AI can do faster, cheaper, and better. There is plenty that scholar in the age of AI can and should do, but it is the creative, imaginative work rather than the semi-manual processing and analysis of data: formulating research questions, developing hypotheses, and, above all, theorizing.

Originally posted on X.

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