California Management Review

American higher education stands at an inflection point. The confluence of declining state support, rising costs, global competition, technological disruption, and eroding public confidence has created what military planners would call a VUCA environment—volatile, uncertain, complex, and ambiguous. In Dynamic Universities, we argue that navigating this environment demands something universities have historically lacked: strategic, entrepreneurial leadership grounded in a coherent management framework. The numbers tell a stark story. State appropriations, which accounted for 46 percent of public university revenues in 1980-81, had fallen to approximately 17 percent by 2020-21. The total cost of attending a four-year public university has risen 163 percent since 1980. Meanwhile, a March 2023 Wall Street Journal survey found that 56 percent of Americans believe a college degree is not worth the time or money—a decline from a decade earlier when 53 percent were confident in higher education’s value. The COVID-19 pandemic served as both stress test and accelerator. More than 1,100 US colleges and universities shuttered classrooms within weeks of the first American death from the virus. Enrollments fell to their lowest level since 2008. The Standard & Poor’s rating service lowered its outlook for more than a quarter of the institutions it rates. As Brian Rosenberg, former president of Macalester College, observed: “If one were to invent a crisis uniquely and diabolically designed to undermine the foundations of traditional colleges and universities, it might look very much like the current global pandemic.” Yet universities also demonstrated their essential value during the crisis. Researchers at scores of institutions turned their attention to understanding the novel coronavirus. Johns Hopkins became a critical resource for policymakers and the public through its Coronavirus Research Center. Research by scientists at the University of Pennsylvania laid the groundwork for the first COVID-19 vaccines. The pandemic underscored both the vital importance of great research universities and the many challenges facing their leaders. Our thesis is straightforward: Universities in the United States and elsewhere have generally not been well managed, and given present challenges, improved leadership and better management is essential. This does not mean universities should be managed exactly like businesses—but they remain unique institutions facing strong competition, and they suffer when they neglect contemporary management concepts and practices. We offer the dynamic capabilities framework as a strategic tool to help campus leaders recognize opportunities, set priorities, execute wisely, and conduct the necessary transformations. The Dynamic Capabilities Framework Dynamic capabilities, first outlined by Teece and Pisano in 1994 and developed more fully by Teece, Pisano, and Shuen in a 1997 article in the strategic management journal, represent an organization’s capacity to sense and shape opportunities and threats, seize those opportunities, and maintain competitiveness through enhancing, combining, protecting, and reconfiguring tangible and intangible assets. The framework was originally developed to explain how Silicon Valley companies succeed and continue to prosper under conditions of technological uncertainty. Today, universities face similar uncertainty—not only technological, but financial, demographic, and political. The critical distinction is between “ordinary” capabilities and “dynamic” capabilities. Ordinary capabilities enable an organization to perform current activities efficiently—what economists call “technical fitness.” Dynamic capabilities, by contrast, enable an organization to change what it does and how it does it—what we call “evolutionary fitness.” In times of stability, ordinary capabilities may suffice. In times of turbulence, dynamic capabilities determine survival. Consider the problem of declining enrollments. An ordinary approach might cut classes, compete on price through lower tuition or increased discounting, and reduce costs to balance budgets. Getting more efficient can happen quickly, and certain elements may be necessary, but efficiency alone is not sustainable. A race to the bottom is not the answer. A dynamic capabilities perspective requires understanding why enrollment is falling and addressing underlying causes. This might mean introducing new programs in areas of high student demand, ensuring those programs align with existing institutional strengths, and orchestrating resources through faculty committees, admissions staff, marketing departments, and financial planners. Leaders must take a systems-level approach, identifying critical internal and external interdependencies. As the Royal Society of Arts advises: “Think like a system, act like an entrepreneur.” The dynamic capabilities framework comprises three clusters of activities: Sensing involves scanning the environment to detect emerging opportunities and threats before they become obvious. This requires what Karl Weick called “sensemaking”—the ability to interpret weak signals and construct meaning from ambiguous information. Universities with strong sensing capabilities monitor technological developments, demographic shifts, policy changes, and competitive moves. They maintain networks that extend beyond campus boundaries and cultivate the intellectual humility to recognize when established assumptions no longer hold. Seizing involves mobilizing resources to address opportunities and threats once they are identified. This requires strategic decision-making, business model innovation, and asset orchestration. Universities with strong seizing capabilities can move quickly from recognition to action. They have governance structures that enable timely decisions, cultures that embrace calculated risk, and leaders who can build coalitions across diverse stakeholders. Transforming involves the continuous renewal of organizational structures, cultures, and capabilities. This is perhaps the most challenging cluster because it requires changing the very foundations on which the institution operates. Universities with strong transforming capabilities can reconfigure their resource base, shed obsolete programs and structures, and build new competencies even when doing so threatens established interests. Berkeley and Stanford: A Tale of Two Universities The contrasting trajectories of Stanford University and the University of California, Berkeley, illustrate how dynamic capabilities shape institutional outcomes. Both are world-class research universities located in the San Francisco Bay Area. Both have produced Nobel laureates, pioneering research, and influential graduates. Yet their approaches to entrepreneurship, industry engagement, and strategic leadership while now converging were markedly different in the past, with significant consequences. Stanford’s transformation from a regional school to a global powerhouse owes much to the entrepreneurial vision of Frederick Terman, who served as dean of engineering and later provost. Terman recognized that Stanford’s future depended on building “steeples of excellence”—concentrating resources in areas where the university could achieve world-class distinction rather than spreading them thinly across all fields. He actively cultivated relationships with industry, encouraged faculty to consult with local companies, and supported the early pioneers of what became Silicon Valley. The creation of Stanford Industrial Park (now Stanford Research Park) in 1951 exemplified this approach. By leasing university land to technology companies, Stanford created a physical infrastructure for university-industry collaboration while generating revenue to support academic programs. The Honors Cooperative Program, launched in the early 1950s, allowed employees of local companies to pursue graduate education part-time, with their employers matching tuition fees. These additional funds were made available to departments to hire additional faculty, strengthening Stanford’s teaching mission while deepening its ties to regional industry. Stanford’s Office of Technology Licensing, established in 1970 under Niels Reimers, formalized the process of identifying commercially promising research and helping faculty capture value from their discoveries. The licensing of the Cohen-Boyer recombinant DNA patents—which earned Stanford and UC San Francisco $255 million by the time the patents expired—demonstrated the potential of this approach and helped launch the modern biotechnology industry. Berkeley, by contrast, has historically been more ambivalent about industry engagement. As a public institution dependent on state appropriations, Berkeley faced different incentive structures and governance constraints. Faculty culture emphasized basic research and skepticism toward commercial entanglements. The university’s complex governance structure, with multiple layers of oversight and shared decision-making, made rapid strategic action difficult. This is not to say Berkeley lacked entrepreneurial faculty or important commercial contributions. Joseph Harris, a Berkeley political science professor, invented the Votomatic punch-card voting machine in the early 1960s, generating considerable licensing revenue. Berkeley researchers have made foundational contributions to fields from nuclear physics to computer science. But the institution as a whole was slower to embrace entrepreneurship as a strategic priority. More recently, Berkeley has moved to strengthen its entrepreneurial capabilities. The SkyDeck accelerator, launched in 2012, provides workspace, mentorship, and seed funding to startups founded by Berkeley students, faculty, and alumni. The Haas School of Business has expanded its entrepreneurship curriculum. The university has worked to streamline technology transfer processes and build stronger connections with Silicon Valley investors and entrepreneurs. Yet Berkeley continues to face challenges that Stanford does not. State funding, which once provided 50 percent of Berkeley’s revenue, now contributes only about 14 percent—making Berkeley, as many observe, almost like a private university but without the governance flexibility or endowment resources that private status would provide. The university must satisfy multiple masters: state legislators, the UC system administration, faculty senates, student governments, and diverse external constituencies. Building consensus for strategic change requires navigating this complex landscape. The Berkeley-Stanford comparison illustrates a broader point: Dynamic capabilities are not simply about having talented people or valuable resources. They depend on organizational structures, governance arrangements, cultural norms, and leadership practices that enable institutions to sense, seize, and transform. Stanford’s advantages in these dimensions have compounded over decades, creating a self-reinforcing cycle of entrepreneurial success. Berkeley’s challenges, while not insurmountable, require deliberate effort to address. Digital Technology: Threat and Opportunity The rise of digital technology presents both existential threats and transformative opportunities for universities. The emergence of massive open online courses (MOOCs) in the early 2010s prompted widespread predictions that traditional universities would be “disrupted” by low-cost online alternatives. Those predictions proved premature—but the underlying forces they identified have not disappeared. Universities with strong sensing capabilities recognized early that digital technology would reshape higher education. MIT’s OpenCourseWare initiative, launched in 2001, made course materials freely available online—not as a revenue-generating strategy but as a way to extend MIT’s educational mission and learn about online learning. This sensing orientation positioned MIT to respond effectively when MOOCs emerged a decade later. Georgia Tech demonstrated strong seizing capabilities with its Online Master of Science in Computer Science (OMSCS), launched in 2014 in partnership with Udacity and AT&T. By offering a rigorous, accredited master’s degree for less than $7,000—a fraction of the cost of comparable on-campus programs—Georgia Tech reached students who could never have attended in person. The program now enrolls more than 10,000 students, making it one of the largest computer science programs in the world. Arizona State University under President Michael Crow exemplifies transforming capabilities applied to digital education. Crow has systematically reimagined ASU’s mission, structure, and operations to serve a broader and more diverse student population. ASU’s online programs now enroll more than 65,000 students. The university has partnered with Starbucks to offer tuition-free degrees to company employees. It has experimented with adaptive learning technologies, competency-based credentials, and other innovations that challenge traditional assumptions about how higher education should work. The emergence of artificial intelligence, including large language models and generative AI, promises further disruption. Universities that develop strong dynamic capabilities now will be better positioned to navigate whatever technological changes lie ahead. Free Speech and Institutional Mission Perhaps no issue has generated more controversy on American campuses than debates over free speech, academic freedom, and the boundaries of acceptable discourse. The Trump administration’s 2025 actions against Harvard and Columbia—freezing federal research funds over allegations of antisemitism and inadequate responses to campus protests—represent the most dramatic government intervention in university affairs in decades. But the underlying tensions have been building for years. We argue that defending free speech and academic freedom is not merely a matter of principle but a strategic imperative. The university’s raison d’être is to be a place for free, open, and reasoned discussion. When that mission is compromised—whether by external political pressure or internal ideological conformity—the institution’s fundamental value proposition erodes. Dynamic capabilities are essential to defending this mission. Sensing capabilities help university leaders recognize emerging threats before they become crises—whether those threats come from government officials, activist groups, or internal factions. Seizing capabilities enable rapid and effective responses when controversies arise. Transforming capabilities allow institutions to rebuild cultures of open inquiry when they have been damaged. The University of Chicago’s 2014 statement on free expression provides a model. The statement affirms that the university’s “fundamental commitment is to the principle that debate or deliberation may not be suppressed because the ideas put forth are thought by some or even by most members of the University community to be offensive, unwise, immoral, or wrong-headed.” More than 100 institutions have since adopted similar statements. But statements alone are insufficient; they must be backed by consistent enforcement and cultural reinforcement. Leadership for the Twenty-First Century What skills do university leaders need to develop and exercise dynamic capabilities? Drawing on research by Paul Schoemaker and colleagues, we identify six essential leadership skills: Anticipate: The ability to detect ambiguous threats and opportunities on the periphery of the organization’s awareness. This requires scanning broadly, building diverse networks, and maintaining intellectual curiosity about developments that may seem distant from immediate concerns. Challenge: The ability to question prevailing assumptions, including one’s own. This requires intellectual honesty, tolerance for dissent, and willingness to consider evidence that contradicts established beliefs. Interpret: The ability to synthesize diverse and often conflicting information into coherent understanding. This requires pattern recognition, tolerance for ambiguity, and the wisdom to know when to act on incomplete information. Decide: The ability to make timely decisions despite uncertainty. This requires balancing deliberation with action, accepting that some decisions will prove wrong, and maintaining the flexibility to correct course. Align: The ability to build coalitions among stakeholders with diverse interests. This requires political skill, emotional intelligence, and the capacity to articulate a compelling vision that transcends parochial concerns. Learn (and Transform): The ability to extract lessons from experience and apply them to future challenges. This requires systematic reflection, honest assessment of failures, and willingness to change established practices when evidence warrants. These skills map directly onto the dynamic capabilities framework. Anticipate, challenge, and interpret support sensing. Decide and align support seizing. Learn and transform support the transforming cluster. Conclusion: Evolutionary Fitness for an Uncertain Future Universities have survived for centuries by adapting to changing circumstances while preserving their core missions of research, teaching, and service. The challenges they face today—financial, competitive, technological, and political—are formidable but not unprecedented. What is required is leadership equal to the moment. We offer the dynamic capabilities framework as a practical tool for campus leaders and trustees. It provides a conceptual lens for understanding critical issues and a useful guide for prioritizing competing demands. Most importantly, it focuses attention on evolutionary fitness—the capacity to adapt and shape the environment—rather than mere operational efficiency. The era of flush budgets, soaring enrollments, and unchallenged international dominance is over. Universities that cling to business as usual will find themselves increasingly marginalized. Those that develop strong dynamic capabilities—the ability to sense emerging opportunities and threats, seize them through decisive action, and transform themselves when circumstances require—will not merely survive but thrive. If university leaders ever had a quiet life, it is no more. The dynamic capabilities framework offers a way forward for those willing to embrace the challenge of strategic, entrepreneurial leadership.

Every startup is told to clearly emphasize its distinctiveness from existing competitors. But when we studied over 31,000 UK startups, we found that many of the most innovative and well-resourced ones describe themselves in strikingly generic terms. Rather than being a failure of communication, this can be a viable strategy for certain startups. In industries awash with investor attention, resource-rich startups gain little from emphasizing their distinctiveness and doing so may even backfire. Symbolic differentiation becomes most important when startups cannot rely on industry buzz and quality signals alone to attract attention. When startups seek investment, one piece of advice is near-universal: make clear what differentiates you from everyone else in the market. Entrepreneurs are therefore often coached to strategically emphasize their startup’s distinctiveness when communicating to potential stakeholders. But when we systematically studied how startups actually describe themselves, a puzzling pattern emerged. In a recent study, published in the Academy of Management Journal, we analyzed how over 31,000 startups described themselves on their websites to understand how much they emphasized their distinctiveness from competitors — what we call symbolic differentiation. All of these startups were founded between 2010 and 2021 in the UK and signaled a clear ambition for growth. What we found challenges the standard advice given to startups: many of the most resource-rich startups — for instance, those with large patent portfolios — did not engage in symbolic differentiation and rather made relatively generic claims about themselves. This is surprising because those startups would be precisely the ones best positioned to convey their distinctiveness. Take DeepMind — one of the most innovative AI ventures of the past decade, backed by over 300 patents and billions in funding. Before being acquired by Google in 2014, DeepMind described itself in relatively generic language that was largely indistinguishable from that of many of its competitors in the AI space, such as being on “a scientific mission to push the boundaries of AI”. In fact, our analysis based on sophisticated computational text analysis methods suggests that DeepMind engaged in less symbolic differentiation than around 84% of all startups in our sample. And DeepMind is not an outlier; it is emblematic of a broader pattern we uncovered across thousands of startups in industries ranging from fintech to renewable energies. What explains this? Not a failure of communication, and not excessive modesty. The answer, our study suggests, lies in the distinction between the ability to claim distinctiveness and the incentive to do so — and in recognizing that for many startups, those two things point in very different directions. Ability Isn’t Everything The standard advice given to startups rests on an assumption so widely shared it rarely gets examined: that startups always have clear incentives to emphasize distinctiveness from their competitors. Hence, researchers and startup advisors alike tend to interpret a lack of differentiation as a failure on the part of the entrepreneur. As Seth Godin famously put it, “in a crowded marketplace, fitting in is failing” — a maxim that startup advisors have repeated so often it has become the default framing for any conversation about positioning. Academic research on how entrepreneurs effectively communicate their startup ideas when talking to potential stakeholders largely reinforced this assumption. The prevailing view holds that founders need to convey their startup’s distinctiveness to stand out and convey competitiveness. From this perspective, startups’ existing resources are seen as the ingredients that allow startups to make convincing claims about their distinctiveness. Startups that hold unique resources, the logic goes, are best positioned to convincingly communicate their distinctiveness. This logic is intuitive. It is also incomplete. What the perspective misses is the distinction between startups’ ability for symbolic differentiation and their incentives to do so. A startup may be perfectly positioned to symbolically differentiate itself — e.g., by simply communicating the distinctive aspects of their technological invention — and yet face an environment in which symbolic differentiation would provide limited benefits. When the external conditions already ensure that the startup will attract favorable attention from investors and other stakeholders, it no longer needs to fight for attention. In fact, startups with highly distinctive resources can even have incentives to actively downplay their distinctiveness. When newly entering an industry, highly innovative startups sometimes rather face the challenge to be recognized as a legitimate player in their industry – and efforts to emphasize their uniqueness may therefore be counterproductive. This reveals a fundamental tension at the heart of startup positioning: the unique resources that enable symbolic differentiation are often the same resources that make it unnecessary — or even counterproductive. This insight shifts attention from startups’ abilities to their incentives for symbolic differentiation. And once you look for the incentive structure, two factors dominate the picture: how much attention investors are paying to the startup’s industry and how its existing resources already signal the startup’s competitiveness. Together, these two factors shape whether a startup has much to gain from symbolic differentiation — or whether doing so would be redundant, and perhaps even backfire. Different Contexts, Different Incentives Our study identifies two distinct scenarios in which startups have clear incentives to emphasize their distinctiveness: 1) Signaling competitiveness in a hot industry. In industries flooded with investor attention – what we call hot industries – startups with fewer resources face a specific challenge: they must prove they can compete with better-resourced players. Think of industries like AI, fintech, biotech, or renewable energy. Symbolic differentiation helps these startups to position themselves as a serious contender in their industry. By communicating a unique competitive positioning, they also signal a plausible pathway toward a competitive advantage. The goal is not to stand apart from the industry but to be seen as a competitive player within it. Consider, for instance, GenomeKey — a biotech startup that lacked the patent portfolios of its better-resourced competitors. Rather than merely positioning itself as a proper biotech company, GenomeKey presented a punchy self-description that explicitly emphasized how its product differs from that of its competitors. For instance, GenomeKey emphasizes that its DNA sequencing technology “diagnoses in hours, not days.” In a hot industry where attention is abundant but competition fierce, symbolic differentiation often serves as a primary tool for signaling competitiveness against better-resourced rivals. 2) Signaling disruption. In industries that startup investors tend to ignore, startups need to fight for any attention at all. When competing in an industry like waste management, construction, or food & beverage manufacturing, merely being recognized as a competitive player rarely makes a startup an attractive investment target. Here, symbolic differentiation serves as a signal of disruption — a declaration that the startup departs from conventional approaches of its industry. The goal is not just to differentiate from competitors but to clearly emphasize how the startup departs from the industry’s conventional approaches. The goal is to appear fundable to investors despite competing in an industry that is seen as relatively unattractive. A case in point is Brightwater, a Scottish water services company. In its self-description, Brightwater declared itself as “an ambitious challenger” that explicitly rejects the industry’s prevailing business model (“We don’t believe in the current ‘switch and forget’ approach to the industry”), while emphasizing its unique approach to water services as “its key differentiator”. Lacking the gravitational pull of a hot industry, startups like Brightwater have to clearly communicate why investors should take notice and take a chance on them. For such startups, symbolic differentiation becomes one of the few tools available to attract the attention of investors who might otherwise overlook the sector entirely. In both cases, startups have a clear incentive to strategically emphasize their distinctiveness even as they do so to address different challenges. But these two scenarios cover only the startups that need to actively fight for attention – what about those startups whose resources already allow them to stand out in in hot industry? When Startups Lack Incentives for Symbolic Differentiation One of the study’s central findings is that resource-rich startups in hot industries are systematically less likely to symbolically differentiate themselves than other startups. In fact, our study shows that startups emphasize the least distinctiveness when they hold a lot of quality-signaling resources that are rare in their industry. Their unique resources already position them as a very competitive contender in an industry poised for growth, already attracting a lot of attention from investors. As they lack strong incentives for symbolic differentiation, resource-rich startups in hot industries don’t ask “how can we differentiate ourselves as much as possible from our competitors?” but often rather seek to ensure that investors recognize them as a proper member of their industry. At the same time, these startups can let their unique resources speak for themselves. Consider MiroBio – a biotech startup that was spun out of the University of Oxford, raised over £50 million in its first years and was later acquired by Gilead Sciences for $405 million. The startup described itself in relatively generic terms that do not indicate any differentiated positioning and may equally apply to dozens of its competitors in the biotech industry (“Our mission at MiroBio is to deliver transformational immunotherapies for people suffering from autoimmune disease”). In a hot industry awash with investor capital, the startup’s ties with a prestigious university and existing patents were sufficient signals to attract interest from investors and corporate partners alike – symbolic differentiation would have added little value. Differentiation Incentives Shift Over Time These incentives are not static. When a once-hyped industry cools down — as investor enthusiasm wanes or media attention shifts elsewhere — startups that previously relied on industry buzz to attract attention may find themselves suddenly fighting for attention. For instance, in another study, we found that startups in the market for online learning platforms did not benefit at all from emphasizing their innovativeness as long as the market was hyped. Yet, once the excitement around the market had cooled off, startups in that space attracted substantially more customers if they made explicit claims about their uniqueness and innovativeness. Similarly, once a previously resource-poor startup has developed a unique resource portfolio, it no longer faces the same incentives for symbolic differentiation. For instance, a startup like GenomeKey started to describe itself in more generic terms after it had raised millions in funding and innovation grants. The optimal positioning strategy is therefore a moving target. Founders need to continuously reassess their evolving incentives for symbolic differentiation. Takeaways for Entrepreneurs and their Advisors Our study suggests that the conventional startup advice to emphasize distinctiveness may be counterproductive for those startups that already stand out due to their resources. When working on their website’s About Us page, social media presence, or pitch deck, founders need to strategically consider the specific benefits they seek from symbolic differentiation. When reflecting on how to publicly present their startup, founders should start by asking themselves two related questions: Is our industry hot right now? Do we have the necessary resources to stand out as a high-quality player in our industry? A startup that answers ‘Yes’ to both questions does not need to lead with how different it is — investors in that context aren’t struggling to take notice of viable new competitors in the space. Looking and sounding like a credible industry insider may thus be more beneficial than forceful efforts to claim a unique positioning within that industry. When publicly describing themselves, these startups can use vocabulary that aligns with industry conventions, while refraining from specific differentiation claims. Counter to common advice, a lack of strategic differentiation may thus be a viable strategic choice for these startups. Getting this calibration wrong carries real costs: a resource-rich startup in a hot industry that aggressively differentiates may signal to investors that it doesn’t understand its own market, while a startup in a cold industry that blends in risks being overlooked entirely. Conversely, startups answering ‘Yes’ to the first but ‘No’ to the second question should strategically think about how they can communicate their distinctiveness and convey a unique positioning within their industry. For founders of such startups, it pays off to invest effort in crafting a compelling narrative that helps stakeholders easily recognize how the startup’s value proposition differs from those of its more endowed competitors. Founders lacking unique resources need to recognize the need to differentiate – and the central role that public communications play in their differentiation efforts. Startups who answer ‘No’ to the first question should reflect about how their self-description can convey their intention to shake up their industry to convey their fundability. In such industries, emphasizing how their approach departs from the conventional industry model can help investors understand why they should even consider investing in an otherwise stagnant industry. The incentives to craft a compelling differentiation narrative are particularly strong for startups that also answer ‘No’ to the second. For these startups, a well-crafted narrative that plausibly positions them as an ambitious disrupter can often become the only chance to attract investors’ attention. For accelerators and other entrepreneurial support organisations, the findings challenge one of the most common elements of startup coaching: when it comes to differentiation and positioning, startups need more tailored advice than a blanket encouragement to emphasize distinctiveness. Startups systematically differ in their differentiation incentives and positioning challenges, and startup advisors can provide a lot of value by helping entrepreneurs to understand and address their startup’s unique positioning challenge. Applying the same positioning playbook across cohorts with radically different contexts risks pushing many startups in the wrong direction. In a world where startup advice often defaults to “differentiate or die,” the truth is that not emphasizing their distinctiveness can be a smart positioning move for some startups. Before seeking to differentiate their startup, entrepreneurs should first reflect on whether doing so is in their best interest. References Karl Taeuscher and Michael Lounsbury, “It Is Not the Whole Story: Toward a Broader Understanding of Entrepreneurial Ventures’ Symbolic Differentiation,” Academy of Management Journal 68, no. 3 (2025): 648–668. Karl Taeuscher and Hannes Rothe, “Entrepreneurial Framing: How Category Dynamics Shape the Effectiveness of Linguistic Frames,” Strategic Management Journal 45, no. 2 (2024): 362–395. Further Reading Rodolphe Durand and Richard F. J. Haans, “Optimally Distinct? Understanding the Motivation and Ability of Organizations to Pursue Optimal Distinctiveness (or Not),” Organization Theory 3, no. 1 (2022). Joel Gehman and Matthew Grimes, “Hidden Badge of Honor: How Contextual Distinctiveness Affects Category Promotion among Certified B Corporations,” Academy of Management Journal 60, no. 6 (2017): 2294–2320. Richard F. J. Haans, “What’s the Value of Being Different When Everyone Is? The Effects of Distinctiveness on Performance in Homogeneous versus Heterogeneous Categories,” Strategic Management Journal 40, no. 1 (2019): 3–27. Michael Lounsbury and Mary Ann Glynn, “Cultural Entrepreneurship: Stories, Legitimacy, and the Acquisition of Resources,” Strategic Management Journal 22, no. 6–7 (2001): 545–564. Michael Lounsbury and Mary Ann Glynn, Cultural Entrepreneurship: A New Agenda for the Study of Entrepreneurial Processes and Possibilities (Cambridge University Press, 2019). Chad Navis and Mary Ann Glynn, “Legitimate Distinctiveness and the Entrepreneurial Identity: Influence on Investor Judgments of New Venture Plausibility,” Academy of Management Review 36, no. 3 (2011): 479–499. Lingling Pan et al., “Sounds Novel or Familiar? Entrepreneurs’ Framing Strategy in the Venture Capital Market,” Journal of Business Venturing 35, no. 2 (2020): 105930. Elizabeth G. Pontikes and William P. Barnett, “The Non-Consensus Entrepreneur: Organizational Responses to Vital Events,” Administrative Science Quarterly 62, no. 1 (2017): 140–178. Yuliya Snihur et al., “Entrepreneurial Framing: A Literature Review and Future Research Directions,” Entrepreneurship Theory and Practice 46, no. 3 (2022): 578–606. Karl Taeuscher et al., “Gaining Legitimacy by Being Different: Optimal Distinctiveness in Crowdfunding Platforms,” Academy of Management Journal 64, no. 1 (2021): 149–179. Karl Taeuscher et al., “Categories and Narratives as Sources of Distinctiveness: Cultural Entrepreneurship within and across Categories,” Strategic Management Journal 43, no. 10 (2022): 2101–2134. Eric Y. Zhao et al., “Optimal Distinctiveness: Broadening the Interface between Institutional Theory and Strategic Management,” Strategic Management Journal 38, no. 1 (2017): 93–113. Christoph Zott and Quy N. Huy, “How Entrepreneurs Use Symbolic Management to Acquire Resources,” Administrative Science Quarterly 52, no. 1 (2007): 70–105.