Karthik Ramachandran

Problem definition: Pivoting is a significant shift from the current product under development to an alternative opportunity. Pivoting is strongly advocated in the popular press and is commonly used in entrepreneurial product development. However, a high proportion of startups that pivot fail subsequently because many entrepreneurs chase pivots that are ultimately not fruitful. We explore how an entrepreneur’s level of experience influences the decision to pivot. Experienced entrepreneurs, possessing a clearer perception of potential obstacles and opportunities, tend to exhibit reduced overconfidence. Methodology/results: We develop a decision-theoretic model to analyze the entrepreneurial choice regarding pivoting, while accounting for overconfidence and market and pivot opportunity uncertainties. We show that lack of experience, that is, a higher level of overconfidence, can systematically influence pivoting decisions. The effect of overconfidence on a pivot decision depends on the market uncertainty of the pivot opportunity relative to that of the already initiated product. More specifically, we show that novice (more overconfident) entrepreneurs are likely to make radical risky pivots, whereas experienced (less overconfident) entrepreneurs are more likely to make safe pivots. We find that the dominant development errors for inexperienced founders relate to pivoting and persisting when faced with risky and safe pivots, respectively. Furthermore, we show that if pivoting can be postponed until after the product launch, novice founders are more likely to postpone safe pivots than experienced founders. We also explore the effects of different types of experience on the entrepreneur’s pivoting decisions. Managerial implications: Our results can benefit entrepreneurs in deciding how to set the duration of their supply chain contracts, choose the types of employees to hire, and select appropriate mentors, based on the experience level of the founders and their likely pivot behavior. For startup incubators and accelerators, our results indicate what type of training programs to provide for entrepreneurs when accounting for their experience level. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.1178 .

The case method has been successfully used in operations management courses and is central to business education, yet its effectiveness depends on consistent student preparation. Many students engage only superficially with cases, and the rise of generative artificial intelligence (AI) tools, such as ChatGPT, further complicates the challenge—providing shortcuts that yield summaries without fostering deeper understanding of trade-offs. This study investigates whether AI can instead be used to strengthen the case method as a pedagogical tool. Partnering with an AI-based upskilling platform, we conducted a field experiment using an AI-supported “quest” structure. We found that students achieved higher quiz scores when using the AI tool to support case preparation than when completing the same type of task without AI-supported preparation. For a group of 38 participants, on average, AI use was associated with an improvement of almost 1 point on a 10-point quiz. This difference was statistically significant using a paired t-test (t(37) = 4.50, p < 0.001). In addition, scores in the AI-supported condition were statistically less variable (standard deviation (SD) = 2.29 versus SD = 1.37), suggesting that AI support may help promote more consistent learning outcomes across students. These findings suggest that rather than undermining the case method, generative AI can enhance case learning when integrated deliberately into preclass preparation.

Problem definition: Pivoting is a significant shift from the current product under development to an alternative opportunity. Pivoting is strongly advocated in the popular press and is commonly used in entrepreneurial product development. However, a high proportion of startups that pivot fail subsequently, since many entrepreneurs chase pivots that are ultimately not fruitful. We explore how an entrepreneur's level of experience influences the decision to pivot. Experienced entrepreneurs, possessing a clearer perception of potential obstacles and opportunities, tend to exhibit reduced overconfidence. Methodology/results: We develop a decision-theoretic model to analyze the entrepreneurial choice regarding pivoting, while accounting for overconfidence and market and pivot opportunity uncertainties. We show that lack of experience, i.e., a higher level of overconfidence, can systematically influence pivoting decisions. The effect of overconfidence on a pivot decision depends on the market uncertainty of the pivot opportunity relative to that of the already initiated product. More specifically, we show that novice (more overconfident) entrepreneurs are likely to make radical risky pivots, whereas experienced (less overconfident) entrepreneurs are more likely to make safe pivots. We find that the dominant development errors for inexperienced founders relate to pivoting and persisting when faced with risky and safe pivots, respectively. Furthermore, we show that if pivoting can be postponed until after the product launch, novice founders are more likely to postpone safe pivots than experienced founders. We also explore the effects of different types of experience on the entrepreneur's pivoting decisions. Managerial implications: Our results can benefit entrepreneurs in deciding how to set the duration of their supply chain contracts, choose the types of employees to hire, and select appropriate mentors, based on the experience level of the founders and their likely pivot behavior. For startup incubators and accelerators, our results indicate what type of training programs to provide for entrepreneurs when accounting for their experience level.

We examine how entrepreneurs optimize their search strategies when using Large Language Models (LLMs) for technology commercialization. The fundamental tension arises from using standardized AI tools to create competitively differentiated value, especially when a contest selects a broad set of performers on a podium, rather than a single winner. We take a two-step approach to this problem. We first build a formal model to characterize podium placement in terms of four variables: contest seeds, friction in using LLMs, workarounds, and product definition capability, when contestants follow either breadth first (B) or depth first (D) search strategies while using LLM. The model separates expected performance from podium-placement probability and shows how contest selectivity influences the efficacy of search strategies. In the second step, we run a randomized control trial to reveal a sharp distinction between process inputs and podium placement. The empirical findings qualify the model-based predictions. When every contestant uses the same LLM, friction, number of ideas, level of LLM usage, and workaround levels are statistically indistinguishable across treatments and performance quartiles. What predicts top-quartile placement is competitive awareness-the ability to recognize what differentiation the contest demands-exceeding the predictive power of product sophistication, elaboration, or prompting behavior. Search strategy shapes not just how many ideas a contestant generates, but whether the contestant perceives the competitive pressure the contest is applying.

As entrepreneurship education has become increasingly prevalent in higher education, its broader consequences for students' career development have attracted growing attention. Supporters argue that entrepreneurship programs generate long-term value by cultivating transferable skills that benefit participants beyond venture creation. Critics, however, contend that such programs may crowd out other educational investments and potentially undermine academic and labor market outcomes. Despite ongoing debate over its net impact, existing research has focused primarily on whether entrepreneurship education increases entrepreneurial activity, leaving its effects on broader career trajectories underexplored. Drawing on ten years of longitudinal data from a university startup incubator, this study examines the impact of an entrepreneurship education program by comparing the post-graduation career trajectories of accepted and rejected applicants. The results show that exposure to entrepreneurship education does not necessarily increase entrepreneurial activity immediately after graduation. However, participation is associated with sustained career advantages, even among students who do not pursue entrepreneurship in the short term. Specifically, participants demonstrate better job matching, as reflected in higher earnings, longer tenure with their initial employers, and greater stability in occupational type. These findings provide empirical evidence that entrepreneurship education can generate enduring positive returns in the broader labor market. They also offer important implications for universities and organizations evaluating whether to invest in entrepreneurship programs, suggesting that the value of such programs extends beyond the creation of new ventures.

Universities play a key role in research commercialization through patenting, licensing, and start-up creation, making performance evaluation in this area essential. A common approach is to benchmark universities against peer institutions. We propose a relative benchmarking framework that aligns with universities' emphasis on rankings. Our framework builds on Data Envelopment Analysis (DEA) for its objectivity. While standard DEA prioritizes maximizing efficiency scores as a measure of productivity, it does not necessarily yield the best possible ranks. Given universities' strategic focus on rank, we develop a rank-oriented version of DEA and uncover a counterintuitive trade-off: rank-oriented DEA produces better ranks at the expense of a lower efficiency scores compared to the standard efficiency-oriented DEA. Using data from the AUTM Licensing Survey, we examine two strategies: (i) uni-dimensionality (concentrating on a single commercialization output) and (ii) multi-dimensionality (diversifying the effort across multiple commercialization outputs). We find that better performance under our rank-oriented benchmarking framework is associated with greater multi-dimensionality, which expands a university's flexibility in weight selection and improves its standing with a broader set of peers. We find that our objective ranking system, unlike a subjective one, assigns more favorable evaluations to universities that exhibit exceptional productivity in either total patent applications or licenses issued dimensions, because it allows the ranking to recognize meaningful strengths rather than diluting them through subjective aggregation.

Consider a client who has a problem she cannot solve by a given due date, and a consultant engaged to help. The consultant sets the price for knowledge outsourcing to maximize his profit. The client sets the quantity of knowledge outsourcing and the portion of the problem she solves in-house to minimize her cost. The client benefits from knowledge outsourcing only to the extent that she has absorptive capacity to understand and utilize that knowledge. In this context, absorptive capacity is driven by the client’s relevant knowledge and the working relationship between the client and the consultant. We examine how absorptive capacity impacts the key operational decisions of both the client and the consultant. Motivated by the experience of one of the co-authors, we introduce a deterministic model and two stochastic extensions that are critical in industries including semiconductor manufacturing. While we find that more absorptive capacity always reduces the client’s total cost; under certain conditions, the consultant’s profit may deteriorate. If project scope uncertainty exists, we find that, in some competitive environments, the availability of an outsourcing option can indirectly incentivize a client to take on more risk. If project completion time uncertainty exists, we show that a client employs a more experienced consultant and forgoes control of the project to lower her expected total cost. Surprisingly, in a time-based competitive environment, the more experienced consultant increases his price; whereas if time-based competition is limited, the consultant’s price is lower. Lastly, extensive numerical experiments are developed based on the synthesis of the deterministic and the two stochastic models. The numerical experiments demonstrate the robustness of the analytical results. Additionally, numerical results are obtained whereby, prior to the start of the game: (i) the client may invest to increase her initial knowledge or (ii) the client and the consultant may invest to improve their working relationship.

For products developed through a collaboration of specialized groups, success requires craft and engineering ingenuity from designers who focus on specific attributes, coupled with synchronized coordination between various design groups. Although shared work experience in the team—or familiarity—can improve coordination, it can deter creativity. We empirically study the impact of team familiarity on market performance of products by focusing on product development in the video game industry by coupling a granular database of development credits with sales data for video games. Although familiarity among coordinators has a positive effect on performance, familiarity among designers has a negative effect. Further, the positive (negative) effect of familiarity among coordinators (designers) is present even if familiarity is built through roles other than coordination (design). Overall, team familiarity is associated with a decrease in the product's market performance. Our results suggest that infusing fluidity in design roles and maintaining stability in coordination roles could improve the performance of new products.

Problem definition: Facing emergent business challenges, entrepreneurs often seek guidance from experienced advisors. When multiple alternatives could potentially solve the entrepreneur’s problem, advisors can lead the entrepreneur’s exploration by choosing which alternative(s) to suggest and in what sequence. Methodology/results: We develop a dynamic game-theoretic model that captures the sequential interaction between an advisor and an entrepreneur. The advisor chooses how to recommend alternative solutions, and the entrepreneur chooses which solution to try. The trial’s success depends on the viability of a solution and the entrepreneur’s execution capability. When a trial of a recommended solution fails, the belief about the viability of the solution is updated. Managerial implications: Our analysis reveals that the advisor should strategically recommend alternatives based on the entrepreneur’s execution capability, trial costs, and correlation between alternatives (among other factors). When the trial of the first alternative fails, the advisor should readily offer a new alternative if the entrepreneur’s capability is either very high or very low. Otherwise, the advisor should encourage the entrepreneur to try the same solution multiple times. In order to motivate and sustain the entrepreneur’s exploration over time and across solutions, the advisor may find it optimal to recommend inferior solutions before superior ones (e.g., when trial costs are different or the entrepreneur can improve her capability with experience) or recommend multiple solutions simultaneously (e.g., when there is correlation between alternatives). Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0361 .

Problem definition: Faced with the challenge of serving beneficiaries with heterogeneous needs and under budget constraints, some nonprofit organizations (NPOs) have adopted an innovative solution: providing partially complete products or services to beneficiaries. We seek to understand what drives an NPO’s choice of partial completion as a design strategy and how it interacts with the level of variety offered in the NPO’s product or service portfolio. Academic/practical relevance: Although partial product or service provision has been observed in the nonprofit operations, there is limited understanding of when it is an appropriate strategy—a void that we seek to fill in this paper. Methodology: We synthesize the practices of two NPOs operating in different contexts to develop a stylized analytical model to study an NPO’s product/service completion and variety choices. Results: We identify when and to what extent partial completion is optimal for an NPO. We also characterize a budget allocation structure for an NPO between product/service variety and completion. Our analysis sheds light on how beneficiary characteristics (e.g., heterogeneity of their needs, capability to self-complete) and NPO objectives (e.g., total-benefit maximization versus fairness) affect the optimal levels of variety and completion. Managerial implications: We provide three key observations. (1) Partial completion is not a compromise solution to budget limitations but can be an optimal strategy for NPOs under a wide range of circumstances, even in the presence of ample resources. (2) Partial provision is particularly valuable when beneficiary needs are highly heterogeneous, or beneficiaries have high self-completion capabilities. A higher self-completion capability generally implies a lower optimal completion level; however, it may lead to either a higher or a lower optimal variety level. (3) Although providing incomplete products may appear to burden beneficiaries, a lower completion level can be optimal when fairness is factored into an NPO’s objective or when beneficiary capabilities are more heterogeneous. History: This paper has been accepted for the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.1031 .