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Journal articleMurtinu S, Park HD, Tucci CL, et al., 2027, , Journal of Business Venturing, Vol: 42, ISSN: 0883-9026
Rapid technological advances in artificial intelligence and other digital technologies play a critical role in enabling established organizations to venture into new entrepreneurial opportunities. These advances present new challenges for corporate venturing (CV): how to organize, transfer information, and execute venturing activities as existing organizational structures and competencies become obsolete. This article introduces a framework for understanding how digital technologies reshape knowledge search in CV contexts under varying levels of environmental complexity, and proposes a typology of human-machine configurations that serves as a diagnostic tool for assessing the degree of machine autonomy in CV decisions. It further explores the opportunities and unintended consequences of these evolving digital venturing practices.
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Journal articleGualtieri G, Dalpiaz E, Hampel C, 2026, , Innovation, ISSN: 1447-9338
Innovations such as AI are the subject of much hype in society. Burgeoning scholarship on hype and entrepreneurship has focused on the sustained high expectations and excitement that hyped innovations attract. This essay reorients extant scholarship by uncovering an unexplored phenomenon: the moral polarisation that regularly accompanies hyped innovations from the outset. We articulate how the emergence of hyped innovations triggers polarised moral views in society as audiences disagree about the innovation’s moral, rather than pragmatic, value, and what challenges this presents for the proponents of hyped innovations. We argue that this dynamic may emerge as the visibility generated by hype moves an innovation’s evaluation beyond experts to audiences relying on conflicting sources, while its framing as highly consequential shifts that evaluation from the technical plane to the moral one. Under these conditions, opposing views may harden into mutually delegitimising camps rather than mere differences of opinion. We illustrate this through the case of 5G technology, a hyped innovation that faced celebration and condemnation at once. Our essay outlines four promising research opportunities about the important topics of how moral polarisation emerges alongside hype, how innovative companies may navigate it, how processes of depolarisation unfold, and how individuals associated with hyped innovations experience the micro-dynamics of this morally charged situation.
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Journal articleLiu H, Wiesemann W, Yue M-C, 2026, , Operations Research, ISSN: 0030-364X
Factored Markov decision processes (MDPs) are a prominent paradigm within the artificial intelligence community for modeling and solving large-scale MDPs whose rewards and dynamics decompose into smaller, loosely interacting components. Through the use of value function approximations, dynamic Bayesian networks and context-specific independence, factored MDPs can achieve an exponential reduction in the state space of an MDP and thus scale to problem sizes that are beyond the reach of classical MDP algorithms. However, factored MDPs are typically solved using custom-designed algorithms that can require meticulous implementationsand considerable fine-tuning. In this paper, we propose a mathematical programming approach to solving factored MDPs. Unlike existing solution schemes, our approach leverages off-the-shelf solvers, which enables streamlined implementation and maintenance. Moreover, we exploit the factored structure in both state and action spaces, and we employ feature representations to unify existing methods while taking advantage of context-specific independence in problem classes that other approaches cannot solve efficiently. To further enhance scalability, we introduce a feature learning scheme that automatically identifies informative features and a dynamic basisapproximation scheme that adaptively refines our value function approximations. Our numerical experiments demonstrate the potential of our approach.
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Journal articleHarutyunyan M, Koca E, 2026, , Management Science, Vol: 72, Pages: 4569-5489, ISSN: 0025-1909
In player-versus-player video games, players' natural desire to win and avoid losing has created an opportunity for game developers to sell ``pay-to-win'' add-ons, digital items that give buyers a competitive advantage against other players. In this paper, we investigate the pricing and design of a pay-to-win add-on, its effect on the base game and profit implications for the developer. When a large share of the players is less price sensitive and highly willing to buy the pay-to-win add-on, we find that, surprisingly, the developer may prefer not to introduce it. Further, when it is optimal to sell the add-on, the developer designs it to be less (more) powerful in games where players are highly (moderately) sensitive to losing. In practice, the developer may design rewards or penalties in the game to influence players' sensitivities to winning and losing, respectively. Our findings suggest that when selling a pay-to-win add-on, the developer ideally prefers players' loss-sensitivity to be neither too high nor too low, whereas high win-sensitivity tends to benefit the developer. The introduction of a pay-to-win add-on influences the optimal pricing and design of the base game. In particular, the developer decreases the base game price and may even sell it as a freemium. Despite the price reduction, investment in game quality may actually increase. From the players' perspective, our analysis shows that the introduction of a pay-to-win add-on can actually improve player surplus.In an extension of our core model, we show that the developer may prefer to customize player-to-player matching probabilities based on ownership of the pay-to-win add-on, making players who own it more likely to be matched against non-owners. However, such customization is not always optimal, and under some market conditions, the developer may optimally abstain from customizing matching probabilities. In another extension, we show that the developer may benefit from setting a negative price for
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Journal articleCollet K, Li B, Wiesemann W, et al., 2026,
A retrospective cohort study mapping real鈥憌orld psychosis care pathways and guideline adherence using integrated electronic health records
, BMJ Connections Mental HealthObjectives: To develop a systematic, data-driven approach to map patient journeys and apply this to care pathways, assessing alignment with published guidance and highlighting potential areas to improve service delivery, using the case study of first diagnosis of psychosis. Design: Using document review, an idealised patient journey was developed. Event data were extracted from electronic health records using a systematic process to map real-world patient journeys across care settings. Setting: North West London’s de-identified integrated care records system (Discover). Participants: 3,219 (mean age 37 years; 55% men) adult patients (18 to 64) registered in North West London in December 2024, with a first episode of psychosis recorded between 2016 and 2019. Main outcomes: Mapping full patient journeys, including care transitions and service contacts (i.e. psychiatric admissions, EIP involvement, and GP management). Comparison of real鈥憌orld pathways against NICE guidelines. Results: Case identification yielded demographic trends comparable with national and local level data. Two-thirds of cases had their diagnosis first recorded in primary care, with many already attending secondary care services (i.e. records may not reflect where diagnosis was clinically established). Only 18% had a clearly identifiable record of an early intervention in psychosis (EIP) service, and the recorded duration of EIP involvement varied widely. Psychiatric admission, readmission, and use of crisis and emergency services were frequent. A large proportion of patient records showed no recorded healthcare contact within either 30 days or 6 months following the index admission. The patient journey models highlighted the complexity and heterogeneity of psychosis care and were difficult to manage and visualise using available models (process maps and matrices). Conclusions: Real-world patient journeys in psychosis, as represented in electronic medical records, appear to diverge widely f
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Conference paperLiu H, Selvi A, Wiesemann W, 2026,
Mind the gap: mixtures of Gaussians in approximate differential privacy
, Forty-Third International Conference on Machine Learning (ICML), Publisher: MLResearchPress, ISSN: 2640-3498We design a class of additive noise mechanisms that satisfy (ε, δ)-differential privacy (DP) for scalar, real-valued query functions with known sensitivities, with a particular focus on moderate and low-privacy regimes. These mechanisms, which we call mixture mechanisms, areconstructed by mixing multiple Gaussian distributions that share the same variance but differ in their means and mixture weights. The resulting distributions can be interpreted as convex combinations of a zero-mean Gaussian (as used in the analytic Gaussian mechanism, Balle & Wang 2018) and additional Gaussians whosemeans depend on the sensitivity of the query function. We derive tight conditions on the variances required for (ε, δ)-DP and provide efficient algorithms to compute them. Compared to the analytic Gaussian mechanism, our mechanisms yield substantially lower expected noise amplitudes (l1-loss) and variances (l2-loss for zero-mean distributions). In the low-privacy regime that motivatesour design, our mechanisms approach optimality,mitigating nearly all of the optimality gap of the analytic Gaussian mechanism.
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Journal articleMishina Y, Yu M, Gomulya D, 2026, , Academy of Management Journal, Vol: 69, Pages: 275-298, ISSN: 0001-4273
When and why might stakeholders react to firm activities in ways that might be different than, or even contradictory to, what we might expect based on the extant research? We draw on expectancy violation theory (EVT) and bring in the notion of heuristics and future-oriented expectations to examine this question, using a sample of investor reactions to earnings surprises from 2013 to 2019. We find that, in addition to comparing earnings to consensus earnings estimates, investors appear to compare the earnings surprises to the firm’s past performance and to its peers. Importantly, their expectations regarding future interactions with the firm appear to shape their decisions and generate anticipatory reactions despite a lack of full certainty about the future, a point notably absent from the EVT literature so far, which has tended to be reactive. Numerous robustness checks and post hoc analyses indicate that this behavior is not necessarily driven by unsophisticated investors, as initially predicted, but seemingly by institutional investors who rely on these multiple expectations, even though that may not be entirely rational. Our theorizing and findings make several contributions to the EVT literature and offer practical insights for managers and investors.
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Journal articleBelbasi R, Selvi A, Wiesemann W, 2026, , Manufacturing & Service Operations Management, ISSN: 1523-4614
Problem definition: A key challenge in supervised learning is data scarcity, which can cause prediction models to overfit to the training data and perform poorly out of sample. A contemporary approach to combat overfitting is offered by distributionally robust problem formulations that consider all data-generating distributions close to the empirical distribution derived from historical samples, where “closeness” is determined by the Wasserstein distance. Although such formulations show significant promise in prediction tasks where all input features are continuous, they scale exponentially when discrete features are present. Methodology/results: We demonstrate that distributionally robust mixed-feature classification and regression problems can indeed be solved in polynomial time. Our proof relies on classical ellipsoid method-based solution schemes that do not scale well in practice. To overcome this limitation, we develop a practically efficient (yet, in the worst case, exponential-time) cutting-plane-based algorithm that admits a polynomial-time separation oracle, despite the presence of exponentially many constraints. We compare our method against alternative techniques both theoretically and empirically on standard benchmark instances. Managerial implications: Data-driven operations management problems often involve prediction models with discrete features. We develop and analyze distributionally robust prediction models that faithfully account for the presence of discrete features, and we demonstrate that our models can significantly outperform existing methods that are agnostic to the presence of discrete features both theoretically and on standard benchmark instances.
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Journal articleThom盲 S, Schiffer M, Wiesemann W, 2026, , Operations Research, ISSN: 0030-364X
Multi-stage decision-making under uncertainty, where decisions are taken under sequentially revealing uncertain problem parameters, is often essential to faithfully model managerial problems. Given the significant computational challenges involved, these problems are typically solved approximately. This short note introduces an algorithmic framework that revisits a popular approximation scheme for multi-stage stochastic programs by Georghiou et al. (2015) and improves upon it to deliver superior policies in the stochastic setting, as well as extend its applicability to robust optimization and a contemporary Wasserstein-based data-driven setting. We demonstrate how the policies of our framework can be computed efficiently, and we present numerical experiments that highlight the benefits of our method.
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Book chapterMishina Y, Yu M, Mikolon S, 2026,
Behavioural Signals
, The Oxford Handbook of Organizational Social Evaluations, Editors: Zavyalova, Rupert, Publisher: Oxford University Press, ISBN: 9780198933137This chapter provides an overview of how behavioural signalling differentially impacts the formation, reinforcement, and decline of social evaluations. Drawing from prior literature, the chapter acknowledges that behavioural signalling serves as a foundational driver across various social evaluations and distil the general manner in which it works across social evaluations. It then turns to the variations and idiosyncrasies that may arise for how behavioural signalling affects different social evaluations. To this end, the chapter examines the relationship between types of behaviour, sources of behaviour, behavioural frequency, and behavioural consistency in their roles in gaining, maintaining, or removing the evaluations, as well as how possession of the social evaluation may impact interpretations of subsequent behavioural signals. It explores the significance of these features in shaping diverse social evaluations and the distinct implications for each evaluation. The chapter concludes by pointing towards promising avenues for future research at the intersection of behavioural signalling and social evaluations.
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