How should organizations divide and sequence decision tasks between human and artificial agents? We develop a computational model of joint sequential adaptation in which two agents differ in a single, precisely specified way: the memory regime governing how past decisions shape subsequent ones. A recency-weighted regime, motivated by behavioral evidence on human adaptation, privileges recent outcomes; a uniform-memory regime, motivated by the scale-free consistency of algorithmic updating, weights a window of past outcomes equally. Situated in the lineage of NK/NKC models but developed on its own terms as a sequential-adaptation model, the framework varies task scope (N), within-task coupling (K), and cross-agent coupling (C) across modular and sequenced task structures. Three mechanisms organize the results. First, threshold dynamics create absorbing high- and low-payoff regimes, so adaptation compounds whatever it inherits. Second, uniform memory amplifies inherited trajectories, for good and for ill, whereas recency weighting corrects locally but is volatile at scale. Third, memoryless stochastic adaptation, though inferior on average, functions as an escape mechanism when inherited trajectories are poor. Consequently, joint performance is maximized not by the prevalent "AI-first" design, but when scale-free adaptation follows a high-performing human; broad stochastic search instead rescues sequences initiated by low-performing humans. The model and its experimental validation (a separate study) offer organization designers a task-structural contingency logic for human-AI collaboration and caution against universal prescriptions for AI-first deployment.

Forthcoming in Strategy Science special issue on AI and Strategy. Analogical reasoning is central to strategy, as it offers a basis for decision making in uncertain and data sparse contexts. Its effectiveness as a process depends not only on retrieving candidate analogies but on correctly matching them to the focal problem, since a poorly chosen analogy can mislead decision makers and produce costly errors of commission. We investigate how humans and large language models (LLMs) perform at analogical reasoning through an exploratory study that extends classic analogical transfer designs by introducing multiple source analogs and target problems. Our results reveal a trade-off: humans in our sample frequently overlooked valid analogies (low recall) but rarely misapplied them (high precision); LLMs, in contrast, did not miss valid analogies (high recall) but often surfaced spurious, even if internally coherent matches (low precision). These findings suggest a complementary division of labor: LLMs might serve as expansive retrieval engines, generating a broad set of candidate analogies, while humans adjudicate their contextual fit through superior causal matching. This highlights a possible pathway for AIhuman collaboration in strategy making, while underscoring the risks of over-reliance on AIgenerated analogies until these models can improve their performance at matching analogies to problems.

Analogical reasoning is central to strategy because it offers a basis for decision making in uncertain and data sparse contexts. Its effectiveness as a process depends not only on retrieving candidate analogies but on correctly matching them to the focal problem because a poorly chosen analogy can mislead decision makers and produce costly errors of commission. We investigate how humans and large language models (LLMs) perform at analogical reasoning through an exploratory study that extends classic analogical transfer designs by introducing multiple source analogs and target problems. Our results reveal a tradeoff: Humans in our sample frequently overlooked valid analogies (low recall) but rarely misapplied them (high precision); LLMs, in contrast, did not miss valid analogies (high recall) but often surfaced spurious, even if internally coherent matches (low precision). These findings suggest a complementary division of labor: LLMs might serve as expansive retrieval engines, generating a broad set of candidate analogies, whereas humans adjudicate their contextual fit through superior causal matching. This highlights a possible pathway for artificial intelligence (AI)-human collaboration in strategy making while underscoring the risks of over-reliance on AI-generated analogies until these models can improve their performance at matching analogies to problems.

This study investigates whether large language models, specifically GPT4, can match human capabilities in analogical reasoning within strategic decision making contexts. Using a novel experimental design involving source to target matching, we find that GPT4 achieves high recall by retrieving all plausible analogies but suffers from low precision, frequently applying incorrect analogies based on superficial similarities. In contrast, human participants exhibit high precision but low recall, selecting fewer analogies yet with stronger causal alignment. These findings advance theory by identifying matching, the evaluative phase of analogical reasoning, as a distinct step that requires accurate causal mapping beyond simple retrieval. While current LLMs are proficient in generating candidate analogies, humans maintain a comparative advantage in recognizing deep structural similarities across domains. Error analysis reveals that AI errors arise from surface level matching, whereas human errors stem from misinterpretations of causal structure. Taken together, the results suggest a productive division of labor in AI assisted organizational decision making where LLMs may serve as broad analogy generators, while humans act as critical evaluators, applying the most contextually appropriate analogies to strategic problems.

Based on 259 articles published in the entrepreneurship, management, and finance literatures during 1990-2022, we provide an integrative review and synthesis of governance in new ventures. We structure our review around the formal contract between the new venture and its investors, and discuss the governance approaches of various pre-initial public offering investors across the three stages of the investment cycle: precontractual, contract design, and post-contractual. Pre-contractual governance and contract formation are explained using a signaling theory lens. To capture the intricacies of post-contractual governance, particularly for late-stage investors such as venture capitalists and private equity firms that have conflicting interests, multiple identities, and overlapping governance roles, we relax the core tenets of agency theory to direct academic inquiry toward a more sophisticated framework-multiple agency theory-that better reflects the complexities of post-contractual governance. Given the limitations of formal contracting in resolving the ambiguities of start-up governance, we integrate our narrative by using a complementary social embeddedness theory lens that highlights the importance of informal governance embedded in personal and social ties in creating implicit obligations based on trust, reciprocity, and reputation. Finally, we discuss how governance breakdowns can cause start-ups to fail, especially in their later stages.

Participating in a multi-party alliance with competitors is an important yet tricky decision for firms. By focusing on interactions with industry peers, the extant literature has largely ignored the role of outside industry participants. We posit that industry outsiders impact this decision by behaving as catalysts that influence the governing principles and the milieu that underlies the alliance. While some catalysts induce an environment of formal governance that reduces inter-partner trust and disincentivizes collaboration among industry rivals, others create an opportune environment for learning that supports collaboration. Given little theoretical guidance by prior literature, we employ a novel methodology that uses pattern recognition by machine learning algorithms to inductively develop and test our theory by analyzing heterogenous deal syndicates in the private equity (PE) industry.

Research Summary We show that the existing alliance portfolio of a firm can impede the adoption of a new business practice. We analyze the private equity industry which features alliances in the form of deal syndication and has recently seen the rise of a novel investment practice: add-on deals. Using algorithm-supported induction, we first document robust empirical patterns using machine learning techniques, and then test the theory we construct to explain these patterns using standard econometric methods in a hold-out sample. We find that when the capabilities required for the new business practice require new partners, existing alliance portfolio members who support current practices can impede access to these new partners (and hence the adoption of the new business practice) through capacity constraints and inter-partner rivalry. Managerial Summary Alliance partners play a valuable role in many industries, and particularly in the PE industry as syndication partners. While the benefits of working with such partners are well understood, we uncover a potential weakness that alliance portfolio managers should be aware of. If a new business practice relies substantially on a new type of alliance partner, the existing alliance portfolio may impede access to this partner, thus impeding the adoption of the new practice. For PE fund managers, in particular, this implies balancing their deal syndication strategy with the development of new relationships with corporate partners to adopt add-on models to complement the traditional LBOs. We also show how to use machine learning techniques to uncover patterns of strategic interest in past transaction data.

Cost-cutting is a central theme for many online service delivery platforms in which they induct freelance workers at a lower wage than was previously prevalent in the system. As workers engage in within-organization wage referencing, this induction can have behavioral implications for the existing workforce. In a natural experiment setting, partnering with a food delivery service in India, we shed light on these anticipated behavioral effects. We find that introduction of a lower-waged peer group can lead to significant unproductive behavior of the existing high-wage workers in the form of a 2.3% increase in food order rejection (86% higher than the baseline rejection rate). This effect however, can be mitigated by recognition (e.g., higher customer ratings) and exacerbated by a negative experience on the platform (e.g., higher customer cancellations). Counterintuitively, we find that such detrimental effects cannot be mitigated effectively by monetary incentives. Our result has a key message for the managers: economic changes can have behavioral consequences and non-monetary solutions can have a stronger lever to reduce workers' unproductive behavior under cost-cutting initiatives which may promote a feeling of “it may happen to me as well” among the existing workforce.

This paper extends the discussion on scope diversification as a means of strategic change to business model diversification as a potential alternative for strategic renewal. Marked by increasing competition, high-priced assets and lower debt ceilings, private equity (PE) firms are adapting their investment strategy from leverage-backed buyouts (LBOs) to creating equity-backed “platforms” – which are aggregates of two to three related investments. Using a large archival dataset of global PE deals and this phenomenon as the backdrop, the paper first isolates key predictors of strategic renewal by using a machine learning algorithm. Consequently, it demonstrates that firms with rigid routines and narrow market exposure find it relatively difficult to adopt a new business model. Moreover, the new model potentially acts as an opportunity for mid-cap firms to differentiate themselves from large established firms. Findings also indicate that when a business model renewal is triggered by a transient scarcity in i...