Research Interests: cognition, game theory, innovation, machine learning, strategic change, strategic decision making
Doctor of Business Administration, Strategy
Bachelor of Arts, Economics and Psychology
My research explores how cognitive processes, namely the mental processes by which individuals interpret situations, and acquire and process information, drive strategic decision-making and strategic interactions. Be it at crucial decision making points such as mergers and acquisitions or expansions, during periods of strategic innovation and change, or when competing with other firms, differences across strategic leaders in these cognitive processes can lead to significant performance differences across the firms. My research investigates some of the ways in which these processes can vary across decision makers, and how these differences affect performance. Thus, it provides a different, cognitive lens with which to understand persistent performance differences across firms, the central issue in strategy.
Anoop Menon (2018), Bringing Cognition into Strategic Interactions: Strategic Mental Models and Open Questions, Strategic Management Journal, 39 (1), pp. 168-192.
Abstract: This paper explicitly introduces cognitive considerations into the treatment of strategic interactions, using the value-based framework as an extended example. Through real-world examples and prior empirical findings, it shows that many of the implicit assumptions of the framework are regularly violated in practice when actors simplify their complex realities into incomplete, inaccurate mental models. These violations lead to outcomes that are often contrary to the predictions of the classical framework. As initial steps towards developing a cognitively grounded theory of strategic interactions, the paper characterizes the core components of strategic mental models that might form the foundation of such a theory and then lays out some open questions that this theory would need to address. These questions, when answered, can point to novel cognitive capabilities.
Exequiel Hernandez and Anoop Menon (2018), Acquisitions, Node Collapse, and Network Revolution, Management Science, 64 (4), pp. 1652-1671.
Abstract: Research on networks emphasizes the addition or deletion of ties as the primary mechanism through which firms alter their networks to obtain valuable positions. This overlooks another mechanism of network change that is at least equally important: the ability of a firm to acquire another firm and inherit its network ties. Such ‘node collapse’ can radically restructure the network in one transaction, constituting a revolutionary change compared to the evolutionary effect of tie additions and deletions. Moreover, acquisitions occur in competitive markets, making it crucial to account for multiple firms simultaneously seeking to reach advantageous network positions. We explore how these issues affect the dynamics of the network at the firm and industry levels through a simulation in which actors acquire one another to span more structural holes. We find that acquisition-driven change affects the distribution of individual firms’ performance and the structural properties of the industry-wide network.
Description: Finalist for the Best Conference Paper Award at the 2015 SMS Annual Conference.
Sen Chai and Anoop Menon (2018), Breakthrough Recognition: Competing for Attention, Research Policy.
Abstract: We introduce to the literature on the recognition and spread of ideas the perspective that articles compete for the attention of researchers who might build upon them, in addition to the bias against novelty view documented in prior research. We investigate these effects by analyzing more than 5.3 million research publications from 1970 to 1999 in the life sciences. In keeping with the competition for attention view, we show that articles covering rarely addressed topics tend to receive more citations and have a higher chance of being a breakthrough paper. We also find evidence consistent with the bias against novelty, as well as that both mechanisms can work simultaneously.
Anoop Menon, Clarence Lee, Haris Tabakovic (Under Review), Using Machine Learning to Predict High-Impact General Technologies.
Abstract: Can machine learning techniques be used to predict high-impact, general technologies? We find that an ensemble of deep learning models that analyze both the text of patents as well as their bibliometric information can ex-ante identify such patents, accurately identifying 80 of the top 100 high generality patents in the hold-out sample. We also find that just the abstracts of patents have enough information content to allow a deep learning model trained on them to outperform models that take into account many quality indicator variables about patents. This ability to identify promising technologies early can be highly valuable for society, helping spot promising technologies in their nascent stages, with implications for national technology policy as well as for the R&D strategies of firms. It also demonstrates that machine learning-driven prediction models can be fruitfully applied even to such complex, socio-culturally driven processes as technology adoption.
Daniel Albert and Anoop Menon (Working), Discounting the Future: Short-termism as a long-term strategy?.
Abstract: We investigate whether and when excessive discount rates yield higher long-term investment returns than discount rates that reflect the true cost of capital. Laying out a canonical repeated investment choice model, and analyzing it using Monte Carlo simulations, we find that excessive discount rates yield greater returns both immediately and over time due to frequent changes in investment choices. When investigating the boundary conditions of these results, we find that intuitive factors, such as the cost of investment, uncertainty in cash flow, and the ability to time investment decisions, only play a small role and sometimes even reinforce the baseline results. Instead, we find that having fewer operational constraints, strong time-dependence of cash flows, and large heterogeneity in long-term returns are factors that cause the use of excessive discount rates to lead to significantly poorer outcomes.
Anoop Menon, Gideon Nave, Sudeep Bhatia (Working), Volatility-Induced Emotions Impact Mergers and Acquisitions by S&P 500 Executives.
Abstract: Risky decisions are at the heart of strategic managerial practice, and research suggests that they might be susceptible to emotional influences. However, the association between emotions and real-world high-stakes decisions made by experienced managers is questionable, because most work to date on the topic has relied on laboratory experiments conducted on college students, and as the capacity to measure emotions in the field has been limited. This work addresses the void in the literature by examining the relationship between emotional expressions of executives of S&P 500 companies (the largest 500 publicly traded firms on the NYSE) and subsequent risky decisions about whether to conduct mergers and acquisitions (M&A), in a large panel dataset. By performing text-based sentiment analysis on the quarterly earnings conference calls of these firms (N = 15,555 calls, conducted between 2005-2016), we reveal that the expression of negative emotions predicts less risk taking by the firms, as reflected by fewer M&A deals (controlling for fundamental financial variables, firm- and year-level effects). The relationship between emotions and M&A decisions is driven by the expression of sadness and fear, emotions that involve appraisals of uncertainty and lack of control, and less by expressions of anger (which involves appraisals of certainty and control), in line with the predictions of exiting theories of emotion and risk taking. We conclude by discussing the implications of our findings for managerial practice and highlight limitations and avenues for future research.
Abstract: This study explores how new text analysis tools can be used in strategy research that examines unstructured textual data. We build on two established Natural Language Processing (NLP) techniques, vector space models and topic modeling, to create text-based measures of several core strategy constructs – strategic change, positioning, and focus. These techniques are applied to 52,392 business descriptions in 10-K annual reports filed from 1996 to 2017. Our analysis shows that these new methods produce novel measures of strategy, opening up previously unexplored avenues of research to strategy scholars. The study advances emerging research utilizing text analysis, demonstrates that NLP techniques can overcome some limitations of traditional text analysis methods, and provides a template for how other machine learning techniques could be introduced into strategy.
Abstract: Research has demonstrated the value of interfirm networks for firm performance, but less attention has been given to how firms strategically modify their networks. We develop a framework that treats strategic network change as a classic decision problem with three elements: objectives, actions, and modifiers. This allows us to consider network change actions beyond tie additions and deletions, to include corporate strategies like mergers, divestitures, entry, and exit. We link these actions to the pursuit of valuable network positions (openness, closure, and status). Our framework generates testable propositions, points to ‘white spaces’ in the literature on network dynamics, and raises the possibility of integrating the literatures on corporate strategy and networks.
Abstract: Using a simulation model, we explore how agents may use experience for first order (updating their beliefs) and second order learning (updating their representation or mental model). We demonstrate how second order learning may allow agents with simpler mental models to outperform agents with more complex and accurate mental models. This beneficial effect of simplicity becomes amplified under conditions of high complexity. These findings have important implications for our understanding of cognitive complexity and simple rules.
Anoop Menon and Dennis Yao (2017), Elevating Repositioning Costs: Strategy Dynamics and Competitive Interactions in Grand Strategy, Strategic Management Journal, 38 (10), pp. 1953-1963.
Abstract: This paper proposes an approach for modeling competitive interactions that incorporates the costs to firms of changing strategy. The costs associated with strategy modifications, which we term “repositioning costs,” are particularly relevant to competitive interactions involving major changes to business strategies. Repositioning costs can critically affect competitive dynamics and, consequently, the implications of strategic interaction for strategic choice. While the literature broadly recognizes the importance of such costs, game-theoretic treatments of major strategic change, with very limited exceptions, have not addressed them meaningfully. We advocate greater recognition of repositioning costs and illustrate with two simple models how repositioning costs may facilitate differentiation and affect the value of a firm’s capability to reduce repositioning costs through investments in flexibility.
This course encourages students to analyze the problems of managing the total enterprise in the domestic and international setting. The focus is on the competitive strategy of the firm, examining issues central to its long- and short-term competitive position. Students act in the roles of key decision-makers or their advisors and solve problems related to the development or maintenance of the competitive advantage of the firm in a given market. The first module of the course develops an understanding of key strategic frameworks using theoretical readings and case-based discussions. Students will learn concepts and tools for analyzing the competitive environment, strategic position and firm-specific capabilities in order to understand the sources of a firm's competitive advantage. In addition, students will address corporate strategy issues such as the economic logic and administrative challenges associated with diversification choices about horizontal and vertical integration. The second module will be conducted as a multi-session, computer-based simulation in which students will have the opportunity to apply the concepts and tools from module 1 to make strategic decisions. The goal of the course is for students to develop an analytical tool kit for understanding strategic issues and to enrich their appreciation for the thought processes essential to incisive strategic analysis. This course offers students the opportunity to develop a general management perspective by combining their knowledge of specific functional areas with an appreciation for the requirements posed by the need to integrate all functions into a coherent whole. Students will develop skills in structuring and solving complex business problems.
This course is concerned with strategy issues at the business unit level. Its focus is on the question of how firms can create and sustain a competitive advantage. A central part of the course deals with concepts that have been developed around the notions of complementarities and fit. Other topics covered in the course include the creation of competitive advantage through commitment, competitor analysis, different organizational responses to environmental changes, modularity, and increasing returns. An important feature of the course is a term-length project in which groups of students work on firm analyses that require the application of the course concepts.
Wharton research using natural language processing techniques advances the field of competitive strategy analysis to previously untested terrain.Knowledge @ Wharton - 2018/05/30