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.
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.
Anoop Menon, Gideon Nave, Sudeep Bhatia (Under Review), Emotional Expressions Predict Risky Decisions 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, as most work to date on the topic has relied on laboratory experiments conducted on college students, the extent to which emotions impact real-world high-stake decisions made by experienced managers is questionable. We address this question by investigating how risky decisions of the S&P 500 firms (a list of 500 of the largest publicly traded firms on the NYSE), on whether to conduct mergers and acquisitions (M&A) are influenced by emotions, induced by volatility in the stock prices of the firms. By performing text-based sentiment analysis on the earnings conference calls of these firms, we confirm that the volatility of the firms’ stocks increases the expression of negative emotions, and in turn leads to fewer M&A deals. The main emotional effect holds even when controlling for volatility itself, ruling out the possibility that the effect is epiphenomenal. Furthermore, the relationship between emotions and M&A decisions is mainly driven by the expression of sadness and fear – emotions that involve appraisals of uncertainty and lack of control – in line with the predictions of the appraisal tendency framework. 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 strategic management 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 constructs in strategy – namely strategic change, positioning, and focus. These techniques are applied to the entire sample of 50,506 business descriptions in 10-K annual reports from 1997 to 2016. Results show that these new methods produce innovative yet meaningful measures of firm strategy which open many previously unexplored avenues for research to strategy scholars. The study advances emerging strategy research utilizing text analysis methods, demonstrates that NLP techniques can overcome the limitations of traditional text analysis methods such as keyword counts and mapping analysis, 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, 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.
Exequiel Hernandez and Anoop Menon (2017), 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.
Anoop Menon and Daniel Albert (Working), Short-termism as a long-term search-strategy.
Abstract: A widely held belief in strategic decision-making is that if information were free and perfectly accurate, acquiring more information about the consequences of one’s choices should always lead to greater long-term performance. In this paper, we revisit this axiomatic belief and explore whether and when this is truly the case. Using computer simulations, we find in most cases that short-term focused decision-making outperforms more foresighted decision-making, both instantaneously and in the long run. For foresightedness to outperform short-termism, we identify a narrow set of environmental conditions that need to apply. Long-termism performs best in environments that are driven by the accumulation of usage specific resources. Long-termism also becomes favorable in environments where strategies are substantially heterogeneous in their performance.
Sen Chai and Anoop Menon (Under Review), Breakthrough Recognition: Competing for Attention.
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.
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