We notice that you are using Internet Explorer to view our website. Please note that we currently do not support that browser for accessing certain features. To improve your browsing experience, please use either Firefox or Chrome.
Organizational search has gained vast popularity among strategic management scholars for its ability to explain essential management phenomena. Yet, recent reviews articulate the need for continued evolution in our understanding of search behavior and suggest the importance of incorporating outside perspectives such as managerial cognition, environmental influence, and empirical approaches to challenge assumptions in the current theory. We respond to these calls and seek to understand how managerial experience affects the nature of search within organizations following an exogenous resource shock, and how this effect is moderated by performance feedback. We draw from the National Basketball Association (NBA) and use data from 31,500 games combined with historical records of the player and coach statistics. Our findings contribute to the literature on organizational search.
AI Tools in Strategic Search: Alleviating and Aggravating Local Search
Aalto University Timo Vuori,
Artificial intelligence technologies could be used to direct executives’ scarce attention to improve the quality of strategic decision-making. However, use of such technologies could also induce local search. We draw from literature on local search, strategy tools and AI technologies to specify how the use of AI tools could affect search in the context of strategic decision-making. We propose how AI tools can broaden or distort search space, change the cognitive-attentional dynamics of the search process, or affect the interpretation of cues, and further, what factors could influence this. We thus contribute to the literature on local search by detailing how technologies might alleviate or aggravate local search, and to discussion on algorithmic decision-making by describing how these technologies influence quality of the strategy process.
Humans vs. Machines: Human Cognition and Computational Modeling to Study Learning from Experience
Franklin & Marshall College Luke Belge,
Franklin & Marshall College
Computational modeling has been instrumental in explicating cognitive and behavioral processes of learning, generating valuable insights in strategy research. In this paper, we argue that human agents and computer agents cognize and behave differently even in fairly confined problem spaces, such as k-armed bandit models. Unlike computer agents, humans are likely to have incomplete mental representations and assign values to alternatives differently. They are also likely to rely on different heuristics for search and selection. We suggest that accounting for and investigating these differences will enable us to consider previously unexplored research questions about learning processes and outcomes.
A Behavioral Perspective of Search in Nonprofit Organizations: How Programmatic Performance Drives Fundraising Efforts
Florida State University Pascual Berrone,
IESE Business School
This paper extends the behavioral theory of the firm to nonprofit organizations. Nonprofits hold multiple goals, including financial and higher-priority nonfinancial performance goals. We term the latter programmatic performance as it relates to program spending directed to fulfill a social mission. We hypothesize that, while financial performance above aspirations decreases fundraising, programmatic performance above aspirations increases fundraising efforts. We also explore moderators in the relations between programmatic performance and fundraising efforts. Factors that reduce dependency on donations or increase confidence in fundraising success should influence the extent of fundraising as a response to attainment discrepancies. We test our hypotheses using a panel dataset of 12,382 nonprofits find support for several of our predictions. We discuss broader implications for studying how multiple organizational goals influence search.