PERFORMANCE, SLACK, AND RISK TAKING IN ORGANIZATIONAL DECISION MAKING.

1986 ◽  
Vol 29 (3) ◽  
pp. 562-585 ◽  
Author(s):  
J. V. Singh
2011 ◽  
Vol 56 (4) ◽  
pp. 530-558 ◽  
Author(s):  
Jennifer Jordan ◽  
Niro Sivanathan ◽  
Adam D. Galinsky

The current investigation explores how power and stability within a social hierarchy interact to affect risk taking. Building on a diverse, interdisciplinary body of research, including work on non-human primates, intergroup status, and childhood social hierarchies, we predicted that the unstable powerful and the stable powerless will be more risk taking than the stable powerful and unstable powerless. Across four studies, the unstable powerful and the stable powerless preferred probabilistic over certain outcomes and engaged in more risky behaviors in an organizational decision-making scenario, a blackjack game, and a balloon-pumping task than did the the stable powerful and the unstable powerless. These effects appeared to be the result of the increased stress that accompanied states of unstable power and stable powerlessness: these states produced more physiological arousal, a direct manipulation of stress led to greater risk taking, and stress tolerance moderated the interaction between power and stability on risk taking. These results have important implications for the way social scientists conceptualize the psychology of power and offer a theoretical framework for understanding factors that lead to risk taking in organizations.


2018 ◽  
Vol 44 (1) ◽  
pp. 154-167 ◽  
Author(s):  
Matteo Valsecchi ◽  
Jutta Billino ◽  
Karl R. Gegenfurtner

2021 ◽  
Vol 2 (2) ◽  
pp. 263178772110046
Author(s):  
Vern L. Glaser ◽  
Neil Pollock ◽  
Luciana D’Adderio

Algorithms are ubiquitous in modern organizations. Typically, researchers have viewed algorithms as self-contained computational tools that either magnify organizational capabilities or generate unintended negative consequences. To overcome this limited understanding of algorithms as stable entities, we propose two moves. The first entails building on a performative perspective to theorize algorithms as entangled, relational, emergent, and nested assemblages that use theories—and the sociomaterial networks they invoke—to automate decisions, enact roles and expertise, and perform calculations. The second move entails building on our dynamic perspective on algorithms to theorize how algorithms evolve as they move across contexts and over time. To this end, we introduce a biographical perspective on algorithms which traces their evolution by focusing on key “biographical moments.” We conclude by discussing how our performativity-inspired biographical perspective on algorithms can help management and organization scholars better understand organizational decision-making, the spread of technologies and their logics, and the dynamics of practices and routines.


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