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Individual and Group Differences Within the Carnegie Perspective

2024· article· en· W4400441449 on OpenAlexaff
Daniela Blettner, Sébastien Brion, Pino G. Audia, Linda Argote, John Kim, Yuxuan Zhu, Serhan Kotiloglu, Thomas Lechler, Jutta Stumpf-Wollersheim, Tim Kanis, Markus C. Becker, Jose Pablo Arrieta, Jerry Guo, Kyosuke Tanaka

Bibliographic record

VenueAcademy of Management Proceedings · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPerspective (graphical)Group (periodic table)PsychologyGeographyChemistryComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Based on the theoretical insights of three seminal books - Administrative Behavior (Simon, 1947), Organizations (March & Simon, 1958), and The Behavioral Theory of the Firm (Cyert & March, 1963) - the Carnegie perspective continues to have a profound influence on the study of organizations (Audia & Greve, 2021; Gavetti et al., 2012). A key feature of this theoretical perspective lies in its orientation toward process-oriented models of the firm. Key concepts and mechanisms such as bounded rationality, search, the dominant coalition, and standard operating procedures all share a concern for “how certain events and experiences set in motion processes of decision making, routine development, or routine selection that change organizational behavior” (Argote & Greve, 2007: 338). Although individuals level processes are prominent in these processes, the individuals who populate organizations are treated in abstract terms. One could argue that the implicit idea behind much of the early theory is that individual level differences do not warrant consideration given their minimal impact on the predictions. The objective of this symposium is to highlight some of the recent work done within the Carnegie perspective that couples a concern for process theorizing with a recognition of the influence of individual differences. The studies featured in this symposium build on an emerging new wave of work that has started to highlight the ways in which individual differences expand in important ways the predictive power of some of the central processes within the Carnegie perspective. Recent examples are: Gaba et al. (2023) who examine how the prior experience of managers influences their decisions in response to low performance; Audia, Rousseau, & Brion (2022) who focus on the influence of CEO power on the choice of social comparisons for the evaluation of performance; and Stumpf-Wollersheim et al. (2023) who study the effect of two emotions, sadness and fear, on routine development. Since individuals generally make organizational decisions in teams, we have included in the symposium also projects regarding how individuals prioritize diverging goals in teams and how they form beliefs that become the foundation for shared knowledge systems. To understand how organizations adapt to their environment, we need to understand how individuals make decisions, how individuals interact with each other in teams, and how individual differences contribute to an understanding of the key building blocks underlying organizational adaptation. This symposium offers a broad array of contributions illustrating diverse approaches to the study of these issues. CEO Affective Dispositions and Self-Enhancing Responses to Ambiguous Performance Feedback Author: Yuxuan Lily Zhu; Washington State U. Author: John Kim; CUHK Business School Changes in Managers’ Risk Perception in Response to Performance Feedback Author: Serhan Kotiloglu; California State U., San Marcos Author: Daniela Blettner; Beedie School of Business Simon Fraser U. Author: Thomas Lechler; Stevens Institute of Technology The Effect of Goal Conflicts on Organizational Routines: Insights from a Lab Experiment Author: Tim Kanis; Technische U. Bergakademie Freiberg Author: Jutta Stumpf-Wollersheim; Technische U. Bergakademie Freiberg Author: Markus C. Becker; U. of Southern Denmark Author: Jose Pablo Arrieta; U. of Amsterdam Communication Networks, Specialization, and Transactive Memory System Updating Author: Jerry M. Guo; Frankfurt School of Finance & Management Author: Kyosuke Tanaka; Aarhus BSS, Aarhus U.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.254

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.048
GPT teacher head0.330
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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