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Evolving Approaches to Spillover Research: The Implications of Diverse, Nonwork Encounters

2024· article· en· W4400442083 on OpenAlexaff
Brandon Mathew Fogel, Jinfeng Chen, Kelly Schwind Wilson, Yu Min, Edward McClain Wellman, Katelyn Zipay, Sophie Pychlau, Troy A. Smith, Amy Bartels, Alexandria Lauren Garcia, Wei Wu, Jordan Nielsen, Lieke Laura Ten Brummelhuis

Bibliographic record

VenueAcademy of Management Proceedings · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSpillover effectPsychologySociologyEconomicsMicroeconomics

Abstract

fetched live from OpenAlex

As management scholars have attempted to paint a more complete picture of the employee experience, the connection between the work and nonwork domains remains a large part of the conversation. While a vast collection of research focuses exclusively on an employee’s work- specific factors, an ever-increasing body of literature acknowledges that the work and nonwork domains consistently spill over into one another (Edwards & Rothbard, 2000; Greenhaus & Beutell, 1985; Greenhaus & Powell, 2006). The literature has long recognized that these domains can come into conflict with one another while simultaneously enriching each other. Yet, the interplay between personal and professional has become increasingly complicated for the modern employee. Changes in the shape and structure of both the family and work domains have proven that these domains are not as static as once thought (Powell, Greenhaus, Allen, & Johnson, 2019). Instead, employees exist beyond the tight bounds of a single work domain and family domain with a spouse and kids. In response, the study of these domains has attempted to look beyond the common parameters of conflict and enrichment and turn instead to the lived experience of individuals as they traverse between the domains. Indeed, the latest concentrations on specific populations, such as breastfeeding mothers (Gabriel, Volpone, MacGowan, Butts, & Moran, 2020), or on specific activities in the nonwork domain, like exercise (Calderwood, Gabriel, ten Brummelhuis, Rosen, & Rost, 2021; ten Brummelhuis, Calderwood, Rosen, & Gabriel, 2022), inform that the nonwork domain contains a wide range of experiences. Recognizing these changes for employees, our symposium takes new angles to common types of spillover (including leisure activities’ influence on work performance and the crossover effects from partners) while also considering new types of social interactions (such as online dating or participating in team-based leisure activities) that spillover in distinct ways. Through these explorations of spillover, we aim to provide novel examples of how the nonwork domain affects the work domain that better represents the modern workforce. Specifically, the papers in our symposium explore well-being outcomes of dating app usage, in-role and extra-role behavioral outcomes of partner sacrifice, proactivity benefits of hobby job participation, and team learning outcomes of team-based leisure activity participation. “Swiping left or right”: Individual dating app experiences and the influence on work Author: Jinfeng Chen; Purdue U., West Lafayette Author: Kelly Schwind Wilson; Purdue U., West Lafayette Author: Jordan Nielsen; Purdue U. Grateful yet guilty: The emotional and behavioral consequences of receiving daily partner sacrifice Author: Min Yu; Arizona State U. Author: Edward McClain Wellman; Arizona State U. Passion projects outside the 9-5: Exploring expressiveness and the nostalgic impact on work outcomes Author: Katelyn Zipay; Purdue U. Author: Sophie Pychlau; Iowa State U. Leisure as a source of team skill-building: Team impacts of group-based leisure learning Author: Brandon Mathew Fogel; U. of Nebraska, Lincoln Author: Amy Bartels; U. of Nebraska, Lincoln Author: Troy Smith; U. of Nebraska, Lincoln Author: Alexandria Lauren Garcia; U. of Nebraska, Lincoln Author: Wu Wei; Wuhan 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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.788
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.312
GPT teacher head0.391
Teacher spread0.079 · 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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