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Beadles, Nicholas A.; Lowery, Christopher M.; Petty, M. M.; Ezell, Hazel F.

doi:10.1023/a:1007828219886

Journal of Business and Psychology

2000

DOI: 10.1023/a:1007828219886

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Nicholas A. Beadles

Christopher M. Lowery

M. M. Petty

et al.

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Cited by 14 publications

(1 citation statement)

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“…Correlations not reported: Aarons & Sawitzky (2006)Alexander, Bloom, & Nuchols (1994)Balkin & Gomez-Mejia (1984)Banaszak-Holl & Hines (1996)Baron, Hannan, & Burton (2001)Beadles, Lowery, Petty, & Ezell (2000)Booth & Hamer (2007)Brannon, Zinn, Mor, & Davis (2002)Cappelli & Neumark (2004)Castle (2005)Castle & Engberg (2005)Castle & Engberg (2006)Dale-Olsen (2006)Donoghue & Castle (2006)Donoghue & Castle (2007)Guthrie, Flood, Liu, & MacCurtain (2009)Harrington & Swan (2003)Kahane & Shmanske (1997)Kash, Castle, & Phillips (2007)Kesner & Dalton (1994)Lavender & Marras (1994)Lucifora (1998)Meier & Hicklin (2008)O'Brien-Pallas et al (2006)Parsons (1972)Plomondon et al (2007)Powell, Montgomery, & Cosgrove (1994)Taplin, Winterton, & Winterton (2003)Walsh (1988)Woltmann et al (2008)Yalabik, Chen, & Kim (2008)…”

mentioning

confidence: 99%

Causes and consequences of collective turnover: A meta-analytic review.

Heavey¹,

Holwerda²,

Hausknecht³

2013

Journal of Applied Psychology

265

320

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Given growing interest in collective turnover (i.e., employee turnover at unit and organizational levels), the authors propose an organizing framework for its antecedents and consequences and test it using metaanalysis. Based on analysis of 694 effect sizes drawn from 82 studies, results generally support expected relationships across the 6 categories of collective turnover antecedents, with somewhat stronger and more consistent results for 2 categories: human resource management inducements/investments and job embeddedness signals. Turnover was negatively related to numerous performance outcomes, more strongly so for proximal rather than distal outcomes. Several theoretically grounded moderators help to explain average effect-size heterogeneity for both antecedents and consequences of turnover. Relationships generally did not vary according to turnover type (e.g., total or voluntary), although the relative absence of collective-level involuntary turnover studies is noted and remains an important avenue for future research. Given growing interest in collective turnover (i.e., employee turnover at unit and organizational levels), the authors propose an organizing framework for its antecedents and consequences and test it using meta-analysis. Based on analysis of 694 effect sizes drawn from 82 studies, results generally support expected relationships across the 6 categories of collective turnover antecedents, with somewhat stronger and more consistent results for 2 categories: human resource management inducements/investments and job embeddedness signals. Turnover was negatively related to numerous performance outcomes, more strongly so for proximal rather than distal outcomes. Several theoretically grounded moderators help to explain average effectsize heterogeneity for both antecedents and consequences of turnover. Relationships generally did not vary according to turnover type (e.g., total or voluntary), although the relative absence of collective-level involuntary turnover studies is noted and remains an important avenue for future research.Keywords: collective turnover, organizational performance, retention, meta-analysis Cause and Consequences 3The issue of collective turnover-that is, "the aggregate levels of employee departures that occur within groups, work units, or organizations" (Hausknecht & Trevor, 2011, p. 353)-has a long history in management and applied psychology research. Discussions of organizational-level turnover rates extend back nearly a century, as seen in early work addressing "rates of departure" (Greenwood, 1919, p. 187) and the "stability of employment" (Fish, 1917, p. 162). Topical interest further formalized via several influential accounts of collective turnover's causes and consequences (March & Simon, 1958;Mobley, 1982;Price, 1977;Staw, 1980). More recently, this attention has intensified in terms of empirical studies (e.g., Batt & Colvin, 2011;Shaw, Dineen, Fang, & Vellella, 2009;Siebert & Zubanov, 2009;Trevor & Nyberg, 2008), theoretical contributions (Dess & Shaw, 20...

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mentioning

confidence: 99%

Causes and consequences of collective turnover: A meta-analytic review.

Heavey¹,

Holwerda²,

Hausknecht³

2013

Journal of Applied Psychology

265

320

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When Coworkers and Managers Quit: The Effects of Turnover and Shared Values on Performance

2006

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Will they stay or will they go? How network properties of WebICs predict dropout rates of valuable Wikipedians

Lerner

Kenis

Raaij

et al. 2011

European Management Journal

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Will they stay or will they go? How network properties of WebICs predict dropout rates of valuable Wikipedians Lerner, J.; Kenis, P.N.; van Raaij, D.P.A.M.; Brandes, U. Published in: European Management Journal Document version:Publisher's PDF, also known as Version of record General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.-Users may download and print one copy of any publication from the public portal for the purpose of private study or research -You may not further distribute the material or use it for any profit-making activity or commercial gain -You may freely distribute the URL identifying the publication in the public portal Take down policy If you believe that this document breaches copyright, please contact us providing details, and we will remove access to the work immediately and investigate your claim. Summary This paper contributes to our understanding of an increasingly prevalent work system, web-based internet communities (WebICs). We are particularly interested in how WebICs are governed given the fact how different they are compared to more classical forms of organization. We study the governance of a WebIC by studying the structure and dynamics of their edit network. Given the fact that the edit network is a relational structure, social network analysis is key to understanding these work systems. We demonstrate that characteristics of the edit network contribute to predicting the dropout hazard of valuable WebIC members. Since WebICs exist only thanks to the activity of their contributors, predicting drop-outs becomes crucial. The results show that reputation and controversy have different effects for different types of Wikipedians; i.e., an actorÕs reputation decreases the dropout hazard of active Wikipedians, while participation on controversial pages decreases the dropout hazard of highly active Wikipedians. ª

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Untitled

Cited by 14 publications

References 11 publications

Causes and consequences of collective turnover: A meta-analytic review.

Causes and consequences of collective turnover: A meta-analytic review.

When Coworkers and Managers Quit: The Effects of Turnover and Shared Values on Performance

Will they stay or will they go? How network properties of WebICs predict dropout rates of valuable Wikipedians

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