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Record W193079371

Retaining Employees in Small and Medium-Sized Firms: Examining the Link with Human Resource Management

2006· article· en· W193079371 on OpenAlexaboutno aff
Terry H. Wagar, Kent V. Rondeau

Bibliographic record

VenueThe Journal of Applied Management and Entrepreneurship · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Downsizing and Restructuring
Canadian institutionsnot available
Fundersnot available
KeywordsHuman resource managementEmployee retentionBusinessHuman resourcesMarketingRestructuringIndustrial relationsManagementEconomics
DOInot available

Abstract

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Executive Summary While the 1990s were a decade characterized by downsizing and restructuring, there has been a recent focus on attracting and retaining quality employees. Very little research has addressed employee retention within small and medium-sized firms. This paper uses data from a national study of manufacturing organizations in Canada to investigate the relationship between employee quit behavior and human resource management issues. The results suggest that employee retention is affected by a host of factors that includes the system of human resource management practices, human resource strategy, and decision-making ideology. Introduction While researchers are now placing considerable attention on the links between human resource management (HRM) practices and the performance of the firm, most of this research has been conducted in large organizations (Wood, 1999; Wright, Gardner, Moynihan & Allen, 2005). Many of these studies have examined the impact of human resource systems on the financial performance of the organization. However, there is a paucity of research investigating whether the adoption of certain human resource management practices has the potential to affect employee retention rates (Stovel & Bontis, 2002; Batt, Colvin & Keefe. 2002). The current study uses data from small and medium-size manufacturing workplaces to investigate the factors associated with the ability of an establishment to retain employees. Why should small firms be concerned about retaining employees? There is a strong body of research indicating that employee turnover can be very costly (Griffith & Horn, 2001). Potential costs (Dess & Shaw, 2001) can include both direct costs (such as separation, replacement, training and general administration costs) and indirect costs (such as lower productivity and reduced customer loyalty). While the loss of quality employees can be devastating for both big and small firms, retaining key individuals may be particularly important for small and medium-size businesses - if a high-quality employee leaves the organization, a smaller firm may be less likely to have a suitable internal candidate or lack the resources to selectively recruit on the external market. Background Although most of the research addressing human resource management and firm performance has been conducted in large firm settings, small and medium-size businesses are now adopting human resource management practices with an expectation that these practices have the potential to positively impact firm performance (Way, 2002). Following from Heneman, Tansky and Camp's (2000) observation that there is a shortage of information on human resource management practices in smaller organizations, it is not surprising that there is very little research addressing the link between employee retention and human resource management. While there is an extensive body of research on employee turnover, almost all of the work by social scientists has been done at the individual (employee) level of analysis (Griffith & Horn, 2001). For instance, Kickul (2001) found that employer breach of factors addressing autonomy and growth (such as providing meaningful work, the opportunity to develop new skills, and recognition of accomplishments) and rewards and opportunities (for instance, providing opportunities for advancement and compensation tied to performance) were associated with a greater intention to quit among small business employees. However, the importance of studying employee turnover at the workplace or organizational level has been noted (Delery, Gupta, Shaw, Jenkins & Gangster, 2000; Batt, 2002). Shaw, Gupta and Delery (2005) recently investigated the relationship between voluntary turnover and organizational performance using data from employers in the trucking industry and organizations involved in the manufacturing of concrete pipe. They found that there was a negative relationship between voluntary turnover and organizational performance but that the relationship was attenuated as the level of voluntary turnover increased. …

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.178

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.183
Teacher spread0.170 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations35
Published2006
Admission routes1
Has abstractyes

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