The effects of person-environment fit on employees' knowledge contribution

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Various individual and organizational factors influencing employees' knowledge contribution to electronic repositories have been identified but their effects often vary across employees and studies. This study proposes that examining the fit between employees and their organizations may explain the variations. Analysis of data from a survey of 211 employees using polynomial regression and response surface methodology indicates that when employees' ideals about the level of skill variety, task identity, organizational innovativeness fit with those in their organizations, they develop strong commitment and engage in more knowledge contributions. In contrast, person-environment (PE) misfit in these aspects (i.e., shortfall or excess) has deleterious effects. Interestingly, excess in job autonomy, task significance, and generalized trust does not have negative impact. The PE fit theory accounts for both employee-side and organization-side effects and enriches our conceptual understanding of the antecedents of knowledge contribution. It also highlights new directions for promoting knowledge contribution in practice.

Original languageEnglish
Title of host publicationInternational Conference on Information Systems, ICIS 2012
Pages1592-1610
Number of pages19
Publication statusPublished - 2012
Externally publishedYes
EventInternational Conference on Information Systems, ICIS 2012 - Orlando, FL, United States
Duration: Dec 16 2012Dec 19 2012

Publication series

NameInternational Conference on Information Systems, ICIS 2012
Volume2

Conference

ConferenceInternational Conference on Information Systems, ICIS 2012
Country/TerritoryUnited States
CityOrlando, FL
Period12/16/1212/19/12

ASJC Scopus Subject Areas

  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Applied Mathematics
  • Library and Information Sciences

Keywords

  • Electronic knowledge repository
  • Knowledge contribution
  • Personenvironment fit
  • Polynomial regression
  • Response surface analysis

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