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AbstractThe paper aims to identify the ways in which explicit and tacit knowledge sharing occur in organizations and to investigate the impact of sharing these two knowledge types on the employee's rewards, performance, and intention to leave.This paper focuses on two hi‐tech companies working in the telecommunications field producing cellular networks. The final sample consisted of 278 completed questionnaires from business departments including finance, R&D, marketing, IT, engineering, and manufacturing.Explicit knowledge sharing is perceived by the employees as having: (1) a direct positive effect on the receipt of monetary rewards; (2) a positive indirect effect on the employee's performance; and it has a (3) positive direct and negative indirect effects on employee's intention to leave. Tacit knowledge sharing is perceived by employees as having: (1) a positive direct effect on the receipt of non‐monetary rewards; (2) a positive direct effect on performance; and (3) it has a positive indirect effect on employee's intention to leave.Although the findings show that increased knowledge sharing results in positive outcomes at the individual level, the research model would benefit from an examination of larger samples for reasons of statistical complexity analysis and in order to allow for generalizability of the results to other companies that operate in similar environments. It would also be worthwhile to conduct a comparative analysis of companies from different industries.Understanding knowledge‐sharing behaviors may prove helpful to managers in developing strategies to encourage organizational knowledge sharing and in the development of an organizational knowledge base. The knowledge sharing outcomes outlined here can give employees a way to predict potential positive outcomes and benefits that are likely to arise as a result of engaging in knowledge sharing activities.The model enables for the first time to substantiate, in a valid way, to hypothesize that knowledge sharing within an organization is perceived by employees to be a rewarding behavior, improving employees' performance, and decrease the intention to leave. Copyright © 2009 John Wiley & Sons, Ltd.

Gender as a correlate of turnover has been inconclusive as a factor in understanding the development of a turnover decision. Yet, it is a decisive factor in the operation of key labor market processes which directly affect the entry and exit of labor to and from work organizations. Two possible conceptual and methodological explanations for this seemingly contradictory situation are proposed which distinguish "intent" from "actual turnover" and disaggregate male and female components of the "sex" variable. The lower and less consistent labor force participation rates of women in contrast to men led us to hypothesize that actual turnover behavior and intent to leave will be gender-specific, as well as influenced by differing sets of labor market and work environment factors. Three propositions testing these hypothesized relationships between gender and "intent" and "actual turnover" were examined. A representative sample of 506 textile workers located at 15 separate worksites was examined. A series of logistic models was devised which first began by clarifying the dual link between gender and both the intent to leave and actual turnover. Identical but separate logistic regression models were performed on men and women. This was followed by assessing if differing sets of biodemographic, perceived work environmental, and labor market variables explain differences for men and women in intent and/or actual turnover behavior. The results establish that women had greater rates of actual turnover than men, but no differences were found in the intent to leave. Both groups perceived their work environment similarly. The logistic analysis, however, confirmed that gender was a significant explanator of actual turnover but lot of intent. Contrasting alternative logistic models disclosed that different antecedent variables explained variations between men and women in both intent and turnover. These results help explain the inconsistent results linking gender to turnover, as well as emphasize that gender differences are crucial to understanding the development of a turnover decision. These results are then interpreted in light of the significance of gender to other forms of organizational behavior.

The predictive ability of most turnover models emanates from a key construct measuring a worker's intention to withdraw from a work organization. Empirical evidence to collaborate this cardinal assumption has, however, only moderate support. To test the predictive value of intention to leave, a logistic regression model of combined cross-sectional and longitudinal survey data on workers'potential and actual turnover was initiated. The results demonstrate that actual turnover and intent are influenced by a separate set of factors with intent a poor predictor of turnover behavior. Age, tenure, wage level, and perceived chances for improvement at the job were found to have a significant impact on actual separation, but not on the intent to leave. Work repetitiveness, importance of improvement, and perception of co-workers' intent influence the intent to move, but not turnover. Perception of co-workers' intentions to leave and importance of improvement played a dual role for both intent and turnover, but much more so for intentions. Further confirmation of these differences was sought by in/excluding intentions into the logistic regression of all the independent variables on actual separation. No improvement in the model occurred indicating the negligible impact of intent on explaining turnover. A parsimonious regression equation to optimize prediction of actual turnover was created, leading to a high and low risk leaver-stayer profile with probabilities ranging from 85.3% to 3.9%. The overall conclusion confirms the distinctiveness of intention and turnover as distinct constructs which are only superficially linked. As they differ in their basic explanatory antecedents, only variables linked to turnover predict work separations. Intentions do not.