Relationship between job satisfaction & job performance: a meta-analysis.
| Author | Davar, S.C. |
| Position | Statistical data - Abstract |
Introduction
Job satisfaction plays an important role for an employee in terms of health and well being (Kornhaurser, 1965; Khaleque, 1981) and for an organization in terms of its productivity, efficiency, employee relations, absenteeism and turnover (Vroom, 1964; Locke, 1976; khaleque, 1984). Job satisfaction is a complex variable and influenced by situational factors of the job as well as the dispositional characteristics of the individual (Sharma & Ghosh, 2006). It can be captured by either a one dimensional concept of global job satisfaction or a multi faceted construct capturing different aspects of job satisfaction that can vary independently.
Research examining the relationship between job satisfaction and job performance has been conducted since at least as early as 1945 (e. g., Brody, 1945) and methodology utilized has varied greatly. Some researchers used established scales to measure job satisfaction, while some developed their own. Some used self-report ratings to assess performance, while others used peer or supervisor ratings.
The idea that job satisfaction leads to better performance is supported by Vroom's (1964) work which is based on the notion that performance is natural product of satisfying the needs of employees. The study relating to the relationship between job satisfaction and job performance has now become a research tradition in industrial-organizational psychology. The relationship between job satisfaction and job performance has been described as the "Holy Grail" of industrial psychologists (Landy, 1989). Many organizational theories are based on the notion that organizations that are able to make their employees happy will have more productive employees. Over the years, scholars examined this idea that a happy worker is a productive worker; however, evidence is not yet conclusive in this regard. Empirical studies have produced several conflicting viewpoints on the relationship between job satisfaction and job performance. Strauss (1968) commented, "Early human relationists viewed the morale--productivity relationship quite simple: higher morale would lead to improved productivity". Siegel & Bowen (1971) and Bagozzi (1980) suggested that job performance leads to job satisfaction but not the reverse. Anderson (1984) indicated that autonomy and feedback from the job is significantly correlated with the performance. Keaveney and Nelson (1993) found a non-significant correlation coefficient between job satisfaction and job performance. Manjunath (2008) found job satisfaction of agricultural scientists significantly correlated with their scientific productivity. Ravindran (2007) found that job satisfaction is non-significantly correlated with job performance.
There are conflicting viewpoints on the relationship between job satisfaction and job performance. The proposed study is to synthesize the results of different studies relating to the relationship between job satisfaction and job performance.
Meta-analytic Studies
Petty et al (1984) provided a limited meta-analysis of the job satisfaction-job performance relationship. They confined their analysis to 16 studies that were published in five journals from 1964 to 1983. Higher and more consistent correlations between overall job satisfaction and performance were indicated than those previously reported. Relationships between job descriptive index measures of job satisfaction and performance were not as high or as consistent as those found between overall job satisfaction and performance. They reported a mean corrected correlation of 0.31 between the constructs.
Laffaldano and Muchinsky (1985) analyzed 217 correlations from 74 studies and found a substantial range in satisfaction-performance correlations across the job satisfaction facets, ranging from a mean "true score" correlation of 0.06 for pay satisfaction to 0.29 for overall job satisfaction. For their primary analysis they averaged the facets performance correlations and reported an average true score correlation of 0.17 between job satisfaction and job performance. In discussing their findings, the authors only made reference to the 0.17 correlation, concluding that job satisfaction and job performance were "Only slightly related to each other".
Because of limitations in the prior analysis, Judge et al. (2001) conducted a new meta analysis on 312 samples. The true correlation between overall job satisfaction and job performance was estimated to be 0.30. Meta analysis was conducted by five facets in the job descriptive index (Smith, Kendall & Hulin, 1969) and found that the average corrected correlation was 0.18 a figure identical to Laffaldano and Muchinsky's (1985) overall estimate. Even with updated meta analysis the facet substantially underestimate the relationship of overall job satisfaction to job performance.
What is Meta-analysis?
A meta-analysis is used to synthesize the results of different studies relating to the relationship between job satisfaction and job performance. Glass (1976) defined meta-analysis as "The statistical analysis of a large collection of studies results for the purpose of integrating the findings". Meta-analysis is regarded as an accurate and objective way to assimilate research findings in the present era. It is a way to summarize, integrate and interpret selected descriptive statistics (e.g., sample correlations) produced by sample studies or experimental outcomes (e.g., d- statistics). There are different methods of meta- analysis. The framework of Rosenthal and Rubin's (1978), Hunter, Schmidt and Jackson (1982), Hedges and Olkin (1985); Davar (2004). Hunter, Schmidt and Jackson (1982) is a popular method used to compute true variance i.e. observed variance net of the measurement error, sampling error and range-restriction. Davar (2004) modified the formulas given by HSJ (1982) framework and provided us with 'An Improved Version' of HSJ (1982). The formulas for two models are given below:
The formulas
Chart -A:
The formulas for true variance models Hunter, Schmidt and Davar (2004) Jackson (1982) framework [bar.r] [[summation][N.sub.i] [sigma] = [[summation] [r.sub.i]/[summation][N.sub.i]] [[r.sub.i]/k [[sigma].sup.2.sub.r] = [[summation] [N.sub.i]([r.sub.i] - [bar.r]).sup.2]/ [summation][N.sub.i] [[sigma].sup.2].sub.r] = [[[summation].sup.k.sub.i=1] [([r.sub.i] - [rho]).sup.2]/k] [[sigma].sup.2.sub.e] [[(1 - [[bar.r].sup.2]).sup.2]/k]/N] [[sigma].sup.2.sub.e] = [[(1 - [[rho].sup.2]).sup.2]/k] [[sigma].sup.2] = [[sigma].sup.2] = [[sigma].sup.2.sub.r] - [[sigma].sup.2.sub.r] - [[sigma].sup.2.sub.e] [[sigma].sup.2.sub.e] [r.sup.-] = Mean correlation; [[sigma].sup.2.sub.r] = Observed variance; [[sigma].sup.2.sub.e] = Sampling error variance; [[sigma].sup.2] = True variance and k= number of studies; N = [summation] [N.sub.i]; [N.sub.i] = number of observations in a sample study.
Objectives of the Study
The first objective of the study is to generate a meta-analtic estimate for the general relationship between job satisfaction and job performance. This estimate indicates the general magnitude...
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