The role of affectivity in job satisfaction: a meta-analysis

Personality and Individual Di€erences 29 (2000) 265±281

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The role of a€ectivity in job satisfaction: a meta-analysis James J. Connolly*, Chockalingam Viswesvaran Psychology Department, Florida International University, Miami, FL 33199, USA Received 6 January 1999; received in revised form 16 June 1999; accepted 17 August 1999

Abstract Correlations between a€ectivity and job satisfaction measures were examined by cumulating research ®ndings across studies. Correlations between (1) job satisfaction and positive a€ectivity, (2) job satisfaction and negative a€ectivity and (3) job satisfaction and a€ective disposition, were separately analyzed. The mean correlation corrected for coecient alpha in the two measures correlated were: 0.49 for positive a€ectivity …N ˆ 3326, k ˆ 15), ÿ0.33 for negative a€ectivity …N ˆ 6233, k ˆ 27† and 0.36 for a€ective disposition …N ˆ 1415, k ˆ 7). Results indicated that 10±25% of variance in job satisfaction could be due to individual di€erences in a€ectivity. No strong moderator variables were found. Implications for a dispositional and situational source of job satisfaction are discussed. 7 2000 Elsevier Science Ltd. All rights reserved.

1. Introduction Job satisfaction has been de®ned as a pleasurable or positive emotional state resulting from an appraisal of one's job (Locke, 1969). Job satisfaction is one of the most important and widely researched variables in industrial±organizational psychology (Locke, 1976). Hackman and Oldham (1976) identi®ed ®ve job characteristics that determined job satisfaction, which are skill variety, task identity, task signi®cance, autonomy and job feedback. Currently, researchers have identi®ed many more determinants of job satisfaction. Agho, Mueller and Price (1993) discuss several more determinants of job satisfaction. These encompass distributive justice, supervisory support, the internal labor market (respective to the organization), integration or friendships among coworkers and * Corresponding author. E-mail address: jconno01@®u.edu (J.J. Connolly). 0191-8869/00/$ - see front matter 7 2000 Elsevier Science Ltd. All rights reserved. PII: S 0 1 9 1 - 8 8 6 9 ( 9 9 ) 0 0 1 9 2 - 0

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pay. Agho et al. (1993) also identi®ed determinants that led to lower job satisfaction as well. These include opportunity of available jobs outside organization, role ambiguity, role con¯ict and role overload. Other determinants of job satisfaction include organizational constraints (O'Connor, Peters, Rudolf & Pooyan, 1982), work±family con¯ict (Bedeian, Burke & Mo€ett, 1988) and work schedules (Erberhardt & Shani, 1984; Jamal & Baba, 1992; Ralston, 1989; Ronen & Primps, 1981). Relationships of job satisfaction with experienced strain (Bogg & Cooper, 1995; Brief, Burke, George, Roberson & Webster, 1988; Viswesvaran & Deshpande, 1996), job performance (I€aldano & Muchinsky, 1985) and turnover (Carsten & Spector, 1987) have been widely researched. Job satisfaction has shown to be signi®cantly related to both organizational commitment and employee turnover (Carsten & Spector, 1987; Schlesinger & Zornitsky, 1991). Despite this voluminous research, most studies have focused on situational variables e€ecting job satisfaction and dispositional factors e€ecting job satisfaction such as personality have garnered far less attention (Brief & Roberson, 1989). The recent years have seen attempts to address this dearth of research in the dispositional factors a€ecting job satisfaction. Researchers (e.g. Watson & Slack, 1993) have attempted to link individual characteristics to job satisfaction. An individual characteristic that has been widely studied is a€ective disposition (Staw, Bell & Clausen, 1986). Although Agho et al. (1993) focused on situational determinants of job satisfaction, they also found that their model of job satisfaction signi®cantly improved when work motivation, positive a€ectivity and negative a€ectivity were added. Furthermore, Arvey, Bouchard, Segal and Abraham (1989) presented evidence that determinants of job satisfaction may be genetically inherited. That is, when monozygotic twins reared apart were examined with the Minnesota Job Satisfaction Questionnaire (MSQ), results indicated that about 30% of the variance in job satisfaction were attributable to genetic components. Additionally, Watson and Slack (1993) found signi®cant correlations for both positive a€ectivity and negative a€ectivity with job satisfaction and that both positive a€ectivity and negative a€ectivity predicted certain job satisfaction facets even 2 years later. The objective in this study is to meta-analytically integrate the literature on the relationship between a€ectivity and job satisfaction. Research on the theoretical explanation of the role of a€ectivity in job satisfaction is pertinent and can be integrated into theories of job satisfaction. Several researchers have posited a needs theory of job satisfaction (e.g. Wolf, 1970). These theories in part detail psychological needs that must be met in order for the individual to experience job satisfaction. Speci®cally, a€ective dispositions provide the perceptual foundation from which these needs are interpreted as being met or not. Hackman and Oldham (1976) present the well known job characteristic theory of job satisfaction. Although, this theory concentrated on the actual tasks performed on the job, Hackman and Oldham added a personality variable, Growth need strength, which moderated the job tasks±job satisfaction relationship. A€ective dispositions may provide the cognitive catalyst from which Growth need strength operate. Indeed, perception and cognition play a signi®cant role in theories of job satisfaction (James & James, 1989). Moyle (1995) has suggested that individuals who are high on the Negative A€ectivity Scale perceive their environment generally in negative terms and thus these individuals perceive work as negative, resulting in low job satisfaction. Levin and Stokes (1989)

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view negative a€ectivity as resulting from a cognitive process that evaluates negative cues from work more acutely, which manifests eventually into low job satisfaction. The same explanation suggests why individuals with positive a€ectivity have higher job satisfaction. In addition, theories of job satisfaction have been advanced that indicate job satisfaction is stable over time (Staw & Ross, 1985) and seems to re¯ect a genetic source (Arvey et al., 1989). If a correlation exists between stable a€ective dispositions and job satisfaction, then such a ®nding explains why job satisfaction is stable over time and covaries within the twin data. That is, the explanation becomes that a€ective disposition and not job satisfaction per se is inherited. Although the meta-analytically-derived correlation by itself does not prove this causality, such a correlation supports this explanation. Further, a€ective dispositions can generally be mapped into the Big Five framework. For example, measures of negative a€ectivity have been classi®ed as measures of emotional stability or neuroticism (Watson & Clark, 1984), whereas positive a€ectivity has been linked to extraversion (George, 1992). Thus, although we focus in this study on the relation between a€ective dispositions and job satisfaction, the results reported can also be interpreted within the Big Five framework. This is important to the extent that this research can be added to the vast research on personality and organizational constructs, as the Big Five framework has established a unifying framework to organize the research on the role of personality at work. Linking positive a€ect to extraversion and negative a€ect to neuroticism establishes the theoretical and empirical foundation for the meta-analysis. Both extraversion and neuroticism have been identi®ed as major personality traits in all theories and models of personality. Linking a€ective disposition to personality traits is important because such links provide the theoretical basis for the hypothesis that negative and positive a€ect are two separate construct dimensions and not bipolar ends of the same dimension. Whether or not positive and negative a€ectivity are the two polar ends of the same construct has been a source of debate among researchers (Schuabroeck, Ganster & Fox, 1992). Factor analytic studies (Watson, 1988; Watson & Pennebaker, 1989) have shown the presence of two factors. However, as the works of Spector and his colleagues indicate, the emergence of two factors may be due to item wording. Thus, it is possible that all positively phrased items load onto a factor whereas all negatively oriented items load onto a second factor. Perhaps, a more persuasive line of evidence to examine whether positive and negative a€ectivity are the polar ends of the same construct is to examine the correlation between them. A high magnitude of the correlation indicates they are bipolar; a low value for the correlation suggests they are not bipolar. The correlation between measures of negative a€ectivity and measures of positive a€ectivity as reported in the extant literature varies from a low of r ˆ ÿ0:05 (Brief & Roberson, 1989) to a high of r ˆ ÿ0:39 (Judge & Locke, 1993). Note, however, that these are observed correlations uncorrected for any artifacts. To investigate whether measures of positive a€ectivity and measures of negative a€ectivity are assessing the same bipolar construct the true score correlation is perhaps more appropriate. Connolly and Viswesvaran (1999) report a moderate correlation of r ˆ ÿ0:30 between positive and negative a€ect, which suggest that the two are also empirically distinct (in addition to the theoretical and conceptual di€erences presented between them). These empirical results coupled with the research on the independence of extraversion and neuroticism dimensions support the independence of positive and negative a€ect.

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Several measures of negative and positive a€ectivity exist in the literature. The Positive and Negative A€ect Schedule (PANAS) scale developed by Watson, Clark and Tellegen (1988) is a commonly used measure. Another measure is the Multidimensional Personality Questionnaire (MPQ; Tellegen, 1982). The PANAS and the MPQ measure both positive a€ectivity and negativity a€ectivity. However, there exist more measures of negative a€ectivity than there are measures of positive a€ectivity. Perhaps this is a re¯ection of the fact that a€ectivity was studied more in the literature on depression in clinical settings before their relevance in explaining normal behavior was recognized. Speci®cally, Watson and Clark (1984) identi®ed 18 measures of negative a€ectivity. These 18 scales encompass a wide variety of personality measures, variously labeled as trait anxiety, neuroticism, ego strength, general maladjustment and repression±sensitization. Recently, Viswesvaran and Sanchez (1998) demonstrated the measurement equivalence of the 18 scales using the principle of tetrad di€erences. The principle of tetrad di€erences implies that the second order determinants should vanish (Spearman, 1904) and this was found to be the case with the 18 measures of negative a€ect (Viswesvaran & Sanchez, 1998). This measurement equivalence suggests that these scales can be grouped together in a meta-analysis as measures of the same construct. In addition, Judge and Hulin (1993) investigate the e€ects of a€ective disposition de®ned as a tendency to respond to classes of environmental stimuli in a pre-determined, a€ect based manner. In the paper, we metaanalyzed the correlations between such measures of a€ective disposition (Judge and Hulin, 1993) and job satisfaction separately from studies reporting the correlation between job satisfaction and positive or negative a€ect. Several Studies have examined the a€ectivity and job satisfaction relation. For example, Levin and Stokes (1989) using a sample of 315 participants from a large international professional service organization found negative a€ectivity to be a signi®cant predictor of job satisfaction. Judge (1993) found using a sample of medical personnel that the job dissatisfaction and turnover relationship was stronger for individuals high on positive a€ectivity. However, Necowitz and Roznowski (1994) failed to ®nd signi®cant correlates between negative a€ectivity and job satisfaction for those who were not in aversive task conditions. The objective in this study is to cumulate, using the principles of psychometric meta-analysis (Hunter & Schmidt, 1990), the extant literature examining the correlation between measures of a€ective dispositions and measures of job satisfaction. Three separate meta-analytic cumulations were undertaken. The correlations between (1) measures of negative a€ectivity and job satisfaction, (2) measures of positive a€ectivity and job satisfaction and (3) measures of a€ective disposition and job satisfaction, were meta-analytically cumulated. We expected negative a€ectivity to correlate negatively with job satisfaction and positive a€ectivity to correlate positively with job satisfaction. Further, we expected the positive a€ectivity±job satisfaction relation to be stronger than the negative a€ectivity±job satisfaction relation. The prediction that positive a€ectivity will correlate more with Job Satisfaction than does negative a€ectivity is based on research on the biological determinants of extraversion and neuroticism (Eysenck, 1990; Strelau & Eysenck, 1987). The correlation between extraversion and physiological responses has been found to be more marked than the correlation between neuroticism and physiological responses. Coupled with research linking positive a€ectivity to extraversion and negative a€ectivity to neuroticism (cf. George, 1996), we expect positive

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Table 1 Hypothesized moderators and their levels analyzed Hypothesized moderator

Levels

Job satisfaction Measures

MSQ JDI

Tenure

less then 5 years 5 years or more

Organization sector

for pro®t non-pro®t

Organizational size

100±999 employees 1000 or more employees

Age

40 years or more 39 years or less

a€ectivity to have stronger relationships with outcomes such as job satisfaction than the relationship of negative a€ectivity to job satisfaction. In addition to a meta-analysis of the job satisfaction±a€ectivity relationship, an analysis of potential moderators of the relationship between job satisfaction and a€ectivity was undertaken (see Table 1). Several potential moderators such as type of job satisfaction measure used, organizational tenure of the participants, organizational sector (i.e. for pro®t and nonpro®t), organizational size in terms of the number of associates employed and age of the participants were examined. A list of potential moderators and their levels are summarized in Table 1. Note, however, that the list of potential moderators and their levels were limited to the data available in the studies. Additionally, the potential moderator analyses were undertaken more in an exploratory spirit.

2. Method 2.1. Database A search to locate all studies reporting a correlation between job satisfaction assessments and measures of a€ective disposition was conducted. First, the PsycLit database was electronically searched. The studies found were then examined to reveal more leads for additional studies to be included in the meta-analysis. Conference presentations were also included. A total of 27 articles were marked as relevant and were included in the database. The measures of a€ective dispositions that went into the database are listed in Table 2. Similarly, the job satisfaction measures used are summarized in Table 3. A complete list of citations of all articles included in the meta-analysis is provided with the references.

270

Scale

Positive & Negative A€ectivity Scale (Watson et al., 1988) Neutral Objects Satisfaction Questionnaire (Weitz, 1952) Multidimensional Personality Questionnaire (Tellegen, 1982) Negative A€ect Scale (Stokes and Levin, 1989) Taylor Manifest Anxiety Scale (Taylor, 1953) Job A€ect Scale (Brief et al., 1988) Trait Personality Inventory (Spielberger, 1979) Gripe Index (Weitz, 1952) Life Orientation Test (Scheier and Carver, 1985) Neuroticism Scale (Eysenck & Eysenck, 1975)

Frequency Positive a€ectivity

Negative a€ectivity

8

9

4

4 4 3 3 3

3

1

A€ective disposition 5

1 1

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Table 2 Personality measures included in the meta-analyses

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Table 3 Job satisfaction measures included in the meta-analyses Scale

Frequency

Job Descriptive Index Minnesota Satisfaction Questionnaire Bray®eld & Roth Index Michigan Organizational Assessment Scale Job Diagnostic Survey Faces Scale Job In General Scale Caplan Job Satisfaction Scale Hoppock Job Satisfaction Scale Facet Free Job Satisfaction Scale

11 12 7 6 4 3 3 1 1 1

2.2. Analysis The correlations were grouped into one of three distributions. One distribution comprised of correlations between measures of negative a€ectivity and measures of job satisfaction, whereas the second distribution included correlations between measures of positive a€ectivity and measures of job satisfaction. Correlations between measures of a€ective dispositions and measures of job satisfaction comprised the third distribution. The artifact distribution based meta-analysis (Hunter & Schmidt, 1990) with the interactive re®nements suggested in Schmidt, et al. (1993) was used. Corrections were made for the following artifacts: sampling error and unreliability in the measures (Schmidt, Viswesvaran & Ones, 1994). In correcting for unreliability, coecient alphas were used (Schmidt & Hunter, 1996) because of our interest was in the intra-rater correlations and not on interrater agreement. Three distributions of observed correlations and four distributions of reliabilities (one each for job satisfaction, positive a€ectivity, negative a€ectivity and a€ective disposition) were constructed. The corrections were made allowing for interactions between artifacts. Three separate meta-analyses were conducted. The input to the meta-analysis was the observed correlations, the associated sample size and the reliability distributions. The sample size weighted mean observed correlation (Mobs) and the sample size weighted mean observed standard deviation (S.D.obs) were computed. The frequency weighted mean of the square root of the reliability estimates were computed and used to correct the sample size weighted mean observed correlation. This mean corrected correlation is referred to as rho. The sample size weighted mean observed correlation (Mobs) was used in the standard sampling error formula for a correlation along with the average sample size (averaged across all estimates meta-analyzed) to estimate the sampling error variance. The artifactual variance caused by di€erences in reliabilities across studies was also estimated. Both sampling error variance and variance caused by reliability di€erences across studies were subtracted from the observed variance to estimate the residual

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variance. This residual variance was multiplied by the square of the ratio of rho to Mobs to obtain the variance of the reliability corrected mean correlation. Thus, for each of the three meta-analyses, the number of correlations meta-analyzed (k ), the total sample size across the k correlations (N ), the sample size weighted mean observed correlation (Mobs), the sample size weighted observed standard deviation (S.D.obs), the reliability corrected mean correlation (rho) and the standard deviation of rho, were computed. Following the three meta-analyses, these distributions were subgrouped by the di€erent levels of each hypothesized moderator. Each subgroup was meta-analyzed (the procedures outlined above were repeated). A moderator in¯uence was inferred if the 90% credibility intervals across di€erent levels of the hypothesized moderator did not overlap. Moderators examined were limited to the availability of data from the cumulated studies. The ®ve moderator variables investigated were: organization size (by employees), employee tenure, job satisfaction measure, employee age and organizational sector. All hypothesized moderators were separated along two extreme levels and the negative a€ectivity±job satisfaction relationship was examined. Not enough studies were available to examine the other possible relationships, such as that between positive a€ectivity and job satisfaction. Organization size was split between organizations with less than 1000 employees and organizations with more than 1000 employees. Employee tenure was divided between employees with 5 years or less service and employees with more than 5 years of service. Job satisfaction measures were also examined as a possible moderator. The two most used job satisfaction measures, the JDI and the MSQ, were analyzed. Employee age was split between employees 40 years old and above and employees age 39 years old or below. Lastly, organizational sector was divided between forpro®t and non-pro®t organizations. 3. Results Table 4 provides the frequency weighted (i.e. unweighted by sample size) mean and standard deviation of the square root of the reliability estimates. Coecient alphas for all three distributions of a€ectivity and one distribution of job satisfaction are summarized. Across the four distributions, the mean value of the reliability estimates is highest for job satisfaction and the lowest for a€ective disposition. This probably re¯ects the fact that scales of job satisfaction Table 4 Descriptive statistics on reliability distributions Reliability distribution

Positive A€ectivity Negative A€ectivity A€ective Disposition Job Satisfaction

Reliability

Square-root of reliability

Mean

S.D.

Mean

S.D.

0.81 0.79 0.76 0.84

0.0708 0.1085 0.0688 0.0634

0.91 0.89 0.87 0.92

0.0412 0.0651 0.0386 0.0346

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Table 5 Summary of a€ectivity and job satisfaction correlationsa A€ectivity constructs

K

N

Mobs

Sdobs

p

S.D.p

Positive A€ectivity Negative A€ectivity A€ective Disposition

15 27 7

3326 6233 1415

0.41 ÿ0.27 0.29

0.1397 0.1013 0.0902

0.49 ÿ0.33 0.36

0.1506 0.0943 0.0691

a

K is the total number of studies, N the total sample size across the studies, Mobs the sample size eighted mean observed correlation, Sdobs sample size weighted standard deviation of the correlations, p sample size weighted mean observed correlation corrected for unreliability in the measures and S.D.p the standard deviation of p.

have been in use for a long time enabling researchers to re®ne the items over the years. Another explanation is that some of the satisfaction measures were much longer then the a€ectivity measures. More importantly, it should be noted that the reliability distributions overlapped and the mean of all of the distributions were above 0.75. The results of the three meta-analyses are summarized in Table 5. The sample size weighted mean observed correlation corrected for coecient alpha ranged from ÿ0.33 to 0.49. The a€ectivity±job satisfaction correlation was highest for positive a€ectivity and lowest for negative a€ectivity. The results suggest that job satisfaction was in¯uenced more by positive a€ect than by negative a€ect. Further, the magnitudes of these correlations suggest that there are many variables a€ecting job satisfaction. The results support the inference that there may be stable individual di€erence correlates of job satisfaction and their relative importance (relative to situational factors) does appear to be substantial. For example, positive a€ectivity (which had the highest correlation with job satisfaction) explained 25% of the variance. Surprisingly, a€ective disposition conceptualized as spanning both positive and negative a€ectivity, had a lower correlation with job satisfaction than positive a€ectivity (0.36 vs. 0.49, respectively). Perhaps, the existing measures of a€ective disposition capture primarily negative a€ectivity. This is also consistent with the ®nding that the correlation between negative a€ectivity and job satisfaction was similar to the correlation between a€ective disposition and job satisfaction (ÿ0.33 vs. 0.36, respectively). Moderator analyses explored the factors that may in¯uence the relationship between negative a€ectivity and job satisfaction. The correlation was ÿ0.33 for the JDI, but much lower in magnitude for the MSQ (ÿ0.24). The correlation was ÿ0.37 for organizations with less than 1000 employees, but was ÿ0.41 for organizations with more than 1000 employees. The magnitude of the correlation was higher in non-pro®t organizations compared to that in for pro®t organizations (ÿ0.41 vs. ÿ0.35, respectively). The magnitude of the correlation was also slightly higher for employees with less than 5 years of tenure as compared with employees with longer tenure (ÿ0.31 vs. ÿ0.28, respectively). Finally, the correlational relationship was stronger (although not signi®cantly) for employees aged 39 or less than it was for older employees (ÿ0.37 vs. ÿ0.23, respectively). The stronger relationship for younger employees may be due to an artifact of the analyses. It is known that job satisfaction rises with age; this could create a ceiling e€ect for older employees, thereby reducing the correlation. Since job

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Table 6 Moderator e€ects on the negative a€ectivity and job satisfaction relationshipa Moderator JS measure JDI MSQ Organization size < 1000 employees 1000+ employees Organization sector For-pro®t Non-pro®t Tenure Less than 5 years 5 years or more Age 40 years or more 39 years or less

K

N

Mobs

Sdobs

p

S.D.p

90% CRED.

6 7

1067 1657

ÿ0.27 ÿ0.20

0.0549 0.0824

ÿ0.33 ÿ0.24

0 0.0625

0.33 0.17±0.29

6 6

1266 1749

ÿ0.30 ÿ0.32

0.0454 0.1086

ÿ0.37 ÿ0.41

0 0.1138

0.36 0.22±0.60

4 7

1167 1739

ÿ0.28 ÿ0.33

0.0331 0.1051

ÿ0.35 ÿ0.41

0 0.1052

0.35 0.24±0.58

7 6

1235 1141

ÿ0.25 ÿ0.23

0.1079 0.0297

ÿ0.31 ÿ0.28

0.0971 0

0.15±0.43 0.28

5 16

1163 3278

ÿ0.19 ÿ0.31

0.0794 0.1124

ÿ0.23 ÿ0.37

0.0554 0.1083

0.14±0.32 0.18±0.54

a

K is the total number of studies, N the total sample size across the studies, Mobs the sample size weighted mean observed correlations, Sdobs the sample size weighted standard deviation of the correlations, p the sample size weighted mean observed correlation corrected for unreliability in the measures and S.D.p the standard deviation of p. 90% credibility intervals are reported in the last column.

satisfaction reliabilities were not computed separately for older employees, this ceiling e€ect does not show up in lower reliabilities and hence would not be `corrected out'. (We thank an anonymous reviewer for this insight.) However, in this database, the average of the reliabilities reported in the studies using older subjects was 0.87; while the average reliability of job satisfaction measures reported in studies using younger participants was 0.83. 90% Credibility intervals for the moderator variables are reported in Table 6. According to this criterion, only the job satisfaction measure (JDI vs. MSQ) moderated the relationship between negative a€ectivity and job satisfaction. All of the remaining four moderator levels had credibility intervals that overlapped, thus indicating no signi®cant moderator e€ect. It is interesting to note that the standard deviation associated with the mean correlation when JDI measures were used was zero, whereas there was unexplained variability when MSQ measures were used. A check of the seven studies using the MSQ indicated that six had used the short form of the MSQ. When the analysis was restricted to these six studies, the mean observed correlation remained the same at 0.20, suggesting that the use of the shorter version of the MSQ did not result in a tradeo€ with the validity of measuring the job satisfaction construct. 4. Discussion The correlations of ÿ0.33 for negative a€ectivity, 0.49 for positive a€ectivity and 0.36 for a€ective disposition suggest that 10±25% of the variance in general job satisfaction can be explained by a€ectivity or a€ective disposition. Although there is a substantially signi®cant

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a€ective component to general job satisfaction, it is important to note that a sizeable percent of the total variance remains unexplained. There also are other factors that contribute to job satisfaction. It appears the JDI captures more of the variance between the a€ectivity and job satisfaction relationship than other measures of satisfaction. Again, this points to the extensive re®nements over the years of the JDI in its improved ability to measure the job satisfaction construct. Organization size does not appear to moderate the a€ectivity±job satisfaction relationship. Additionally, organization sector and tenure also do not appear to moderate the a€ectivity±job satisfaction relationship. Neither does employee age appear to moderate the negative a€ectivity±job satisfaction relationship. Further, the correlation between negative a€ectivity and job satisfaction was ÿ0.33, whereas the correlation between positive a€ectivity and job satisfaction was 0.49. This di€erential pattern of correlation also suggests that positive and negative a€ectivity may be independent constructs (at least when existing measures are considered). This is also consistent with a larger body of literature on the personality dimensions of extraversion and neuroticism. Several limitations to these meta-analyses should be noted. First, although the number of studies was sucient, it may be that the moderator analyses were in¯uenced by the small number of studies. That is, the small number of studies may have increased sampling error, both primary and secondary, in the moderator analyses. Also, a closer examination of the facets of job satisfaction with the a€ectivity measures would have been informative, but due to the limited number of studies, this analysis was not practical. In addition, the studies examined were all correlational, which precludes casual conclusions. In fact, there is evidence for a reciprocal causation relationship between job satisfaction and employee job perceptions (James & Jones, 1980). Additionally, most of the studies relied on self-report measures, which points to monomethod biases a€ecting the results, although the nature of the constructs is such that the use of self-reports is inevitable. Also, the levels of the individual moderators may have been too broad to capture any moderator e€ect. In addition, another limitation to this study was the unavailability of data to investigate the positive a€ectivity±job satisfaction relationship in terms of the moderator analyses. In interpreting the magnitude of the correlations reported here, we should take into account the e€ects of range restriction on estimated correlations (Thorndike, 1949). Brief et al. (1995) suggested that individuals with extreme levels of negative a€ect (emotionally disturbed), may not be represented in these studies. Although range restriction corrections are possible, the lack of studies raises questions about the appropriateness of such corrections. Further, it is possible that individuals su€ering from extreme levels of negative a€ect are not part of the labor force. Barrick and Mount (1991) reported high values for the ratio of restricted to unrestricted standard deviation of personality scores (0.85). Although, such high values seem to suggest that range restriction is not a problem, it is important to note that such high values use as unrestricted standard deviation national norms from normal adults (to the standard deviation of applicant scores). It does not address the fact that extreme scorers in neuroticism are not included in the national norms. Future empirical and conceptual development in this area will be useful. An issue to consider is whether item overlap explains the negative a€ect±job satisfaction correlation. Arguments (Chen & Spector, 1991) have been presented that one of the negative

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a€ect measures (i.e., the Taylor Manifest Anxiety Scale) shares item content with health strains (which presumably a€ect job satisfaction). To estimate the extent to which the confound is operating, we reanalyzed the negative a€ect±job satisfaction correlation after deleting all scales that employed the Taylor Manifest Anxiety Scale (there were three correlations). The correlation dropped from 0.3282 (line 3 Table 5) to 0.3240; a di€erence of 0.004 that is practically within rounding error. In addition, the mean observed correlation only dropped from 0.27 to 0.26, which suggests that the Taylor Manifest Anxiety Scale (Taylor, 1953) may be comparable both operationally and at the construct level with other measures of negative a€ect. The question of item overlap is one facet of a greater issue, namely construct contamination. Stone-Romero (1996) has presented conceptual arguments to suggest that all existing measures of negative a€ectivity are contaminated with dissatisfaction measures. Thus, one would expect a stronger negative a€ect±satisfaction relationship than a positive a€ect± satisfaction relation. However, our results indicate the converse. Positive a€ectivity had a stronger relationship than negative a€ectivity with job satisfaction. Further, Viswesvaran and Sanchez (1998) reported that the intercorrelations among measures of negative a€ectivity were in the 0.80's, whereas as reported in this paper, the average observed negative a€ectivity±job satisfaction correlation is only 0.27. This is further evidence of discriminant validity between job satisfaction and negative a€ectivity measures, although this does not imply that the measures are uncontaminated. Discriminant validity is still possible (the two are separate constructs), even though the correlation (between the two constructs) is in¯ated by contamination. However, given the results summarized here, it becomes incumbent on those who believe that existing measures of negative a€ectivity are contaminated to present empirical evidence (beyond more conceptual arguments) to support their claims. Negative a€ectivity is an important construct and should not be neglected in industrial±organizational psychology (Brief et al., 1988). This study has several theoretical implications. This study is speci®cally supportive of interactionist theories, in that both situational as well as personality/dispositional determinants of job satisfaction are relevant. The data is also supportive of the consistency model posited by Staw and Ross (1985); and by the genetic disposition of job satisfaction researched by Arvey et al. (1989). Researchers have treated a€ective disposition as a personality trait and other researchers have attributed personality development partially to genetic in¯uences (Tellegen et al., 1988). This also suggests the need for more behavioral genetics to be incorporated in several theories. Speci®cally, theories on mood, subjective-well being and life±job satisfaction need to orientate their research e€orts more towards dipositional determinants. Several practical implications of this research exist. Organizations may have less ability to in¯uence job satisfaction than originally. Indeed, global interventions to increase job satisfaction may not be as e€ective and individual a€ective dispositions must be considered. For example, employee compensation systems present one of the strongest motivators in the work environment (Flannery, Hofrichter & Platten, 1996). Speci®cally, successful linkage of merit pay to performance can increase positive outcomes such as job motivation and job satisfaction (Heneman, 1992). This means that normal compensation systems may be missing the target if they do not consider how a€ective dispositions a€ect their employees. In light of

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the e€ects of positive mood on leadership job satisfaction and group performance (George, 1995), managerial personality style must be assessed to provide feedback to managers so that they can be a more e€ective leader. Also employee development/training can be improved by giving employees a better understanding on how they appraise their work environment. Furthermore, a€ective dispositions can inform employers on correct interventions to decrease unwanted absences and/or turnover (Judge, 1993). This research points to the gains obtained from both a€ective and personality based research in organizational settings. Additionally, consider the use of employee reactions as criteria to evaluate the impact of a training program. Typically researchers (Alliger & Janak, 1989) have found that the correlation between trainee reactions and the other three levels of training criteria (Kirkpatrick, 1987) such as learning, behavior and outcomes, is low. Perhaps to the extent the training program is remedial (or participants were mandated to attend), individual di€erences in negative and positive a€ect will reduce the correlation between reactions and other types of criteria. The research reported by Quinones (1995) is also in line with this interpretation. Another practical implication of the results reported here is for the evaluation of applicant reactions to personnel selection system characteristics (Arvey & Sackett, 1993). Merely asking applicants whether a predictor is face valid may be misleading. In fact, this concern is applicable to all self-report measures. Further, job satisfaction has been studied in the literature on downsizing, layo€s and survivor guilt. Given that the results reported in this paper indicate a larger correlation between negative and positive a€ect compared to the correlation between negative a€ect and job satisfaction, it seems reasonable to question whether job satisfaction and job dissatisfaction are bipolar. In any event, pending further research, it may not be wise to treat job satisfaction and dissatisfaction as proxies of one another. If the intent is to assess job dissatisfaction following layo€s, the use of satisfaction measures appears questionable. Researchers interested in assessing respondents' negative attitudes should not rely on measures of job satisfaction. Nothing in this research is to be construed as a suggestion to select people based either on job satisfaction or on negative a€ect measures. As long as a dissatis®ed employee discharges his or her job satisfactorily, a€ect should not be a selection criterion. Only if an a€ect±job performance link can be shown, should a€ect serve as a predictor for personnel selection. Employing a€ect as a predictor without documenting this link raises a whole host of ethical questions. For example, should an individual who is su€ering from a slight depression be denied a job that s/he is capable of performing satisfactorily? It would be pro®table to examine the discussion of these issues in the personality research literature (cf. Barrick & Mount, 1991). Future research can concentrate on possible moderator variables other than organizational size, employee tenure or type of organization (for pro®t or non-pro®t) to both positive and negative a€ective relationships with job satisfaction. Given that job satisfaction is an important construct in industrial±organizational psychology and given that most of the prior research on job satisfaction has focused on situational determinants, this study was a preliminary attempt to systematically examine the dispositional factors. It is hoped that future research will build on this work to ultimately realize a more comprehensive understanding of job satisfaction and its correlates.

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Further reading Articles included in the cumulation Chen, P. Y., O'Connell, B. J. & Spector, P.E. (1993). Impact of mood and negative a€ectivity on stressor-strain relationships. Paper presented at the Annual Conference of the Society for Industrial and Organizational Psychology, San Francisco, CA. Czajka, J. M. & Begly, T. M. (1990). The e€ects of dispositional a€ectivity on work attitudes and behavior. Paper submitted for publication. Decker, P. J., & Borgen, F. H. (1993). Dimensions of work appraisal: stress, strain, coping, job satisfaction and negative a€ectivity. Journal of Counseling Psychology, 40, 470±478. Erez, A. & Judge, T. A. (1997). Psychological processes underlying the dispositional source of job satisfaction. Paper presented at the 12th Annual Conference of the Society for Industrial and Organizational Psychology, St. Louis, MO. George, J. M. (1991). Time structure and purpose as a mediator of work-life linkages. Journal of Applied Social Psychology, 21, 296±314. Harris, S. G., & Mossholder, K. W. (1996). The a€ective implications of perceived congruence with culture dimensions during organizational transformation. Journal of Management, 22, 527±547.

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Kemery, E. R., Bedeian, A. G., & Zacur, S. R. (1996). Expectancy-based job cognitions and job a€ect as predictors of organizational citizenship behaviors. Journal of Applied Social Psychology, 26, 635±651. Kim, S. W., Price, J. L., Mueller, C. W., & Watson, T. W. (1996). The determinants of career intent among physicians at a US air force hospital. Human Relations, 49, 947±976. Long, B. C. (1993). Coping strategies of male managers: a prospective analysis of predictors of psychosomatic symptoms and job satisfaction. Journal of Vocational Behavior, 42, 184±199. Mastrangelo, P. M., Guy, J. L., Greenamyer, C. J., Melanson, K. M. (1997). Life and job satisfaction: to disaggregate or not to disaggregate. A Presentation at the Twelfth Annual Conference of the Society for Industrial and Organizational Psychology, St. Louis, MO, April. Moorman, R. H. (1993). The in¯uence of cognitive and a€ective based job satisfaction measures on the relationship between satisfaction and organizational citizenship behavior. Human Relations, 46, 759±776. Spector, P. E., & O'Connell, B. J. (1994). The contribution of personality traits, negative a€ectivity, locus of control and type A to the subsequent reports of job stressors and job strains. Journal of Occupational and Organizational Psychology, 67, 1±11. Williams, L. J., Gavin, M. B., & Williams, M. L. (1996). Measurement and nonmeasurement processes with negative a€ectivity and employee attitudes. Journal of Applied Psychology, 81, 88±101.