Understanding is worker turnover decisions: is it job satisfaction or job fit with quality of life goals?

Taylor, David S. ; Chin, Wynne W.

ABSTRACT

Over 12,000 academic and practitioner studies have been performed relating job satisfaction with voluntary turnover. However, researchers have been frustrated in explaining more than 20 percent of the variance in turnover. This paper presents the notion that traditional measures of job satisfaction may not fully capture the reason for staying or quitting. A new construct is presented that examines the congruence of fit between the job and the person's quality of life goals. By utilizing a PLS structural equation model on a sample of 135 Information Systems workers, this construct is empirically shown to be a better predictor of various measures of turnover decision (i.e., thoughts of quitting, expectation of quitting, and intention to quit) with an average explained variance of 0.50.

INTRODUCTION

The prominent paradigm in the field of voluntary turnover research is that job satisfaction is related to the decision to leave an organization. The purpose of this study is to evaluate whether or not job satisfaction is a broad enough measure of a person's overall feelings about their job or whether a new construct that measures the congruence of fit between the job and the person's goals for quality of life would be a better predictor of turnover.

Understanding the turnover decision is a relevant topic regardless of where an organization is in its business cycle. Even in times like the present when the number of Information Systems job seekers exceeds the number of open positions in the U.S., practitioners are still concerned with attracting the right people and avoiding dysfunctional turnover. Dysfunctional turnover results when the organization loses the personnel that it can least afford to lose such as those with specific skills and/or abilities (Hollenbeck & Williams, 1986). Additionally, a recent ComputerWorld job satisfaction survey of Information Systems workers (ComputerWorld 12/8/2003), determined that 42 percent of IS employees were dissatisfied with their companies. Such a large amount of dissatisfaction potentially results in increased turnover when the job market improves.

LITERATURE REVIEW

Most studies of voluntary employee turnover trace the genesis of the field to the work of March and Simon in 1958. March and Simon introduced the notion of voluntary turnover resulting from an employee's perception of ease of movement and desirability of movement. During the last four decades, job satisfaction and employee turnover have become one of the most studied topics in both academic and practitioner research with over 12,400 studies by 1991 (Hom, Griffeth & Sellaro, 1984; Hom, Caranikas-Walker, Prussia & Griffeth, 1992; Spector, 1996; Brief, 1998; Lee, Mitchell, Holtom, McDaniel, & Hill, 1999).

A significant step forward was taken when Mobley (1977) introduced the notion that turnover was actually a process. "The actual event of quitting is merely the final act following some series of mechanisms that leads to an intent and decision to resign. Thus, the sequence and duration of these mechanisms become of particular interest for the study of turnover." (Dickter, Roznowski, & Harrison, 1996). Mobley started the turnover process with job dissatisfaction being the catalyst. This catalyst then initiated thoughts about quitting and job searching, ultimately leading to an intention to quit and actually quitting. Until Mobley's work, turnover theory and research had not advanced much beyond the general framework of March and Simon (Muchinsky & Tuttle, 1979). Extensive research has followed, refining and expanding Mobley's model, but has still only resulted in explaining less than 20 percent of the variance in turnover (Healy, Lehman, & McDaniel, 1995; Mobley, Griffeth, Hand, & Meglino, 1979). Observed correlations between job dissatisfaction and turnover seldom exceed 0.40 (Hom, Griffeth, & Sellaro, 1984). In fact, in a meta-analysis of 47 studies, Carsten and Spector (1987) revealed a corrected correlation between job satisfaction and turnover of -0.26.

Job satisfaction is the extent to which an employee expresses a positive affective orientation toward his or her job (Gupta, Guimaraes, & Raghunathan, 1992). It has been measured by previous researchers either on a global or faceted basis. Tett and Meyer (1993) concluded from their meta-analysis of 155 studies "the assessment of overall satisfaction is not unduly compromised by the use of facet-based scales" (pg. 281). In other words, there is no difference in the predictive power of models using either the global or faceted measurement approaches. Thus, this study employs a global measure of job satisfaction.

Global measures of satisfaction are typically worded as "All in all, how satisfied would you say you are with your job" (McFarlin & Sweeney, 1992). Although the questions ask about the "job", the position of this paper is that there is room for interpretation as to how the respondent might consider this question. Do respondents answer it by focusing only on the job itself or are they also thinking of the job fit in terms of its' broader impact on his/her life? Perhaps traditional research focusing on job satisfaction is missing the true beliefs that make an employee begin having thoughts about quitting. The defining example is the schoolteacher who dislikes her job but does not intend to quit because the work hours satisfy her family needs. This person would answer a survey saying that they were dissatisfied with their job, but did not intend to quit--a possible anomaly for an empirical research project.

To answer this question, a new construct is introduced as an alternative to job satisfaction for predicting turnover. This construct evaluates the quitting process as a result of a failure of the job to fit with the quality of life goals the employee has for him or herself. In considering quality of life, George and Jones (1996) noted that "Well-being in life has three complimentary aspects of well being: value attainment (e.g., how one's life is evaluated relative to one or more standards or values such as virtue or success); life satisfaction (the extent to which one is satisfied with one's life or has come to evaluate one's life in positive terms); and the extent to which a person experiences positive feelings or moods" (pg. 318). Brief (1998) described terminal needs in life as professional, social, and personal. Professional goals include professional growth or advancement and status within the community. Social goals include needs for recreation and social relationships. Personal goals are financial, work/family life balance, and spiritual/ethical goals. In this research, measures were developed to tap into these facets of the congruence of fit of the job to the professional, social, and personal goals as well as to overall quality of life goals. Measures to reflect this construct (Table 1) had to be developed because no empirically validated measures were available.

In order to evaluate this new construct against the traditional job satisfaction measures, an empirical study was designed that would gather both job satisfaction and congruence of fit data coupled with 3 representative turnover decision constructs used in turnover literature. By triangulating the predictive power across three different dependent variables, a better understanding of the relative strengths of these antecedents in predicting voluntary turnover can be determined. While, for many, the preferred dependent variable might be actual turnover behavior, that data was not available at the time and therefore surrogate turnover decision variables were used. Specifically, thoughts about quitting, the expectation of quitting, and the formation of the intention to quit were used as surrogates or immediate antecedents to the actual turnover behavior. According to Dalton, Johnson, and Daily (1999), "The key issue with regard to the appropriate use of a surrogate variable, however, is its relationship to the actual variable. The usual assumption is that the surrogate variable is highly correlated with its actual behavioral counterpart" (p. 1338).

The validity of the relationship between intention and behavior has been established in a number of different studies. A series of meta-analysis (Steel & Ovalle, 1984; Hom et al., 1992; Tett & Meyer, 1993) have reported weighted average correlations between intention to quit and turnover behavior of 0.50, 0.36, and 0.52 respectively. The genesis of this well-correlated relationship can be traced back to Ajzen and Fishbein's theory of reasoned action which says "according to the theory of reasoned action, attitudes follow reasonably from the beliefs people hold about the object of the attitudes, just as intentions and actions follow reasonably from attitudes" (Ajzen, 1988, p.32). A number of turnover studies have used this approach with success (Joy, 1989; Doran, Stone, Brief & George, 1991; Sager & Menon, 1994; Igbaria & Greenhaus, 1992; Sager, Griffeth & Hom, 1998; Dalton, Johnson & Daily, 1999; Vandenberg & Nelson, 1999). Moreover, beyond behavioral turnover, these decision constructs can also be argued as potentially predictive of an employee's level of productivity, quality of work, or other withdrawal behaviors.

THE STUDY

A questionnaire was administered to 150 information systems workers in an independent school district. There were 135 usable responses representing a 90 percent response rate. The instrument included the measures of congruence of fit listed in Table 1 along with global measures of job satisfaction adopted from previous research as listed below:

1. All in all, how satisfied would you say you are with your job?

2. How would you rate your satisfaction with your job?

3. Overall, I am quite pleased with my job

Responses were given on a 7-point Likert scale and analyzed using the structural equation modeling software PLS Graph version 3.0 (build 1060). Our model, as shown in Figure 1, uses both job satisfaction and congruence of fit to predict each of the three turnover decision constructs of thoughts about quitting, expectation of quitting, and intention to quit. In addition, model runs were made where only one exogenous construct (either congruence of fit or job satisfaction) was used to predict each turnover decision. These nine analyses using three dependent variables provide multiple criterion validity to the research.

[FIGURE 1 OMITTED]

Measurement Model

The results of our analyses of the measurement model show an excellent fit to the constructs. This conclusion begins by examining the loadings of the individual reflective measures to their respective construct. According to Chin (1998) loadings should be 0.70 or higher, although loadings over 0.60 and even 0.50 can be acceptable if there are sufficient good measures reflecting the same construct. All factor loadings were in excess of .90.

The second and more detailed test of measurement model validity is to see how each item relates to other constructs. Not only should each measure be strongly related to the construct it attempts to measure, but it should not have a stronger connection with another construct. Otherwise, such a situation would imply that the measure in question is unable to discriminate as to whether it belongs to the construct it was intended to measure or to another (i.e., discriminant validity problem). Table 2 provides the correlations of each item to its intended construct (i.e., loadings) and to all other constructs (i.e., cross loadings). As Chin (1998) notes, going down a particular construct column, you should expect to see item loadings to be higher than the cross loadings. Similarly, if you scan across a particular item row, you should expect to see that item be more strongly related to its construct column than any other construct column. This was indeed the case. The items exhibit discriminant validity by loading more highly on their own construct than on other constructs and that all constructs share more variance with their measures than with other constructs.

Structural Model

The structural model reflects the hypothesized linkages between the constructs and defines the strength of the various causal relationships. The test of validity of the structural model can be accomplished by first, determining the amount of variance explained in the dependent constructs and second, by examining the paths among the latent variables to determine the statistical significance of each of the causal relationships.

The variance explained is a measure of the predictive power of the model and is reflected by the R-square values. Table 3 lists the R-square values computed for each of the nine analyses and shows that our model has very high predictive power. As was mentioned earlier, previous research has traditionally explained no more than 20 percent of the variance in turnover

In addition, Table 3 provides the relative impact of our constructs. When job satisfaction and congruence of fit are run individually, each produces a relatively high path coefficient suggesting that each of these constructs can represent a good predictor of the quitting thoughts, expectations, and intentions. However, when evaluated together the congruence of fit becomes the dominant factor and reduces job satisfaction to being statistically non-significant for two of the turnover decisions (thoughts about quitting and intention to quit). In the case of expectation to quit, congruence of fit was more than twice as important.

Although secondary to this study, Table 4 provides information on the relative impact of the congruence of fit facets to overall job fit. These facets can be used to determine which sub areas of job-to-quality of life fit are most important in producing an overall feeling of fit for our IS worker sample. The overall R-square of 0.897 indicates that we've obtained a relatively comprehensive set of facets. Moreover, we find that fit to professional growth and advancement had more than twice the impact than any of the other facets.

DISCUSSION

A student recently said, "My Dad hated his job, but he worked at it for 20 years so that he could put my sister and I through college." The Dad hated the attributes of the job, but stayed with it because it fulfilled a quality of life goal to attain a certain level of financial well being. In response to a job satisfaction survey, the Dad would answer that he was dissatisfied with his job, but had no intentions of quitting. This dichotomy may represent a statistical confound for the traditional job satisfaction-to-turnover models. It was therefore posited that measures of job satisfaction do not necessarily reflect the congruence of fit with overall quality of life goals and thus a new measure could improve the prediction power of a voluntary turnover model.

Our research question thus became: "Do some people look at their jobs in a broader context than just the attributes of the job itself?" In other words if a person is asked about their level of satisfaction with their job, can they differentiate the job itself from its overall influences on the quality of life. Our belief is that not all people are answering these questions in a consistent manner. Some respondents see job satisfaction as it relates strictly to the job itself and others see job satisfaction as the job relates to the quality of life. The lesson for the researcher is that this dichotomy may exist and in order to improve the results of models using the job satisfaction construct, specific instructions should be given to the respondent as to how that question should be evaluated.

On the other hand, the congruence of fit between quality of life goals and the job, with the associated measures used in this study, would appear to be a superior yardstick for future research. This construct eliminates the definitional problems associated with job satisfaction measures and represents a new point of departure from previous studies. Nonetheless, some limitations of this study should be noted. First, the sample size is somewhat limited. That was one of the reasons for using PLS rather than covariance-based methods such as LISREL, EQS, or AMOS. PLS can often produce valid results even under conditions of smaller sample sizes (Chin, 1998; Chin & Newsted, 1999). Minimal recommendations for sample size using PLS range from 30 to 100 cases; while covariance-based methods generally require 200 to 800.

Second, the generalizability of the results could be questioned. The respondents in this study were information systems workers in a school district. The preponderance of them were para-professional help desk people assigned to the individual schools to assist with teacher problems. Therefore, this sample does not generalize to the normal skill mix typically found within the Information Systems department of other businesses. However, despite these limitations the study provides compelling evidence for future work to both validate these findings and gain further insight into the turnover process.

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David S. Taylor, Sam Houston State University

Wynne W. Chin, University of Houston

Table 1. Questions Used to Measure Fit of Job to Quality of
Life Goals *

 SUB
CONSTRUCT COMPONENT MEASURES

Global 1 Overall, my job is aligned with
 the quality of life goals I have
 set for myself

 2 In general, my job fits with my
 overall goals in life

Professional Growth & 1 My job fits the goals I have for
 Advancement professional growth

 2 My job aligns with my goals for
 professional advancement

Professional Status 1 My job fits the goals I have for
 status within the community

 2 My job is consistent with the
 level of community status I seek

Social Recreational 1 My job fits my needs for
 recreational opportunities

 2 My job aligns properly with my
 needs for recreation

Social Relationships 1 My job fits my goals for social
 relationships

 2 My job is consistent with my
 social relationships

Personal Financial 1 My job aligns with my goals for
 financial accomplishment

 2 My job properly fits with my
 financial objectives

Personal Work/Family 1 My job fits my goals for work/
 Balance family life balance

 2 My job aligns with the work/
 family life balance I seek

Personal Spiritual/ 1 My job is consistent with my
 Ethical spiritual/ethical goals

 2 My job aligns with my spiritual/
 ethical goals

* 7 pt scale Likert scale from -3 to +3 (strongly disagree,
disagree, somewhat disagree, neither agree or disagree, somewhat
agree, agree, strongly agree)

Table 2. Measure Cross Loadings

 Intention
 to Quit Global Fit Growth Spiritual

Q5.06 0.9853 -0.6673 -0.6211 -0.4374
Q5.12 0.9851 -0.7297 -0.6637 -0.4543
Q1.08 -0.6533 0.9613 0.8400 0.7574
Q1.16 -0.7107 0.9646 0.8729 0.6742
Q1.01 -0.6638 0.8292 0.9525 0.6194
Q1.09 -0.5831 0.8689 0.9568 0.5984
Q1.07 -0.3538 0.6707 0.5356 0.9625
Q1.15 -0.5116 0.7594 0.6878 0.9708
Q1.06 -0.2861 0.4671 0.3744 0.4681
Q1.14 -0.2412 0.5879 0.5077 0.6828
Q1.05 -0.5480 0.7467 0.6737 0.5705
Q1.13 -0.5966 0.7275 0.6535 0.6557
Q1.04 -0.3135 0.5832 0.4647 0.4105
Q1.12 -0.4781 0.6567 0.6027 0.5211
Q3.01 -0.6688 0.8173 0.7016 0.5724
Q3.02 -0.5913 0.7811 0.6723 0.5481
Q3.03 -0.5952 0.7756 0.6375 0.4896
Q5.01 0.6238 -0.6235 -0.4696 -0.4757
Q5.15 0.7338 -0.6458 -0.5843 -0.4130
Q5.05 0.7813 -0.6038 -0.5780 -0.5606
Q5.16 0.8842 -0.6886 -0.6167 -0.5468
Q1.02 -0.6506 0.8247 0.7843 0.7129
Q1.10 -0.4911 0.7278 0.6552 0.5571
Q1.03 -0.1419 0.2737 0.1954 0.3446
Q1.11 -0.1562 0.3795 0.3184 0.5407

 Work/
 Family Global
 Balance Financial Social Satisfaction

Q5.06 -0.2883 -0.5623 -0.4183 -0.6053
Q5.12 -0.2517 -0.6053 -0.4083 -0.6517
Q1.08 0.6415 0.7245 0.6305 0.7251
Q1.16 0.4437 0.7458 0.6243 0.8407
Q1.01 0.4924 0.5939 0.6103 0.6383
Q1.09 0.4091 0.7159 0.4683 0.6786
Q1.07 0.5532 0.5574 0.4709 0.4988
Q1.15 0.6352 0.6630 0.4785 0.5662
Q1.06 0.9397 0.3278 0.4230 0.3837
Q1.14 0.9624 0.3969 0.5956 0.4390
Q1.05 0.3387 0.9663 0.3621 0.6784
Q1.13 0.4048 0.9645 0.4534 0.6613
Q1.04 0.4895 0.3912 0.9492 0.6965
Q1.12 0.5483 0.4130 0.9601 0.6352
Q3.01 0.4506 0.6879 0.7032 0.9603
Q3.02 0.4168 0.6758 0.6730 0.9864
Q3.03 0.3999 0.6609 0.6504 0.9752
Q5.01 -0.3210 -0.5137 -0.4597 -0.4823
Q5.15 -0.3806 -0.5529 -0.6076 -0.6590
Q5.05 -0.4528 -0.5560 -0.4029 -0.5754
Q5.16 -0.2648 -0.5574 -0.5274 -0.6159
Q1.02 0.5617 0.6862 0.6230 0.7656
Q1.10 0.4973 0.7276 0.3607 0.5808
Q1.03 0.6112 0.2277 0.5183 0.2605
Q1.11 0.6344 0.3832 0.4575 0.2313

 Thoughts Expect
 Quit Quit Status Recreation

Q5.06 0.7508 0.8870 -0.5943 -0.1603
Q5.12 0.6956 0.8646 -0.6317 -0.1534
Q1.08 -0.6383 -0.6362 0.8275 0.4636
Q1.16 -0.6772 -0.6917 0.7949 0.2260
Q1.01 -0.5313 -0.6435 0.6970 0.2866
Q1.09 -0.5584 -0.5730 0.7940 0.2545
Q1.07 -0.4503 -0.5340 0.6020 0.4914
Q1.15 -0.4684 -0.5983 0.7272 0.4513
Q1.06 -0.3706 -0.3365 0.5598 0.5784
Q1.14 -0.3541 -0.3703 0.5367 0.6725
Q1.05 -0.4838 -0.5147 0.7358 0.2797
Q1.13 -0.6285 -0.6288 0.7364 0.3726
Q1.04 -0.4908 -0.3838 0.4846 0.4213
Q1.12 -0.6090 -0.5597 0.5438 0.5492
Q3.01 -0.6569 -0.6633 0.7048 0.2291
Q3.02 -0.5662 -0.5940 0.7310 0.2701
Q3.03 -0.5800 -0.5886 0.7081 0.2583
Q5.01 0.9190 0.6546 -0.5466 -0.3720
Q5.15 0.9391 0.7729 -0.6353 -0.2220
Q5.05 0.6448 0.9308 -0.5644 -0.3386
Q5.16 0.7966 0.9504 -0.6030 -0.2991
Q1.02 -0.6186 -0.6662 0.9346 0.4481
Q1.10 -0.5603 -0.4725 0.9152 0.3329
Q1.03 -0.2713 -0.3193 0.3422 0.9204
Q1.11 -0.3139 -0.3168 0.4444 0.9594

Table 3. Structural Model Results

 R-square of Path
Endogenous Exogenous Endogenous Coefficient
Variable Variable Variable (Satisfaction)

Thoughts about Satisfaction only 0.3940 0.6280
Quitting Congruence of 0.4660
 fit only
 Both 0.4780 n.s.
Expectation Satisfaction only 0.4010 0.6330
of Quitting Congruence of 0.4750
 fit only
 Both 0.4900 0.2160
Intention to Satisfaction only 0.4080 0.6380
Quit Congruence of 0.5050
 fit only
 Both 0.5150 n.s.

 Path
Endogenous Exogenous Coefficient
Variable Variable (Fit)

Thoughts about Satisfaction only
Quitting Congruence of 0.6830
 fit only
 Both
Expectation Satisfaction only 0.5320
of Quitting Congruence of 0.6890
 fit only
 Both 0.5130
Intention to Satisfaction only
Quit Congruence of 0.7100
 fit only
 Both 0.5650

n.s. = not statistically significant obtained via 500
bootstrap resamples

Table 4. Relative Impacts of Facets to Overall Congruence of Fit
(overall R-square 0.897).

 Standardized
 PLS path
CONSTRUCT SUB COMPONENT estimate

Professional Growth & Advancement 0.429
Professional Status 0.178
Social Recreational 0.118
Social Relationships 0.187
Personal Financial 0.162
Personal Work/Family Balance n.s.
Personal Spiritual/Ethical 0.167

n.s. = not statistically significant obtained via 500 bootstrap
resamples