EMPLOYEE ATTITUDES AND CUSTOMER SATISFACTION: MAKING THEORETICAL AND EMPIRICAL CONNECTIONS

PERSONNEL PSYCHOLOGY 1995.48

EMPLOYEE ATTITUDES AND CUSTOMER SATISFACTION: MAKl NG THEORETICAL AND EM PI REAL CONNECTIONS

MARK J. SCHMIT Department of Management

University of Florida

STEVEN €! ALLSCHEID Stanard & Associates, Inc.

Previous research has established links between employee attitudes and customer satisfaction. Little theory has been used to help explain and build on the results of these studies. A theoretical model of the employee attitude-customer satisfaction process is proposed based on Bagozzi’s (1992) model of attitudes, intentions and behavior. Em- ployee attitudes and intentions, and customer satisfaction data from a service-oriented organization with 160 offices is used to provide an initial test of the usefulness of the model. Cross-validations of the model with and without common method variance were conducted. The results provide strong support for the model. Suggestions are offered on extensions of the model in future research.

In the face of current economic realities, U.S. business organizations are searching for ways to remain competitive. One front on which these efforts have been made is that of product and service quality improvement. This has been the case particularly for organizations in the growing service sector. Many of these organizations are searching for practical ways to improve customer satisfaction with both tangible and intan- gible products and services. The anticipated result of improved service quality is an improvement in the bottom line of the organization (Tomp- Ens, 1992; Weaver, 1994).

A productive avenue of research that has addressed the issue of service quality improvement and its potential economic benefits has come from the concomitant work of organizational behavior and. marketing researchers (e.g., Bowen & Schneider, 1988; Zeithaml, Parasuraman, & Berry, 1990). This line of research began with an exploration of the relationships between employee and customer perceptions of service quality. For example, it was found that when bank employees reported the existence of a service imperative in their branch, customers reported that

Correspondence and requests for reprints should be addressed to Mark J. Schmit, Department of Management, University of Florida, 201 Business Building, Gainesville FL 32611 or e-mail at [email protected].

COPYRIGHT 0 1995 PERSONNEL PSYCHOLOGY, INC.

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they received high quality service (Schneider, Parkington, & Buxton, 1980). A follow-up study found that employee work attitudes were positively related to customer perceptions of quality service (Schneider & Bowen, 1985). Several studies have replicated these findings. At NCR, employee perceptions of service-quality climate were strongly related to objective quality ratings (Ulrich, Halbrook, Meder, Stuchlik, & Thorpe, 1991); and at Ford Motor Credit Company it was found that attitudes regarding workloadstress, trainingldevelopment, job/company satisfaction, and work group/teamwork were all significantly related to customer satisfaction (Johnson, Ryan, & Schmit, 1994). Finally, it has been shown that employees who report higher levels of satisfaction also believe they are able to deliver excellent service (Schlesinger & Zornitsky, 1991).

The service-quality line of research was extended next to the issue of bottom-line outcomes resulting from improved customer satisfaction. Employee attitudes and employee and customer perceptions of service quality all have been shown to be conditionally related to the profitability of an organization by various researchers (see Schneider, 1991 for a review; also see Johnson et al., 1994).

Although the currently identified relationships suggest important connections between the alternative variable sets, further conceptual and empirical evidence is needed to establish the links among all of these variables. Schneider and Bowen (1992) suggested that service quality can be improved by the implementation of a service climate; that is, a climate in which everyone from upper management to public-contact employees emphasizes a commitment to service and is given the required training, equipment, facilities, and so forth, to facilitate high quality service. Schneider and Bowen also suggest that a positive climate for employee well-being (i.e., a climate that leads to job satisfaction) is needed to facilitate quality service. Measurement of employee perceptions of both the climate of well-being (i.e., an assessment of policies and practices that lead to positive experiences) and the climate of service (i.e., an assessment of policies and practices that support employee efforts to serve the customer well) involves the employee in an attitudinal appraisal of the work situation. Thus, the relationship between customer satisfaction and employee attitudes needs to be put into the context of an attitudinal model that links attitudes and outcomes.

Bagozzi (1992) recently developed a model of attitudes, intentions, and behavior relationships which he proposed to replace the deficient attitude models currently popular in psychology (e.g., Ajzen, 1991; Ajzen & Fishbein, 1980). Essentially, his model elaborates on a more general model developed by Lazarus (1991) that links appraisal, emotional response, and coping in a sequential process. Bagozzi (1992) suggests that the appraisal process involves the assessment of “outcome-desire units.”

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Outcome-desire conflicts occur when an individual fails to meet a goal or experiences an unpleasant event; outcome-desire fulfillment ~ ~ c u r s when a goal is met or the individual has a pleasant experience. These outcome-desire experiences are followed by negative or positive emotional responses, respectively. In turn, negative responses are followed by coping intentions to reduce the conflict, and positive responses are followed by the coping intent to maintain, increase, or share the outcome (i.e., coping responses). Finally, behavior follows from intent.

Gotlieb, Grewel, and Brown (1994) used the Bagozzi (1992) model to explain the sequential relationship among customers’ perceptions of quality (i.e., an appraisal), customers’ satisfaction (i.e., an emotional response), and customers’ intentions (i.e., Coping) to use a service in the fume. The model can also be applied to the other side of the customer- service provider relationship. The measurement of the Organizational climates of well-being and service involve employee appraisals of their work situations. These measures typically involve assessments of goal

(e.g., Burke, Borucki, & Hurley, 1992; James & James, 1989), all of which are related to the employee’s ability to meet goals and have positive work experiences. Further, the appraisal of climate has been shown to be substantially related to both job satisfaction (James &James, 1989) and mood at work (see George & Brief, 1992). In other words, evidence for the appraisal-emotional response link is strong.

There is also strong evidence for the emotional response-coping link that is pertinent to the customer-service provider relationship. Positive mood has been found to facilitate helping behaviors and related actions in laboratory (e.g., Isen, 1970) and natural settings (e.g., Isen, 1987); job satisfaction has been shown to be related to prosocial behaviors, including helping behaviors, in organizations (e.g., Puffer, 1987; Smith, Organ, Lk Near, 1983). Thus, it might be expected that happy or satisfied employees should have intentions to share and extend these emotions with customers (cf. Brief & Motowidlo, 1986), which should spawn an aPPraisal-OUtCOme chain of events leading the customer to be satisfied and to have intentions to use the service again, B la Gotlieb et al. (1994).

The study we conducted was designed to put the relationship between employee attitudes and customer satisfaction into a comprehensive conceptual framework (i.e., the Bagozzi framework) and test the efficacy Of this model in explaining the relationships. Therefore, the objective was to show that employee appraisals of the climates of well-being and service are positively related to a latent affective variable, which conse- quently affects intentions to provide quality service to customers. These intentions should be positively related to actual customer service behavior. The model we tested is presented in Figure 1. Four measured

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variables, management, supervisor, monetary and service support, were classified as climate appraisal variables. Consistent with the Bagozzi (1992) model, these four appraisal variables were hypothesized to repre- sent an underlying emotional response or affect toward the organization. Although in the Bagozzi model appraisal precedes emotional response, the point at which measurement of climate takes place presupposes that individuals have made many previous appraisals of their situations and formed affective responses, which now drive their responses to the measured variables. In the next step, the latent affect variable is linked to the coping response or intention variable labeled service intentions. A measure of employees’ perceptions of their ability to provide customers with quality service and products (i.e., perceived service-product quality) was used to identify the latent variable of service intentions. Finally, intentions were hypothesized to be linked to actual behavior, customer service, as measured by a customer satisfaction survey.

Aggregation Issues

Organizational climate can only be expressed in terms of an individual’s perceptions of the elements of the climate (Schneider, 1990). However, the aggregation of individuals’ perceptions is appropriate if the grouping makes conceptual sense given the researcher’s objective (Schneider, 1990). In the current study, the participating organization was decentralized into many offices nationwide, each being held to general company policy although given the latitude to respond to local labor and economic market influences, as is the case with many organizations that have nationwide service locations. Ostroff (1992,1993a) noted that job satisfaction, and other work-related attitudinal variables, may be as much influenced by situational variables as individual differences. Ag- gregating attitudes within organizational units is based on a belief in the existence of climate’s influence on satisfaction (Ostroff, 1993b) and the workings of the attraction-selection-attrition model (Schneider, 1987) to create work units with similar levels of satisfaction. In addition, the company under study collects customer satisfaction data at the office level to determine which offices are producing satisfied customers; interven- tions are also made at the office level. Accordingly, it made both conceptual and practical sense to aggregate employee climate perceptions gathered in the current study at the office level. However, James (1982) noted that statistical assessment of between- versus within-group variance should be conducted before aggregation is considered appropriate. Accordingly, we conducted these tests and provide empirical support for the aggregation decision.


Method

Sample

Approximately 9,000 employee attitude surveys were distributed to the employees of a large security systems company with offices nationwide. Representative samples of employees were surveyed in 361 offices of the larger organization, which overlapped all geographical areas in the United States. A total of 5,085 employees completed the survey for a response rate of approximately 57%. Of this original sample, 160 offices had both personnel whose primary position requirements involved direct customer contact through sales and service functions and an office size greater than 3 individuals (10 offices had 3 or less employees and the remaining offices were corporate sales offices with little direct customer contact or administrative offices, such as monitoring centers, records offices, etc.). This smaller sample of 3,464 individuals responding to the survey was used in the current study (response rate for this group was still about 57%) but aggregated at the office level, so the sample size is effectively 160. The average office size was about 37 people. A total of 31,362 customers served by the 160 offices completed a customer satisfaction survey. The overall response rate for the survey was 10.7%.

Measures

An employee attitude survey was developed and conducted as part of the organization’s on-going commitment to gain input on important organizational variables from employees to understand and improve the operation of the business. A principal axis factor analysis of data from a sample of 5,085 employees suggested a five-factor structure (results available from the first author). Item responses were made on a 4-point scale ranging from 1 = strongly disagree to 4 = strongly agree. The first subscale was labeled Supervisor Support. This scale was composed of 14 items (a = .95) including the following sample items: “My supervisor lives up to hisher promises,” and “My supervisor encourages an open and participative work environment.” The second subscale was labeled Management Support. This scale was composed of 7 items (a = .91) including the following sample items: “I have confidence in the fairness of management,” and “Management is really interested in the welfare of associates.” The third subscale was labeled Service Support. This scale was composed of 8 items (a = 37) including the following sample items: “(Company X) is making the necessary effort to improve external customer service,” and “(Company X) is making the necessary effort to improve internal customer service.” The fourth subscale was labeled


Monetary Support. This scale was composed of 3 items (a = .75) including the following sample item: “I am paid fairly for the kind of work I do.’, The final subscale was labeled Perceived Service-Product Quality. This scale was composed of 4 items (a = .71) including the following sample items: “Our company’s products and services meet the needs of our customers,” and “I’m proud of the quality of our company’s products and services.”

Customer Satisfaction Survey

The customer satisfaction survey was developed and conducted to gain input from customers regarding the company’s effectiveness in providing products and services. Customers responded to eight items on a scale ranging from strongly disagree (1) to strongly agree (4). The items included: “My sales representative demonstrates a comprehensive knowledge of (the company’s) products and services,” “(The company’s) main- tenance/service representatives are competent, courteous, and knowl- edgeable,” and “I am satisfied with (the company) as a provider of services.,’ This scale was labeled the Customer Satisfaction scale (a = .95), though it could be argued that the measure was a combination of both perceived quality and satisfaction, as these variables have been traditionally defined in the marketing literature (cf. Oliver, 1981). Overall customer satisfaction was calculated for each office by taking the average of the percent of that office’s customers responding to the eight questions with a 3 or 4 (tend to agree or strongly agree). The method was the company’s way of reporting the data and was the only measure available to us. This is a common method of reporting this type of data (cf. Peterson &Wilson, 1992).

Results

Two types of intraclass correlations were computed to justify the aggregation of data at the office level (James, 1982). ICC(1) is a compar- ison of the between sum of squares to the total sum of squares, based on the results of a one-way ANOVA in which offices are specified as the independent variable. ICC( 1) estimates the proportion of variance in individuals’ responses that can be accounted for by office differences; James noted that ICC( 1) values have ranged from 0 to .5 in previous studies, with a median of .12. ICC(2) estimates the reliability at the group level (Le., the correlation between the mean and the mean of another hypothetical group drawn from the same population). It is an expression of the mean square between minus the mean square within,


TABLE 1 Means Standard Deviations, and Intercomelatiom

of Measures for Both Samples (N = 160)

Measure M S D 1 2 3 4 5 6 Samule group 1 1. Management support 24.03 3.63 - 2. Supervisor support 51.99 6.59 .68' - 3. Monetary support 10.87 1.23 .39" .19* - 4. Servicesupport 28.09 3.46 .78' .59* .37* - 5. Perceivedservice/prod. quality 13.95 1.69 .63* .43* .35* .65* -

Samde 00~1) 2 6. Customer satisfaction 85.96 6.54 .24" .05 .35* .W .27* -

1. Management support 23.81 3.93 - 2. Supervisor support 51.43 7.13 .69* - 3. Monetary support 10.82 1.37 .47* .23* - 4. Servicesupport 28.20 3.74 .77' .55* .48' - 5. Perceived semceiprod. quality 14.25 1.67 .68* .43* .53* .68* - 6. Customer satisfaction 85.96 6.54 .24* .05 .38* .24* .30* -

* p < .05

divided by the mean square between. The ICC(1) and ICC(2) values for the 4 climate measures and 1 service intention measure were computed; the between-group effects were significant at p < .01 for all five measures. The ICCs were found to be adequate for the five measures, as ICC(1)s were at or above the median level of previous studies and ICC(2)s were all above .70: management support (.15, .72), supervisor support (.12, .70), monetary support (.12, .70), service support (.16, .74), perceived service-product quality (.15, .72).

Given the size of the sample and number of offices available, a cross- validation of covariance models approach (MacCallum, Roznowski, Mar, & Reith, 1994) was used to test the proposed model (see Figure 1). Self-report data at the individual level for each office were randomly divided into two groups and then aggregated, forming two sets of data with 160 office level data points for each variable, with a total sample size of 1,732 individuals in each group. Because customer satisfaction data were available only in the aggregated form (i.e., at the office level), the original and cross-validation samples shared the same data on this variable. Table 1 contains the means, standard deviations, and zero-order correlations for each of the six measured variables within the two groups.

The test of the fit of the proposed model to the data was conducted using LISREL 8 (Joreskog & Sorbom, 1993). Four indicators of fit were used to assess the models tested, including x2, root mean square error of approximation (RMSEA; Browne & Cudeck, 1993), the Normed Fit In- dex (NFI; Bentler & Bonett, 1980) and the Comparative Fit Index (CFI; Bentler, 1990). A non-significant x2 indicates a good fit between model


and data; however, x2 has been shown to be affected by sample size (Bentler & Bonett, 1980), therefore other indexes of fit were also exam- ined. A RMSEA of .05 has been suggested as an indicator of close fit, whereas .08 suggests a reasonable fit of the model to the data (Browne & Cudeck, 1993); NFI and CFI values greater than .90 also suggest a reasonable fit (Bentler & Bonett, 1980).

As recommended by Anderson and Gerbing (1988), we first tested the measurement model for the latent affect variable (Group 1 data only); the four climate measures were expected to have significant loadings on affect. The model fit reasonably well, ~ ' ( 9 ) = 4.93, p = .09; RMSEA = .09; NFI = .98; CFI = .99. All loadings were significant (see Figure 1).

The entire structural model was tested next, once again using only data from Group 1. Note that in this model single observed variables were available for the latent variables service intention and customer service. As recommended by Williams and Podsakoff (1989), factor loadings for these two variables were set at the square root of the reliability of the measured variables (the Q coefficients noted earlier were used in this procedure), and the random measurement error was fixed at the quantity 1 minus the reliability multiplied by the variance. The test of the structural model fit the data fairly well, x2(9) = 28.23, p < .05; RMSEA = .12; NFI = .93; CFI = .95. The standardized path coefficient between affect and service intentions (.83; t = 9.85, p < .05) suggested a strong relationship. A more moderate relationship between service intentions and customer service was indicated by the standardized path coefficient (.33; t = 3.71, p < .05). A significant indirect effect of affect on customer service was also indicated by a moderate standardized path coefficient (.28; t = 3.61, p < .05).

Several alternative models could have fit the data. To test for this possibility, additional analyses were conducted. First, a direct path from affect to customer service was added to the model. This addition did not result in a significant improvement of fit, x&[(l) = 0.23, p < .88. Also, the path was not significant. A second possibility might be that affect directly affects customer satisfaction, and customer satisfaction then affects employees' perceived service-product quality. In other words, future service intentions are affected by customer satisfaction through some feedback mechanism. This model, however, did not fit nearly as well as the proposed model, x2(9) = 111.48,~ c.05; RMSEA = .27; NFI

The cross-validation of the model was conducted using multi-sample = .73; CFI = .74.


TABLE 2 Cross-Sample Intercorelatiom of Measures for Both Samples

(N = 160 for Each Set of Of-Diagonal Correlations)

Measure 1 2 3 4 5 6

1. Management support .35* .19* .08 .29* .28* .24* 2. Supervisor support .08 .13 .I0 .14 .15* .05 3. Monetary support .23* .07 .15* .17* .27* .35* 4. Service support .29* .08 .17* .38* .29* .23* 5. Perceived service/prod quality .32* .I2 .19* .25* 33' .27* 6. Customer satisfaction .24* .05 .35* .23* .27* 1.0

*p< .05

LISREL analysis (Joreskog & Sorbom, 1993). A hierarchical process of testing several cross-validation models, ranging from tight to loose constraint, was planned, following MacCallum et al. (1994). However, the most tightly constrained model fit the data reasonably well, ~ ~ ( 3 0 ) = 88.02, p < .05; RMSEA = .08; NFI = .90; CFI = .93, so no other models were tested. The test showed that factor loadings, structural paths, and error variances were all invariant across samples providing strong support of cross-validation.

A potential problem in the interpretation of the previous results stems from the fact that five of the six measured variables were self- report variables. Thus, common method variance may inflate the estimates of some of the relationships found in the initial analyses. Pod- sakoff and Organ (1986) suggested that this problem might be over- come in an aggregated sample by randomly splitting the aggregated units and then correlating the different variables across groups. In the current study we used the same sample split that was used for the cross- validation. Correlating the six variables from one sample with the six variables from the other sample resulted in a 6 x 6 correlation matrix. Above and below the diagonal elements were two distinct sets of correlations, in which eachvariable from one sample was correlated with each variable from the other sample. This correlation matrix is presented in Table 2. A test of the equivalence of these two sets of off-diagonal correlations was conducted using MULTICORR (Version 2.4; Steiger, 1987; also see Steiger, 1980a, 1980b). The test suggested that the two sets of correlations were not significantly different, ~ ~ ( 1 5 ) = 14.42, p = .49. Therefore, we used the average covariances of the two cross-sample covariance matrixes to retest the proposed model in Figure 1. The variances used for the diagonal elements were the average variances of the measures across the two samples. The loadings and error variances of perceived service-product quality and customer satisfaction were fixed in the same manner as in the previous models tested.


The model with common methodvariance controlled fit the data reasonable well, x2(9) = 20.29, p < .05; RMSEA = .08; NFI = .90; CFI = .93. On Figure 1, the standardized path coefficients for the model are the second numbers inside the parentheses. The only major differences between this model and the same model where common method variance was not controlled were in the loadings of the 4 climate appraisal variables on the latent affect variable. The structural relations differ very little across the two analyses. These findings provide further support for the adequacy of the proposed model in explaining the relationships found in the data.

Discussion

The findings in this study were consistent with the Bagozzi (1992) model. As predicted, climate appraisals were strongly associated with an underlying emotional response, which was strongly associated with coping responses or intentions; intentions were found to be moderately related to behavior, in this case customer service. Tests of alternative models supported the finding that affect had an indirect impact on customer service through the mediator variable service intentions. The initial findings were further supported by both a cross-validation analysis and an analysis that controlled for common method variance.

The contribution of the current study is to place the relationship between employee attitudes and customer satisfaction in a framework that can facilitate understanding and extend previous findings in this area. Bagozzi’s (1992) notion of outcome-desire units can go a long way toward enhancing the knowledge of the mechanisms ultimately responsi- ble for customer satisfaction. For example, supervisors may play a very supportive role and cause employees to have very pleasant experiences; however, upper management may have many policies and procedures that keep employees from meeting the desired organization goals; or an under-staffed or under-qualified human resources department may not provide programs to support quality service delivery (e.g., training) so employees cannot meet a desired organizational goal. In this case, when the employees undertake a climate appraisal, the additive effects of the different outcome-desire units may amount to a general negative affect, which in turn is likely to negatively impact service intentions and ultimately, service behavior.

In the current study, management support and service support were most strongly related to affect in both of the analyses (i.e., with and without common method variance). These variables appear to be more highly related to organizational goal fulfillment than supervisor support and monetary support. In fact, supervisor support may have more of a direct impact on the pleasantness of the experiences of employees. In


the analysis of the model without common method variance, the loading of supervisor support on affect is substantially lower in magnitude than in the analysis with common method variance. This may be because the part of the variance associated with common method variance is affect related to pleasant experiences in the workplace and this affective glow influences the appraisals of all elements of the climate to some degree. This is all primarily conjecture, but the point is that the present research and the Bagozzi (1992) framework provide a base from which many future efforts might begin to better explain the employee attitude- customer satisfaction relationship.

The finding that management support and service support were most strongly related to affect is consistent with current thinking on quality customer service and has practical implications for organizations. It has been suggested that a service climate must begin at the top with top management support of customer service imperatives and employee relations efforts necessary to be successful in the service industry (Schneider & Bowen, 1995). The management support and service support variables measured in the current study were indicative of the top level in- volvement in the establishment of a service climate. In branches where these forms of support were better communicated or made evident to the employees through organizational contacts, affect was increased, and by extension both service intentions and customer satisfaction increased. These findings suggest that top management must take an active role in establishing a service climate by showing direct support for service imperatives and concern for employees’ welfare.

Although the results of the current study include what amount to two cross-validations, there are some problems that limit the usefulness of the study. First, the measured variables in the study were empirically, rather than conceptually designed. That is, an organization made available to us measures that could be loosely placed into the Bagozzi (1992) framework. A much better approach would be to develop measures that are more consistent with the model. Bagozzi (1992) and Lazarus (1991) provide guidance in this respect.

Second, it appears that the measurement of customer satisfaction has a set of unique problems that may make the measurement of that construct more tenuous than the measurement of employee attitude constructs. Peterson and Wilson (1992) showed that due to the nature of most self-report customer satisfaction measures, these measures tend to have very negatively skewed distributions. They noted that this skew will lead to a underestimation of the “true” relationship between customer satisfaction measures and measures of other variables. The distribution of responses to the customer satisfaction measure used in this study, however, did not appear to be skewed to the point where the results might be adversely impacted (i.e., skew = -.56).

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The response rate on employee surveys was adequate in the current study, but the response rate for customer satisfaction surveys was not nearly as good. Thus, it is unclear what the effects of this low response rate could be on the results. Peterson and Wilson (1992) suggest that the most likely outcome of low response rates would be a skewed or bi- modal distribution, this did not appear to be the case in the current study. Further, Peterson and Wilson showed that in 49 studies, satisfaction percentages were not related to response rate percentages. Thus, the low response rate in the current study may not have influenced the results.

Another potential problem with the current study was the simplicity of the model tested (James, 1980). The structural equation modeling approach taken here tends to favor rather simple models; that is, they are easier to fit than more complex models. Accordingly, the relationship between employee perceptions and customer perceptions is likely to be spuriously high in the current study. Future research is needed to expand the current model to include other pertinent variables.

Climate variables not included in this study have been identified in previous research (Burke et al., 1992: goal emphasis, non-monetary re- ward orientation, work group cooperation; James & James, 1989: role stress and lack of harmony, job challenge and autonomy, workgroup cooperation, warmth, and friendliness) that may play a part in the forma- tion of outcome-desire units which are important to consider in the employee attitude-customer satisfaction framework. Other variables that are likely to affect customer satisfaction include individual differences of the customers (e.g., negative affectivity, impulsivity), the degree to which the customer co-produces the service, product, or both, the ap- pearance of the service facility and employees, the degree of customer focus across organizational functions such as human resources, marketing, and operations, and the expectations and needs of the customers (Schneider & Bowen, 1995). Finally, the cuqtomer satisfaction side of the model could be expanded to reflect the entire Bagozzi (1992) model from both the employee and customer sides. This model would essentially replicate both the current study and the Gotlieb et al. (1994) study. We could not attempt this model as our customer satisfaction measure contained items that have traditionally been linked to either perceived quality (i.e., appraisal) or customer satisfaction (i.e., affect).

An alternative way to look at the data from the current study would be to set aside the latent variable model based on Bagozzi’s (1992) model and simply examine the zero-order correlations. An examination of the correlations in both Tables 1 and 2 suggests that monetary support may have a direct impact on customer satisfaction. In the latent variable model monetary support was less related to affect than both management and service support, but monetary support is also likely to have remaining specific variance unrelated to affect which may be related to


customer satisfaction (e.g., pay or rewards may motivate the employee to give the customer good service regardless of his or her affect). This is evidenced by an examination of the zero-order correlations. The correlation between monetary support and customer satisfaction is .35. If perceived service quality is partialed out of this relationship, the partial correlation is still a significant .28. This point highlights the potential problem with the simplicity of the current model and demonstrates the need for models that include, but go beyond, the simple mediated relationship between affect and customer satisfaction.

Finally, the current study included data from a single organization, at a single point. These limitations lead to questions about generaliz- ability and the direction of causality. For example, when data are aggregated at the office level, factors in the external environment of the office, such as economic conditions, could affect either customers, employees, or both in ways that might change the proposed relationships between the groups’ attitudes (i.e., climate appraisals or customer satisfaction). Another possibility is that profitable offices have the luxury of providing better working environments that start a chain of events that lead to greater customer satisfaction. If this is the case, then cost-benefit con- siderations should receive more consideration in the research relating employee attitudes to customer satisfaction. These issues could not be addressed in this study and need to be considered in future research.

Although the direction of causality from employee affect to customer satisfaction is left in question because of the cross-section nature of the current study, previous research supports the implied direction. Many lab and field experiments have been conducted that collectively support the notion that positive affect influences organizational behavior (see Isen & Baron, 1991 for a review). More specifically, positive affect increases helping behaviors (Isen, 1970, 1987), which logically would include helping customers in a positive manner (Brief & Motowidlo, 1986). Our study is consistent with Isen’s work and supports the logical extension made by Brief and Motowidlo.

The current study adds to the growing body of evidence supporting the links between employee attitudinal variables and customer satisfaction. In addition, it places the findings in a conceptual model that begins to explain the relationships. Future research should be aimed at the further development of the model. This research is important in the development of theory that will lead to organizational programs effective in their positive influence on customer satisfaction.

REFERENCES

Ajzen I. (1991). The theory of planned behavior: Some unresolved issues. Organizafwml Behavior and Human Decision Processes, 50,17921 1.


Ajzen I, Fishbein M. (1980). Understanding attitudes and predicting social behavior: Engle- wood Cliff, NJ: Prentice-Hall.

Anderson JC, Gerbing DW. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103,411423.

Bagozzi Rl? (1992). The self-regulation of attitudes, intentions, and behavior. Social Psychology Quarterb, 55,178-204.

Bentler PM. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107,238-246.

Bentler PM, Bonett DG. (1980). Significance tests and goodness-of-fit in the analysis of covariance structures. Psychological Bulletin, 88,588-606.

Bowen DE, Schneider B. (1988). Service marketing and management: Implications for organizational behavior. In Staw BM, Cummings LL (Eds.), Research in organizational behavior (Vol. 10, pp. 43-80). Greenwich, CT: JAI Press.

Brief Ap, Motowidlo SJ. (1986). Prosocial organizational behavior. Academy of Manage- ment Review, 11,710-725.

Browne MW, Cudeck R. (1993). Alternative ways of assessing model fit. In Bollen KA, Long JS (Eds.), Testing structuml equation models (pp. 136-162). Newbury Park, C A Sage.

Burke MJ, Borucki CC, Hurley AE. (1992). Reconceptualizing psychological climate in a retail service environment: A multiple-stakeholder perspective. Journal of Applied

George JM, Brief M. (1992). Feeling gooddoing good: A conceptual analysis of the mood at work-organizational spontaneity relationship. Psychological Bulletin, 112, 3 10-329.

Gotlieb JB, Grewal D, Brown SW. (1994). Consumer satisfaction and perceived quality: Complimentary or divergent constructs? JoumalofAppfied Psychology, 79,875485.

Isen, AM. (1970). Success, failure, attention, and reaction to others: The warm glow of success. Journal of Personality and Social Psychology, 294-301.

Isen AM. (1987). Positive affect, cognitive processes, and social behavior. In Berkowitz L (Ed.), Advances in aperimental socialpsychology (Vol. 20, pp. 203-253). New York Academic Press.

Isen AM, Baron RA. (1991). Positive affect as a factor in organizational behavior. Re- search in Organizational Behavior; 13,l-53.

James LR. (1980). The unmeasured variables problem in path analysis. Journal ofApplied Psychotegy, 65,415-421.

James LR. (1982). Aggregation bias in estimates of perceptual agreement. Journal of Applied Psychology, 67,219-229.

James LA, James LR. (1989). Integrating work environment perceptions: Explorations into the measurement of meaning. Journal ofApplied PTchology, 74,739-751.

Johnson RH, Ryan AM, Schmit MJ. (1994, April). Employee attitudes and branch performance at Ford Motor Credit. In Rotchford N (Chair), Linking Employee Survey Data to Organizatwnal Outcome Measures. Practitioner Forum conducted at the Ninth Annual Conference of the Society of Industrial and Organizational Psychol- ogy, Nashville, TN.

Joreskog KG, Siirbom D. (1993). LISREL 8: Structural equation modeling with the SIM- PLZS command hnguage. Chicago, I L Scientific Software International.

Lazarus RS. (1991). Emotion and adaptation. New York Oxford University Press. MacCallum RC, Roznowski M, Mar CM, Reith J\! (1994). Alternative strategiesfor cross-

validation of covariance structure models. Multivariate Behavioml Research, 29, 1- 32.

Oliver RL. (1981). Measurement and evaluation of satisfaction process in retail settings. Journal of Retailing, 57,2546.

Psychology, TI, 717-729.


Ostroff C. (1992). The relationship between satisfaction, attitudes, and performance: An organizational level analysis. Joumal of Applied Psychology, 77,963-974.

Ostroff C. (1993a). Comparing correlations based on individual-level and aggregated data. Journal of AppZied Psychology, 78,569-582.

Ostroff C. (1993b). The effects of climate and personal influences on individual behavior and attitudes in organizations. Organizational Behavior and Human Decision Processes, 56,56-90.

Peterson RA, Wilson WR. (1992). Measuring customer satisfaction: Fact and artifact. Journal of the Academy of Marketing Science, 20,61-71.

Podsakoff PM, Organ DW. (1986). Self-reports in organi~ational research: Problems and prospects. Journal of Management, 12,531-544.

Puffer SM. (1987). Prosocial behavior, noncompliant behavior, and work performance among commission salespeople. Journal of Applied Psychology, 72, 615-621.

Schlesinger LA, Zornitsky J. (1991). Job satisfaction, service capability, and customer satisfaction: An examination of linkages and management implications. Human Resource Planning, 14,141-150.

Schneider B. (1987). The people make the place. PERSONNELPSYCHOLOW, 40,437453. Schneider B. (1990). The climate for service: Application of the construct. In Schneider

B (Ed.), Organizational climate and culture (pp. 383-412). San Francisco: Jossey- Bass.

Schneider B. (1991). Service quality and profits: Can you have your cake and eat it, too? Human Resource Planning, 14,151-157.

Schneider B, Bowen DE. (1985). Employee and customer perceptions of service in banks: Replication and extension. Journal of Applied Psychofogy, 70,423433.

Schneider B, Bowen DE. (1992). Personnelhuman resources management in the service sector. Research in Personnel and Human Resources Management, 10,l-30.

Schneider B, Bowen DE. (1995). Winning theservicegame. Boston, MA: Harvard Business School Press.

Schneider B, Parkington JJ, Buxton VM. (1980). Employee and customer perceptions of service in banks. Administrative Science Quarterly, 25,252-267.

Smith CA, Organ DW, Near Ip. (1983). Organizational citizenship behavior: Its nature and antecedents. Joumal of Applied Psychology, 68,453-463.

Steiger JH. (1980a). Tests for comparing elements of a correlation matrix. Psychological Bulletin, 87,245-251.

Steiger JH. (1980b). Testing pattern hypotheses on correlation matrices: Alternative statistics and some empirical results. Multivariate Behavioral Research, 15, 335-352.

Steiger JH. (1987). How to use MULTZCORR (Version 2.4). Unpublished manual, Calgary, Alberta, Canada: University of British Columbia.

Tompkins NC. (1992, November). Employee satisfaction leads to customer service. HR Magazine, 37, 93-95.

Ulrich D, Halbrook R, Meder D, Stuchlik M, Thorp S. (1991). Employee and customer attachment: Synergies for competitive advantage. Human Resource Planning, 14,

Weaver JJ. (1994, February). Want customer satisfaction? Satisfy your employees first. HRMagazine, 39, 112,110.

Williams LJ, Podsakoff PM. (1989). Longitudinal field methodsfor studying reciprocal relationships in organizational behavior research: Toward improved causal analysis. Research in Organizational Behavior; 11,247-292.

Zeithaml VA, Parasuraman A, Berry LL. (1990). Delivering service quai@. New York: Free Press.

89-104.

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EMPLOYEE ATTITUDES AND CUSTOMER SATISFACTION: MAKING THEORETICAL AND EMPIRICAL CONNECTIONS