Assessing asymmetric effects in the formation of employee satisfaction

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Tourism Management 28 (2007) 1093–1103 www.elsevier.com/locate/tourman

Research article

Assessing asymmetric effects in the formation of employee satisfaction Kurt Matzlera,, Birgit Renzlb a

Department of International Management, Johannes Kepler University Linz, Altenberger Strasse 69, A 4040 Linz, Austria b Department of Strategic Management, Marketing and Tourism, Innsbruck University School of Management, Austria Received 5 July 2006; accepted 13 July 2006

Abstract Employee satisfaction is a central concern in the service industry in general and in hospitality and tourism in particular. Employee satisfaction is typically viewed as a multi-factorial construct, assuming that some satisfaction factors are more important than others. In this study we investigate whether there is an asymmetric relationship between satisfaction involving single satisfaction factors and overall employee satisfaction. Two theories serve as a framework: prospect theory and the three-factor theory of customer satisfaction, which can be understood as an extension of Herzberg’s two-factor theory, adapted to explain the formation of customer satisfaction. The authors report the findings of an empirical study (N ¼ 752) in the Austrian hotel industry that tests this asymmetric relationship using a regression analysis with dummy variables. The authors find an asymmetric relationship between satisfaction involving individual factors and overall employee satisfaction, thereby confirming the three-factor theory in the context of employee satisfaction. r 2006 Elsevier Ltd. All rights reserved. Keywords: Employee satisfaction; Prospect theory; Three-factor theory of customer satisfaction; Herzberg’s two-factor theory

1. Introduction Employee satisfaction has become a critical issue in the last two decades. Management concepts such as the Balanced Scorecard, Total Quality Management, Intangible Assets Navigator, etc. highlight its role as a driver of firm success. The Balanced Scorecard emphasizes a causal relationship between Innovation and Learning, Internal Business Perspective, Customer, and Financial Performance (Kaplan & Norton, 1996). Within the Innovation and Learning perspective, employee satisfaction plays an important role. Considering the EFQM-Model the criterion People Results focuses, among other things, on employee satisfaction, which is seen as an important performance indicator. Companies such as the Swedish insurance and financial services company Skandia started to report an Intellectual Capital Supplement in their annual reports which allows for information on the development of intangible assets (Edvinsson, 1999; Sveiby, Corresponding author. Tel.: +43 732 2468 9449; fax: +43 732 2468 9135. E-mail addresses: [email protected] (K. Matzler), [email protected] (B. Renzl).

0261-5177/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.tourman.2006.07.009

1997). The firm’s intangible assets reside in the employees and their know-how and skills. Thus, satisfaction, loyalty and commitment of employees are crucial for increasing firm performance. Employee satisfaction is considered especially important in the service industry in general and in the hotel industry in particular (Lam, Zhang, & Baum, 2001). A number of studies found a positive relationship between employee satisfaction, customer satisfaction and company performance (Homburg & Stock, 2004, 2005). A very popular conceptualization is the ‘‘service–profit chain’’ (Heskett, Jones, Loveman, Sasser, & Schlesinger, 1994; Heskett, Sasser, & Schlesinger, 1997) which includes several relationships between employee satisfaction, customer loyalty and company performance. The link between employee satisfaction and guest satisfaction has also been tested in the hotel industry (Spinelli & Canavos, 2000). Therefore, many companies monitor employee satisfaction and implement programs to enhance satisfaction and loyalty of the employees. Employee satisfaction typically is viewed as a multidimensional construct. There exists a number of standardized scales to measure satisfaction on several dimensions (e.g. Bettencourt & Brown, 1997; Hoffman & Ingram,

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1992; Homburg & Stock, 2000; Rogers, Clow, & Kash, 1994). It is assumed that some of the dimensions are more important to employees than others. One of the most influential and most criticized works in this area is Herzberg’s (Herzberg, Mausner, & Snyderman, 1959) two-factor theory of motivation. It distinguishes between factors that can increase job satisfaction (‘‘motivators’’) versus those that can prevent dissatisfaction but do not lead to satisfaction (‘‘hygiene factors’’). Herzberg’s work, however, has been strongly criticized for a number of reasons (e.g. King, 1970; Korman, 1971; Waters & Waters, 1972). First, the results could only be reproduced with the same method (critical incident technique); studies using other methods failed. Hence, the two-factor theory is widely considered as an artefact. Second, Herzberg does not take individual or situational differences into account; third, his theory is regarded as an oversimplification. Despite this criticism, Herzberg’s basic ideas have not passed out of literature (Furnham, Petrides, Jackson, & Cotter, 2002). Brief (1998), for an instance, writes: I will reconsider job satisfaction as affect and introduce evidence suggesting that positive and negative affect likely are independent of one another. Thus, of the ideas advanced by Herzberg, I remain somewhat attached to the possibility that job satisfaction is not necessarily the opposite of job dissatisfaction. Herzberg’s basic ideas have also been adopted in marketing theory, where multi-attribute models are used to understand and explain the formation of customer satisfaction. Multi-attribute models imply that some attributes are more important to the customer and some attributes are less important. Some studies suggest that there is an asymmetric relationship between attribute satisfaction and overall satisfaction. Whereas earlier studies that found such a relationship lacked a sound theoretical argument for the observed asymmetries (e.g. Anderson & Sullivan, 1993; Oliva, Oliver, & Bearden, 1995; Oliver, 1993), Mittal, Ross, and Baldasare (1998) used prospect theory (Kahneman & Tversky, 1979) and memory for negative versus positive instances as theoretical underpinnings for asymmetric relationships.

diminishing sensitivity (marginal values of both gains and losses decrease with their size). This asymmetric relationship between losses, gains and value is expressed in an S-shaped value function (see Fig. 1). The basic idea of loss aversion has been used to explain risky choice and risk aversion in money (Kahneman & Tversky, 1979), but also in the context of riskless decisions (Thaler, 1980), suggesting that receiving a good has a much smaller valuation than losing the same good. Researchers identified loss aversion in many contexts, including areas that are of special importance to management and marketing, e.g. pricing and customer satisfaction (Novemsky & Kahneman, 2005). In the context of customer satisfaction, negative performance on an attribute should have a greater impact on overall satisfaction than the same magnitude of positive performance on that attribute (Mittal et al., 1998). Another theoretical argument that has been used to explain the stronger impact on satisfaction of negative incidents when compared to positive incidents is the fact that negative information elicits stronger responses than positive information (Peeters & Czapinski, 1990). As satisfaction judgments require memory-based information processing (Oliver, 1980) and not all aspects of a transaction in an exchange relationship are equally accessible (Gardial, Clemons, Woodruff, Schumann, & Burns, 1994) the overall satisfaction judgment depends on the accessibility of single incidents that are relevant to satisfaction. Accessibility strongly depends on stimulus salience (Taylor, 1982) and as negative information is perceptually more salient, is given more weight, and elicits stronger physiological response than positive information (Peeters & Czapinski, 1990), it is argued that negative incidents or performance have a greater impact on satisfaction judgments than a positive incident or performance of the same magnitude. Indeed, Mittal et al. (1998) provide strong empirical evidence for the asymmetric impact of negative and positive attribute-level performance on overall satisfaction. In their third in a series of studies on customer satisfaction, however, they found that ‘‘interior roominess’’ of a car has a stronger impact on overall satisfaction when

2. Theoretical underpinnings of asymmetric effects Kahneman and Tversky’s (1979) theory of choice (prospect theory) describes how individuals form decisions and react to losses and gains. Prospect theory argues that: (1) carriers of value or utility are not states but rather changes relative to a reference point (Camerer, 2000; Kahneman & Tversky, 1979), which means that individuals’ judgments show reference dependence; (2) individuals have a loss aversion, that is—changes for the worse (losses) are more heavily weighted than equivalent changes for the better (Kahneman & Tversky, 1979; Tversky & Kahneman, 1991), or in other words: ‘‘losses loom larger than gains’’ (Einhorn & Hogarth, 1981); and (3) a

Value (Overall satisfaction behavioral intention)

Loss (Negative attribute performance)

Gain (Positive attribute performance)

Fig. 1. S-shaped value function in prospect theory.

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attribute-level performance was high than when it was perceived as low. Later studies came to similar results suggesting that service or product attributes fall into one of the following three categories, each having a different impact on customer satisfaction (Anderson & Mittal, 2000; Matzler & Sauerwein, 2002; Oliver, 1997). Basic factors (dissatisfiers) are minimum requirements that cause dissatisfaction if not fulfilled but do not lead to customer satisfaction if fulfilled or exceeded; negative performance on these attributes has a greater impact on overall satisfaction than positive performance. Excitement factors (satisfiers) are the factors that increase customer satisfaction if delivered but do not cause dissatisfaction if they are not delivered; in other words, positive performance on these attributes has a greater impact on overall satisfaction than negative performance. Excitement factors surprise the customer and generate ‘‘delight’’. Performance factors lead to satisfaction if performance is high and to dissatisfaction if performance is low. To summarize, there is mounting evidence that the importance–satisfaction relationship is more complex than traditional multi-attribute models assume. The ‘‘loss aversion’’ (prospect theory) can be found within basic factors. Within excitement factors, the opposite can be observed: a positive perception is more heavily weighted than a negative perception. Originally developed by Kano (1984) this three-factor model has its origins in Herzberg’s two-factor theory. Nevertheless, there are some clear differences with Herzberg’s two-factor theory (Matzler, 2000). First, Herzberg clearly defines which factors can be classified as hygiene factors (e.g. company policy and administration, supervision, relationship with supervisor, working conditions) and which factors are motivators (e.g. achievement, recognition, work itself, responsibility). In Kano’s model, however, basic, performance and enhancement factors are not defined a priori: the classification depends on customer expectations and varies between industries and even market segments. Hence, it takes individual and situational differences into account. Second, whereas Herzberg identified two factors, Kano’s model is based on three factors. This is an important difference. Several studies which tried to apply Herzberg’s two-factor theory to customer satisfaction found attributes that had an impact on dissatisfaction as well as on satisfaction, but they were considered a falsification of the two-factor theory (Maddox, 1981). Third, Kano’s model of customer satisfaction can be interpreted as a dynamic model. Today’s enhancement factors can convert to performance factors and can eventually become basic factors. These recent advances in customer satisfaction research, which have their roots in job satisfaction research, lead to the question whether the same principles and relationships that are found in customer satisfaction responses also apply to employee satisfaction. Like customer satisfaction, employee satisfaction can be considered as a multi-factorial construct (e.g. Bettencourt & Brown, 1997; Hoffman & Ingram, 1992; Homburg &

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Stock, 2000; Rogers et al., 1994). Therefore, the same relationships (strength and form, i.e. symmetric or asymmetric) can be expected to be found, which would be of particular interest to researchers and practitioners in human resource management as well. Hence, drawing on the same theories, we test a number of hypotheses. Prospect theory and studies on memory for negative versus positive instances suggest that negative performance on one factor has a stronger impact on overall satisfaction than the same magnitude of positive performance, leading to the following hypothesis: H1. Low satisfaction with a factor has a stronger impact on overall employee satisfaction than the same magnitude of high satisfaction. Using this three-factor theory (Kano’s model of customer satisfaction with its origins in Herzberg’s two-factor theory) as a framework to explain the formation of employee satisfaction leads to the following hypotheses: H2. There is an asymmetric relationship between satisfaction with single factors and overall employee satisfaction. H2.1. For some factors, low satisfaction has a greater impact on overall satisfaction than high satisfaction with the same factor (basic factor, satisfier, hygiene factor). H2.2. For some factors, high satisfaction has a greater impact on overall satisfaction than low satisfaction with the same factor (excitement factor, enhancement factor, satisfier, motivator). H2.3. For some factors, high satisfaction has the same impact on overall satisfaction as the same magnitude of low satisfaction with the same factor (hybrid factor). The Human Resource Management literature in general, and research in hospitality and tourism in particular, has found a number of factors, mediators and moderators on the level of the individual, the organization, and the situation that influence employee satisfaction. Literature in personality psychology, for example, has identified some specific personality traits as predictors of employee satisfaction (Furnham et al., 2002; Judge, Heller, & Mount, 2002). Karatepe, Uludag, Menevis, Hadzimehmedagic, and Baddar (2006) found that self-efficacy and effort are positively related to frontline employees’ job satisfaction, competitiveness has an indirect effect—via effort—on job satisfaction. Numerous studies related demographic variables to job satisfaction. Lam, Pine, and Baum’s (2003) findings indicate that job satisfaction is also influenced by the individual’s subjective norms. Lam et al. (2001), for instance, found that age, educational level, length of employment, and marital status are related to job satisfaction in a study on Hong Kong hotel employees. Work role and family role variables have been found to be predictors of job satisfaction in a study in the Turkish Hotel industry (Karatepe & Sokmen, 2006). A number of studies have also focused on organizational variables that influence frontline

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employee performance and job satisfaction (Brown & Peterson, 1993). One of the situational variables that has great importance in tourism, and has received little attention in employee satisfaction research, is seasonality (Lee-Ross & Johns, 1995). Seasonality has been described as one of the most predominant, yet least understood, features in tourism (Highman & Hinch, 2002), which requires extraordinary resources in terms of recruitment, selection, training and retention of staff (Jolliffee & Fransworth, 2003). As seasonal jobs are non-permanent and end at a specific time, typically when the seasonal peak is over, seasonal workers find themselves in different situations from nonseasonal workers. They come shortly before and leave immediately after the seasonal peak. Hence, they have shorter time for training, less time and opportunities to form close relationships with colleagues and superiors, and to form trust. Seasonal workers may also have different motivations and expectations. Thus, job factors such as remuneration, relationships with peers and superiors, etc. will play a different role for seasonal workers than for nonseasonal workers. We therefore hypothesized that H3. The importance of satisfaction factors differs between seasonal and non-seasonal workers. 3. Study To test the hypotheses, employee satisfaction in the Austrian Hotel industry (N ¼ 752) was measured. For this purpose, a standardized questionnaire with closed-response questions using rating scales was developed, based on the literature on employee satisfaction (Bettencourt & Brown, 1997; Hoffman & Ingram, 1992; Homburg & Stock, 2000; Matzler, Fuchs, & Schubert, 2004; Rogers et al., 1994). The questionnaire included questions concerning employee satisfaction within the nine different work satisfaction areas summarized in Table 1. Each of the different employee satisfaction areas was measured by at least two standardized statements using a 5-point rating scale (from 1 ¼ very satisfied to 5 ¼ very dissatisfied). Overall employee satisfaction was measured

Table 1 Employee satisfaction areas included in the study

1 2 3 4 5 6 7 8 9

Employee satisfaction area

Number of items

Top management Superiors Colleagues Job conditions Remuneration Job content Recognition Responsibility Personal development

2 7 4 3 3 4 3 2 2

using a 100 percent scale. Both these selected areas and the item-variables used were derived from well developed and empirically tested scales to measure employee satisfaction proposed in literature (Homburg & Stock, 2000; Matzler et al., 2004). A total number of 770 questionnaires were collected. 18 questionnaires had to be excluded because of excessive missing values. Of the remaining 752 questionnaires, some still had missing values. These were substituted with means, which was not considered to be problematic, as a maximum of three percent missing values per variable was replaced. Table 2 reports the characteristics of the sample. In the first data analysis step, an exploratory factor analysis using principal components and Varimax rotation was employed in order to identify the underlying dimensions of the construct and to purify the employee satisfaction scale. Items with a factor loading of less than .4 and items strongly loading on more than one factor were excluded again, resulting in seven factors. The seven factors extracted in this analysis explain 70.16% of the variance (9 iterations, Kaiser–Meyer–Olkin ¼ .920, Bartlett’s test of sphericity, sign. ¼ .000): peers (6 items, a ¼ .90), superior (5 items, a ¼ .86), development (5 items, a ¼ .83), remuneration (3 items, a ¼ .87), responsibility (3 items, a ¼ .86), time and environment (3 items, a ¼ .69), content (2 items, a ¼ .83). Thus, reliabilities are well above the lower limits of acceptability (Hair, Anderson, Tatham, & Black, 1998). To test whether this model fits the data and whether the measures show good psychometric properties (e.g. average variance extracted), the purified scales were then used to compute a confirmatory factor analysis (CFA) with AMOS 5.0, resulting in the following fit indices: w2 value of 745.514 (df ¼ 293, p ¼ .000; w2/df ¼ 2.544), root mean-square error of approximation (RMSEA) of .04, goodness-of-fit index

Table 2 Sample characteristics Characteristic

%

Gender Male Female

35.0 65.0

Age Under 25 25–34 years 35–44 years 45–54 years 55–64 years

44.2 27.8 17.9 8.4 1.7

Employment Seasonal workers Non-seasonal workers

54.8 45.2

Hotel category Four stars Five stars

78.6 21.4

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(GFI) ¼ .93, AGFI ¼ 91, CFI ¼ .96, and Tucker–Lewis index (TLI) ¼ .95. The RMSEA with the value of .04 clearly indicates a good model fit and meeting recommendations, whereby the RMSEA should be o.05 (Hu & Bentler, 1999). The GFI, at .93, is above the general recommended threshold of .90. Our measurement model showing a CFI value of .96 far exceeds the lower bound of .90 and therefore can be considered as an indicator for good model fit. Also TLI, which is less susceptible to non-normality of data (West, Finch, & Curran, 1995) and sample size (Marsh, Balla, & McDonald, 1988), yields a corroborating value for good

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model fit of .95. The adjusted GFI was .91. Summarizing, the hypothesized model fits the sample data. In the next step, reliability of the measures was tested calculating the composite reliability (CR) of the constructs and the average variance extracted (AVE) (Fornell & Larcker, 1981). The results are reported in Table 3 and show satisfactory reliability. In order to test the asymmetric relationship as proposed in Hypotheses H1 and H2, a dichotomized type of regression analysis with dummy variables was used (Anderson & Mittal, 2000; Matzler, Bailom, Hinterhuber, Renzl, & Pichler, 2004; Matzler & Sauerwein, 2002; Mittal

Table 3 Reliability and validity of the measures Factor

Item (1 ¼ very satisfied, 5 ¼ very dissatisfied)

Mean

S.D.

Item loading

Peers

Cooperation Team spirit Support Trust Exchange of information Atmosphere among peers

1.78 1.93 1.90 2.09 2.04

.86 .90 .91 1.03 .96

.81 .82 .80 .78 .72

1.75

.91

.74

Fairness Recognition of good performance Communication Trust Exchange of information

1.79 2.16

.94 1.13

.70 .77

1.96 2.09 2.12

1.01 1.03 1.07

.72 .65 .70

Rewards Personal recognition Personal development training Further training Career opportunities

2.45 3.27 2.43

1.14 1.03 1.14

.56 .77 .63

2.24 2.57

1.08 1.11

.56 .56

Remuneration

Transparency Fairness Achievement oriented

2.17 2.23 2.42

1.13 1.11 1.13

Responsibility

Decision-making power Responsibility Scope of action

1.79

Composite reliability

Average variance extracted

.90

.61

.84

.50

.76

.39

.73 .87 .86

.87

.69

.84

.80

.87

.69

2.12 1.83

1.03 .90

.87 .83

Working hours

1.89

1.02

.56

.72

.47

Work place Working environment

1.82 1.67

.96 .80

.68 .79

Content

Job is varied Job is challenging

1.82 1.87

.93 .91

.75 .94

.84

.72

Overall satisfaction

Overall satisfaction (100%-scale)

78.73

18.50

Superior

Development

Time and environment

—

—

—

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amount added to the constant) associated with high satisfaction with a certain workplace attitude. If utility preservation exceeds utility enhancement, the attribute in question is a utility preserving (i.e. hygiene) factor. Otherwise, if the utility enhancing value outweighs the utility preserving value, the workplace attitude observed can be considered an enhancement (i.e. motivating) factor (utility enhancer). If utility preservation and utility enhancing are equal, the attitude leads to satisfaction when performance is high as well as to dissatisfaction when performance is low. Hence, it is a typical performance factor (hybrid). The above-discussed Penalty–Reward Contrast Analysis based on dichotomized dummy variables is stated in the following equation form using a multiple regression model:

et al., 1998). One set of dummy variables was created and used to quantify utility enhancement factors, while another set was created to quantify utility preserving (i.e. hygiene) factors. Hence, in order to conduct the analysis, the z-standardized factor values were recoded as follows: Factor score values in the lowest tertile were used to form one dummy variable to quantify enhancement factors (value of 1), while factor values in the highest tertile were used to form the second dummy variable to quantify preserving factors (value of 1). The empty cells of the dummy variables were assigned the value of zero as they were defined as expressing indifference (e.g. average satisfaction) and comprised a reference group. Based on this recoding, a multiple regression analysis was conducted to empirically quantify utility preserving (i.e. minimum work satisfaction requirements) and utility enhancing factors in a workplace setting using the overall satisfaction item variable as the dependent variable and the two dummy variables as independent variables. Thus, the constant in the regression equation is the average of all the reference groups on total employee satisfaction. ‘‘Penalties’’ (utility preservers) are expressed as an incremental decrease (i.e. amount subtracted from the constant) associated with low satisfaction, while ‘‘rewards’’ (utility enhancers) are expressed as an incremental increase (i.e.

ES tot ¼ b0 þ b1fact:1 dummy1fact:1 þ b2fact::1 dummy2fact:1 þ . . . þ b1n dummy1fact:n þ b2n dummy2fact:n , where EStot is the total employee satisfaction, n is equal to 7 (i.e. for seven analytically deduced factors, see Table 3), dummy1 the dummy set indicating highest employee satisfaction levels, dummy2 the dummy set indicating lowest employee satisfaction levels, b1 the incremental increase in total employee satisfaction associated with high satisfaction

Table 4 Mean satisfaction per item for the three groups (high, low and medium factor satisfaction) Factor

Item

Mean high satisfaction (S.D.)

Mean average satisfaction (S.D.)

Mean low satisfaction (S.D.)

Peers

Cooperation Team spirit Support Trust Exchange of information Atmosphere among peers

1.08 1.15 1.13 1.25 1.33 1.12

(.27) (.36) (.33) (.46) (.53) (.32)

1.70 1.86 1.80 2.02 1.92 1.61

(.49) (.44) (48) (67) (58) (65)

2.56 2.78 2.77 2.99 2.85 2.52

(.90) (.87) (.89) (.99) (.97) (.99)

Superior

Fairness Recognition of good performance Communication Trust Exchange of information

1.19 1.67 1.34 1.18 1.60

(.39) (.87) (.58) (.40) (.71)

1.53 1.89 1.72 1.50 1.94

(.53) (.90) (.58) (.56) (.77)

2.64 2.91 2.84 2.53 3.01

(1.02) (1.17) (1.09) (1.05) (1.11)

Development

Rewards Personal recognition Personal development training Further training Career opportunities

2.01 1.85 1.51 1.60 1.71

(.92) (.84) (.65) (.73) (.75)

2.93 2.33 2.13 2.32 2.58

(.96) (1.01) (.82) (.80) (.78)

3.18 3.78 3.09 3.40 3.44

(1.12) (1.03) (1.07) (1.06) (1.01)

Remuneration

Transparency Fairness Achievement oriented

1.49 (.55) 1.31 (.50) 1.49 (.66)

2.09 (.54) 2.07 (.56) 2.32 (.75)

3.05 (.94) 3.36 (1.00) 3.50 (.91)

Responsibility

Decision-making power Responsibility Scope of action

1.30 (.51) 1.12 (.31) 1.13 (.35)

2.06 (.66) 1.71 (.49) 1.75 (.54)

3.14 (.97) 2.66 (.83) 2.73 (.90)

Time and environment

Working hours Work place Working environment

1.69 (.96) 1.10 (.31) 1.14 (.34)

1.71 (.89) 1.59 (.49) 1.51 (.49)

2.30 (1.09) 2.80 (.95) 2.38 (.87)

Content

Job is varied Job is challenging

1.20 (.41) 1.27 (.48)

1.59 (.54) 1.70 (.62)

2.69 (.96) 2.63 (.97)

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levels and b2 the incremental decline in total employee satisfaction associated with low satisfaction levels. Table 4 reports the average satisfaction scores of each item for the three groups formed for the regression analysis with dummy variables. Table 5 and Fig. 2 report the results of the regression analysis. In the last column a ratio is computed that indicates whether a satisfaction factor is a utility preserver or a utility enhancer. The regression coefficient obtained when satisfaction with the factor is high is divided by the regression coefficient obtained when satisfaction is low. Thus, a ratio below one indicates that the factor is a utility preserver; a ratio above one indicates that the factor is a utility enhancer and a value of one or close to one indicates a hybrid factor.

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Job content has a strong negative impact when satisfaction with this factor is low but has no impact if satisfaction is high. Thus, it is a full utility preserver. Superiors, peers and development have a stronger impact on overall satisfaction when employees are strongly dissatisfied with these factors than when they are strongly satisfied. Hence, these factors can also be categorized as utility preservers. Remuneration and responsibility have a slightly stronger impact when satisfaction is low; however, the ratio is close to one, and thus, these two factors are closer to hybrids. Time and work environment with a ratio of 2.16 are much stronger predictors of overall employee satisfaction (the impact is more than twice as large when satisfaction is high), when employees express a high satisfaction with this factor than when employees express low satisfaction. Thus,

Table 5 Dummy variable regression results Dependent variable: overall satisfaction

Dummy variable regression coefficients

Ratio (impact high/impact low)

Factors

High satisfaction

Low satisfaction

Peers

.089**

.128***

Superior

.072**

.284***

Development

.108***

.157***

Remuneration

.104***

.130***

Responsibility

.125***

.108***

Time and environment

.171***

.079**

Content

.00 n.s.

.195***

.69 (Utility preserver) .25 (Utility preserver) .69 (Utility preserver) .80 (Utility preserver—hybrid) 1.16 (Utility enhancer—hybrid) 2.16 (Utility enhancer) Full utility preserver

R2 .407, **po.01, ***po.001, n.s. ¼ not significant, F-value 37.784 (Sig. ¼ .000).

Utility Enhancersand UtilityPreservers for Employee Satisfaction

0.2

0.171∗∗∗ 0.108∗∗∗

0.1

0.089∗∗

0.125∗∗∗ 0.104∗∗∗

0.072∗∗

0.00n.s.

0 Peers

Superior

Development

Remuneration

Responsibility

Time and environment

Content

-0.079∗∗

-0.1 -0.108∗∗∗ -0.130∗∗∗

-0.128∗∗∗ -0.157∗∗∗

-0.2

-0.3

∗∗∗ -0.195

∗∗∗ -0.284

Indices Are Standardized Regression Coefficients Significancelevels 5%∗∗ 1%∗∗∗ Fig. 2. Utility preservers, utility enhancers and hybrids.

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this factor can be classified as a utility enhancer. Hence, H1, which states that low factor satisfaction has a stronger impact on overall employee satisfaction than high factor satisfaction (prospect theory), has to be rejected. Responsibility is a clear utility enhancer. Consequently, hypothesis two is supported. There are asymmetric relationships between factor satisfaction and overall employee satisfaction as Kano’s model suggests. There are utility preservers, utility enhancers and hybrids.

H3 predicts that satisfaction factor importance differs between employee segments, i.e. seasonal and non-seasonal workers. To test this hypothesis, the same procedure of data analysis to identify utility preservers, utility enhancers and hybrids was applied as before. Table 6, Figs. 3 and 4 report the results, which clearly show that satisfaction drivers differ between the two groups. In the seasonal worker group, peers and superiors have a stronger influence on overall satisfaction when satisfaction with

Table 6 Dummy variable regression results (seasonal versus non-seasonal workers) Dependent variable: overall satisfaction Factors

Seasonal workers

Non-seasonal workers

High satisfaction

Low satisfaction

Ratio (impact high/ impact low)

Peers

.107*

.155***

Superior

.089*

.325***

Development

.127**

.135**

Remuneration

.131**

.099*

Responsibility

.079+

.159***

.69 (Utility preserver) .27 (Utility preserver) .94 (Hybrid) 1.32 (Utility enhancer) .49 (Utility preserver)

Time and environment

.168***

.031 n.s.

Content

.00 n.s.

Full utility enhancer

Full utility preserver

.124**

High satisfaction

Low satisfaction

Ratio (impact high/ impact low)

.043 n.s.

.137**

Full utility preserver

.066 n.s.

.257***

Full utility preserver

.091+

.180***

.055 n.s.

.174***

.69 (Utility preserver) Full utility preserver

.149**

.082+

.152***

.181**

1.16 (Utility enhancer— hybrid) .83

.285***

(Utility preserver— hybrid) Full utility preserver

.02 n.s.

Reward for Satisfaction

R2 .391, +po.10, ** po.01, *** po.001, n.s. ¼ not significant, F-value 13.436 (Sig. ¼ .000). R2 .453, ** po.01, *** po.001, n.s. ¼ not significant, F-value 19.767 (Sig. ¼ .000).

Penalty and Reward Indicesfor Employee Satisfaction 0.168∗∗∗

0.2 0.107∗

0.127∗∗

0.131∗∗

0.089∗

0.079+

0.1

0.000

0 Peers

Superior

Development

Remuneration Responsibility

Time and environment

Content

Penalty for Dissatisfaction

-0.031n.s.

-0.1

-0.099∗ -0.135∗∗.

-0.155∗∗∗∗

-0.124∗∗ -0.159∗∗∗

-0.2

-0.3 -0.325∗∗∗∗

-0.4 Indices Are Standardized Regression Coefficients Significance levels 10%+ 5%∗ 1%∗∗ 0%∗∗∗ Fig. 3. Utility preservers, utility enhancers and hybrids (seasonal workers).

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Penalty and Reward Indicesfor Employee Satisfaction 0.2

0.149∗∗

0.152∗∗∗

0.091+

0.1 0.043n.s.

0.066n.s.

0.055n.s. 0.02n.s.

0 Peers

Penaltyfor Dissatisfaction

1101

Superior

Development

Remuneration Responsibility

-0.1

Time and environment

Content

-0.082+ -0.137∗∗

-0.2

-0.180∗∗∗

-0.174∗∗∗

-0.181∗∗

-0.257∗∗∗

-0.3

-0.285∗∗∗

-0.4 Indices Are Standardized Regression Coefficients Significance levels 10%+ 5%∗ 1%∗∗ 0%∗∗∗ Fig. 4. Utility preservers, utility enhancers and hybrids (non-seasonal workers).

this factor is low than when it is high. In the non-seasonal worker group the regression coefficient for high factor satisfaction for peers and superiors is not significant. Hence, it is a full utility preserver. In addition, the role of development differs between the groups. Whereas for seasonal workers it is a hybrid factor (ratio of .94), for non-seasonal workers it is rather a utility preserver (ratio of .69). One of the biggest differences between the groups is remuneration. It is a utility enhancer (ratio of 1.32) for seasonal workers and a full utility preserver for nonseasonal workers. Responsibility is a utility preserver for seasonal workers and a hybrid for non-seasonal workers. Another great difference can be found in the role of time and environment, which is a full utility enhancer in the seasonal workers group and more of a hybrid in the nonseasonal workers group. The only satisfaction factor that is classified in the same way by both groups is job content. Thus, H3 is supported.

4. Conclusions In this study, we investigated the asymmetric relationship between satisfaction with single factors and overall employee satisfaction, testing two opposing theories in the Austrian hotel industry. The results confirm that prospect theory needs to be rejected in favor of a three-factor theory. This three-factor theory states that employee satisfaction is a function of three categories of satisfaction factors. These findings are consistent with recent findings in marketing research, where the extension of Herzberg’s (Herzberg, Mausner, & Snyderman, 1959) two-factor theory led to a three-factor theory of customer satisfaction

which has been widely recognized (e.g. Anderson & Mittal, 2000; Matzler, Hinterhuber, Bailom, & Sauerwein, 1996). These findings have some interesting implications for Human Resource Management. First, in the overall sample, factor 1 (peers), factor 2 (superior), factor 3 (development), and factor 7 (content) were classified as utility preservers. They are unimportant as long as employees are satisfied with them but become important if satisfaction decreases. Factor 4 (remuneration) and factor 5 (responsibility) were classified as hybrids. Overall satisfaction linearly increases with an improvement of these factors. Time and environment, finally, is a utility enhancing factor. Thus it is shown that the relationship between satisfaction with single factors and overall satisfaction is asymmetric, and the three-factor theory is confirmed. It is evident that managers need to know how to classify factors in order to be able to set the right priorities in enhancing employee satisfaction. It is crucial to consider these asymmetries in Human Resource Management. If human resource managers know the relative importance of the satisfaction factors and how this changes when satisfaction changes, they can take more effective measures to increase employee satisfaction. This, however, can effectively been done only, as other important findings of this study suggest, when differences in work groups are taken into account. The results of the study show that there are situational differences that influence the role of the satisfaction factors (i.e. seasonal versus non-seasonal workers). As a consequence, measures to increase employee satisfaction (e.g. remuneration systems, training programs, career plans, relationship and trust building behavior, working hours, etc.) may be

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effective for one group of employees and not effective for another group. Finally, there seems to be a systematic difference between seasonal and non-seasonal workers. For nonseasonal workers, more factors are classified as utility preservers. One possible explanation might be the dynamic nature of satisfaction factors, as suggested by the threefactor theory. It is argued that satisfaction is a function of the confirmation of expectations (Oliver, 1980). Expectations evolve over time, and are also influenced by past experiences. Hence, as satisfaction with a job-related factor, according to the expectation–disconfirmation paradigm, depends on the expectations employees have, this model has a dynamic nature. Utility enhancers will become hybrids and utility preservers in the future as employees get used to them and come to expect them. As non-seasonal workers have been with the employer for a longer time— compared to seasonal workers—more satisfaction factors might have turned into utility preservers. This hypothesis, however, has not explicitly been tested in this study. Nevertheless, the results seem to point towards this direction and this hypothesis offers an interesting issue for future research.

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