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The effects of customer voice on hotel performance
International Journal of Hospitality Management 44 (2015) 77–83
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International Journal of Hospitality Management journal homepage: www.elsevier.com/locate/ijhosman
The effects of customer voice on hotel performance A. George Assaf a,∗ , Alexander Josiassen b , Ljubica Kneˇzevic´ Cvelbar c , Linda Woo a a
Isenberg School of Management, University of Massachusetts, Amherst, United States Copenhagen Business School, Denmark c Faculty of Economics, University of Ljubljana, Slovenia b
a r t i c l e
i n f o
Keywords: Hotel performance Customer satisfaction Customer complaints Equity model
a b s t r a c t This paper investigates the effects of two critical customer voice variables on hotel performance. Specifically, the research provides a customer equity model in which the influences of both customer satisfaction and complaints are considered. The impact of the customer voice variables on hotel performance is investigated while considering the potential for moderating effects by hotel size and star rating. We use a more robust approach to measure firm performance than is traditionally used in satisfaction-performance studies. Finally the paper reports on the results of these investigations and outlines implications for both theory and practice. © 2014 Elsevier Ltd. All rights reserved.
1. Introduction A significant portion of the service literature focuses on assessing the impact of customer satisfaction on firm performance (Anderson and Mittal, 2000; Johnston, 1995; Johnston et al., 1990; Mersha & Adlakha, 1992). Customer satisfaction is a form of customer voice. Specifically it is a post-consumption consumer response that leads to greater customer loyalty (Anderson and Sullivan, 1993; Fornell, 1992; LaBarbera & Mazursky, 1983) and help firms “secure future revenues, reduce the costs of future transactions, decrease price elasticities, and minimize the likelihood that customers will defect if quality falters” (Anderson et al., 1997, p. 129). Positive word-of mouth from satisfied customers also makes it simpler and less expensive to attract new customers (Anderson, 1998; Luo, 2009). Customer satisfaction also links to improve overall reputation, economic return, and shareholder value (Anderson et al., 2004; Fornell et al., 2006). In service industries such as hotels, customer satisfaction is not only an important goal, it is also a vital marketing tool for attracting future customers and ensuring stronger market positions (Luo and Homburg, 2007). However, customers may not only voice their satisfaction but also their dissatisfaction, and recently, scholars have also investigated customer complaints as an important customer voice (Luo, 2007, 2009). Despite the attention and contributions to understand customer satisfaction and complaints on firm performance, the relationships
∗ Corresponding author. Tel.: +1 4135454192. E-mail addresses: [email protected] (A.G. Assaf), [email protected] (A. Josiassen), [email protected] (L. Kneˇzevic´ Cvelbar), [email protected] (L. Woo). http://dx.doi.org/10.1016/j.ijhm.2014.09.009 0278-4319/© 2014 Elsevier Ltd. All rights reserved.
have not been explored fully, and despite their importance to hotels – and academic interest – few studies analyze how they affect hotel performance (Chi & Gursoy, 2009). For example, hotels use many resources to improve customer satisfaction and attract/retain customers with the purpose of increasing performance, but the literature offers contradicting evidence regarding the impact of customer satisfaction on hotel performance (Barsky, 1992; Chi & Gursoy, 2009; Sun & Kim, 2013). It is not necessarily the case that customer satisfaction leads to improved firm performance; many reasons exist to suggest customer satisfaction does not improve firm performance (Anderson et al., 1997). Studies in the service literature also provide inconsistent conclusions regarding the longitudinal relationship between customer satisfaction and firm performance (Anderson, 1994; Anderson and Mittal, 2000; Ittner & Larcker, 1998; Johnston, 1995; Johnston et al., 1990; Mersha & Adlakha, 1992). If a firm improves performance by downsizing, it might achieve an increase in performance in the short-term, but future profitability might be threatened since lack of back-and front-office personnel influences both customer satisfaction and complaints negatively. Scholars note trade-offs between customer satisfaction and firm performance across several heterogeneous industries such as airlines, banking, education, hotels, and restaurants (Anderson et al., 1997). Customer satisfaction is also a given or expected factor in some service industries. In the hotel industry, for example, high satisfaction does not necessarily result in higher performance because customers expect to be satisfied when choosing one hotel over another (Gursoy & Swanger, 2007). The goal of this paper is to contribute to the literature on customer satisfaction in a service context, focusing on three important gaps. First, customer satisfaction and complaints are two essential customer voice variables. Although these two have been analyzed
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separately regarding their impacts on firm performance, to date no study has included their impact on firm performance in a single model. This is a major gap; as Luo and Homburg (2008) suggest, “managers should no longer value satisfaction and complaint in isolation. Rather, both good news (of “angel” customers) and bad news (of “devil” customers) should be considered in one model” (p. 29). It might be more important for managers to reduce customer complaints than improve customer satisfaction (Luo and Homburg, 2008) since potential negative impacts of customer complaints on performance might matter more than upside gains in terms of customer satisfaction. Hence, it is important to also consider customer complaints for two reasons: (a) to provide a more robust assessment of customer satisfaction on performance and (b) to compare the impacts of both customer satisfaction and customer complaints on hotel performance. Thus, we provide an investigation of such a more complete customer equity model in which we analyze the impact of both customer satisfaction and customer complaints on hotel performance simultaneously. Second, when analyzing the impact of customer satisfaction on hotel performance, the literature has yet to consider moderating variables, with few exceptions. This research gap of potential moderating influences is even more pronounced for the customer complaint–hotel performance relationship. We suggest that hotel size and star ratings moderate the influence of customer satisfaction and customer complaints on performance. We also include these moderators to advance a contingency view of customers’ impact on hotel performance. Third, this study uses a more robust approach to measure firm performance than extant satisfaction–performance studies. Instead of using financial indicators (e.g., ROA, Tobin’s q1 ) used commonly in the literature, we focus on technical efficiency gap, which offers two advantages. It measures overall firm performance based on multiple inputs and outputs, not partial indicators alone, providing a comprehensive, realistic assessment of firm performance. It also reveals a company’s efficiency gap when benchmarked against optimum, best-performing competitors. Hence, it provides a complete assessment of performance by measuring performance of every firm relative to the maximum performance it can achieve.
2. Conceptual framework and hypotheses Consumers’ post-consumption responses (e.g., expressing satisfaction or complaining) are important to managing loyalty and repeat purchases, and understandably, study of such responses attracts research from various fields (Anderson and Mittal, 2000; Johnston, 1995; Johnston et al., 1990; Mersha & Adlakha, 1992). Expressing low satisfaction is not synonymous with expressing dissatisfaction by complaining, and vice versa; research from sociology and psychology (Cashdan, 2001; Larsen et al., 2001) suggest positive and negative dispositions are distinct. We investigate whether creating satisfied customers leads to higher performance. Further, we investigate whether firms with less complaining customers perform better than firms with more complaining customers. We are interested in discovering whether the most efficient strategy is to (a) mostly allocate resources to increase satisfaction, (b) mostly allocate resources to avoid complaints, (c) balance resources between increasing satisfaction and lowering complaints, or (d) accept lower satisfaction, while satisfying the majority, and allow for some complaints. Our framework that includes the impact of consumer responses on firm performance is illustrated in Fig. 1.
1
Tobin’s q is the ratio between total market value of the firm and total asset value.
The hospitality management literature suggests that customer satisfaction is at “the core of hospitality operations” (Sun and Kim, 2013, p.70). Hotel revenues rely heavily on the service quality delivered by its employees, and consequently, customer satisfaction occupies an important role in the hotel industry, leading to improved brand reputation, faster market penetration, accelerated cash flows (Anderson et al., 2004), steady future sales (Anderson et al., 1994; Reichheld & Sasser, 1990), and higher shareholder value (Anderson et al., 1994, 2004). According to Barsky and Labagh (1992), the objective of satisfying customers is to improve profitability in a chain of effects from increased customer loyalty, improved product reputation, and increased sales. Since loyal customers provide increased repeat business with less effort and costs than finding new customers, and increased positive word-of mouth (Bowen & Chen, 2001), maintaining high satisfaction is essential, and has direct and indirect effects on hotel performance (O’Neill & Mattila, 2004). Although the literature supports these theoretical advantages of customer satisfaction, the empirical evidence remains inconclusive. Banker et al. (2005) demonstrate that while Hotelcorp enjoyed positive effects on revenue as a result of implementing an incentive plan to improve customer satisfaction, the impact on operating costs was negative. Customer satisfaction influences a firm’s revenue positively, but it might not always result in increased profits (Bernhardt et al., 2000; Schneider, 1991; Tornow & Wiley, 1991; Wiley, 1991). For example, to increase customer satisfaction, firms often invest in training and upgraded facilities (Chi & Gursoy, 2009), but this might affect profits and obscure the potential relationship between customer satisfaction and firm performance – at least in the short term (Bernhardt et al., 2000). The service literature suffers from the same gap regarding the direction and strength of the customer satisfaction/firm performance relationship. Heskett et al. (1997) found a weak relationship between customer satisfaction and customer loyalty in a serviceprofit chain. Novelty-seeking customers might be very satisfied but not loyal, and some customers are price sensitive, searching for better deals even if satisfied with a hotel’s property (Shoemaker & Lewis, 1999). In view of contradicting findings in this literature, it is difficult to hypothesize the nature and direction of the relationship between customer satisfaction and hotel performance. Following theoretical arguments found in the majority of the literature (Anderson et al., 2004; Luo and Homburg, 2007), we expect customer satisfaction to influence hotel performance positively. H1. Customer satisfaction correlates positively with hotel performance. 2.1. Customer complaints and hotel performance The focus on decreasing complaints should also be important for hotels just as customer satisfaction is. In a recent extension of the satisfaction literature, marketing scholars have focused on customer complaints (Luo, 2007; Luo and Homburg, 2007). For most firms, the cost of generating a new customer is higher than retaining a customer (Fornell & Wernerfelt, 1987; Yavas et al., 2004), and because complaints is a more extreme effect of being dissatisfied, these complaining customers may exit. Therefore managing customer complaints is important, particularly in industries such as hotels in which competition is fierce and customers can easily switch among service providers. Customer complaints also relate to the effectiveness of operations. For example, complaining is a way of providing direct feedback regarding a hotel’s operating processes, and it is useful for initiating corrective actions (Banker et al., 2005). Service research has particularly designed complaint management strategies to reduce customer complaints and turnover. Fornell and Wernerfelt (1987) found that better handling of
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Fig. 1. Conceptual framework: the relationship between consumer responses and firm performance.
dissatisfied customers and having a complaint management system in place results in more positive word of mouth and higher customer loyalty. When researchers include complaints in their studies, they often use complaints as a proxy for customer satisfaction (Steven et al., 2012). More typically, however, in much of the extant literature customers’ complaining behaviors are viewed as a consequence of low satisfaction, but recent studies demonstrate that complaints do not always originate from dissatisfaction, and dissatisfaction does not always result in complaints (Day et al., 1981; Singh & Pandya, 1991; Tronvoll, 2007). According to Hirschman (1970) exit-voice theory, dissatisfied customers have two options: exit or voice; customers either stop buying (i.e., exit the firm) or voice complaints to a firm. Customer complaints is one of the two feedbacks for dissatisfaction, and dissatisfaction in turn is a necessary but insufficient condition for customer complaints (Jacoby & Jaccard, 1981). Thus, customer complaints should not be used as a reverse proxy for customer satisfaction. This study distinguishes customer satisfaction from complaints. Thus: H2. Customer complaints correlate negatively with hotel performance. 2.2. Moderation Hotel size. Hotel size has been linked to the resources that a hotel has. It also represents a prominent contingency variable that distinguishes hotels (Dahlstrom et al., 2009; Huang et al., 2012). Hotel size can, for example, influence a hotel’s learning opportunities, and the size of promotions (Barros and Dieke, 2008). Larger hotels with greater resources will want to draw on experiences from those of their hotels which have particularly positive customers, and then implement corresponding procedures across other hotels in the hotel chain (Assaf and Agbola, 2011). Larger hotels have more resources and can better to capitalize on such higher-satisfaction information in promotional campaigns. Such greater opportunities for organizational learning and promotion may in turn increase customer loyalty behaviors (e.g., word of mouth, cross-buying behaviors, etc.). As such, larger hotels draw both internal (e.g., learning from the experiences of other hotels in the chain) and external (e.g., leverage higher customer satisfaction) benefits from size. Therefore, regarding customer satisfaction, we predict that size will strengthen the impact of customer satisfaction on hotel performance. Regarding customer complaints, we argue that firm size weakens the negative impact of complaints on firm performance. Larger hotels are better able to identify the threshold when complaints reach an unsustainable level (Liu, 1995). Smaller hotels have less opportunity to identify hotels within the chain to use as benchmarks across the chain, and a hotel manager might perceive that some complaints are inevitable in the context in which the hotel operates. Larger hotels can use internal benchmarking regarding complaints, and invest in complaint management. Complaint
management induces repurchasing behaviors and increases brand equity (Fornell and Wernerfelt, 1988). An opposite effect of hotel size is, however, possible for the impact of both customer satisfaction and complaints. Larger firms tend to be less flexible, and react more slowly to environmental changes (Fiegenbaum & Karnani, 1991). If customers increasingly complain, larger firms might react more slowly than smaller firms do (Perry-Smith & Blum, 2000). Larger firms are subject to inertial forces and rigidity that limit change. Systems and processes that characterize smaller firms make them inherently more flexible and receptive (Perry-Smith & Blum, 2000). Similarly for customer satisfaction, larger hotels may be less flexible in terms of reacting to customer satisfaction once identified (Baum & Wally, 2003) Overall, we do not expect flexibility to outweigh positive size mechanisms, and suggest a strengthening effect of hotel size on the customer satisfaction-hotel performance relationship, and a weakening effect of hotel size on the customer complaint-hotel performance relationship. H3a. The impact of customer satisfaction on firm performance is stronger for larger firms than for smaller firms. H3b. The impact of customer complaints on firm performance is weaker for larger firms than for smaller firms. Star rating. The star-rating system in the hotel industry is a quality signal (Israeli, 2002) that allows higher-rated hotels to support premium-pricing strategies (Bull, 1994). We argue that star rating has a moderating influence on the relationship of both customer voice variables on performance. Regarding the impact of customer satisfaction on hotel performance, we argue that this relationship is stronger for higher rated hotels than for lower rated hotels. This is the case as higher rated hotels are in a better position to leverage high levels of customer satisfaction than are lower rated hotels (Barros and Dieke, 2008). Higher rated hotels are more likely to have systems in place in terms of promotion to leverage positive customer satisfaction (Israeli, 2002; Siu & Fung, 1998). Regarding customer complaints, we argue that hotels with a higher star rating experience a more negative relationship between customer complaints and hotel performance. We rely on the expectancy-confirmation theory (Ahluwalia and Gürhan-Canli, 2000) and the notion of negativity bias which states that negative disconfirmations (in this case indicated by customer complaints) are punished harder when the expectation for quality is higher such in the case of higher rated hotels. Recent studies (Darke et al., 2010) also emphasized that negativity bias such that negative disconfirmation is punished harder than positive disconfirmation is rewarded. H4. The impacts of (a) customer satisfaction, and (b) customer complaints on firm performance are stronger for firms with a higher rating than for hotels with a lower rating.
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2.3. Customer satisfaction and complaints Researchers are increasingly focusing on the importance of complaints and whether complaint management represents an opportunity to increase profitability (Heskett et al., 1990; Johnson et al., 2001; Kahneman & Tversky, 1979; Tversky & Kahneman, 1992). Prospect theory asserts that “people are more sensitive to losses than gains. Thus, in most service encounters, customers perceive service failures as losses, and weigh failures heavily” (Smith et al., 1999, p. 360). Lou and Homburg (2008) test the effects of both customer satisfaction and complaints on firms’ stock-value gap, finding that customer complaints have a stronger impact on gaps than satisfaction. Smith et al. (1999) investigate complaints in restaurant and hotel industries, the results of which suggest customer complaints depend on type and magnitude of service failure. This implies that negative experiences of service failure are more impactful than positive experiences (Luo and Homburg, 2008). Marketing research demonstrates that negative product feedback has a stronger impact on attracting new customers than an equal amount of positive feedback (Berry & Parasuraman, 1991; Mahajan et al., 1984). Based on these arguments, we suggest that the effect of complaints is stronger than satisfaction on hotel performance. H5. Customer complaints have a stronger influence on hotel performance than customer satisfaction does. 3. Methods The paper adopts a two-step procedure to test the hypotheses. First, we estimated performance using the technical efficiency gap concept. Second, we estimated the impact of customer satisfaction, customer complaints, and moderators on the gap. Table 1 presents a correlation matrix for all variables for the full sample. We tested hypotheses on a sample of 56 hotels from Slovenia and Croatia for the 2009–2012 period (56 × 4 years = 224 observations). Both countries are known well internationally for their tourism and hotel industries. The Croatian economy depends highly on tourism; the direct contribution of tourism to GDP was 11% in 2011. Major international tourism markets for Croatian hotels include Austria, Italy, Slovenia, Germany, and Czech Republic. In 2011, there were 656 hotels in Croatia, and 57,435 hotel rooms. Hotels from our sample represented 19% of all rooms in Croatia, generating 21% of all income in the hospitality industry in 2011. Tourism is also important to Slovenia. In 2011, tourism’s contribution to GDP was 12.8% (WTTC, 2012). Slovenia reported 2.9 million tourism arrivals and 9.6 million overnight stays (SORS, 2012). In 2011, there were 298 hotels, offering 18,719 hotel rooms. Most hotel rooms (90%) had 3 or 4 stars (SORS, 2012), and approximately 40% of all hotel capacities were small hotels with fewer than 30 rooms. Hotels in our sample represented 30% of all hotel rooms in Slovenia, and generated 32% of all income in the hospitality industry (AIJPES, 2012). 3.1. Performance We measured performance using the technical efficiency gap metric; the degree of producing as much output as technology and inputs allow or using as few inputs as required by technology and production. The concept recently gained increased popularity in the hospitality and marketing literature (Dutta et al., 1999; Barros and Mascarenhas, 2005; Krasnikov et al., 2009; Luo and Homburg, 2008; Murthi et al., 1996) due to several advantages over simpler performance metrics such as ROA and Tobin-q. For example, the technical efficiency gap is a relative measure of performance that compares a firm’s performance with its optimum performance. It provides the distance (i.e., gap) between a firm’s performance and the
maximum performance it could achieve. In contrast to other, simpler measures of performance, the technical efficiency gap is also a more comprehensive measure of performance since it depends on multiple inputs and outputs. Measurement of the technical efficiency gap involves estimating a stochastic frontier production function in “which a firm’s output is a function of its inputs and a standard, two-sided error term that measures the effects of unobservables, and another technical efficiency gap term that has a minimum value of zero” (Assaf et al., 2012, p. 197). The stochastic frontier production function is expressed as: yit = xit ˇ + vit − uit , for each firm i = 1, ..., n, at time
t = 1, ..., T
(1)
where yit is firm output, xit is a k × 1 vector of input and explanatory variables, and i is a k × 1 vector of parameters. vit , and uit denote measurement error distributed as normal N(0, v2 ) and the technical inefficiency gap distributed as half-normal N + (0, u2 ). Hence, the technical efficiency gap enters the production function negatively since it measures the shortfall of a firm’s performance to its optimum performance. Eq. (1) is usually estimated using the maximum-likelihood (ML) method since the combined error (vit − uit ) is not distributed normally. For specification of input and output variables, we followed the hotel literature (Assaf & Josiassen, 2012; Barros, 2005), selecting total revenue (output), number of hotel rooms (input), number of employees (input), cost of materials (input), and other operational costs excluding labor (input). We normalized the technical efficiency gap (uit ) between zero and 1 (or 100%) since this simplifies comparisons across firms, and Eq. (2) enables straightforward interpretation of results (zero means the firm achieved optimum technical efficiency). The larger the parameter, the wider the gap is between a firm’s performance and optimum performance. We collected data for input and output variables from hotels’ financial statements, available from the Agency of the Republic of Slovenia for Public Legal Records and Related Services and the Financial Agency.
3.2. Customer satisfaction, customer complaints, moderators, and control variables Data for customer satisfaction and complaints were collected directly from hotels, all of which collect data on these variables regularly. We calculated average customer satisfaction per year for each hotel, and for complaints, we summed the number of complaints per year for each hotel. Data on star ratings were collected from hotel websites. We measured size as the log of total assets, in line with the literature (Assaf et al., 2012; Lu & Beamish, 2001). We added control variables to the model, including the number of years the hotel had been in business, type of ownership (i.e., independently owned versus part of a group), and advertising expenditures. The first two control variables are used in most performance studies on the topic (e.g., Assaf & Agbola, 2011; Barros & Dieke, 2008). Several studies test the impact of advertising expenditures on firm performance (Joshi and Hanssens, 2010; Luo and Donthu, 2005). In the present context, advertising might help firms reap benefits from customer satisfaction or decrease the negative impact of customer complaints.
4. Results Technical efficiency gap was the dependent variable, and independent variables were customer satisfaction, customer complaints, the moderators, and control variables. Since technical efficiency gap was bound to 0–1, we used a two-limit Tobit model
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Table 1 Correlation matrix and descriptive statistics.
1. TEG 2. Satisfaction 3. Complaint 4. Star rating 5. Size 6. Years in business 7. Part of a group 8. Advertising spending
Mean
SD
1
2
3
4
5
6
7
0.64 193.69 5.64 3.55 14.02 0.27 0.40 1,173,773
0.15 208.98 0.77 0.62 1.40 0.44 0.49 3,049,400
−0.02 0.04 −0.08 0.02 −0.07 0.18 0.05
0.01 0.04 0.43 0.18 −0.14 0.29
0.23 0.06 0.06 0.14 0.12
0.24 0.20 0.01 0.07
0.33 −0.43 0.62
−0.31 0.31
−0.3
to estimate hypotheses.2 The model was: TEGi,t+1 = ˇ0 + ˇ1 CuSati,t + ˇ2 CuComi,t + ˇ3 CuSati,t × Sizei,t +ˇ4 CuSati,t × Stari,t + ˇ5 CuComi,t ×Sizei,t + ˇ6 CuComi,t × Stari,t + ˇ7 Sizei,t + ˇ8 Stari,t + ˇControls Controlst +εi,t+1 and TEGi,t+1 =
∗ TEGi,t+1
∗ if 0 ≤ TEGi,t+1 ≤1
0
∗ ∗ if TEGi,t+1 < 0 or 1 if TEGi,t+1 >1
(2)
where TEGi,t+1 is the technical efficiency gap, CuSati,t is the lag of customer satisfaction, CuComi,t is the lag of customer complaints, Sizei,t is the lag of hotel size, Stari,t is the lag of hotel star rating, and εi,t+1 is error. Since panel data involves unobserved, cross-sectional heterogeneity that can lead to bias, we also estimated a randomparameter, robust Tobit model to account for bias. The model also accounted for both firm-wise and time-wise heteroskedasticity (Greene, 2003). The results of both models are shown in Table 2. Since the dependent variable is technical efficiency gap, a variable that has a negative estimated effect (i.e., a negative impact on technical efficiency gap) has a positive impact on performance (i.e., closing the technical efficiency gap). All variables seem to be correctly signed. The control variables seem also to be in line with the literature. For example, advertising has a positive impact on performance supporting previous studies in the literature (Osinga et al., 2011). Both Tobit models in Table 2 (columns 2 and 6) support H1. For example, increased customer satisfaction had a negative influence on technical efficiency gap (i.e., a positive impact on performance). Both models also support H2 (columns 2 and 6). Customer complaints, for example, had a positive impact on technical efficiency gap; when customer complaints increase, technical efficiency gap increases. We hypothesized with H3a and H4a that the impact of customer satisfaction on firm performance is stronger for larger hotels and hotels with higher ratings. Results shown in Table 2 (columns 4 and 8) support these hypotheses since both moderators were negative (i.e., positive impact on performance), indicating both size and rating strengthened the impact of customer satisfaction on firm performance. We hypothesized with H3b and H4b that the impact of customer complaints on firm performance is weaker for larger hotels and stronger for hotels with higher ratings. Results in Table 2 (columns 4 and 8) indicate that size reduced the impact of complaints on increasing the technical efficiency gap, but the interaction of complaints and ratings was not significant. Hence, only H4b was supported. We suggested with H5 that customer complaints have a stronger impact than customer satisfaction on hotel performance. As shown
2 Note that one can also avoid the two-stage approach and include the variables in (2) directly in the inefficiency term in (1) and estimate the model simultaneously. Both approaches are deemed to be statistically correct (Coelli et al., 2005).
in Table 2, customer complaints had a stronger impact than customer satisfaction on firm performance. To confirm the hypothesis, we conducted a Wald coefficient test to confirm statistical significance between the two. Results suggested rejection of the null hypothesis (F = 16.322, p < 0.05); customer complaints and satisfaction had the same impact on firm performance. Hence, H5 was supported. 4.1. Theoretical and managerial implications This study advances the traditional customer equity model by analyzing the impact of both customer satisfaction and complaints on firm performance simultaneously. Results suggest increasing customer satisfaction affects firm performance positively, and increasing customer complaints has a negative effect on firm performance. These findings confirm extant theoretical assumptions regarding relationships with firm performance. Relationships often change (i.e., weaken, strengthen, or reverse) when added to a more complete model in comparison to individual testing. These results empirically confirm that both concepts are important and must be included in the same model. To the best of our knowledge, this is the first time that both variables have been investigated simultaneously. The next question was whether a firm’s limited resources are better allocated toward increasing satisfaction or limiting complaints? This is a key question for any service firm. Including customer satisfaction and complaints in the same model allowed us to test which of the two had the strongest impact on performance. Results show that customer complaints had the stronger impact. Demonstrating that a negativity bias exists contributes to customer service literature. We also address another gap in customer satisfaction and firm performance literature: lack of studies that use a contingency approach to examine circumstances under which customer satisfaction is a more or less important determinant of firm performance. Regarding hotel size, results show that the impact of customer satisfaction on firm performance was stronger for larger hotels. As suggested, the impact of customer complaints on firm performance was weaker for larger hotels. Concerning the impact of perceived quality, we used hotel ratings, and findings suggest performance of hotels with higher ratings is influenced more by customer satisfaction than for hotels with lower ratings. Although hotels with higher ratings were more sensitive to variations in customer satisfaction, highly rated hotels were no more sensitive regarding customer complaints. Results thus show that the effect of customer complaints on firm performance is similar for hotels regardless of rating. This study also has several implications for practitioners. The finding that customer complaints have a stronger effect on hotel performance than satisfaction allows hotel managers to allocate limited service management resources better. This implication also gives impetus to future research trying to find thresholds at which optimum resource allocation switches between satisfaction and complaints. Although this study suggests that hotels benefit more from investing in lowering complaints rather than investing in
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Table 2 Impact of customer satisfaction and complaints on the technical efficiency gap. Variable
Customer satisfaction Customer complaints Customer satisfaction, hotel size Customer complaints, hotel size Customer satisfaction, star rating Customer complaints, star rating Star rating Size Years in business Part of group versus individual Advertising spending
Column 1
Two-limit robust Tobit Model
Hypothesis
Column 2 Coefficient
Column 3 p-value
Column 4 Coefficient
Column 5 p-value
Column 6 Coefficient
Column 7 p-value
Column 8 Coefficient
Column 9 p-value
H1 H2 H3a H3b H4a H4b
−1.811 2.493
**
−1.107 2.507 −1.807 −1.699 −1.404 1.567 −0.501 −0.523 1.345 −1.780 −2.469
**
−1.816 2.491
**
−1.124 2.487 −1.804 −1.698 −1.376 1.489 −0.506 −0.527 −1.344 −1.783 −2.469
**
−0.491 −0.566 −1.470 −1.527 −2.470
**
*
n.s. ** ** **
Random parameter Tobit Model
**
**
** ** **
ns n.s. n.s. ** ** **
−0.478 −0.564 −1.469 −1.525 −2.473
*
n.s. ** ** **
** ** ** **
ns n.s. n.s. ** ** **
*
p < 0.10, p < 0.05, n.s., not significant. **
increasing satisfaction, the added value of investing in lowering complaints decreases the lower the number of complaints. Similarly, the value of increasing satisfaction might be higher the lower satisfaction is. There might exist degrees of customer satisfaction and complaints with which hotels do better allocating resources to increasing satisfaction, an interesting opportunity for future research. Managers of larger hotels should particularly allocate resources to managing customer satisfaction, and managers of smaller hotels should minimize complaints rather than increase satisfaction. Hotel managers should also consider ratings. Customer satisfaction should be a focus for highly rated hotels, and customer complaints are equally important for all hotels, those with both low and high ratings. This study shows that customer satisfaction and complaints are important customer equity variables that influence hotel performance. We provide several insights into their relative importance, including contingency criteria in which they are particularly important to determining hotel performance.
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Contents lists available at ScienceDirect
International Journal of Hospitality Management journal homepage: www.elsevier.com/locate/ijhosman
The effects of customer voice on hotel performance A. George Assaf a,∗ , Alexander Josiassen b , Ljubica Kneˇzevic´ Cvelbar c , Linda Woo a a
Isenberg School of Management, University of Massachusetts, Amherst, United States Copenhagen Business School, Denmark c Faculty of Economics, University of Ljubljana, Slovenia b
a r t i c l e
i n f o
Keywords: Hotel performance Customer satisfaction Customer complaints Equity model
a b s t r a c t This paper investigates the effects of two critical customer voice variables on hotel performance. Specifically, the research provides a customer equity model in which the influences of both customer satisfaction and complaints are considered. The impact of the customer voice variables on hotel performance is investigated while considering the potential for moderating effects by hotel size and star rating. We use a more robust approach to measure firm performance than is traditionally used in satisfaction-performance studies. Finally the paper reports on the results of these investigations and outlines implications for both theory and practice. © 2014 Elsevier Ltd. All rights reserved.
1. Introduction A significant portion of the service literature focuses on assessing the impact of customer satisfaction on firm performance (Anderson and Mittal, 2000; Johnston, 1995; Johnston et al., 1990; Mersha & Adlakha, 1992). Customer satisfaction is a form of customer voice. Specifically it is a post-consumption consumer response that leads to greater customer loyalty (Anderson and Sullivan, 1993; Fornell, 1992; LaBarbera & Mazursky, 1983) and help firms “secure future revenues, reduce the costs of future transactions, decrease price elasticities, and minimize the likelihood that customers will defect if quality falters” (Anderson et al., 1997, p. 129). Positive word-of mouth from satisfied customers also makes it simpler and less expensive to attract new customers (Anderson, 1998; Luo, 2009). Customer satisfaction also links to improve overall reputation, economic return, and shareholder value (Anderson et al., 2004; Fornell et al., 2006). In service industries such as hotels, customer satisfaction is not only an important goal, it is also a vital marketing tool for attracting future customers and ensuring stronger market positions (Luo and Homburg, 2007). However, customers may not only voice their satisfaction but also their dissatisfaction, and recently, scholars have also investigated customer complaints as an important customer voice (Luo, 2007, 2009). Despite the attention and contributions to understand customer satisfaction and complaints on firm performance, the relationships
∗ Corresponding author. Tel.: +1 4135454192. E-mail addresses: [email protected] (A.G. Assaf), [email protected] (A. Josiassen), [email protected] (L. Kneˇzevic´ Cvelbar), [email protected] (L. Woo). http://dx.doi.org/10.1016/j.ijhm.2014.09.009 0278-4319/© 2014 Elsevier Ltd. All rights reserved.
have not been explored fully, and despite their importance to hotels – and academic interest – few studies analyze how they affect hotel performance (Chi & Gursoy, 2009). For example, hotels use many resources to improve customer satisfaction and attract/retain customers with the purpose of increasing performance, but the literature offers contradicting evidence regarding the impact of customer satisfaction on hotel performance (Barsky, 1992; Chi & Gursoy, 2009; Sun & Kim, 2013). It is not necessarily the case that customer satisfaction leads to improved firm performance; many reasons exist to suggest customer satisfaction does not improve firm performance (Anderson et al., 1997). Studies in the service literature also provide inconsistent conclusions regarding the longitudinal relationship between customer satisfaction and firm performance (Anderson, 1994; Anderson and Mittal, 2000; Ittner & Larcker, 1998; Johnston, 1995; Johnston et al., 1990; Mersha & Adlakha, 1992). If a firm improves performance by downsizing, it might achieve an increase in performance in the short-term, but future profitability might be threatened since lack of back-and front-office personnel influences both customer satisfaction and complaints negatively. Scholars note trade-offs between customer satisfaction and firm performance across several heterogeneous industries such as airlines, banking, education, hotels, and restaurants (Anderson et al., 1997). Customer satisfaction is also a given or expected factor in some service industries. In the hotel industry, for example, high satisfaction does not necessarily result in higher performance because customers expect to be satisfied when choosing one hotel over another (Gursoy & Swanger, 2007). The goal of this paper is to contribute to the literature on customer satisfaction in a service context, focusing on three important gaps. First, customer satisfaction and complaints are two essential customer voice variables. Although these two have been analyzed
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separately regarding their impacts on firm performance, to date no study has included their impact on firm performance in a single model. This is a major gap; as Luo and Homburg (2008) suggest, “managers should no longer value satisfaction and complaint in isolation. Rather, both good news (of “angel” customers) and bad news (of “devil” customers) should be considered in one model” (p. 29). It might be more important for managers to reduce customer complaints than improve customer satisfaction (Luo and Homburg, 2008) since potential negative impacts of customer complaints on performance might matter more than upside gains in terms of customer satisfaction. Hence, it is important to also consider customer complaints for two reasons: (a) to provide a more robust assessment of customer satisfaction on performance and (b) to compare the impacts of both customer satisfaction and customer complaints on hotel performance. Thus, we provide an investigation of such a more complete customer equity model in which we analyze the impact of both customer satisfaction and customer complaints on hotel performance simultaneously. Second, when analyzing the impact of customer satisfaction on hotel performance, the literature has yet to consider moderating variables, with few exceptions. This research gap of potential moderating influences is even more pronounced for the customer complaint–hotel performance relationship. We suggest that hotel size and star ratings moderate the influence of customer satisfaction and customer complaints on performance. We also include these moderators to advance a contingency view of customers’ impact on hotel performance. Third, this study uses a more robust approach to measure firm performance than extant satisfaction–performance studies. Instead of using financial indicators (e.g., ROA, Tobin’s q1 ) used commonly in the literature, we focus on technical efficiency gap, which offers two advantages. It measures overall firm performance based on multiple inputs and outputs, not partial indicators alone, providing a comprehensive, realistic assessment of firm performance. It also reveals a company’s efficiency gap when benchmarked against optimum, best-performing competitors. Hence, it provides a complete assessment of performance by measuring performance of every firm relative to the maximum performance it can achieve.
2. Conceptual framework and hypotheses Consumers’ post-consumption responses (e.g., expressing satisfaction or complaining) are important to managing loyalty and repeat purchases, and understandably, study of such responses attracts research from various fields (Anderson and Mittal, 2000; Johnston, 1995; Johnston et al., 1990; Mersha & Adlakha, 1992). Expressing low satisfaction is not synonymous with expressing dissatisfaction by complaining, and vice versa; research from sociology and psychology (Cashdan, 2001; Larsen et al., 2001) suggest positive and negative dispositions are distinct. We investigate whether creating satisfied customers leads to higher performance. Further, we investigate whether firms with less complaining customers perform better than firms with more complaining customers. We are interested in discovering whether the most efficient strategy is to (a) mostly allocate resources to increase satisfaction, (b) mostly allocate resources to avoid complaints, (c) balance resources between increasing satisfaction and lowering complaints, or (d) accept lower satisfaction, while satisfying the majority, and allow for some complaints. Our framework that includes the impact of consumer responses on firm performance is illustrated in Fig. 1.
1
Tobin’s q is the ratio between total market value of the firm and total asset value.
The hospitality management literature suggests that customer satisfaction is at “the core of hospitality operations” (Sun and Kim, 2013, p.70). Hotel revenues rely heavily on the service quality delivered by its employees, and consequently, customer satisfaction occupies an important role in the hotel industry, leading to improved brand reputation, faster market penetration, accelerated cash flows (Anderson et al., 2004), steady future sales (Anderson et al., 1994; Reichheld & Sasser, 1990), and higher shareholder value (Anderson et al., 1994, 2004). According to Barsky and Labagh (1992), the objective of satisfying customers is to improve profitability in a chain of effects from increased customer loyalty, improved product reputation, and increased sales. Since loyal customers provide increased repeat business with less effort and costs than finding new customers, and increased positive word-of mouth (Bowen & Chen, 2001), maintaining high satisfaction is essential, and has direct and indirect effects on hotel performance (O’Neill & Mattila, 2004). Although the literature supports these theoretical advantages of customer satisfaction, the empirical evidence remains inconclusive. Banker et al. (2005) demonstrate that while Hotelcorp enjoyed positive effects on revenue as a result of implementing an incentive plan to improve customer satisfaction, the impact on operating costs was negative. Customer satisfaction influences a firm’s revenue positively, but it might not always result in increased profits (Bernhardt et al., 2000; Schneider, 1991; Tornow & Wiley, 1991; Wiley, 1991). For example, to increase customer satisfaction, firms often invest in training and upgraded facilities (Chi & Gursoy, 2009), but this might affect profits and obscure the potential relationship between customer satisfaction and firm performance – at least in the short term (Bernhardt et al., 2000). The service literature suffers from the same gap regarding the direction and strength of the customer satisfaction/firm performance relationship. Heskett et al. (1997) found a weak relationship between customer satisfaction and customer loyalty in a serviceprofit chain. Novelty-seeking customers might be very satisfied but not loyal, and some customers are price sensitive, searching for better deals even if satisfied with a hotel’s property (Shoemaker & Lewis, 1999). In view of contradicting findings in this literature, it is difficult to hypothesize the nature and direction of the relationship between customer satisfaction and hotel performance. Following theoretical arguments found in the majority of the literature (Anderson et al., 2004; Luo and Homburg, 2007), we expect customer satisfaction to influence hotel performance positively. H1. Customer satisfaction correlates positively with hotel performance. 2.1. Customer complaints and hotel performance The focus on decreasing complaints should also be important for hotels just as customer satisfaction is. In a recent extension of the satisfaction literature, marketing scholars have focused on customer complaints (Luo, 2007; Luo and Homburg, 2007). For most firms, the cost of generating a new customer is higher than retaining a customer (Fornell & Wernerfelt, 1987; Yavas et al., 2004), and because complaints is a more extreme effect of being dissatisfied, these complaining customers may exit. Therefore managing customer complaints is important, particularly in industries such as hotels in which competition is fierce and customers can easily switch among service providers. Customer complaints also relate to the effectiveness of operations. For example, complaining is a way of providing direct feedback regarding a hotel’s operating processes, and it is useful for initiating corrective actions (Banker et al., 2005). Service research has particularly designed complaint management strategies to reduce customer complaints and turnover. Fornell and Wernerfelt (1987) found that better handling of
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Fig. 1. Conceptual framework: the relationship between consumer responses and firm performance.
dissatisfied customers and having a complaint management system in place results in more positive word of mouth and higher customer loyalty. When researchers include complaints in their studies, they often use complaints as a proxy for customer satisfaction (Steven et al., 2012). More typically, however, in much of the extant literature customers’ complaining behaviors are viewed as a consequence of low satisfaction, but recent studies demonstrate that complaints do not always originate from dissatisfaction, and dissatisfaction does not always result in complaints (Day et al., 1981; Singh & Pandya, 1991; Tronvoll, 2007). According to Hirschman (1970) exit-voice theory, dissatisfied customers have two options: exit or voice; customers either stop buying (i.e., exit the firm) or voice complaints to a firm. Customer complaints is one of the two feedbacks for dissatisfaction, and dissatisfaction in turn is a necessary but insufficient condition for customer complaints (Jacoby & Jaccard, 1981). Thus, customer complaints should not be used as a reverse proxy for customer satisfaction. This study distinguishes customer satisfaction from complaints. Thus: H2. Customer complaints correlate negatively with hotel performance. 2.2. Moderation Hotel size. Hotel size has been linked to the resources that a hotel has. It also represents a prominent contingency variable that distinguishes hotels (Dahlstrom et al., 2009; Huang et al., 2012). Hotel size can, for example, influence a hotel’s learning opportunities, and the size of promotions (Barros and Dieke, 2008). Larger hotels with greater resources will want to draw on experiences from those of their hotels which have particularly positive customers, and then implement corresponding procedures across other hotels in the hotel chain (Assaf and Agbola, 2011). Larger hotels have more resources and can better to capitalize on such higher-satisfaction information in promotional campaigns. Such greater opportunities for organizational learning and promotion may in turn increase customer loyalty behaviors (e.g., word of mouth, cross-buying behaviors, etc.). As such, larger hotels draw both internal (e.g., learning from the experiences of other hotels in the chain) and external (e.g., leverage higher customer satisfaction) benefits from size. Therefore, regarding customer satisfaction, we predict that size will strengthen the impact of customer satisfaction on hotel performance. Regarding customer complaints, we argue that firm size weakens the negative impact of complaints on firm performance. Larger hotels are better able to identify the threshold when complaints reach an unsustainable level (Liu, 1995). Smaller hotels have less opportunity to identify hotels within the chain to use as benchmarks across the chain, and a hotel manager might perceive that some complaints are inevitable in the context in which the hotel operates. Larger hotels can use internal benchmarking regarding complaints, and invest in complaint management. Complaint
management induces repurchasing behaviors and increases brand equity (Fornell and Wernerfelt, 1988). An opposite effect of hotel size is, however, possible for the impact of both customer satisfaction and complaints. Larger firms tend to be less flexible, and react more slowly to environmental changes (Fiegenbaum & Karnani, 1991). If customers increasingly complain, larger firms might react more slowly than smaller firms do (Perry-Smith & Blum, 2000). Larger firms are subject to inertial forces and rigidity that limit change. Systems and processes that characterize smaller firms make them inherently more flexible and receptive (Perry-Smith & Blum, 2000). Similarly for customer satisfaction, larger hotels may be less flexible in terms of reacting to customer satisfaction once identified (Baum & Wally, 2003) Overall, we do not expect flexibility to outweigh positive size mechanisms, and suggest a strengthening effect of hotel size on the customer satisfaction-hotel performance relationship, and a weakening effect of hotel size on the customer complaint-hotel performance relationship. H3a. The impact of customer satisfaction on firm performance is stronger for larger firms than for smaller firms. H3b. The impact of customer complaints on firm performance is weaker for larger firms than for smaller firms. Star rating. The star-rating system in the hotel industry is a quality signal (Israeli, 2002) that allows higher-rated hotels to support premium-pricing strategies (Bull, 1994). We argue that star rating has a moderating influence on the relationship of both customer voice variables on performance. Regarding the impact of customer satisfaction on hotel performance, we argue that this relationship is stronger for higher rated hotels than for lower rated hotels. This is the case as higher rated hotels are in a better position to leverage high levels of customer satisfaction than are lower rated hotels (Barros and Dieke, 2008). Higher rated hotels are more likely to have systems in place in terms of promotion to leverage positive customer satisfaction (Israeli, 2002; Siu & Fung, 1998). Regarding customer complaints, we argue that hotels with a higher star rating experience a more negative relationship between customer complaints and hotel performance. We rely on the expectancy-confirmation theory (Ahluwalia and Gürhan-Canli, 2000) and the notion of negativity bias which states that negative disconfirmations (in this case indicated by customer complaints) are punished harder when the expectation for quality is higher such in the case of higher rated hotels. Recent studies (Darke et al., 2010) also emphasized that negativity bias such that negative disconfirmation is punished harder than positive disconfirmation is rewarded. H4. The impacts of (a) customer satisfaction, and (b) customer complaints on firm performance are stronger for firms with a higher rating than for hotels with a lower rating.
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2.3. Customer satisfaction and complaints Researchers are increasingly focusing on the importance of complaints and whether complaint management represents an opportunity to increase profitability (Heskett et al., 1990; Johnson et al., 2001; Kahneman & Tversky, 1979; Tversky & Kahneman, 1992). Prospect theory asserts that “people are more sensitive to losses than gains. Thus, in most service encounters, customers perceive service failures as losses, and weigh failures heavily” (Smith et al., 1999, p. 360). Lou and Homburg (2008) test the effects of both customer satisfaction and complaints on firms’ stock-value gap, finding that customer complaints have a stronger impact on gaps than satisfaction. Smith et al. (1999) investigate complaints in restaurant and hotel industries, the results of which suggest customer complaints depend on type and magnitude of service failure. This implies that negative experiences of service failure are more impactful than positive experiences (Luo and Homburg, 2008). Marketing research demonstrates that negative product feedback has a stronger impact on attracting new customers than an equal amount of positive feedback (Berry & Parasuraman, 1991; Mahajan et al., 1984). Based on these arguments, we suggest that the effect of complaints is stronger than satisfaction on hotel performance. H5. Customer complaints have a stronger influence on hotel performance than customer satisfaction does. 3. Methods The paper adopts a two-step procedure to test the hypotheses. First, we estimated performance using the technical efficiency gap concept. Second, we estimated the impact of customer satisfaction, customer complaints, and moderators on the gap. Table 1 presents a correlation matrix for all variables for the full sample. We tested hypotheses on a sample of 56 hotels from Slovenia and Croatia for the 2009–2012 period (56 × 4 years = 224 observations). Both countries are known well internationally for their tourism and hotel industries. The Croatian economy depends highly on tourism; the direct contribution of tourism to GDP was 11% in 2011. Major international tourism markets for Croatian hotels include Austria, Italy, Slovenia, Germany, and Czech Republic. In 2011, there were 656 hotels in Croatia, and 57,435 hotel rooms. Hotels from our sample represented 19% of all rooms in Croatia, generating 21% of all income in the hospitality industry in 2011. Tourism is also important to Slovenia. In 2011, tourism’s contribution to GDP was 12.8% (WTTC, 2012). Slovenia reported 2.9 million tourism arrivals and 9.6 million overnight stays (SORS, 2012). In 2011, there were 298 hotels, offering 18,719 hotel rooms. Most hotel rooms (90%) had 3 or 4 stars (SORS, 2012), and approximately 40% of all hotel capacities were small hotels with fewer than 30 rooms. Hotels in our sample represented 30% of all hotel rooms in Slovenia, and generated 32% of all income in the hospitality industry (AIJPES, 2012). 3.1. Performance We measured performance using the technical efficiency gap metric; the degree of producing as much output as technology and inputs allow or using as few inputs as required by technology and production. The concept recently gained increased popularity in the hospitality and marketing literature (Dutta et al., 1999; Barros and Mascarenhas, 2005; Krasnikov et al., 2009; Luo and Homburg, 2008; Murthi et al., 1996) due to several advantages over simpler performance metrics such as ROA and Tobin-q. For example, the technical efficiency gap is a relative measure of performance that compares a firm’s performance with its optimum performance. It provides the distance (i.e., gap) between a firm’s performance and the
maximum performance it could achieve. In contrast to other, simpler measures of performance, the technical efficiency gap is also a more comprehensive measure of performance since it depends on multiple inputs and outputs. Measurement of the technical efficiency gap involves estimating a stochastic frontier production function in “which a firm’s output is a function of its inputs and a standard, two-sided error term that measures the effects of unobservables, and another technical efficiency gap term that has a minimum value of zero” (Assaf et al., 2012, p. 197). The stochastic frontier production function is expressed as: yit = xit ˇ + vit − uit , for each firm i = 1, ..., n, at time
t = 1, ..., T
(1)
where yit is firm output, xit is a k × 1 vector of input and explanatory variables, and i is a k × 1 vector of parameters. vit , and uit denote measurement error distributed as normal N(0, v2 ) and the technical inefficiency gap distributed as half-normal N + (0, u2 ). Hence, the technical efficiency gap enters the production function negatively since it measures the shortfall of a firm’s performance to its optimum performance. Eq. (1) is usually estimated using the maximum-likelihood (ML) method since the combined error (vit − uit ) is not distributed normally. For specification of input and output variables, we followed the hotel literature (Assaf & Josiassen, 2012; Barros, 2005), selecting total revenue (output), number of hotel rooms (input), number of employees (input), cost of materials (input), and other operational costs excluding labor (input). We normalized the technical efficiency gap (uit ) between zero and 1 (or 100%) since this simplifies comparisons across firms, and Eq. (2) enables straightforward interpretation of results (zero means the firm achieved optimum technical efficiency). The larger the parameter, the wider the gap is between a firm’s performance and optimum performance. We collected data for input and output variables from hotels’ financial statements, available from the Agency of the Republic of Slovenia for Public Legal Records and Related Services and the Financial Agency.
3.2. Customer satisfaction, customer complaints, moderators, and control variables Data for customer satisfaction and complaints were collected directly from hotels, all of which collect data on these variables regularly. We calculated average customer satisfaction per year for each hotel, and for complaints, we summed the number of complaints per year for each hotel. Data on star ratings were collected from hotel websites. We measured size as the log of total assets, in line with the literature (Assaf et al., 2012; Lu & Beamish, 2001). We added control variables to the model, including the number of years the hotel had been in business, type of ownership (i.e., independently owned versus part of a group), and advertising expenditures. The first two control variables are used in most performance studies on the topic (e.g., Assaf & Agbola, 2011; Barros & Dieke, 2008). Several studies test the impact of advertising expenditures on firm performance (Joshi and Hanssens, 2010; Luo and Donthu, 2005). In the present context, advertising might help firms reap benefits from customer satisfaction or decrease the negative impact of customer complaints.
4. Results Technical efficiency gap was the dependent variable, and independent variables were customer satisfaction, customer complaints, the moderators, and control variables. Since technical efficiency gap was bound to 0–1, we used a two-limit Tobit model
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81
Table 1 Correlation matrix and descriptive statistics.
1. TEG 2. Satisfaction 3. Complaint 4. Star rating 5. Size 6. Years in business 7. Part of a group 8. Advertising spending
Mean
SD
1
2
3
4
5
6
7
0.64 193.69 5.64 3.55 14.02 0.27 0.40 1,173,773
0.15 208.98 0.77 0.62 1.40 0.44 0.49 3,049,400
−0.02 0.04 −0.08 0.02 −0.07 0.18 0.05
0.01 0.04 0.43 0.18 −0.14 0.29
0.23 0.06 0.06 0.14 0.12
0.24 0.20 0.01 0.07
0.33 −0.43 0.62
−0.31 0.31
−0.3
to estimate hypotheses.2 The model was: TEGi,t+1 = ˇ0 + ˇ1 CuSati,t + ˇ2 CuComi,t + ˇ3 CuSati,t × Sizei,t +ˇ4 CuSati,t × Stari,t + ˇ5 CuComi,t ×Sizei,t + ˇ6 CuComi,t × Stari,t + ˇ7 Sizei,t + ˇ8 Stari,t + ˇControls Controlst +εi,t+1 and TEGi,t+1 =
∗ TEGi,t+1
∗ if 0 ≤ TEGi,t+1 ≤1
0
∗ ∗ if TEGi,t+1 < 0 or 1 if TEGi,t+1 >1
(2)
where TEGi,t+1 is the technical efficiency gap, CuSati,t is the lag of customer satisfaction, CuComi,t is the lag of customer complaints, Sizei,t is the lag of hotel size, Stari,t is the lag of hotel star rating, and εi,t+1 is error. Since panel data involves unobserved, cross-sectional heterogeneity that can lead to bias, we also estimated a randomparameter, robust Tobit model to account for bias. The model also accounted for both firm-wise and time-wise heteroskedasticity (Greene, 2003). The results of both models are shown in Table 2. Since the dependent variable is technical efficiency gap, a variable that has a negative estimated effect (i.e., a negative impact on technical efficiency gap) has a positive impact on performance (i.e., closing the technical efficiency gap). All variables seem to be correctly signed. The control variables seem also to be in line with the literature. For example, advertising has a positive impact on performance supporting previous studies in the literature (Osinga et al., 2011). Both Tobit models in Table 2 (columns 2 and 6) support H1. For example, increased customer satisfaction had a negative influence on technical efficiency gap (i.e., a positive impact on performance). Both models also support H2 (columns 2 and 6). Customer complaints, for example, had a positive impact on technical efficiency gap; when customer complaints increase, technical efficiency gap increases. We hypothesized with H3a and H4a that the impact of customer satisfaction on firm performance is stronger for larger hotels and hotels with higher ratings. Results shown in Table 2 (columns 4 and 8) support these hypotheses since both moderators were negative (i.e., positive impact on performance), indicating both size and rating strengthened the impact of customer satisfaction on firm performance. We hypothesized with H3b and H4b that the impact of customer complaints on firm performance is weaker for larger hotels and stronger for hotels with higher ratings. Results in Table 2 (columns 4 and 8) indicate that size reduced the impact of complaints on increasing the technical efficiency gap, but the interaction of complaints and ratings was not significant. Hence, only H4b was supported. We suggested with H5 that customer complaints have a stronger impact than customer satisfaction on hotel performance. As shown
2 Note that one can also avoid the two-stage approach and include the variables in (2) directly in the inefficiency term in (1) and estimate the model simultaneously. Both approaches are deemed to be statistically correct (Coelli et al., 2005).
in Table 2, customer complaints had a stronger impact than customer satisfaction on firm performance. To confirm the hypothesis, we conducted a Wald coefficient test to confirm statistical significance between the two. Results suggested rejection of the null hypothesis (F = 16.322, p < 0.05); customer complaints and satisfaction had the same impact on firm performance. Hence, H5 was supported. 4.1. Theoretical and managerial implications This study advances the traditional customer equity model by analyzing the impact of both customer satisfaction and complaints on firm performance simultaneously. Results suggest increasing customer satisfaction affects firm performance positively, and increasing customer complaints has a negative effect on firm performance. These findings confirm extant theoretical assumptions regarding relationships with firm performance. Relationships often change (i.e., weaken, strengthen, or reverse) when added to a more complete model in comparison to individual testing. These results empirically confirm that both concepts are important and must be included in the same model. To the best of our knowledge, this is the first time that both variables have been investigated simultaneously. The next question was whether a firm’s limited resources are better allocated toward increasing satisfaction or limiting complaints? This is a key question for any service firm. Including customer satisfaction and complaints in the same model allowed us to test which of the two had the strongest impact on performance. Results show that customer complaints had the stronger impact. Demonstrating that a negativity bias exists contributes to customer service literature. We also address another gap in customer satisfaction and firm performance literature: lack of studies that use a contingency approach to examine circumstances under which customer satisfaction is a more or less important determinant of firm performance. Regarding hotel size, results show that the impact of customer satisfaction on firm performance was stronger for larger hotels. As suggested, the impact of customer complaints on firm performance was weaker for larger hotels. Concerning the impact of perceived quality, we used hotel ratings, and findings suggest performance of hotels with higher ratings is influenced more by customer satisfaction than for hotels with lower ratings. Although hotels with higher ratings were more sensitive to variations in customer satisfaction, highly rated hotels were no more sensitive regarding customer complaints. Results thus show that the effect of customer complaints on firm performance is similar for hotels regardless of rating. This study also has several implications for practitioners. The finding that customer complaints have a stronger effect on hotel performance than satisfaction allows hotel managers to allocate limited service management resources better. This implication also gives impetus to future research trying to find thresholds at which optimum resource allocation switches between satisfaction and complaints. Although this study suggests that hotels benefit more from investing in lowering complaints rather than investing in
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Table 2 Impact of customer satisfaction and complaints on the technical efficiency gap. Variable
Customer satisfaction Customer complaints Customer satisfaction, hotel size Customer complaints, hotel size Customer satisfaction, star rating Customer complaints, star rating Star rating Size Years in business Part of group versus individual Advertising spending
Column 1
Two-limit robust Tobit Model
Hypothesis
Column 2 Coefficient
Column 3 p-value
Column 4 Coefficient
Column 5 p-value
Column 6 Coefficient
Column 7 p-value
Column 8 Coefficient
Column 9 p-value
H1 H2 H3a H3b H4a H4b
−1.811 2.493
**
−1.107 2.507 −1.807 −1.699 −1.404 1.567 −0.501 −0.523 1.345 −1.780 −2.469
**
−1.816 2.491
**
−1.124 2.487 −1.804 −1.698 −1.376 1.489 −0.506 −0.527 −1.344 −1.783 −2.469
**
−0.491 −0.566 −1.470 −1.527 −2.470
**
*
n.s. ** ** **
Random parameter Tobit Model
**
**
** ** **
ns n.s. n.s. ** ** **
−0.478 −0.564 −1.469 −1.525 −2.473
*
n.s. ** ** **
** ** ** **
ns n.s. n.s. ** ** **
*
p < 0.10, p < 0.05, n.s., not significant. **
increasing satisfaction, the added value of investing in lowering complaints decreases the lower the number of complaints. Similarly, the value of increasing satisfaction might be higher the lower satisfaction is. There might exist degrees of customer satisfaction and complaints with which hotels do better allocating resources to increasing satisfaction, an interesting opportunity for future research. Managers of larger hotels should particularly allocate resources to managing customer satisfaction, and managers of smaller hotels should minimize complaints rather than increase satisfaction. Hotel managers should also consider ratings. Customer satisfaction should be a focus for highly rated hotels, and customer complaints are equally important for all hotels, those with both low and high ratings. This study shows that customer satisfaction and complaints are important customer equity variables that influence hotel performance. We provide several insights into their relative importance, including contingency criteria in which they are particularly important to determining hotel performance.
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