بهبود عملکرد کسب و کار در هتل ها : نقش نوآوری و مشتری مداری
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|2311||2012||10 صفحه PDF||سفارش دهید||9113 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Hospitality Management, Available online 21 November 2012
Innovation management and customer orientation have been widely recognized as key factors in enhancing the business performance of hotels. Our research investigates the interplay between customer orientation, innovation, and business performance in the Alpine hospitality industry. The study contributes to current innovation research by jointly investigating hotel innovativeness and innovation behavior as two distinct dimensions of the innovation concept. Analyzing data from 203 hotel managers, this study shows that the effect of hotels’ customer orientation exceeds the effects of innovativeness and innovation behavior on financial and non financial business performance. Mediation analysis shows that innovation behavior partially mediates the effect of customer orientation on business performance. The results of the study provide hotel management with relevant insights into the customer orientation innovation performance chain.
In an era of globalization, technological evolution and stagnating tourism demand, competition in the hotel industry has become fierce (Tseng et al., 2008). Hotels that track and respond to customers’ needs and preferences perform at higher levels while achieving sustained success and maintaining a strong competitive position (Sin et al., 2005 and Zhou et al., 2007). Innovation in this context allows hotel managers to introduce new services that improve quality, thereby both meeting the changing requirements of potential customers and increasing their market share, sales and profits (Chen et al., 2009). This is particularly important for the Alpine hospitality industry, which consists predominantly of small- and medium size hotels that are unable to obtain a low-cost advantage (Smeral, 1996). Alpine hotels therefore maintain their competitive position by focusing on differentiation strategies (Pikkemaat and Peters, 2005 and Pikkemaat, 2008), offering new services, and providing quality standards that meet the expectations of their customers (Weiermair and Fuchs, 1999). Although marketing research investigates the relationship between customer orientation and business performance in various small, medium, and large companies (Narver and Slater, 1990 and Jaworski and Kolhi, 1993), to date there has been scant research that has addressed the influence of customer orientation on hotel performance (Wang et al., 2012, Sin et al., 2005 and Zhou et al., 2007) and found a positive relationship between them. In addition to customer orientation, innovation also significantly influences a hotel's business outcome in terms of enhanced competitiveness and substantial improvements in performance (Orfila-Sintes and Mattsson, 2009). The existing literature suggests that customer orientation is an important driver of innovation. A customer-oriented company is more likely to adopt services and products that meet customers’ needs and wants (e.g., Campbell and Cooper, 1999, Jaworski and Kolhi, 1993, Deshpandé et al., 1993, Atuahene-Gima, 1996 and Kuusisto and Riepula, 2008). Several scholars have investigated how customer orientation and innovation together influence business performance (e.g., Narver and Slater, 1990, Deshpandé et al., 1993, Jaworski and Kolhi, 1993 and Slater and Narver, 1994). In the hotel context, the scant body of research that has examined the customer orientation–innovation–performance chain so far (Agarwal et al., 2003 and Sandvik and Sandvik, 2003) has argued that the relationship between customer orientation and performance is not direct but mediated through innovation. In order to understand how customer orientation influences the competitive parameters of a company, it is essential to investigate innovation as a mediator. However, current studies in the hospitality sector measure innovation by focusing either on the behavioral dimension of innovation, referring to the number of innovations implemented (e.g., Orfila-Sintes et al., 2005 and Agarwal et al., 2003) or on its innovativeness, referring to management's openness to new ideas. They thus describe a more attitudinal dimension of innovation (e.g., Hurley and Hult, 1998 and Tajeddini, 2010). This research contrasts with that of Sandvik and Sandvik (2003) and Agarwal et al. (2003), who focused on innovation behavior as a mediating variable, and it adds to the literature by considering innovativeness as an additional mediating variable in the customer orientation–innovation–performance chain. Our study is the first to jointly investigate the attitudinal and behavioral dimensions of innovation as mediating variables. By measuring both a hotel's innovation behavior and its innovativeness, we gain deeper insights into the relationships between innovation and the business performance of hotels. The purpose of this study, therefore, is to investigate the interplay between innovation, customer orientation, and business performance indicators in Alpine hotels. We take into account the inter-sectoral heterogeneity of tourism services by focusing on companies belonging to a single industry (Han et al., 1998 and Orfila-Sintes and Mattsson, 2009). Because the tourism industry encompasses a wide range of activities in a variety of sectors such as restaurants, transportation, accommodation, and entertainment, there are also differences in customer orientation and innovation management. The hotel industry, however, is characterized as a homogenous industry that provides a substantial part of the tourism product (Borooah, 1999). Our study is important for the Alpine hospitality industry for several reasons. First, over the past half century, tourism has become an increasingly significant economic factor in the Alps (Weiermair et al., 2007). The numbers speak for themselves: some 4.5 million tourist beds (about 1.2 million of them in hotels) and more than 300 million annual bed nights indicate the hospitality industry's importance to the economy of the Alpine region (Bartaletti, 2011). Second, the accommodation sector is a fundamental part of the Alpine tourist experience since lodging is usually the first service that guests encounter when they arrive at a destination (Orfila-Sintes et al., 2005). Third, in terms of arrivals, the Alpine region ranks second only to the Mediterranean coast as the world's top tourist destination (Bartaletti, 2011). And finally, although some innovation research has studied Mediterranean destinations (Orfila-Sintes et al., 2005, Sundbo et al., 2007, Martinez-Ros and Orfila-Sintes, 2009 and Orfila-Sintes and Mattsson, 2009), it has largely ignored the Alpine hospitality industry. Taking the Swiss hospitality industry as an example for Alpine hotels, Tajeddini, 2010 and Tajeddini, 2011 and Tajeddini and Trueman (2012) take an in-depth look into the interplay of innovation, customer orientation, and performance. Our study, which elaborates on their findings, is set in a broader context of Alpine hotels. Taking all these reasons into account, this study chooses the Alpine hotel industry as a suitable focus for research on customer orientation, service innovation, and hotel performance. The remainder of the paper is structured as follows: a literature review discusses the influence of customer orientation on innovation behavior, innovativeness and business performance and then establishes the study hypotheses. We develop two alternative conceptual models and test them with data obtained from 203 hotel managers from five Alpine countries: Austria, Germany, Italy, Liechtenstein, and Switzerland. Subsequent sections then describe the methodology, the data analysis, and the results. The study concludes by providing implications for research and management, discussing limitations, and making recommendations.
نتیجه گیری انگلیسی
4.1. Measurement model Items of each construct were evaluated by means of Cronbach's alpha, item-to-total correlations (ITTC), exploratory factor analysis (EFA), and confirmatory factor analysis (CFA). Cronbach's α values for financial performance (.91), customer retention (.78), and customer orientation (.90) reveal the high reliability of the constructs. Two out of five items measuring innovativeness (i.e., “Technical innovation, based on research results, is readily accepted.” and “Innovation in our hotel is perceived as too risky and is resisted.”) and one item measuring reputation (“We have had an important competitive advantage.”) were dropped from further analyses due to insufficient α values (<.70) of the respective constructs. This procedure resulted in satisfactory α values for innovativeness (.84) and reputation (.88). Construct reliabilities for innovativeness (.84), financial performance (.91), customer retention (.78), reputation (.88), and customer orientation (.88) are also in line with the conventional threshold of .60. Table 2 shows the results for the average variance extracted (AVE). All the values surpass the recommended threshold value of .50. Table 2 also indicates that discriminant validity is fulfilled due to the fact that the AVE values exceed the square of the correlations between pairwise matched factors (Fornell and Larcker, 1981). Table 3 shows means, standard deviations, composite reliabilities, and estimates of the latent constructs. To sum up, all the items and constructs used in the model show very good reliability and validity and thus are acceptable for further analysis.4.2. Structural models We analyzed the postulated relationships of H1, H2, H3, H4, H5, H6 and H7 with the help of structural equation modeling using the statistical software Mplus 5.21 (Muthén and Muthén, 2009). The results indicate a very good model fit for the initial structural model. The chi-square value of 197.731 (df = 96, p = .018; χ2/df = 2.05) is in line with the related literature (Hu and Bentler, 1999). The value of .07 of the root mean square error of approximation (RMSEA), which takes into account the effects of large sample sizes in the evaluation of model fit, is lower than the threshold value of .08 suggested by Browne and Cudeck (1993). The comparative fit index (CFI) value of .94 and the Tucker–Lewis index (TLI) value of .93 clearly surpasses the conventional cutoff value of .90. In summary, according to well-established fit criteria, the initial model is a good representation of the data. Table 4 reports the path coefficients of the initial model. The results indicate that innovation behavior is positively influenced by innovativeness (H1) and customer orientation (H3). In support of H2, customer orientation also influences innovativeness. Innovation behavior, however, positively influences financial performance (H4a), customer retention (H4b) and reputation (H4c).To test the mediation effects proposed in H5, H6 and H7, we followed the recommendations of Preacher and Hayes (2008) and conducted a detailed multiple mediation analysis. We used the suggested bootstrap method in combination with the model indirect command of the Mplus software (Preacher and Hayes, 2008). This analysis revealed that partial mediation holds for the effect of customer orientation on financial performance and customer retention. There is no mediating effect of innovation behavior between customer orientation and reputation. Therefore, H5a and H5b are supported while H5c is not supported. We applied the same procedures to test H6 and H7. The analysis shows that the effect of customer orientation on business performance indicators is not mediated by innovativeness, so H6 was not supported. We found support for H7a (βIV-IB-FP = .04, p < .10) and H7b (βIV-IB-CR = .05, p < .10), which implies that the effect of innovativeness on financial performance and customer retention is mediated by innovation behavior. Table 5 summarizes the results of the multiple mediation analysis.Even though the initial model represents the data well, we continued our analysis in order to investigate whether this model could be improved by adding direct paths from customer orientation and innovativeness to business performance indicators. Fig. 2 shows this alternative model and reports its path coefficients. The alternative model shows an even better fit than our initial model (χ2 = 133.501; df = 90; p = .002; χ2/df = 1.48; CFI = .98, TLI = .97, RMSEA = .04). A chi-square difference test between the two models supports the supposition that the alternative model better fits the data (Δχ2 (6) = 64.23, p < .001).The findings indicate that the direct effect of customer orientation on financial success is comparable to the effect of innovation behavior on financial success. The direct effects of customer orientation on retention and reputation are substantial and highly significant. When controlling for the direct effects of customer orientation and innovativeness, the effect of innovation behavior on reputation becomes insignificant. In addition, the alternative model indicates that innovativeness has no direct effects on business performance indicators. The comparison of these two competing models improves our analysis and reveals that the alternative model provides even more interesting insights than the initial model. In order to check for possible effects of hotel size (number of rooms and employees) and star category on the alternative model, we conducted a series of moderation tests by running three two-group models in Mplus and using the controls as grouping variables. We compared models that differed only with respect to the effect of one control variable on one path at a time. We compared two nested models: a restricted model and a general model, which allowed the respective parameter to vary freely across the two groups. The general model always has one degree of freedom less than the restricted model. If the χ2 value improves significantly when freeing the respective path, then the grouping variable can be seen as a moderator (Muthén and Muthén, 2009; see Ryu and Han, 2011 for an application of this moderation test). This analysis shows that star categorization is a moderator on the relationship between customer orientation and financial performance (see Table 6). While the (direct) effect of customer orientation on financial performance is not significant (β = .05, ns) for hotels with a 1 to 3 star rating (N = 113), this effect is highly significant (β = .42, p < .001) for 4 to 5 star hotels (N = 75). No moderation effect however, is found for hotel size.