A performance evaluation matrix (PEM) is an evaluation tool for assessing customer satisfaction and the importance of service items across various services. In addition, inferences based on point estimates of sample data can increase the risk of misjudgment due to sampling errors. Thus, this paper creates a decision-making model for a performance evaluation matrix based on upper confidence limits to provide various service operating systems for performance evaluation and decision making. The concept is that through the gap between customer satisfaction and the level of importance of each service item, we are able to identify critical-to-quality (CTQ) service items requiring improvement. Many studies have indicated that customer satisfaction and the importance of service items follow a beta distribution, and based on the two parameters of this distribution, the proposed indices for customer satisfaction and the importance of service items represent standardization. The vertical axis of a PEM represents the importance index; the horizontal axis represents the satisfaction index. Since these two indices have unknown parameters, this paper uses the upper confidence limit of the satisfaction index to find out the CTQ service items and the upper confidence limit of the importance index to determine the order of improvement priority for each service item. This paper then establishes a decision-making model for a PEM based on the above-mentioned decision-making rules. Since all decision-making rules proposed in this paper are established through upper confidence limits, the risk of misjudgment caused by sampling errors can be reduced. Finally, this article uses a practical example to illustrate how to use a PEM to find CTQ service items and determine the order of improvement priority for these service items that need to be improved.
This study empirically investigates the effects of online consumer reviews on hotel accommodation performance in an e-commerce context. Online consumer reviews include two types: online consumer satisfaction and electronic word-of-mouth (eWOM). eWOM was also regarded as the proxy of consumer loyalty. Hotel-level online consumer reviews from three well-known online travel agencies (i.e., Agoda.com, Expedia.com, and Trip.com) and financial data from 88 hotels were combined and analyzed using the Hayes’ PROCESS Macro. Based on the service-profit chain (SPC) framework, the two forms of online consumer reviews, satisfaction, and eWOM, were hypothesized to have positive effects on performance. The hypothetic effects were assessed in terms of the concurrent model and three lagged models. The results indicate that satisfaction has a positive effect on eWOM. However, to our great surprise, the two forms of online consumer reviews did not directly affect hotel accommodation performance across the concurrent model and the three lagged models. Additionally, online consumer satisfaction did not influence hotel accommodation performance via eWOM. The results have several important theoretical and practical implications for online consumer relationship management in the hospitality and tourism industry. The results of this study can further clarify the relationships among online consumer satisfaction and eWOM (customer loyalty), and performance.
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