• Journal of Distribution Research
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• v.17 no.1
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• pp.1-36
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• 2012

Service failure is one of the major reasons for customer defection. As the business environment gets tougher and more competitive, a single service failure might bring about fatal consequences to a service provider or a firm. Sometimes a failure won't end up with an unsatisfied customer's simple complaining but with a wide-spread animosity against the service provider or the firm, leading to a threat to the firm's survival itself in the society. Therefore, we are in need of comprehensive understandings of complainants' attitudes and behaviors toward service failures and firm's recovery efforts. Even though a failure itself couldn't be fixed completely, marketers should repair the mind and heart of unsatisfied customers, which can be regarded as an successful recovery strategy in the end. As the outcome of recovery efforts exerted by service providers or firms, recovery of the relationship between customer and service provider need to put on the top in the recovery goal list. With these motivations, the study investigates how service failure and recovery makes the changes in dynamics of fundamental elements of customer-firm relationship, such as customer affection, customer trust and loyalty intention by comparing two time points, before the service failure and after the recovery, focusing on the effects of recovery satisfaction and the failure severity. We adopted La & Choi (2012)'s framework for development of the research model that was based on the previous research stream like Yim et al. (2008) and Thomson et al. (2005). The pivotal background theories of the model are mainly from relationship marketing and social relationships of social psychology. For example, Love, Emotional attachment, Intimacy, and Equity theories regarding human relationships were reviewed. As the results, when recovery satisfaction is high, customer affection and customer trust that were established before the service failure are carried over to the future after the recovery. However, when recovery satisfaction is low, customer-firm relationship that had already established in the past are not carried over but broken up. Regardless of the degree of recovery satisfaction, once a failure occurs loyalty intention is not carried over to the future and the impact of customer trust on loyalty intention becomes stronger. Such changes imply that customers become more prudent and more risk-aversive than the time prior to service failure. The impact of severity of failure on customer affection and customer trust matters only when recovery satisfaction is low. When recovery satisfaction is high, customer affection and customer trust become severity-proof. Interestingly, regardless of the degree of recovery satisfaction, failure severity has a significant negative influence on loyalty intention. Loyalty intention is the most fragile target when a service failure occurs no matter how severe the failure criticality is. Consequently, the ultimate goal of service recovery should be the restoration of customer-firm relationship and recovery of customer trust should be the primary objective to accomplish for a successful recovery performance. Especially when failure severity is high, service recovery should be perceived highly satisfied by the complainants because failure severity matters more when recovery satisfaction is low. Marketers can implement recovery strategies to enhance emotional appeals as well as fair treatments since the both impacts of affection and trust on loyalty intention are significant. In the case of high severity of failure, recovery efforts should be exerted to overreach customer expectation, designed to directly repair customer trust and elaborately designed in the focus of customer-firm communications during the interactional recovery process to affect customer trust rebuilding indirectly. Because it is a longer and harder way to rebuild customer-firm relationship for high severity cases, low recovery satisfaction cannot guarantee customer retention. To prevent customer defection due to service failure of high severity, unexpected rewards as a recovery will be likely to be useful since those will lead to customer delight or customer gratitude toward the service firm. Based on the results of analyses, theoretical and managerial implications are presented. Limitations and future research ideas are also discussed.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.171-183
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• 2012

In the development of technologies, the exhibition industry has received much attention from governments and companies as an important way of marketing activities. Also, the exhibitors have considered the exhibition as new channels of marketing activities. However, the growing size of exhibitions for net square feet and the number of visitors naturally creates the competitive environment for them. Therefore, to make use of the effective marketing tools in these environments, they have planned and implemented many promotion technics. Especially, through smart environment which makes them provide real-time information for visitors, they can implement various kinds of promotion. However, promotions ignoring visitors' various needs and preferences can lose the original purposes and functions of them. That is, as indiscriminate promotions make visitors feel like spam, they can't achieve their purposes. Therefore, they need an approach using STP strategy which segments visitors through right evidences (Segmentation), selects the target visitors (Targeting), and give proper services to them (Positioning). For using STP Strategy in the smart exhibition environment, we consider these characteristics of it. First, an exhibition is defined as market events of a specific duration, which are held at intervals. According to this, exhibitors who plan some promotions should different events and promotions in each exhibition. Therefore, when they adopt traditional STP strategies, a system can provide services using insufficient information and of existing visitors, and should guarantee the performance of it. Second, to segment automatically, cluster analysis which is generally used as data mining technology can be adopted. In the smart exhibition environment, information of visitors can be acquired in real-time. At the same time, services using this information should be also provided in real-time. However, many clustering algorithms have scalability problem which they hardly work on a large database and require for domain knowledge to determine input parameters. Therefore, through selecting a suitable methodology and fitting, it should provide real-time services. Finally, it is needed to make use of data in the smart exhibition environment. As there are useful data such as booth visit records and participation records for events, the STP strategy for the smart exhibition is based on not only demographical segmentation but also behavioral segmentation. Therefore, in this study, we analyze a case of the promotion methodology which exhibitors can provide a differentiated service to segmented visitors in the smart exhibition environment. First, considering characteristics of the smart exhibition environment, we draw evidences of segmentation and fit the clustering methodology for providing real-time services. There are many studies for classify visitors, but we adopt a segmentation methodology based on visitors' behavioral traits. Through the direct observation, Veron and Levasseur classify visitors into four groups to liken visitors' traits to animals (Butterfly, fish, grasshopper, and ant). Especially, because variables of their classification like the number of visits and the average time of a visit can estimate in the smart exhibition environment, it can provide theoretical and practical background for our system. Next, we construct a pilot system which automatically selects suitable visitors along the objectives of promotions and instantly provide promotion messages to them. That is, based on the segmentation of our methodology, our system automatically selects suitable visitors along the characteristics of promotions. We adopt this system to real exhibition environment, and analyze data from results of adaptation. As a result, as we classify visitors into four types through their behavioral pattern in the exhibition, we provide some insights for researchers who build the smart exhibition environment and can gain promotion strategies fitting each cluster. First, visitors of ANT type show high response rate for promotion messages except experience promotion. So they are fascinated by actual profits in exhibition area, and dislike promotions requiring a long time. Contrastively, visitors of GRASSHOPPER type show high response rate only for experience promotion. Second, visitors of FISH type appear favors to coupon and contents promotions. That is, although they don't look in detail, they prefer to obtain further information such as brochure. Especially, exhibitors that want to give much information for limited time should give attention to visitors of this type. Consequently, these promotion strategies are expected to give exhibitors some insights when they plan and organize their activities, and grow the performance of them.