Search results

The purpose of this study is to investigate the impact of dissatisfaction and anger, driven by the failure of the self-service technology of banks, on customers' post-purchase behavioural reactions, such as complaints, negative word-of-mouth (NWOM) and supplier change. The stability of the failure is proposed to moderate these relationships.

The proposed research model was tested through data collected from an online survey of a Tunisian sample of 300 respondents, using the scenario method.

The study validates the positive impact of dissatisfaction on anger and negative word-of-mouth, as well as that of anger on complaint behaviour and negative word-of-mouth. The relation between dissatisfaction and negative word-of-mouth is mediated by anger. When the failure is stable, dissatisfied users of the self-service technology seek to enhance their negative word-of-mouth and supplier change. The results also show that the stability of the failure enhances the effect of anger on complaint behaviour.

Banks should invest efforts to accelerate the recovery of services to reduce consumer dissatisfaction and anger and prevent adverse behavioural outcomes. Further, they need to ensure that failures are not repeated, as failure stability activates some otherwise non-significant behavioural outcomes, like supplier change.

Previous works have focused on the impact of dissatisfaction and negative emotions for interpersonal services, but very few works have come to associate dissatisfaction, anger, complaint, negative word-of-mouth and supplier change in an integrative framework for an self-service technology failure.

Preferential treatment of selected customers is one of the strategies employed by companies seeking to implement relationship marketing. However, it remains unclear whether or not this policy negatively affects relationships with customers not receiving the above‐mentioned preferential treatment, as existing literature focuses, for the most part, on the beneficiaries. The purpose of this paper is to study whether or not the perception of lack of preferential treatment has a positive impact on dissatisfaction following a service failure, after accounting for the effects of attribution.

The sample used in this paper consists of 344 subjects who, over the last six months, have experienced service failures. The data are collected via personal interviews using a structured survey. Structural equation modelling is employed in order to test the relationship between lack of preferential treatment and dissatisfaction.

The results of this investigation indicate that lack of preferential treatment has an additional explanatory power with regard to customer dissatisfaction, after considering the effects of attributions, and that negative emotions have a mediation effect on the relationship between these cognitive antecedents and the aforementioned dissatisfaction.

This paper examines only one service context; as a consequence, caution is needed when generalizing the results.

Given the findings of this paper, managers are advised to consider the “merits” of preferential treatment to help strengthen customer relationships.

This paper provides an initial step towards understanding the impact of systematic and deliberate use of preferential treatment as a relationship marketing strategy from a non‐beneficiaries perspective.

How do online customers judge a product's attributes in cyberspace? Previous studies of online product category suggest that all goods are not equal on the Web, because products have different attributes. Furthermore, the literature assumes that the customer's ability to evaluate product quality on the Web differs according to product attributes. Based on these considerations, the purpose of this study is to determine whether a customer's dissatisfaction and propensity to complain on the Web differ depending on product category. This study examines how selected variables (i.e. monetary, and non‐monetary effort, and the degree of involvement) influenced the impact of product category on customer dissatisfaction. The analysis was performed using survey data, collected both online and offline. The findings suggest the most appropriate strategies online companies should employ for each product category in question.

The purpose of this paper is to explore routes of dissatisfaction feedback transferrals within a Swedish machine industry segment. The study focuses upon transferrals from dissatisfied users to the product development organizations. There is also an interest in determining whether the feedback is reliable and, if not, how to improve the reliability of this information to create a better basis for decision‐making.

The paper presents the results from a qualitative interview‐based study of 16 product development organizations and their customer dissatisfaction feedback systems. About 84 percent of the companies within a machine industry segment in Sweden are covered. Based on the empirical investigation, a typology describes four different dissatisfaction feedback constructs, depending on whether the feedback system is active or passive, and on whether the feedback is codified or personalized.

The study indicates that parallel usage of codified and personalized dissatisfaction feedback, compared to using these transferrals in isolation only, improves reliability of dissatisfaction information and puts product developers in a better position when deciding on future actions. However, a real challenge is how to turn passive dissatisfaction routes into active ones. Managing passive dissatisfaction routes with service personnel and call centres as knowledge carriers more actively in product development can certainly reveal many of the hidden needs of users.

Our project is essentially managerial, aiming to provide managers and other decision‐makers with a framework to establish reliable and adequate customer feedback systems for more effective product development.

Despite double deviation being an acknowledged phenomenon in services marketing, less research has been devoted to the evaluation of the underlying relationships between cognitive appraisals, customer forgiveness and postrecovery actions following a double deviation. Therefore, this study aims to develop and empirically test a conceptual model to determine the role of customer forgiveness and its boundary conditions in double-deviation scenarios based on the stress and coping theory.

This study aggregated 290 survey data by adopting the retrospective experience sampling method and examined the proposed model using structural equation modeling and bootstrapping analysis.

The results confirm that customer forgiveness mediates the link between service recovery dissatisfaction and postrecovery customer complaints (i.e. online and third-party complaints). Additionally, attribution-based factors (i.e. stability and controllability attributions) positively moderated the service recovery dissatisfaction–customer forgiveness relationship. Finally, these findings exhibit that relationship-based factors (i.e. relationship duration and affective commitment) had negative moderating effects on the service recovery dissatisfaction–customer forgiveness link.

Without ensuring customer forgiveness, customers who experience failure twice in a row may act more aggressively to damage service firms. Yet, knowledge of customer forgiveness in a double-deviation scenario is still lacking. The results make twofold contributions to the service recovery literature. First, this study emphasizes customer forgiveness as an integral coping response that has a mediating role in the relationship between service recovery dissatisfaction and postrecovery customer complaints. Second, this study shed insights into boundary conditions of customer forgiveness by identifying attribution- and relationship-based factors as moderators.

Although unfavorable pricing errors (UPEs) cost customers billions of dollars each year, research has not yet examined customers’ reactions to UPEs. This paper aims to fill this gap by examining customers’ reactions to UPEs in terms of frequency, magnitude and the interaction between frequency and magnitude. Also, this study explores the moderated mediating role of price consciousness.

Three experimental studies were conducted to examine customers’ reactions to UPEs in terms of frequency, magnitude and the interaction between frequency and magnitude. PROCESS Model 6 and 84 along with multivariate regression analysis and MANOVA were used to test the hypotheses.

The results show that high-frequency and high-magnitude UPEs lead to increased perceived deception and dissatisfaction, resulting in a higher negative attitude toward the grocery store, decreased re-patronage intentions and increased negative word-of-mouth (NWOM). Also, results show that regardless of customers’ price consciousness level, customers display negative reactions when encountering UPEs.

This paper only investigates UPEs in the brick and mortar setting; future studies should examine UPEs in different settings.

The findings show that UPEs can cause significant problems for grocery stores. Thus, managers should take precautionary measures (e.g. constantly checking shelves) to ensure that the advertised price and the checkout price match.

This paper represents the first attempt to empirically examine the relationship between UPEs frequency and magnitude, on the one hand, and perceived deception, dissatisfaction, customer attitude, re-patronage intentions, NWOM and price consciousness on the other.

Discusses customer dissatisfaction and how it is expressed. Reports on a study showing that customers will complain to management if the problem is severe and if they are encouraged to participate in service management, whereas if service firms create barriers within the complaint‐handling process and set up luxury‐oriented and aloof service images, customers will adopt more harmful negative word of mouth forms of complaining. Proposes ways of managing customer complaints, for controlling the more harmful types of dissatisfaction and for maximizing opportunities to develop loyal customer bases through effective handling of problems.

The purpose of this study is to analyze online hotel reviews produced by customers to identify and compare factors known as satisfiers and dissatisfiers based on Herzberg’s two-factor theory. This approach was applied to compare full-service and limited-service hotels, which can show different levels of customer expectation.

A content analysis of 919 satisfaction- and dissatisfaction-indicating reviews of 100 hotels in both full-service and limited-service hotel segments in New York City on Trip Advisor was conducted.

Results show that satisfiers and dissatisfiers in full-service hotels were distinct, with the exception of two common service-related factors, namely, “staff and their attitude” and “service”. On the other hand, “staff and their attitude” and four room facilities-related factors, “room cleanliness/dirtiness”, “bed”, “bathroom” and “room size”, were revealed as common satisfiers and dissatisfiers in limited-service hotels. To fulfill customer satisfaction and resolve dissatisfaction in both full-service and limited-service hotels, satisfiers and dissatisfiers should be highlighted according to the hotel class; the most critical factor is “staff and their attitude”.

Analysis of online hotel reviews provides understanding of customers’ satisfiers and dissatisfiers, and the results are very useful to hotel management. Therefore, hotel operators should monitor electronic word-of-mouth, recognizing and acting upon previous and current customers’ satisfactory and unsatisfactory reactions.

As technologies such as social media develop, customers are increasingly sharing their satisfactory and unsatisfactory experiences on consumer-generated online review sites. These have become a major source of information not only for customers deciding on a hotel stay but also for hotel managers trying to understand their customers and competitors.

This study aims to establish a comprehensive satisfaction analysis framework by mining online restaurant reviews, which can not only accurately reveal consumer satisfaction but also identify factors leading to dissatisfaction and further quantify improvement opportunity levels.

Adopting deep learning, Cross-Bidirectional Encoder Representations Transformers (BERT) model is developed to measure customer satisfaction. Furthermore, opinion mining technique is used to extract consumers’ opinions and obtain dissatisfaction factors. Furthermore, the opportunity algorithm is introduced to quantify attributes’ improvement opportunity levels. A total of 19,133 online reviews of 31 restaurants in Universal Beijing Resort are crawled to validate the framework.

Results demonstrate the superiority of Cross-BERT model compared to existing models such as sentiment lexicon-based model and Naïve Bayes. More importantly, after effectively unveiling customer dissatisfaction factors (e.g. long queuing time and taste salty), “Dish taste,” “Waiters’ attitude” and “Decoration” are identified as the three secondary attributes with the greatest improvement opportunities.

The proposed framework helps managers, especially in the restaurant industry, accurately understand customer satisfaction and reasons behind dissatisfaction, thereby generating efficient countermeasures. Especially, the improvement opportunity levels also benefit practitioners in efficiently allocating limited business resources.

This work contributes to hospitality and tourism literature by developing a comprehensive customer satisfaction analysis framework in the big data era. Moreover, to the best of the authors’ knowledge, this work is among the first to introduce opportunity algorithm to quantify service improvement benefits. The proposed Cross-BERT model also advances the methodological literature on measuring customer satisfaction.

Few scholars have so far explored how healthcare service quality affects patient dissatisfaction, leading to negative behavior responses when a healthcare service fails. The purpose of this paper is to examine how different service quality attributes affect patient dissatisfaction leading to a variety of asymmetric negative behavior responses.

Following a survey of 453 dissatisfied outpatients in Korea, structural equation modeling with a series of post hoc analyses is used to test the research model. It consists of five hypotheses.

Outcome quality is found to be the most significant variable affecting patient dissatisfaction, followed by administrative quality, interactive quality, and environmental quality. Dissatisfied patients tend to engage more in active behaviors (e.g. negative word-of-mouth, switching, and complaining) than in remaining passive in a non-linear way. Also, the mediating role of dissatisfaction is found to be significant.

This paper has empirically identified the most significant service quality attributes that lead to dissatisfied patients and negative behaviors on their part. These findings indicate that different quality attributes of service failure lead to different actions. However, this study has suffered from a few limitations as a result of its research context and scope.

This paper is one of the very few empirical studies examining the relationships among the output and process quality attributes, patient dissatisfaction, and actual behaviors in a healthcare service failure context.