Dysfunctional customer behavior is common in service settings. For frontline employees, negative encounters can cause short-term despondency or have profound, long-term psychological effects that often result in both direct and indirect costs to service firms. Existing research has explored the influence of dysfunctional customer behavior on employee emotions, but it has not fully investigated the psychological mechanism through which customer misbehavior transforms into employee responses. To maintain service quality and employee well-being, it is important to understand the impact of customer misconduct on employee emotions and its effect on subsequent service behavior. To assess the process through which dysfunctional customer behavior manifests as negative emotions in frontline service employees, and the influence of negative employee emotions on their prosocial service behavior, we surveyed 185 frontline banking service employees. We sought information on service employee experiences, attitudes, and feelings regarding dysfunctional customer behaviors, the perceived level of supervisor support, and employee prosocial service behavior intentions. Structural equation modeling and hierarchical linear modeling were used for statistical analysis and hypothesis verification. Results indicate that dysfunctional customer behavior has a positive relationship with bank service employee negative emotions and a negative influence on employee prosocial service behavior. The study found that negative emotions fully mediated the relationship between dysfunctional customer behavior and prosocial service behavior. The moderating role that perceived supervisor support plays on the relationships between dysfunctional customer behavior and negative emotion was also investigated. The results show that perceived supervisor support moderates the relationship between dysfunctional customer behavior and negative employee emotions. Finally, the study provides bank managers with effective strategies to assist frontline employees to manage and deter dysfunctional customer behavior, and presents employees with internal recovery strategies when encountering dysfunctional customer behavior.

Small and medium-sized enterprises (SMEs) play an indispensable role in China’s national economic system, and they can play a critical role in promoting economic growth and full employment. The main reason for the disruption and impediment to the development of SME clusters is that the enterprises in the clusters are experiencing a “capital shortage,” as a result of the double contradiction between the clusters’ financial benefits and the difficulties in financing. Grey theory is a relatively new concept in the field of information processing. It proposes theories and methods for processing and analyzing incomplete information systems using mathematical methods. This study uses finance in supply chain as a new research perspective to investigate the effectiveness of finance in supply chain in resolving SMEs’ financing problems using the Grey theory model, with the goal of resolving SMEs’ financing problems. When comparing the results of the Grey theory model and the regression analysis model, the relative error of the Grey theory model is on average 12.2% lower than that of the regression analysis model, indicating that the Grey theory model greatly improves accuracy. As a result, using the Grey theory model to solve supply chain financing and SMEs’ difficulties can share risks, share credit, reduce transaction costs, weaken information asymmetry, and achieve mutual benefits and a win-win situation. It is expected to promote the development of the finance in supply chain model, mobilise enterprise enthusiasm for using finance in supply chain, improve finance in supply chain operational efficiency, and promote finance in supply chain development.

As market competition intensifies, companies recognize the value of attracting customers to participate in activities and loyalty programs (LPs) that encourage repeat purchases and maintain customer loyalty. Literature on LP design explores the positive impact of program structure and rewards on the acquisition of customers. However, research is lacking on the role of LP information transparency on customer participation intention. This study uses 280 college students in China as the survey object to explore the influence of LP information transparency on willingness to participate in such programs. Using experimental design methods, the authors verify whether the type of merchant and channel customers select affect willingness to participate when customers redeem rewards. This study also explains the internal psychological mechanism of information transparency, merchant and channel types, and customer participation intention from the perspective of perceptual psychological distance in construal level theory (CLT) and the elaboration likelihood model (ELM). Both information visibility and accessibility have a positive impact on customer intention to participate in LPs. When a customer redeems a reward from a LP operator, information visibility has a more positive impact on willingness to participate than redeeming a reward from an alliance partner. Moreover, when a customer redeems a reward from online channels, the positive impact of information accessibility on willingness to participate is greater than redeeming from offline channels. Under the influence of multiple psychological distance effects, the synergistic effect of merchant type and channel type is not significant in the relationship between information transparency and willingness to participate in LPs. This article will provide design strategies and management suggestions for retail managers to attract customers to participate in LPs.

No abstract

Consumers are constantly generating a large amount of data, thanks to the arrival of the big data era and the advancement of mobile edge computing capabilities. Massive behavioral data points to the need to mine and analyze potentially valuable information. The commonality and individuality of customer groups’ consumption behaviors must be researched before marketing decisions and strategies can be implemented. Because of the unique advantages of mobile edge computing technology, the Internet’s application has become more and more widespread, and businesses are increasingly paying attention to network marketing. With the system’s long-term use, decision-makers began to wonder if useful information could be extracted from vast amounts of historical data to help them summarize or even predict changes in customer demand and purchasing behavior. This assumption is possible thanks to the rise and development of data mining technology. Association rules are increasingly being applied to customer behavior analysis as the most active branch of data mining in the last ten years. The majority of association rule research currently focuses on one-dimensional data association analysis of a user’s package using classical algorithms. The use of association rules mining on multidimensional data with multiple attributes in the telecom service industry is limited due to the complexity of the data structure and algorithm.