The relationship of service failure severity, service recovery justice and perceived switching costs with customer loyalty in the context of e-tailing

Given that e-tailing service failure is inevitable, a better understanding of how service failure and recovery affect customer loyalty represents an important topic for academics and practitioners. This study explores the relationship of service failure severity, service recovery justice (i.e., interactional justice, procedural justice, and distributive justice), and perceived switching costs with customer loyalty; as well, the moderating relationship of service recovery justice and perceived switching costs on the link between service failure severity and customer loyalty in the context of e-tailing are investigated. Data collected from 221 useful respondents are tested against the research model using the partial least squares (PLS) approach. The results indicate that service failure severity, interactional justice, procedural justice and perceived switching costs have a significant relationship with customer loyalty, and that interactional justice can mitigate the negative relationship between service failure severity and customer loyalty. These findings provide several important theoretical and practical implications in terms of e-tailing service failure and recovery.     

Satisfaction and post-purchase intentions with service recovery of online shopping websites: Perspectives on perceived justice and emotions

This study explores post-recovery satisfaction and post-purchase intentions with service recovery of online shopping customers from the perspectives on perceived justice and emotions based on twenty scenarios that combine five service failures and four recoveries. Analytical results indicating that in addition to enhancing post-purchase intentions and post-recovery satisfaction among customers, distributive justice increases positive emotions and decreases negative ones. Additionally, procedural justice enhances post-recovery satisfaction as well as increases positive emotions and decreases negative ones, while interactional justice only increases post-recovery satisfaction of customers. Moreover, while positive emotions increase post-recovery satisfaction, and vice versa. Post-recovery satisfaction positively affects post-purchase intentions. Results of this study provide a valuable reference for operators and managers of online shopping websites.     

The role of big data analytics capabilities (BDAC) in understanding the challenges of service information and operations management in the sharing economy: Evidence of peer effects in libraries

Information and operations management in libraries presents a unique opportunity to provide insights for the sharing economy. Libraries correspond to a special type of sharing goods, named common-pool resources. Such resources have two characteristics: they are non-exclusive, but rival to each other. Service operations in libraries involve thousands of operations every year, making them a perfect context for the use of big data analytics capabilities (BDAC) to provide real-world evidence on the potential existing challenges in the sharing economy. Employing a novel dataset related to 723,798 library transactions, made by 16,232 individual users during a 10-year period (2006–2015), we estimate peer effects among users via regression analysis, considering the number of books each user borrows. Our main results suggest that a rise in the number of loans among a user’s peer group correlates with her own loans, an evidence of positive peer effects. However, a closer look at the data suggests a high degree of heterogeneity, in terms of behavioral patterns. First, we suggest that peer effects do not occur in the case of users who are not subject to monetary fines. Second, peer effects vary according to users’ category (student or non-student), and area of study (management, accounting, economics, and other courses). Third, there is evidence of different magnitudes of peer effects according to time in school, which suggests the existence of learning effects in a library setting. The results reported in this paper highlight the important role of big data analytics capabilities to uncover new challenges of the sharing economy, having important implications, both in theoretical and practical terms.     

新疆生产性服务业与制造业协调发展研究

基于1995—2012年新疆的时间序列数据,采用耦合协调度方法及灰色关联模型,探讨新疆生产性服务业与制造业协调发展的关系及影响因素。研究表明,新疆生产性服务业与制造业耦合协调度较低,处于周期性失调的状态。新疆生产性服务业与制造业协调影响程度排序为:经济发展结构资本累积科学创新金融支持力度经济外向性程度人力资本。建议两者从产业融合、科学创新、固定资产投资和外商投资方面做出调整,实现新疆生产性服务业与制造业协调发展。