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This research investigates whether and how perceived firm remorse (PFR) influences consumers’ coping behaviors in the digital media service recovery context. It also examines how an apology should be delivered to generate PFR.

In Study 1, 452 mobile application service users were recruited for a survey study, and Structural Equation Modeling was used to test the research hypotheses. In Study 2, 1,255 mobile application service users were recruited for an experimental study.

Study 1 shows that PFR negatively influences blame attribution and positively influences emotional empathy. Emotional empathy negatively affects coping behaviors. According to this study, blame attribution and emotional empathy do not have any serial mediation effect on the relationship between PFR and coping behaviors. Only emotional empathy mediates the effect of PFR on coping behaviors. Study 2 finds that response time and apology mode jointly influence PFR.

This research establishes the relationship between PFR and coping behaviors and shows the mediating role of emotional empathy in this relationship.

Service providers should consider response time and apology mode, as the two factors jointly influence the extent of PFR, which affects consumers’ coping behaviors through emotional empathy. A grace period, in which PFR does not decrease, is present when a public apology is offered. Such an effect does not exist when a private apology is offered.

This research explains how PFR influences coping behaviors and demonstrates how apology mode moderates the effect of response time on PFR in the digital media service recovery context.

The purpose of this paper is to demonstrate the applications of big data in personal knowledge management (PKM).

Five conventional knowledge management dimensions, namely, the value of data, data collection, data storage, data application and data presentation, were applied for integrating big data in the context of PKM.

This study concludes that time management, computer usage efficiency management, mobile device usage behavior management, health management and browser surfing management are areas where big data can be applied to PKM.

While the literature discusses PKM without considering the impact of big data, this paper aims to extend existing knowledge by demonstrating the application of big data in PKM.