When consumers find that something critically out of the ordinary has occurred, they direct attention to evaluate such a critical incident more closely. The results of this evaluation may put consumers on a switching path or it might lead them to engage in unfavorable behaviors from the perspective of the organization, such as engaging in negative word-or-mouth online. The negative consequences of some product (goods or services) failures go beyond simple product attribute defects, leading customers to terminate the relationship with the organization. This dissertation, which is composed of three essays, investigates how consumers engage in negative word-of-mouth on social media channels in response to their various product failures and explores an important relationship event of betrayal, which can be triggered by certain product failures. It investigates how betrayal is perceived by customers and influences a range of their behaviors across business-to-consumer and business-to-business contexts.

In two independent and thematically relevant chapters, I empirically investigate consumers’ mobile channel usage behaviors. In the first chapter, I examine the impact of mobile use in online higher education. With the prevalence of affordable mobile devices, higher education institutions anticipate that learning facilitated through mobile access can make education more accessible and effective, while some critics of mobile learning worry about the efficacy of small screens and possible distraction factors. I analyze individual-level data from Massive Open Online Courses. To resolve self-selection issues in mobile use, I exploit changes in the number of mobile-friendly, short video lectures in one course (“non-focal course”) as an instrumental variable for a learner’s mobile intensity in the other course (“focal course”), and vice versa, among learners who have taken both courses during the same semester. Results indicate that high mobile intensity impedes, or at most does not improve course engagement due mainly to mobile distractions from doing activities unrelated to learning. Finally, I discuss practical implications for researchers and higher education institutions to improve the effectiveness of mobile learning. In the second chapter, I investigate the impact of mobile users’ popular app adoption on their app usage behaviors. The adoption of popular apps can serve as a barrier to the use of other apps given popular apps’ addictive nature and users’ limited time resources, while it can stimulate the exploration of other apps by inspiring interest in experimentation with similar technologies. I use individual-level app usage data and develop a joint model of the number of apps used and app usage duration. Results indicate that popular app adoption stimulates users to explore new apps at app stores and allocate more time to them such that it increases both the number of apps used and app usage duration for apps excluding the popular app. Such positive spillover effects are heterogeneous across app categories and user characteristics. I draw insights for app developers, app platforms, and media planners by determining which new apps to release in line with the launch of popular apps, when to release such apps, and to whom distribution should be targeted.

In two thematically related chapters, I explore the benefits incurred as companies actively respond to consumers who share positive word of mouth in digital environments (eWOM). This research takes a multi-method approach by first addressing the psychological impact of company response on the sharing consumer, followed by an examination of real behavioral consequences in a social media setting. Across six studies in Chapter 1, I find support for a conceptual model indicating that consumers who receive a company response to their positive eWOM experience greater satisfaction compared to no response, leading to increased intentions to engage in future positive eWOM on behalf of the company, both through social media and online review websites. Furthermore, I find that consumer perceptions of response personalization lead to judgments of company effort and that these two elements mediate the effect of response on consumer satisfaction. In Chapter 2, using a dataset of firm responses to positive consumer feedback on Twitter (tweets) from 79 apparel retailers, I find that company responses to positive consumer tweets can generate consumer engagement behavior in the form of continued interaction. Company responses that use consumer-oriented language increase the likelihood of consumer interactivity. However, this effectiveness depends on whether the consumer's audience is the company or their broader network of followers. I also show that, in some conditions, companies achieve higher consumer engagement by personalizing responses with the consumer's name. Together, the findings from these two chapters point to the need for companies to strategically practice positive eWOM management, both to promote consumer engagement behaviors and to avoid the negative outcomes associated with unresponsiveness.

Anomaly is a deviation from the normal behavior of the system and anomaly detection techniques try to identify unusual instances based on deviation from the normal data. In this work, I propose a machine-learning algorithm, referred to as Artificial Contrasts, for anomaly detection in categorical data in which neither the dimension, the specific attributes involved, nor the form of the pattern is known a priori. I use RandomForest (RF) technique as an effective learner for artificial contrast. RF is a powerful algorithm that can handle relations of attributes in high dimensional data and detect anomalies while providing probability estimates for risk decisions.

I apply the model to two simulated data sets and one real data set. The model was able to detect anomalies with a very high accuracy. Finally, by comparing the proposed model with other models in the literature, I demonstrate superior performance of the proposed model.