The Antecedents of Customer Self-Disclosure to Online Virtual Advisors

This study proposes and tests a model of the antecedents to customer self-disclosure to an online virtual advisor during the requirements elicitation stage of a skin care product. In such a context, a customer is required to provide personal information for the advisor to identify a suitable skin care product. Consistent with the view of self-disclosure as a form of social exchange, we propose that a customer’s intention to self-disclose is affected by the extent to which she believes the disclosures will enable her to obtain a better product, and her perceived information misuse risk. Furthermore, we posit that the customer’s perception of the advisor’s responsiveness acts both as an antecedent to her disclosure intentions, as well as her perceived benefits and costs. The results of an experimental investigation support the theoretical model

Factors That Enhance Consumer Trust in Human-Computer Interaction: An Examination of Interface Factors and Moderating Influences

The Internet coupled with agent technology presents a unique setting to examine consumer trust. Since the Internet is a relatively new, technically complex environment where human-computer interaction (HCI) is the basic communication modality, there is greater perception of risk facing consumers and hence a greater need for trust. In this dissertation, the notion of consumer trust was revisited and conceptually redefined adopting an integrative perspective. A critical test of trust theory revealed its cognitive (i.e., competence, information credibility), affective (i.e., benevolence), and intentional (i.e., trusting intention) constructs. The theoretical relationships among these trust constructs were confirmed through confirmatory factor analysis and structural equation modeling. The primary purpose of this dissertation was to investigate antecedent and moderating factors affecting consumer trust in HCI. This dissertation focused on interface-based antecedents of trust in the agent-assisted shopping context aiming at discovering potential interface strategies as a means to enhance consumer trust in the computer agent. The effects of certain interface design factors including face human-likeliness, script social presence, information richness, and price increase associated with upgrade recommendation by the computer agent were examined for their usefulness in enhancing the affective and cognitive bases for consumer trust. In addition, the role of individual difference factors and situational factors in moderating the relationship between specific types of computer interfaces and consumer trust perceptions was examined. Two experiments were conducted employing a computer agent, Agent John, which was created using MacroMedia Authorware. The results of the two experiments showed that certain interface factors including face and script could affect the affective trust perception. Information richness did not enhance consumers’ cognitive trust perceptions; instead, the percentage of price increase associated with Agent John’s upgrade recommendation affected individuals’ cognitive trust perceptions. Interestingly, the moderating influence of consumer personality (especially feminine orientation) on trust perceptions was significant. The consequences of enhanced consumer trust included increased conversion behavior, satisfaction and retention, and to a lesser extent, self-disclosure behavior. Finally, theoretical and managerial implications as well as future research directions were discussed

E-Commerce Digital Information Transparency and Satisfaction. Can We Have Too Much of a Good Thing?

Despite core product and service quality improvements and advances in shopping processes and technology, customers often report being unsatisfied with their online purchases. One plausible reason for lower customer satisfaction rates is too much or too little information that is shared with the customers about their orders. We show that when forming their perceptions about the purchases, customers form digital information satisfaction (DIS) levels as they evaluate supplementary informational services in addition to the core product being purchased. We believe that DIS is one of the dimensions of overall customer satisfaction. We also show that supplementary informational services are essential in meeting the increased informational needs of online shopping and, thus, can explain the decreased overall customer satisfaction level through the decreases in DIS. We develop and test the Digital Information Transparency and Satisfaction (DITS) model that shows how supplemental informational services influence digital information satisfaction (DIS_ in e-commerce. By doing so, this dissertation introduces a new dimension of satisfaction in the era of online shopping. This helps close the knowledge gap in the current research on overall customer satisfaction by showing that too much information transparency can harm the overall experience of the customers, thus leading to decreases in DIS. The study results provide a platform for future research on the influence of informational services provided during online shopping. Explaining the role of information shared with the customers in their perceptions of transparency and, consequently, DIS may help provide crucial practical business insights. Thus, by proposing the DITS model, this dissertation brings contributions to both theory and praxis by enhancing the understanding of DIS, which can serve as a robust foundation for future research on decreasing levels of overall customer satisfaction in a digital setting, as well as help companies improve their customer relationships

Children’s Social Behaviors and Peer Interactions in Diverse Cultures

This chapter lays out five principles to guide research on peer relationships in cultural context that reflect both current and earlier bodies of research literature: (1) Cultural scripts for socialization in peer relationships are evident in early childhood. (2) Both across and within cultural communities, children’s own active role in the socialization process becomes increasingly evident as they grow older. (3) Because children are active agents in their own socialization, they can not only make choices, they can also negotiate, deflect, and resist socializing attempts by others. (4) Children’s choices and preferences (self-socialization) during middle childhood have measurable and lasting effects on their developmental outcomes during adolescence. (5) Periods of rapid social change create exceptional stresses as well as opportunities for childhood peers

Beyond Rio+20: governance for a green economy

This repository item contains a single issue of the Pardee Center Task Force Reports, a publication series that began publishing in 2009 by the Boston University Frederick S. Pardee Center for the Study of the Longer-Range Future.As an intellectual contribution to the preparations for the 2012 United Nations Conference on Sustainable Development (UNCSD, a.k.a. Rio +20), the Boston University Frederick S. Pardee Center for the Study of the Longer-Range Future convened a task force of experts to discuss the role of institutions in the actualization of a green economy in the context of sustainable development. A stellar group of experts from academia, government and civil society convened at the Pardee Center and were asked to outline ideas about what the world has learned about institutions for sustainable development from the past, and what we can propose about the governance challenges and opportunities for the continuous development of a green economy in the future. The Task Force members were encouraged to think big and think bold. They were asked to be innovative in their ideas, and maybe even a little irreverent and provocative. They were charged specifically NOT to come to consensus about specific recommendations, but to present a variety and diversity of views. This report presents their thoughts and ideas

Evaluating Wireless Carrier Consolidation Using Semiparametric Demand Estimation

The US mobile phone service industry has dramatically consolidated over the last two decades. One justification for consolidation is that merged firms can provide consumers with larger coverage areas at lower costs. We estimate the willingness to pay for national coverage to evaluate this motivation for past consolidation. As market level quantity data is not publicly available, we devise an econometric procedure that allows us to estimate the willingness to pay using market share ranks collected from a popular online retailer, Amazon. Our semiparametric maximum score estimator controls for consumers%u2019 heterogeneous preferences for carriers, handsets and minutes of calling time. We find that national coverage is strongly valued by consumers, providing an efficiency justification for across-market mergers. The methods we propose can estimate demand for other products using data from Amazon or other online retailers where quantities are not observed, but product ranks are observed. Since Amazon data can easily be gathered by researchers, these methods may be useful for the analysis of other product markets where high quality data are not publicly available.

Evaluating Wireless Carrier Consolidation Using Semiparametric Demand Estimation

The US mobile phone service industry has dramatically consolidated over the last two decades. One justification for consolidation is that merged firms can provide consumers with larger coverage areas at lower costs. We estimate the willingness to pay for national coverage to evaluate this motivation for past consolidation. As market level quantity data is not publicly available, we devise an econometric procedure that allows us to estimate the willingness to pay using market share ranks collected from a popular online retailer, Amazon. Our semiparametric maximum score estimator controls for consumers' heterogeneous preferences for carriers, handsets and minutes of calling time. We find that national coverage is strongly valued by consumers, providing an efficiency justification for across-market mergers. The methods we propose can estimate demand for other products using data from Amazon or other online retailers where quantities are not observed, but product ranks are observed. Since Amazon data can easily be gathered by researchers, these methods may be useful for the analysis of other product markets where high quality data are not publicly available.Technology and Industry