Mehrdad Koohikamali

Location disclosure on LB-SNAs

Location based social network applications (LB-SNAs) allow business access to real-time customer location information. Despite concerns about individual privacy, the popularity of LB-SNAs is rising. This research considers how marketers can motivate people to disclose their location even when they are aware of the risks involved. The data from these applications encourages customers to try new businesses and allows those businesses to provide services that increase loyalty among current customers. The proposed LB-SNA model was tested using survey research with active online LB-SNA users. Findings show that privacy concerns do not moderate the correlation between attitude and location disclosure and that the decision to disclose location information is correlated with incentives. We also show that opinion leadership is a new antecedent for understanding attitudes about location disclosure. This study fills a research gap by positing and testing how an external factor such as incentives can alter a persons attitude about sharing private information. We also refine correlated factors to attitudes, discovering that if a person perceives that they are an opinion leader, they will also be more inclined to have a positive attitude about LB-SNA. In conjunction to testing these new motives this research is the first to modify Internet privacy concerns to fit LB-SNAs specifically, test a model that is contextualized to LB-SNAs, and include incentives and opinion leadership as dimensions within a privacy model. These findings are relevant to practitioners interested in motivating user participation, as well as academics searching for better understanding of privacy behaviors. We investigate the role of incentives in location disclosure behavior on LB-SNA.Opinion leadership is offered as a new construct in the IS literature.Opinion leadership, social norm, risk, and benefit predict attitude toward LB-SNA.

Rumor and Truth Spreading Patterns on Social Network Sites During Social Crisis: Big Data Analytics Approach

Social network sites give their users the ability to create contents and share it with others. During social crisis, the spread of false and true information could have profound impacts on users. Lack of prior studies to compare differences between diffusion patterns of rumors and truths during social crisis is the motivation of this study. In this study, we examine the role of information credibility, anxiety, personal involvement, and social ties on rumor and truth spread during social crisis. Building on the rumor theory, we propose a research model to examine differences between spread of rumors and truths. Using the Tweeter data collected during the Baltimore riots in 2015, we test the research model. Theoretical contributions and practical implications will be outlined based on the findings of the study. We anticipate findings will provide new avenues of research by determining characteristics of truths and rumors in online contexts.