Online social shopping emerges from the idea of using social networking features to benefit traditional e-commerce activities. Technology-driven shopping environments not only support shopping task completion and self-entertainment, more importantly, these new shopping environments become alternate outlets for consumers to interact with others. This dissertation aims to understand the effects of atmospheric cues on consumers\u27 behavioral intentions in online social shopping environments. This dissertation study proposes and validates a research model that predicts consumers\u27 diverse behavioral intentions (approach and avoidance) toward using online shopping environments due to website atmospheric cues. This research model is constructed based on theoretical perspectives including stimulus-organism-response framework, the technology acceptance model, the theory of affordances, and activity theory. The empirical study used a three-factorial between-subject field experiment approach to validate the research model and hypotheses. A total of 360 valid responses were collected from Amazon Mechanical Turk. Each of the subjects was randomly assigned to one of the eight experimental conditions. Data was analyzed using three-way MANOVA and PLS-SEM techniques. Analysis results largely supported the research model. Three path coefficients surprisingly had different signs from their correlation coefficients, and further mediation analysis indicated that: perceived usefulness fully mediated the effects of perceived utilitarian affordances, perceived sociability of use fully mediated the effects of perceived social affordances, and that perceived usefulness and perceived fun fully mediated the effects of perceived sociability of use on behavioral intentions. This dissertation theoretically contributes to online social shopping research by building a well-grounded research model that integrates several theories from different disciplines. The instrument for measuring perceived affordances provides an operationalized solution to understand interaction mechanism between technology-driven environments and users. Practically, investigating the effects of atmospheric cues and decomposing process-based and outcome-based evaluations suggest different aspects that online merchants can work on to improve consumer experiences
