Abstract

The acceptance and use of information technology (IT) and information systems (IS) are two of the most critical topics in IT and IS research. In these fields, intention has usually functioned as a direct and immediate predictor of actual behavior in most theories and models of behavioral change. However, participants usually fail to translate their intention into behavior, and accumulated studies have indicated there is a substantial gap between intentions and actual behavior (Sheeran & Webb, 2016). This intention-behavior gap has been largely unexamined by IT and IS researchers, who have kept using intention as a straight effect or as a surrogate for actual usage. To address this shortcoming, we focus our current research on how scholars can bridge the gap between behavioral intention and actual usage, especially in the IT and IS fields. Practically, as concerns about online privacy have been growing over the years, our model should help fill the gap between users’ declared intention to protect their privacy and the actual privacy protection behavior. Implementation intention was designed by Gollwitzer (1993, 1999), who linked critical situational cues to specific behavioral responses in an if-then format (“If I encounter situation Y, then I will initiate action Z!”). Implementation intention has been widely used in psychology to help people initiate, pursue, and achieve their goal behavior. For the present study, we hypothesized that integrating implementation intention into the traditional behavioral models of IT usage, such as TAM, TPB, and UTAUT, will mediate the effect of intention on actual behavior. Moreover, our study will focus on how to design appropriate situational cues that could result in the target action in IT/IS research. We will gather data through the worldwide crowdsourcing platform Amazon Mechanical Turk (MTurk) with a two-survey approach involving 400 subjects from the United States. Participants will be requested to watch a video about privacy issues and available tools that may be used to protect their privacy in their daily lives for the first study. Following the video, their desire to protect their privacy will be evaluated on a five-point scale (range from not at all to very high), with higher scores indicating a stronger intention to protect their privacy. After then, the participants will be separated into two groups: treatment and control (no treatment). The treatment group will be instructed to create their own implementation intention, which will detail when, when, and how they plan to use the tools to protect their online privacy. A month later, all participants in both groups will get a follow-up questionnaire on their actual protection behavior in their daily lives. Our current research contributes significantly to the field. First, our research extends existing behavioral models between intentional and behavioral relationships. Additionally, when measuring IT acceptance and usage, our study sheds light on the significant gap between intention and behavior. Furthermore, we will give recommendations to users on how to safeguard their privacy, aid privacy protection tool designers in developing privacy-protection tools, and make policy recommendations to improve general public privacy protection.

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