Unexpected usage of user data has made headlines as both governments and commercial entities have encountered privacy-related issues. Like other social networking sites, LinkedIn provides users to restrict access to their information or allow for public viewing; information available in the public view was used unexpectedly (i.e., profiling). A non-profit entity called ICWATCH used tools to gather information on government mass surveillance programs by scraping publicly accessible user data from LinkedIn. Previous research has shown that privacy concerns influence behavior intention in contrived scenarios. What remains unclear is whether LinkedIn users, whose data was scraped by ICWATCH (an actual situation), would have similar privacy concerns and subsequently express the intent to take privacy-preserving action.
This study proposed to answer three research questions in the context of an actual privacy-centric situation, using an explanatory sequential mixed methods design. First, what is the user\u27s disposition towards privacy? Second, to what extent does this influence users\u27 privacy concerns regarding the inclusion of their LinkedIn profile information within ICWATCH? Third, to what extent do these concerns influence their stated intention to modify their LinkedIn profile/settings to minimize/eliminate this inclusion? The two-phase approach performed quantitative analysis on collected survey data, followed by analysis on follow-up interview data to provide context.
The resulting analyses found significant support for each hypothesis and divergence of underlying factors between degrees of the hypotheses and variable representations. Those participants who were not inclined to privacy and were not concerned with the situation, as expected, did not intend to modify their LinkedIn profile. However, they did express underlying factors such as control and privacy risk belief, unlike their counterparts. Those participants who were more inclined and more concerned about the situation did express an intent to modify their profile and revealed underlying factors such as regulations and usage. The findings support the extension of the existing literature onto actual privacy-centric situations. The results also highlight challenges with population demographics in actual situations and suggestions for construct prioritization when investigating future situations
