Full length articleLiving in a big data world: Predicting mobile commerce activity through privacy concerns
Section snippets
Big data
The collection of personal data, once the exclusive domain of governments and state agencies, is now an inescapable part of every day life for U.S. consumers (Lyon, 2001). Half a century after computers entered mainstream society, personal data has begun to accumulate to the point where it is increasingly difficult to grasp its magnitude, much less manage the potential implications it represents to business and society Defined by Mayer-Schoenberger and Cukier (2013) as data sets so large that
Information privacy and trust
Consumers' mobile information privacy concerns are largely rooted in the rapidly expanding big data ecosystem (Cleff, 2007). Conceptualized as the rights of individuals whose information is communicated to others, information privacy and the protection of personal data have long been viewed as fundamental human rights. Currently, human recognition (or “personally-identifiable information”) is portrayed as the legal threshold condition for the loss of anonymity or privacy (Schwartz & Solove, 2011
Personalized mobile advertising
As consumers increasingly integrate mobile technologies into their lives, the information amassed by these devices not only offers users an array of conveniences and benefits, but also presents opportunities for third parties to access their personal data for other purposes. Personalized mobile advertising utilizing big data has evolved almost as quickly as the technology that makes it possible. Industry studies reveal that expenditures on mobile advertising have skyrocketed in recent years and
Theoretical foundation
Advancing technology has generated new forms of communication that span the structural and functional characteristics of mass mediated and interpersonal communication. As a result, some scholars look to mass communication theories such as uses and gratifications (U&G) to explain online and mobile communication behaviors (e.g., Jun and Lee, 2007, LaRose and Eastin, 2004); while others argue that interpersonal communication paradigms such as expectancy violations theory (Dolnicar & Jordaan, 2007)
Sample
Participants consisted of 416 U.S. adults (46% male, 54% female) from a broad range of ages (35% 18–24, 34% 25–34, 14% 35–44, 11% 45–54, 8% 55+) and educational backgrounds (3% no degree, 11% high school graduate, 39% completed some college, 36% college graduate, 12% some graduate school experience). Smartphone usage also varied among the sample with 4% indicating they did not own a smartphone. Among those who did, 11% used one for a year or less, 14% used one for 1–2 years, 18% used one for
Results
After entering the demographic variables of gender, age, income and prior mobile phone use were entered into block 1, block 2 indicated that while the privacy concerns of collection (H2b), awareness (H2c) and location (H2e) were not significant predictors of mobile commerce activity, control (H2a) and unauthorized access (H2d) were significant predictors. Further, as predicted in H1 and H3, trust in mobile advertisers and attitude toward mobile commerce were significant predictors of mobile
Discussion
The way in which U.S. marketers and policy makers address the ownership, collection, and use of personal data is of increasing importance to consumers. As predicted by CPM theory, findings from the current study demonstrate that concerns about perceived control and unauthorized access to personal information have a significant negative influence m-commerce activity. While many marketers feel they have grasped the ability to deliver effective messages based on consumers' personal information,
Limitations and conclusions
As with most research, the present study has limitations that should be noted. To begin, while the sample was diverse across age, gender, and education, using an online panel to examine issues related to technology could present a bias. Related, ceiling effects were detected in the collection, awareness, and unauthorized secondary use measures. This type of effect typically restricts scale variance. That said, ceiling effects and variance restrictions only work to attenuate relationships, and
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