Employing a Mixed Method to Explore Mobile Social Media Users' Big Data Privacy Concerns

Employing a Mixed Method to Explore Mobile Social Media Users' Big Data Privacy Concerns

Kenneth C. C. Yang (The University of Texas – El Paso, USA) and Yowei Kang (Kainan University, Taiwan)
DOI: 10.4018/978-1-4666-8125-5.ch017

Abstract

Since its introduction in the early 21st century, mobile social media have played an indispensable part in contemporary human experiences. The convergence of social networking and mobile technologies and services creates a fascinating circumstance because the pervasive nature of mobile social networking technologies has impacted on users' privacy. The chapter employed a mixed research method to collect and analyze mobile social media users' experiences and privacy concerns in the age of Big Data. A total of 57 participants were included in this study. Collected data was analyzed by examining mobile social media users' experiences and their concerns over privacy. Findings from this study showed the rising concerns over personal privacy as a result of convergence of mobile social media and Big Data practices by the advertising industry. Theoretical and practical implications were discussed.
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Introduction

Mobile Social Media: An Emerging Advertising Platform

Since its introduction in the early 21th century, mobile social media have played an indispensable part in contemporary human lives. Mobile social media (such as Twitter, Facebook, and MySpace) can be accessed through texting, calling, or using mobile Internet networks that are currently available at many hotspots (Salehan & Negahban, 2013; Varnali, Toker, & Yilmaz, 2011). Emerging mobile social media that have attracted advertisers’ attention include Facebook, Twitter, Google+, and other smartphone-enabled mobile apps (Dlamini, 2008; Kaplan, 2012). Facebook has launched in May 2014 its mobile advertising network, The Facebook Audience Network, to better target consumers through the analysis of their demographics, interests, and social media behaviors (such as “likes”) (Delo, 2014; Parsons, 2015). In his speech to f8, Facebook CEO Mark Zuckerberg said, “The mobile ecosystem needs a way to deliver these native personalized ads to people” (cited in Delo, 2014, para. 4).

Recent trends in multi-platform advertising have augmented the importance of social media and their mobile counterparts in advertising campaign planning by integrating these emerging media into generating consumer engagement and participation after watching traditional 30-second commercials (Wong, 2010). Ad spend in social media is expected to reach $8.5 billion in 2014 and $14 billion in 2018, according to a recent data from BI Intelligence (Hoelzel, 2014).

The rosy prediction that many advertisers share is often attributed to the number of mobile Internet connections that have grown exponentially after the launch of Apple’s I-Phone in 2007. Presently, there are 234.18 million mobile Internet users in the United States. The number is expected to increase to 325.24 million in 2020 (IBISWorld, 2014). Thanks to the drastic cost reduction of mobile broadband access, it is expected that mobile social media population will increase. Lee (2013) concluded the convergence of smartphone and social media has permitted users to access their Facebook, Twitter, and other social media anywhere and anytime. Lenhart, Purcell, Smith, and Zichuhr (2010) found that 81% of adults between 18-29 years old and 63% of adults between 30-49 years old use wireless Internet. A recent Pew report found that 58% of American adults own a smartphone that provides easy and ubiquitous access to social media. The report also found that 60% of mobile phone users access the Internet through their mobile device (Pew Internet Research Project, 2014). Increasingly, more and more people are accessing their social media platforms through these mobile devices. However, tablets, smartphones, and Wi-Fi laptop computers are often intrusive, location-based, and pervasive with their ability to reach a person at any time and any location by creating an encompassing communication environment.

Key Terms in this Chapter

Event-Contingent Protocol: requires participants to provide their responses only when event of interest occurs.

Consumer Privacy: The concept is defined as consumers’ ability to control when, how, and to what extent their personal and private information is to be transmitted to other entities.

Interval-Contingent Protocol: The protocol refers to the procedure to request participants to provide ESM responses at per-arranged intervals (e.g., every 30 minutes) or at the same time every day.

Privacy by Design (PbD): The concept was proposed by the Federal Trade Commission in its 2012 report, “Protecting Consumer Privacy in an Era of Rapid Change.” The concept refers to how organizations can integrate consumer privacy protection mechanisms (such as limited data collection and retention, reasonable security, and procedures) into every phase of product’s design and development.

Communication Privacy Management (CPM) Theory: Originally called, communication boundary management, CPM was developed by Sandra Petronio to examine how consumers determine their boundaries in disclosing personal private information. CPM postulates the consumers make conscious decisions about how they perceive and set boundaries for their individual privacy and examine the negotiation process in which personal information is shared, co-owned, and disseminated. CPM has been applied to emerging media technologies such as blogging and Facebook in various communication contexts.

Experience Sampling Method (ESM): This research method refers to a quasi-naturalistic method that uses sending signaling questions to participants at random times throughout the day.

Mixed Research Methods: The term refers to a type of social scientific research methods that emphasize the understanding of consumers in their real-life cultural contexts, with multi-level philosophical perspectives, and with both quantitative and qualitative methods.

Location-Based Advertising (LBA): The term refers to an emerging form of advertising that cobmines both location-based services and mobile advertising. LBA allows advertisers to deliver location-sensitive ads to consumers’ mobile devices at particular locations.

Do-Not-Track (DNT): A regulatory mechanism mandated by the Federal Trade Commission that allows individual consumers to opt out the businesses’ data collection activities during consumers’ search and other activities online.

Signal-Contingent Protocol: The protocol is defined as when participants are required to provide ESM responses once prompted by a signaling device at a randomly pre-determined time in the day.

Big Data: The term refers to the dataset that has large, more varied, and complex structure, accompanies by difficulties of data storage, analysis, and visualization. Big Data are characterized with their high-volume, -velocity and –variety information assets.

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