6 DATA ANALYSIS Chapter Four Data Analysis Introduction The purpose of the

6
DATA ANALYSIS
Chapter Four
Data Analysis
Introduction
The purpose of the data analysis exercise is to explore the effects of commodification of data on the users. To accomplish this objective, a qualitative research method is employed. This method is particularly useful in examining users’ perceptions of companies gathering their data for commercial purposes. Additionally, the data collected focuses on the issues associated with privacy, confidentiality, and security problems posed by the mass gathering and analysis of internet user data by major companies. A Google survey technique is utilized to collect data from major tech professionals and different user groups on their perceptions regarding the commodification of user data. The results are analyzed using thematic content assessment tools.
Unit Analysis
The study utilized data acquired from five different big data firms examining different ways private internet users’ information is gathered and commoditized. The firms were selected depending on their notoriety and popularity among the market niche. On the other hand, five fields including banking, health, engineering, education, and ordinary internet users were selected, with ten random individuals picked from each field for selection, for inclusion in the study. The participants were sampled through a purposive sampling technique whereby researchers decided, types of individuals to be included in the final representative sample of a population (Denieffe, 2020). This sampling method of participant selection was superior within these circumstances, since sample balancing is important, for comprehensive results whereby, feedback from a crucial domain was with the studies intentions aligned, towards understanding complete effects of commoditization of internet user data.
Data collection
Data was collected through an online survey, through semi-structured questionnaires delivered to respondents through individual email addresses. This method was highly dependent on the adoption of the internet widely by the target specimen, easily accessible by people of different social classes. In the email was a link to a website specifically designed for persons, preferably unable to communicate through email due to privacy and cyber security reasons. The questionnaires were flexible, enabling participants to participate in the research regardless of their location and time. However, feedback was obtainable as soon as the questionnaire was filled besides easy and quick computation for results due to simple accessibility by software and applications. Data reliability was enhanced through the test-retest method, validating the tool for use and helping improve the instrument where necessary. A pilot study was conducted where five random individuals from each field before the final survey was conducted. About the research objective, the research instrument proved reliable. Also, emphasis was placed on data collection sampling ensuring appropriate sampling and data collection through purposive sampling ensuring individuals from all age demographics and social classes were represented. Data consistency was measured through Cronbach’s alpha method which was calculated from pairwise correlations between items (Eisinga et al., 2012) The table below guided the acceptance or rejection criteria.
Data analysis
The collected data were subjected to qualitative analysis, seeking to examine the interconnections in rich complex data sources. Statistical tools for Quantitative methods separate pieces of data in ways that defeat the purpose. Similarly, qualitative researchers often find themselves overwhelmed by the amount of data and equally in need of tools to enhance their abilities. To this extent several software packages have been created, a commonly overlooked software package is Microsoft Excel. In many cases, qualitative methods generate large volumes of data, which must be coded and analyzed thoroughly and professionally (Swallow et al., 2003). Commercial software packages can assist in this analysis. However, their operational licenses are difficult and prohibitory. A rich thematic description of all the data collected was performed ensuring the findings were a representation of a valid reflection of all the data sets. An inductive approach was implemented, coding the data, and categorizing it for analysis, ensuring it was not fitted to determine the coding frames. This ensured the analysis process was driven by data collected during the sampling process rather than any analytic preconceptions. Theme identification, whether semantic or latent, was considered.
Findings
Two groups of participants were administered with semi-structured questionnaires to determine their attitudes and perceptions towards online firms gathering user private data for commercial purposes. They included end-users, tech firms, and information technology professionals from different sectors. The main dominant themes that emerged from the surveys include fear of privacy violations, breach of trust, security problems, and ethics of wealth inequality. The end-users of such data are the main groups that raised inequality concerns. More specifically, they stated that firms that sell their data to unauthorized parties should also include the data owners in the revenue-sharing process. Professionals from the tech sector raised security concerns by stating that such data may land in the hands of untrustworthy buyers, who may use them to carry out security breaches, such as personification, cyber fraud, espionage, and disclosure of user data to unauthorized parties. Out of the population sampled, Figure I indicates that the most critical problems that relate to commodification in their order of significance include security concerns, privacy, confidentiality, and wealth disparity.
Table 1
Results of the Key Themes Identified
Commodification Issues
Individual Perception
Privacy
53
Confidentiality
34
Security concerns
28
Wealth disparity
22
Figure 1
A Graphical Illustration of the Results
Summary
From the aforementioned results, the study echoes the hypothesis that the commodification of private data poses major ethical issues, such as privacy, confidentiality, security problems, and wealth disparity. Additionally, privacy and confidentiality are the leading problems that the participants are concerned with relating to commodification. As such, the findings reaffirm previous studies, which show that internet users’ self-efficacy has significantly declined to the extent that online users’ private and confidential data is no longer secure since the internet big data firms are increasingly commercializing client private data. In that regard, Oravec (2017) explains that the IoT internet-enabled devices can be controlled remotely, which not only presents the risk of sabotage but also can endanger lives through terrorist attacks? For instance, emote supervision of IoT devices can control household devices including those that perform heating, water, and lighting functions (Oravec, 2017). In this regard, individual and organizational private data can be issued out to third parties who may, in turn, interfere remotely with crucial individual and organization services. Khadam et al. (2020) raise similar concerns about the risks associated with IoT technologies. Thus, the absence of a common framework in the IoT makes it possible for manufacturers, wholesalers, and retailers to retain clients’ private and confidential information.
References
Denieffe, S. (2020). Commentary: Purposive sampling: complex or simple? Research case examples. Journal of Research in Nursing, 25(8), 662–663.
Eisinga, R., Grotenhuis, M. T., & Pelzer, B. (2012). The reliability of a two-item scale: Pearson, Cronbach, or Spearman-Brown? International Journal of Public Health, 58(4), 637–642.
Nulty, D. D. (2008). The adequacy of response rates to online and paper surveys: what can be done? Assessment & Evaluation in Higher Education, 33(3), 301–314.
Swallow, V., Newton, J., & van Lottum, C. (2003). How to manage and display qualitative data using ‘Framework’ and Microsoft®Excel. Journal of Clinical Nursing, 12(4), 610–612.