2 Literature Review and Methodology (CHAPTER TWO AND THREE) Chapter Two: Literature

2
Literature Review and Methodology
(CHAPTER TWO AND THREE)
Chapter Two: Literature Review
Introduction
Privacy and confidentiality issues often arise as a result of poor management of internet-users data by online data-analytical firms. Users of the internet including individuals and companies are vulnerable to privacy breaches through different ways that they use the internet. Unethical use of internet users’ data by data-analytics firms poses serious challenges regarding the type of information that can be shared with third parties. With the heavy reliance on the internet of things (IoT), the privacy and security of users of the internet pose serious concerns in different domains including the healthcare and banking sectors. Therefore, it is crucial to not only quantify the threats posed by the issuance of private user data by data-analytical firms but also demonstrate the extent to which privacy breaches impact the socioeconomic lives of internet users. The commodification of internet-user data poses security risks on societal socioeconomic welfare because leaked private data can be used to interfere with physical and socio-economic welfare and to manipulate society in making choices that they would otherwise have avoided.
Conceptual Framework
The conceptual framework used for the current study is the social cognitive theory (SCT). According to Choi et al. (2020), SCT holds that individual, environmental, and the interaction of different factors influence peoples’ behaviors and actions. In this regard, self-efficacy is one of the essential factors that motivate users of the internet to use IoT without the fear of privacy breaches. Historically, the invention of computers and the internet was regarded as targeted means of increasing self-efficacy in the sense that the computer and the internet would enable people to perform their own personal and professional activities with minimal interference. In the early times of the internet and computers, there were no major issues of cyber-attacks and collection of internet-user data by analytical firms (Choi et al., 2017). However, the increasing adoption of IoT by individuals and organizations has directed marketing efforts toward studying internet users’ online behaviors.
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. 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. According to the authors, the absence of a common framework in the IoT makes it possible for manufacturers, wholesalers, and retailers to retain clients’ private and confidential information. Moreover, since many IoT devices can share data on multiple platforms, internet users’ private details are gathered, analyzed, and shared for different reasons including improvement of decision-making functions, efficiency, and customizing services to suit users (Khadam et al., 2020).
IoT technology has been improving over time, and the improvement of the technology has been associated with privacy issues. According to Oravec (2017), the increasing privacy breaches over time have necessitated major shifts in business approaches, and public policy concerns. A few decades ago, household technology users had adopted the idea that if users had nothing to hide, they ought not to be concerned about their privacy when they are on the internet (Oravec, 2017). At the time, people used to put a lot of detailed personal information on social media and other websites with little or no regard for privacy and confidentiality notices. However, the increasing cyber-attacks on websites including Yahoo and Google have made individuals reconsider the volume of information to reveal about themselves.
Historical Background
Some historical privacy and confidentiality concerns that have persisted to the present moment of the IoT technology include remote control capabilities. While the internet experience is convenient, fast, and fascinating, breaches of privacy and confidentiality are a security threat to the IoT. According to Alsubaei et al. (2017), many of the currently used medical devices applying the internet of medical things (IoMT) have brought different attack pathways from going by historical analysis of the application of internet-users data. The IoMT includes internet-enabled medical devices used to improve access to healthcare services, manage illnesses, monitor health status, improve patient experience, and lower the cost of healthcare services. Despite such benefits, Alsubaei et al. (2017) explain that the IoMT has posed privacy threats based on the past use of patients’ sensitive data by corporate organizations to make profits through marketing and advertisements. Moreover, there have been incompatibility and complexity issues from a combination of the many networks and devices connected to IoMT devices (Alsubaei et al., 2017). Electronic health record (EHR) systems are examples of pathways through which patients’ private and confidential information has been leaked without their consent. Alsubaei et al. (2017) explain that unethical professionals in the healthcare sector, internet service providers, and big data firms are among the entities that have leaked outpatient private information in the past.
Research Gap
Although the study of privacy and confidentiality breaches has been done by many researchers, there is a gap in the commoditization of big data by online firms. Researchers have studied different ways through which private internet users’ information is collected and sold to third parties with different objectives (Alferidah & Jhanjhi, 2020). The aim of collecting IoT users’ private information has also been studied (Bastos et al., 2018). However, no attention has been paid to the commercialization of internet users’ data and the implications of doing so. Therefore, this study seeks to fill that gap in research by analyzing the effects of the commercialization of internet users’ private and confidential information.
Summary
The collection of user data by big data firms and service providers has always vied through the positive lens of improving the internet-users experience. However, the paradigm shift from improving internet-users experience to commercializing user data by big data firms and internet service providers calls for cyber-security protection regarding privacy and confidentiality matters. Mendez et al. (2017) explain that the positive aspects of the IoMT including intelligence, mobility, heterogeneous essence, and low restrictions are also the same attributes that increase privacy and security risks. These positive attributes of the IoT have been reduced by the commercialization of users’ private information including the selling of sensitive data to third parties by big data firms. Unauthorized use and selling of private information should be avoided due to the dangers they pose to clients including theft of intellectual property.
Chapter Three: Methodology
Methodology
The study will utilize a qualitative method since it seeks to examine the effects of commodification of IoT users’ private and confidential information. The qualitative methodology fits this study since qualitative approaches are suitable for studies in which researchers want to understand the causes and effects of specific social phenomena that affect a significant number of people (Kim et al., 2017). In this study, the descriptive correlational qualitative approach will be applied. The correlational approach is a type of non-experimental design that aids in the prediction and explanation of the relationship that exists between the variables (Seeram, 2019). Apart from the ability to measure the relationship between two or more variables, correlational design reveals the variables that interact and how they interact, which enables researchers to make different predictions depending on the nature of their interaction (Seeram, 2019). Since the study is aimed at examining the effects of commoditization of online users’ data, the correlational approach will suit the purpose of the study. Through the descriptive correlational approach, it will be possible to study the relationship between commoditization of internet-users private information and other variables including their online behaviors, and the security of the clients. The approach will also make it possible to examine the interaction between the variables, and the implications of the interactions including ethical and legal matters, and the degree to which individuals and organizations embrace IoT technologies.
Unit of analysis
The study will utilize data from five different big-data firms to examine different ways that they use internet users’ private information. The firms will be selected based on their reputation and popularity. Additionally, ten individuals from different fields including education, health, engineering, banking, and ordinary internet users will be included in the study. The purposive sampling technique will be applied to select the participants. In purposive sampling, researchers decide the types of individuals that will be included to get the final representative sample of a population (Campbell et al., 2020). Since the study is aimed at examining the effects of commoditization of internet users’ data, the purposive sampling method of participant selection will be convenient since it will be possible to balance the sample to include different types of IoT users. Doing so will make the results comprehensive in that it will include feedback from the most crucial domains of application of IoT thereby increasing the generalizability of the study findings and recommendations. The units of analysis include individuals and organizations that frequently use the IoT in many aspects of their personal and professional activities.
Data Collection Methodology
Data will be collected using online surveys. The surveys will contain semi-structured questionnaires that will be sent to the respondents at the email address of their choice. The significance of using online surveys in the study includes the wide range of adoption of the internet by people from different socioeconomic classes (Evans & Mathur, 2018). It will thus be easy for the respondents to participate in the study even with internet-enabled mobile phones. Participants will have an option of filling the questionnaires on a website that will specifically be designed for the study in case they do not prefer to communicate using email due to privacy concerns. Online questionnaires surveys are flexible to participants in that they can freely participate in the research regardless of their physical location or time (Evans & Mathur, 2018). Online surveys are also fast and flexible since feedback can be obtained as soon as the questionnaires have been responded to, and it is possible to format the questions in different ways using software tools to suit different types of devices (Evans & Mathur, 2018). The importance of using semi-structured questionnaires is that respondents are allowed to respond in their way while at the same time enabling researchers to obtain crucial information.
Trustworthiness (Reliability and Validity)
The trustworthiness of the study will be boosted by increasing its reliability and validity. Reliability refers to the extent to which measurement tools effectively control random errors (Mohajan, 2017). In this regard, reliability can be increased through the assessment of different possible errors sources concurrently rather than focusing on each one at a time (Mohajan, 2017). Doing so will significantly reduce random errors that often reduce the reliability of qualitative studies. Moreover, the reliability of the results will be increased by involving different types of participants from different socioeconomic classes to reduce the likelihood of biases. On the other side, validity refers to the degree to which measurement tools effectively measure the items that they were intended to measure (Mohajan, 2017). The validity of the study will be increased by applying appropriate participant sampling and data-collection methods including purposeful sampling, and online surveys to increase the internal validity. Validity will also be increased through saturation of the results to ensure that the collected results capture all aspects of commoditization of online users’ data. External validity will be increased through non-reactive measures and the use of a heterogeneous sample.
Summary
The study will utilize the qualitative approach since it seeks to examine the effects of commodification of IoT users’ private and confidential information. The qualitative methodology fits this study since qualitative approaches are suitable for studies in which researchers want to understand the causes and effects of specific social phenomena that affect a significant number of people (Kim et al., 2017). The purposive sampling technique will be applied to select the participants. In purposive sampling, researchers decide the types of individuals that will be included to get the final representative sample of a population (Campbell et al., 2020). Data will be collected using online surveys. The surveys will contain semi-structured questionnaires that will be sent to the respondents at the email address of their choice. The trustworthiness of the study will be boosted by increasing its reliability and validity through the application of appropriate sampling methods and data analysis tools.
References
Alferidah, D. K., & Jhanjhi, N. Z. (2020). A Review on Security and Privacy Issues and Challenges in the Internet of Things. International Journal of Computer Science and Network Security IJCSNS, 20(4), 263-286.
Alsubaei, F., Abuhussein, A., & Shiva, S. (2017, October). Security and privacy on the internet of medical things: taxonomy and risk assessment. In 2017 IEEE 42nd Conference on Local Computer Networks Workshops (LCN Workshops) (pp. 112-120). IEEE.
Bastos, D., Giubilo, F., Shackleton, M., & El-Moussa, F. (2018, December). GDPR privacy implications for the Internet of Things. In 4th Annual IoT Security Foundation Conference (Vol. 4, pp. 1-8).
Campbell, S., Greenwood, M., Prior, S., Shearer, T., Walkem, K., Young, S., … & Walker, K. (2020). Purposive sampling: complex or simple? Research case examples. Journal of Research in Nursing, 25(8), 652-661.
Choi, D., Lowry, P. B., & Wang, G. A. (2020). The Design of Personal Privacy and Security Risk Scores for Minimizing Consumers’ Cognitive Gaps in IoT Settings. In Hawaii International Conferences on Systems Sciences (HICSS-53), Maui, January (pp. 8-10).
Evans, J. R., & Mathur, A. (2018). The value of online surveys: A look back and a look ahead. Internet Research, 28(4), 854-884.
Khadam, U., Iqbal, M. M., Alruily, M., Al Ghamdi, M. A., Ramzan, M., & Almotiri, S. H. (2020). Text data security and privacy in the internet of things: threats, challenges, and future directions. Wireless Communications and Mobile Computing, 2020. 1-15.
Kim, H., Sefcik, J. S., & Bradway, C. (2017). Characteristics of qualitative descriptive studies: A systematic review. Research in nursing & health, 40(1), 23-42.
Mendez, D. M., Papapanagiotou, I., & Yang, B. (2017). Internet of things: Survey on security and privacy. arXiv preprint arXiv:1707.01879, 1-16.
Mohajan, H. K. (2017). Two criteria for good measurements in research: Validity and reliability. Annals of Spiru Haret University. Economic Series, 17(4), 59-82.
Oravec, J. A. (2017, July). Emerging “cyber hygiene” practices for the Internet of Things (IoT): professional issues in consulting clients and educating users on IoT privacy and security. In 2017 IEEE International Professional Communication Conference (ProComm) (pp. 1- 5). IEEE.
Seeram, E. (2019). An overview of correlational research. Radiologic Technology, 91(2), 176- 179.