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Online Personalized Advertisements
The U.S federal trade commission concluded as early as 1998 that as many as 92% of web sites collected personal information of consumers for the purpose of possible future marketing (Jay & Cude, 2009). Such data collection is still highly relevant and used (Aguirre et al., 2015; Zhu & Chang, 2016). The data collected provide companies with information regarding characteristics of geographic, demographic and psychographic nature (Jay & Cude, 2009; Lekakos & Giaglis, 2004). It is further stated by Jay and Cude (2009) that such information is not only gathered in a primary way, i.e. by the companies themselves, but also from third parties that specialises in collecting information about consumer groups with the sole purpose of selling it. The databases with consumer information that companies have collected and stored are used to personalize advertising towards individual consumers and consumer groups (Baek & Morimoto, 2012; Jay & Cude, 2009; Köster et al., 2015;). Owing to the development of online technology, the diversity and the types of online personalized advertisements have significantly increased, ranging from website banners (Bleier & Eisenbeiss, 2015), to online personalized e-mails, to more technological advanced online personalized websites, which use cookies to track and record consumers’ online behaviour to create suitable, online personalized advertisements (Jay & Cude, 2009). The use of cookies involves the process of planting small text files on consumers’ hard drives to track their online behaviour, and it is the most prevalent method to track consumers online (Miyazaki, 2008). It is further argued by Miyazaki (2008) that the use of cookies can generate concerns in relation to an invasion of privacy, as the process is sometimes done in a covert manner and with a lack of information given to consumers of how it is used. Pavlou and Stewart (2000) argued that these advancements in online technology would cause a shift from mass communication to more targeted and online personalized communication, which would alter the traditional marketing focus of mass advertising to a more targeted audience.
Fowler, Pitta and Leventhal (2013) discuss the implementation of online personalized advertising, that firms need to master four basic concepts that varies from the concept of collecting information to putting it to use, namely identify consumers, differentiate individual consumers, interact with each consumer and customize products for each consumer. To identify consumers, companies use the collected information to gain a sophisticated understanding of potential future consumers and such an understanding further allows companies to identify those consumers with the highest lifetime value. Zeithaml, Rust and Lemon (2001) argue that once a company has identified the possible profitable consumers, and excluded those who are deemed non-profitable i.e. consumers who will not purchase the company’s products or services, the firm is able to maximize the profitability of its marketing efforts. The process of identifying and excluding consumers who will never purchase anything which the company offers, is of excellent value to any organisation. It allows them to stop wasting resources in the attempt to attract consumers who are not likely to respond to the advertisement, and instead focus those resources on potential future consumers or the already existing profitable ones (Fowler, Pitta & Leventhal, 2013; Zeithaml, Rust & Lemon, 2001). When organizations differentiate consumers on an individual level, they recognize that consumers have unique needs, as well as from the organization. Interacting with each consumer is important to organizations because every interaction with a consumer is an opportunity to learn more about the consumer and the needs of that individual consumer, as well as the value the consumer may have to the organization (Fowler, Pitta & Leventhal, 2013). The process of customizing products for each consumer involves the process of producing and delivering a product personalized to consumers individually, which is argued to be the most difficult step to put in practice (Fowler, Pitta & Leventhal, 2013).
Consumer Perceived Benefits
Personalization is meant to increase the relevance of information to the consumer, with less effort required. It is meant to save the consumer from tedious tasks and instead place that responsibility with the marketer, allowing them to anticipate such needs and personalize offers for the consumer. Such online personalized offers can be done, thanks to extensive databases and recording of past behaviours (Montgomery & Smith, 2009).
Zhu and Chang (2016) explore the role of relevance in relation to online personalized advertising. The role of relevance, in this context, refers to the “degree to which consumers perceive an object to be self-related or in some way instrumental to achieving their personal goals and values” (p. 443). It is further stated that relevance of an advertisement influences consumer reactions, such as showing favourable attitudes towards the advertisement, and higher attention paid towards the advertisements, contributing to better advertisement effectiveness and showing a higher purchase intentions (Zhu & Chang, 2016). The study examines the influence which relevance has on consumers’ perceptions on privacy concerns and future intentions towards online personalized advertisements. Findings suggest that online personalized advertisement relevance indeed mitigates the privacy concerns of consumers, and that future intentions towards online personalized advertisements were positively enhanced through perceived relevance (Zhu & Chang, 2016).
De Keyser, Dens and De Pelsmacker (2015) support the findings presented by Zhu and Chang (2016), stating that personalization can develop a more favourable response from consumers because of the increase in personal relevance of the advertisement. Moreover, Tucker (2014) displays that among the benefits of consumers from online personalized advertisements, is that such advertisements might be beneficial in terms of interest and appeal. For instance, the content of the advertisement might be more aligned with a consumer’s own preferences of products and services. Similarly, Wang et al. (2015) state that consumers which are subjected to online personalized advertisements are able to more efficiently encounter offers which align with the consumers interests and preferences.
Consumer Perceived Risks
According to Dinev and Hart (2004), privacy concern as a topic of interest has been explored in multiple scientific disciplines for many years. Extant literature regarding privacy concerns in online personalized advertisements bases its foundations in general online environments and subsequent research. The major element, examined as a part of privacy concerns in such research, is known as perceived risk or perceived vulnerability (Aguirre et al., 2015; Dinev & Hart, 2004; Liebermann & Stashevsky, 2002), referred to as perceived risk in this study.
Perceived risk pertains to the risk which may be experienced by individuals when disclosing personal information, stemming from an innate expectation that those institutions which have this information will exploit it, and thus negatively affecting the individual (Dinev & Hart, 2004). Strongly associated with an emotional depth, a negative experience may induce threatening feelings regarding an individual’s general well-being and security (Aguirre et al., 2015). However, as noted by Dinev and Hart (2004), an experience of a positive nature in relation to information disclosure will repercuss in such a manner that privacy concerns will have decreased compared to outcomes through negative experiences. Essentially, negative or positive perceptions of the results of the information disclosure will affect the privacy concerns of an individual (Aguirre et al., 2015; Dinev & Hart, 2004).
Moreover, contemporary research specifically applying privacy concerns in online personalized advertisements, as mentioned in the introductory chapter of this study, is manifold, yet in close relation to the practices conducted in general online environments. Aguirre et al. (2015), extending the research on perceived risk, in relation to the data accumulation processes of companies, conclude that the strategies utilized in these processes are vital to the consumers’ reactions towards online personalized advertisements. Applied on Facebook, they explore the degree of personalization of an advertisement, whether the information collection is covert or overt, and whether there are any means of confirming the information handling (Aguirre et al., 2015). When discussing a covert or overt information collection process, Aguirre et al. (2015) denote these two concepts to reflect whether or not visitors on websites are purposefully made aware that their information is being collected by the website. This can be done through visual cues such as cookies disclaimers. The instance when consumers are informed of this process is thus called overt, while the opposite process is known as covert. The results from Aguirre et al., (2015) suggest that when data from consumers is covertly collected to enhance personalization of advertisements, consumers are likely to associate the advertisement with negative perceptions. Continuously, an overt data collection method was concluded to minimize these negative experiences, resulting in increased trust and higher effectivity of the personalized advertisement (Aguirre et al., 2015). Moreover, providing visitors with means of confirming how the information that they disclose is handled, also increases the subsequent effectivity of the advertisement. This can be done through providing access to a website’s privacy policy (Aguirre et al., 2015).
Concrete denotations of perceived risk have been expanded upon by Liebermann and Stashevsky (2002). Although their proposed hypotheses included nine elements with significant influence on privacy concerns, only two hypotheses were central to their results: internet credit card theft, and supplying personal information (Liebermann & Stashevsky, 2002). While these results concluded in implications for marketers and advertisers opting for online personalized advertisements, it should likewise be noted upon that the generalizability of the research had cultural limitations. Despite this, the study provides support for concrete components of perceived risk (Liebermann & Stashevsky, 2002).
Methodology
The third chapter of this study displays the process through which the given study was conducted. It includes both an explanation to each methodological aspect as well as a justification for each aspect’s use in relation to the study.
Deductive Research Approach
In any given study, a research approach pertains to the nature of the relationship between theory and empirical material (Bryman & Bell, 2011). As a continuation, a deductive research approach primarily concerns the accumulation of pre-existing theory, wherein a researcher bases theoretical assumptions on such theory (Hyde, 2000). According to Bryman and Bell (2011), the theory and the subsequent assumptions are based on the relevance they hold in relation to the specified phenomenon. In this study, a deductive research approach allowed for a problematization regarding the pre-existing theories on the theoretical foundations of online personalized advertisements, privacy concerns, consumer perceived benefits and risks. Moreover, the process of the approach can be considered to appear linear in nature, in that it is initiated through the collection of theory, continuing with assumptions, or in some cases hypotheses, which themselves must be put in relation to empirical findings (Hyde, 2000; Popper, 2005). These assumptions can subsequently be analysed through a wide array of instruments, in an effort of temporarily confirming or rejecting the preceding assumptions (Bryman & Bell, 2011; Popper, 2005). While this study did not attempt to confirm or reject assumptions, a deductive research approach allowed analysis of empirical material which was operationalized from pre-existing theoretical foundation. An operationalization concerns the action through which the theories of a given research are translated into concepts or definitions related to the context of the given study (Saunders, Lewis & Thornhill, 2009).
While a deductive research approach is more common in quantitative research, it is equally viable in qualitative research (Hyde, 2000). Moreover, the advantages of using a deductive research approach in alignment with the given study, concern its linearity, or perhaps, its non-linearity. As argued by Bryman & Bell (2011), the most common perception of deductive research approaches revolves around that they are linear. Yet, the two authors continue, stating that theoretical foundations may require adjustment as the research advances. Given the two primary building blocks of this study, consumers perceived benefits and consumer perceived risks, which are continuously discussed in contemporary research, a deductive research approach allowed this study to be amended or modified in the event of new generated research in the given research process. On par with this, accumulated data of the current research may not be of relevance to the original assumptions of the given study (Bryman & Bell, 2011). A deductive research approach thus facilitated alterations to theoretical foundations, in the event of such data.
Table of contents :
1 Introduction
1.1 Background
1.2 Problem Discussion
1.3 Purpose
1.4 Research Questions
2 Theoretical Chapter
2.1 Online Personalized Advertisements
2.1.1 Consumer Perceived Benefits
2.1.2 Consumer Perceived Risks
3 Methodology
3.1 Deductive Research Approach
3.2 Qualitative Research Method
3.3 Exploratory Research Design
3.4 Data Collection Method: Focus Groups
3.4.1 Operationalization
3.4.2 Interview guide
3.5 Sampling
3.6 Ethical Considerations
3.7 Method of Analysis
3.8 Quality Criteria
3.8.1 Trustworthiness
3.9 Methodological Summary
4 Empirical Material
4.1 Consumer Perceived Benefits
4.2 Consumer Perceived Risks
5 Analysis
5.1 Consumer Perceived Benefits
5.2 Consumer Perceived Risks
6 Conclusion
7 Implications
7.1 Practical Implications
7.2 Theoretical Implications
7.3 Future Research
References
Appendices
Appendix A
