Academic publishing in Europe and N. America

Archive Publication ethics Submission Payment Contacts
In the original languageTranslation into English

Toward a conceptual framework for the impact of trust, motivation, and context on marketing comfort

Authors

Anas Esmat Kazem, Zhou Xiaohong

Rubric:Marketing
764
6
Download articleQuote
764
6

Annotation

Over the past decade, as companies utilized personal information for marketing purposes, it increased privacy concerns and sparked debates over holding large data sets. The communication privacy management (CPM) theory guides research on an individual’s comfort with companies using personal information shared on social media for marketing purposes, or marketing comfort. This study expanded theory application by defining the antecedents, outcomes, and the process, which integrate research from multiple theories and disciplines, influencing trust, motivation, and context, which are three of the factors of the theory. Several propositions are made based on the presented conceptual model. This study postulated the impact of the three factors on marketing comfort based on the CPM theory. The research outlined how people’s discomfort with marketing data practices and privacy concerns might be decreased.

Keywords

marketing comfort
marketing ethics
data practices
privacy attitudes and behaviors.
communication privacy management theory

Authors

Anas Esmat Kazem, Zhou Xiaohong

Toward a conceptual framework for the impact of trust, motivation, and context on marketing comfort

Over the past decade, as companies utilized personal information for marketing purposes, it increased privacy concerns and sparked debates over holding large data sets. The communication privacy management (CPM) theory guides research on an individual’s comfort with companies using personal information shared on social media for marketing purposes, or marketing comfort. This study expanded theory application by defining the antecedents, outcomes, and the process, which integrate research from multiple theories and disciplines, influencing trust, motivation, and context, which are three of the factors of the theory. Several propositions are made based on the presented conceptual model. This study postulated the impact of the three factors on marketing comfort based on the CPM theory. The research outlined how people’s discomfort with marketing data practices and privacy concerns might be decreased.

Keywords: communication privacy management theory, marketing comfort, marketing ethics, data practices, privacy attitudes and behaviors.

 

 

Introduction

Social media has amplified companies’ leverage of online data. Social media’s efficacy in marketing has spurred companies to create a digital presence. Such efficacy results from the granted access to information. This access is done through companies’ surveillance of communication cycles or engaging with social media users (SMUs) (Akter et al., 2016, Sivarajah et al., 2020). Such data practices do not act under marketing ethics or privacy regulations. Companies still cannot define an ethical borderline or the boundaries of marketing data practices. Thus, reflecting individuals’ beliefs on marketing data practices, leading to greater discomfort with utilizing personal information for marketing. Thus, engendering arguments of companies knowing individuals’ personal information. As companies understand the value of data, these data practices contribute to creating a dilemma.

Despite that, the CPM theory guided the research, but several theories are supporting the three factors of the theory. First, the theory of trust of Mayer et al. (1995) defined the factors affecting trust. Second, the ideology of factors affecting the context is supported by privacy calculus theory, economic theory, and equality theory. Third, motivation follows the research of CPM theory. The proposed model is based on the analysis of the research of Afifi (2003), Petronio (1991), Petronio et al. (1998), Petronio (2002), Petronio (2007). Several theories and studies support the causes of motivation. First, the social representation theory and the structuration theory support the research on social norms and social benefits. Furthermore, the research on social benefits is supported by the CPM theory. Second, value expectancy theory supports the research on social benefits and financial benefits. In addition, the research on financial benefits is supported by Genesys Telecommunication laboratories. Third, the research on informational sensitivity is supported by the CPM theory. Fourth, control of the personal information is supported by the extension of the prospect theory of Choi (2014). Fifth, transparency is supported by information boundary management theory and communication privacy management theory. Finally, contextual-conditional usage is based on the research of Haghirian and Madlberger (2005), Boerman et al. (2021), Smith (1997), Boerman et al. (2017).

Social media marketing comfort

Marketing comfort is defined as individuals’ comfort with utilizing available personal information on social media (Jacobson et al., 2020). Comfort has a few aspects that are agreed on. First, comfort is subjective. Second, one’s internal and external factors can influence one’s comfort. Third, comfort is viewed as a reaction to surroundings (De Looze et al., 2003)

Advertising has remained stagnant with its primary purpose being informing. However, marketing tradition of embracing new technology has enabled marketers to eradicate boundaries and provide real-time interactivity (Sharma and Verma 2018). Today, the facilitation is more of systemizing and targeting data-driven and/or data-informed advertising content. The flows of information are facilitated because of the data-intensive business environment (Glazer, 1991). However, in some cases, online marketing data practices do not go along with consumers’ comfort. Such as Cambridge Analytica-Facebook data scandal (Criddle, 2020), and Facebook-WhatsApp data sharing practices (CURRY, 2022). Digital privacy concerns are not just related to a certain aspect of data practices. This might be because of the deficiency in confidence in companies’ data practices (Genesys, 2020). From an individual’s perspective, companies’ diligence in focusing on privacy policies and terms of service regularly encounter with one’s privacy and create concerns (Auxier et al., 2019, Genesys, 2020). Therefore, the absence of comfort is observed in this context when privacy concerns are presented.

Marketing ethics

Despite challenges faced, practitioners should possess ethical virtues such as integrity, fairness, respect, and empathy (Murphy, 1999). If marketing practices are linked to an ethical theory, it would be easier to justify marketing practices to social critics. Therefore, it can foster trust in the marketing system (Laczniak and Murphy, 2019). However, Marketers’ strategies are based on consumers’ susceptibility to manipulation because businesses’ proposition requires that shopping behavior can be predicted and manipulated (Nadler and McGuigan 2018).

It is unacceptable for SMPs to justify privacy breaching of users (Lucas and Borisov), or justify it by the accessibility of data (Boyd and Crawford, 2012). Privacy concerns are exacerbated by the black-boxing of data-mining processes and the negative consequences of data mining. In the meantime, data collection practices by marketers is justified by notice and consent protocol for advertisements and commercial offers (Nadler and McGuigan, 2018, Kennedy and Moss, 2015, Barocas and Nissenbaum, 2014). From marketers’ standpoint, the availability of personal information is not marketer’s responsibility. Thereby, marketers can shape data responsibility to maintain one’s interests (Cluley, 2020). Eventually, marketing ethics dilemma is viewed when marketers understand the value of consumers and data but still cannot define an ethical borderline.

Trust

Trust is defined by Mayer et al. (1995) as the willingness of a trustor to be vulnerable to actions of the trustee, which are not monitored or controlled, while the other party is a free agent whose behavior cannot be entirely controlled or even predicted (Gefen, 2000). Therefore, any negative consequence of the nonoccurrence of an event is greater than the positive consequence if it is confirmed (Deutsch, 1958). It results in rising concerns and increasing distrust and discomfort. Thus, urges the co-owner of information to lessen turbulence by reestablishing and coordinating boundaries (Afifi, 2003, Petronio et al., 1998). It is understood that marketing data practices disenfranchise people from controlling how information is utilized (Foxman and Kilcoyne, 1993). This might be because of the abuse of consumers’ privacy or a deficiency in transparency (Bright et al., 2021).

The proposed model of trust by Mayer et al. (1995) is the one being followed to define factors affecting trust. It states that ability, integrity, benevolence, and propensity to trust form the factor trust but with considering that the best cultural predictor for propensity to trust is cultural tightness which is the strength of social norms and sanctioning in society (Gelfand et al., 2006). However, it is postulated that companies’ ability with using personal information for marketing purposes should have a negative relationship with trust, as it increase privacy concerns. Giffin (1967) has demonstrated that interpersonal and intrapersonal trust can be significantly affected. Today, this is seen by how social media platforms (SMPs) are introduced to reality. Additionally, companies intend to enhance user’s perceived supportive climate, acceptance, or sense of psychological safety. Thus, it creates individuals’ favorable disposition of marketing data practices. Thereafter, an individual will engage in risk-taking behavior while considering the impact of perceived risk. Eventually, it will yield a desired outcome (Mayer et al., 1995)

Proposition 1: individuals’ perceived trust has a positive relationship with one’s comfort with marketers using publicly available social media information.

Based on the theory of trust of Mayer et al. (1995), the following hypothesis are postulated.

 Proposition 1a: companies’ ability to use personal information for marketing purposes has a negative relationship with the Trust.

Proposition 1b: companies’ benevolence has a positive relationship with individuals’ perceived trust.

Proposition 1c: companies’ integrity has a positive relationship with individuals’ perceived trust.

Proposition 1d: individuals’ propensity to trust has a positive relationship with one’s perceived trust.

Proposition 1e: perceived social norms have a positive relationship with one’s propensity to trust.

Proposition 1f: Propensity to trust has a moderation effect on the relationship between ability and trust.

Proposition 1g: Propensity to trust has a moderation effect on the relationship between benevolence and trust

Proposition 1h: Propensity to trust has a moderation effect on the relationship between integrity and trust.

Motivation

Motivation is defined by American-psychological-association (2022a) as the impetus that provides purpose or direction to behavior. It operates at a conscious or unconscious level. As psychological deficits increase individuals’ needs to behave in a certain way, that fulfills one’s needs (Alvandi, 2020). In social environments, CPM theory exhibit that in order to develop a bond or express an interest in forming a bond, an individual will disclose information (Afifi, 2003). Thereby, motivation is critical in deciding whether to disclose or conceal information (Petronio, 2007). However, as the social environment demands that individuals open boundaries, individuals respond to these demands with the best of one’s abilities. The responses are guided by the extent to which individuals are motivated to meet these demands (Petronio et al., 1998). Finally, information disclosure is guided by the benefit or advantages desired and sought, which in this study is a social benefits or financial benefits resulting from information exchange, that will provide satisfaction (Petronio, 1991, Petronio, 2007). This is in parallel with the value expectancy theory, which indicate that an individual is motivated to do certain behavior if it is believed that the behavior can achieve a certain desired and significant goal (Almuqrin, 2018).

Proposition 2: Consumers’ or social media users’ perceived motivation has a positive relationship with comfort with marketers using publicly available social media information.

The proposed model for the process in which the factor motivation reflects in the outcome and the Positive affect is represented in the following figure:

(Insert figure 1 of the appendix)

Based on a literacy review within the context of the research, the causals of the motivation to are as follows:

4.1. Information sensitivity

As the disclosure of sensitive information may cause harm (Nguyen, 2021), CPM theory supposing that a tension is created because a person may desire to disseminate or conceal information. Therefore, boundaries are established to choose with who and what information to reveal (Petronio, 1991). The significance of sensitivity can be attributed to the existence of risk and the possibility of creating harm (Belen Sağlam et al., 2022).

Milne et al. (2017) indicate that the riskier the information, the more sensitive it is and the less likely to be shared with marketers. Thus supporting Markos et al. (2017) who illustrated that information sensitivity increases privacy concerns and decreases willingness to share information. Individuals are comfortable with the disclosure of information if benefits are present, or if it is required for the use case. However, individuals are not as comfortable with the exchange of sensitive data, and value the social aspects when disclosing information (Markos et al., 2017).

Proposition 2a: the use of insensitive personal information has a positive relationship with motivation.

4.2.Information transparency and awareness

Schnackenberg and Tomlinson (2016) highlighted the perpetual nature of transparency and defined it as the perceived quality of intentionally shared information from a sender. Within the fields of business ethics and information ethics, transparency is referred to as information visibility, which is enhanced by the reduction or elimination of obstacles. CPM theory indicates that it is important to know the communication context for deciding about personal data disclosure. One would create a set of rules for the disclosure decision (Petronio, 2002). While information boundary management theory demonstrate that situational factors, which represent the degree of personalization and transparency offered to a customer, moderate a person’s privacy concerns and risk assessment (Hansen, 2007). Consumers are demanding to know what information is collected and who it is sold to (Dinev et al., 2013). Eventually, to increase comfort, businesses should prioritize control and transparency (Culnan Mary and Milberg Sandra, 1999).

Proposition 2b: transparency has a positive relationship with motivation.

4.3.Financial benefits

A literacy review has revealed the importance of the additional value provided to the co-owner of information for information dissemination and usage, including future usage (Kokolakis, 2017, Genesys, 2020). The research of Genesys (2020) on defined the financial benefit as the advantageous additional value. Humans are influenced by the perceived reward maximization attitude (Homans, 1958). Even when consumers display claims of anxiety, consumers recognize it has financial value (Genesys, 2020). Therefore, individuals exchange it, which constitutes a privacy paradox. The regulatory focus theory highlights that it depends on the mindset––a promotion or prevention focused mindset––which increases the individual’s perception that the decision made is right, which will transfer value to the decision outcome (Higgins et al., 2003).

Proposition 2c: financial benefits have a positive relationship with motivation.

Privacy paradox and social influence

Despite rising privacy concerns, SMUs intend to share personal information, which creates a privacy paradox that is defined by Kokolakis (2017) as the phenomenon illustrating the dichotomy between the information privacy attitude and actual behavior.

Various theories delineated the phenomenon, one is by demonstrating the presence of strong motivation for self-disclosure stemming from the essentiality of disclosure for maintaining social lives (Blank et al., 2014). Another perspective that has been used by Lutz and Strathoff (2014) indicates that a collision will end up favoring the emotional rewards of belonging to a community as it outweighs the calculated risks of data misuse (Kokolakis, 2017).

One of the social theories used to enhance the understanding of the phenomenon and the impact of society is the structuration theory. Thus indicating that the reason for having a dichotomy is that social representation of online privacy is not yet developed as people are relying on established schemes (Kokolakis, 2017). Thereby, social influence affects individuals’ behaviors. It includes both the social benefits and the scheme that people understand and follow regarding privacy concerns. As the outcomes of the users’ online behavior are not weighted equally, it can be attributed to the fact that the expected benefits of sharing are valued more than the potential risks (Lee et al., 2013). Therefore, it indicates that social influence is a catalyst for a change in comfortability with the usage.

4.4.Social benefits

Social benefits are the social advantages gained as the result of the affiliation with a virtual community (Hennig-Thurau et al., 2004). By disclosing information online to fulfill a certain need, an individual is understanding how the information is going to be used within the social context. Similarly, within the marketing context, the online content shared is seen as e-WOM (Powell et al., 2017). However, users tend to underestimate the privacy dangers of self-disclosure (Taddicken, 2014). The social benefits are as follows: 1. social bonding of Powell et al. (2017), 2. approval and impression management of Powell et al. (2017), 3. obtaining a social reward of Hallam and Zanella (2017).

Proposition 2d: social benefits have a positive relationship with motivation.

4.5.Social norms

Epstein (2001) defined social norms as self-enforcing behavioral regularities, but once entrenched, it is confirmed without thinking about it. Social norms can either emerge through two methods. First, the interaction of personal preferences and social factors on the behavioral choices of agents. Second, it can be transmitted by imitation because, in economic settings, agents imitate successful strategies rather than a calculation of cost and benefit (Elsenbroich and Gilbert, 2014). Social influence is viewed through the social norms that are correlated with society’s impact on individuals, which are as follows: 1-injunctive norms, 2-subjective norms, and 3-descriptive norms.

Proposition 2e: Social norms have a positive relationship with motivation.

4.6.Motives of gratification and the contextual conditional usage “advantageous” (CCU)

According to the uses and gratification theory, SMUs will choose different platforms for fulfilling different needs and achieving different gratifications (Weaver Lariscy et al., 2011). These needs can be considered motivation for anticipating in information disclosure behavior (Malik et al., 2016). The intention to fulfill such a need is a motive for gratification, which changes from one platform to another. In this context, it involves:

Contextual usage: the usage of the information within preferred contexts, which are marketing contexts tailored to individuals’ preferences and interests. Some consumers are interested in the advertisement substance that is tailored to one’s interests (Haghirian and Madlberger, 2005). However this might be considered a two-sided weapon, as users might hold negative attitudes resulting from being targeted by personalized ads (Boerman et al., 2021).

Positive conditionality: following the definition of Smith (1997), it is defined within this context as individuals’ promise of the positive-affect in the exchange for fulfilling particular conditions by companies. This can be represented by adding value through advertisements, such as decreasing opportunity costs or increasing knowledge, as the informativeness of the advertising information can add value and incentivize positive reactions to the message (Haghirian and Madlberger, 2005).

Proposition 2f: contextual conditional usage has a positive relationship with motivation.

4.7.Control

The prospect theory demonstrate that, from the companies’ perspective, as long as the individual has been consented, concerns do not matter. However, from an individual perspective, the focus is on whether consent is given and meaningful. If a consent is given, it does not always mean that individuals are acting in their own best interest (Choi, 2014). However, Insufficient knowledge about the potential outcomes of revealing personal information represents the problem of skewed decision-making (Solove, 2012), which reflects in individuals’ control over personal information. Therefore, it is considered that controlling personal information is the core of privacy, which is defined by Clarke (1999) as the interest an individual has in controlling, or at least significantly influencing, the handling of individuals’ data. However, if individuals cannot control personal information, then no one else should have it (Choi, 2014). Finally, providing individuals with control over personal information is mainly to increase comfort (Culnan Mary and Milberg Sandra, 1999).

Proposition 2g: control over the usage of personal information has a positive relationship with motivation.

5.     Context

Context is defined as the conditions or circumstances in which a particular phenomenon occurs (American-psychological-association, 2022b). Following the research of Ashworth and Free (2006) on considering information collection as exchange, it is understood that the collection and usage are exchanged with online benefits. Based on that context, there are two repercussions. 1) the existence of negative outcomes, 2) the presence of judgments about fairness and justice. Eventually, such an exchange might be unfair. This can be the reason co-owners of information appreciate financial benefit as a fair exchange (Genesys, 2020).

By considering the three aspects of comfort mentioned by De Looze et al. (2003), the nature of the factor context is subjective. As an individual intend to maximize one’s benefits, judgments/estimation of the risks and benefits is affecting the factor context. While considering information collection as an exchange, rules of fairness and outcome equality or rules of equity are the circumstances in which comfort occur. Therefore, the context is formed by consumers’ or SMUs’ estimation of the benefits and the risks. 2- fairness, and 3- rules of equality/equity.

Proposition 3: The perceived context has a positive relationship with the comfort of marketers using publicly available social media information.

5.1. Estimation

Based on the privacy calculus theory, an individual makes a calculation between privacy loss and potential gains of disclosure. The final behavior is determined by the privacy trade-off. Disclosure is more likely when the benefits outweigh the risks (Kouklakis, 2017). Disclosure is driven by social needs (Debatin et al., 2009). Therefore, one’s decision is based on one’s opinion of the pros and cons of using personal information. When the calculation yields a positive estimation, comfort is established.

Proposition 3a: positive estimation /judgements of the benefits and risks has a positive relationship with context.

5.2. Fairness

The fairness of the outcome is the organization’s perception that it has received a fair share of the divided pie of outcomes and gains from the collaboration. It can provide insight into the perceived reliability and integrity of the other party (Jap, 2001). The utilization of social media data can yield results that are scientifically dubious and ethically problematic (Leonelli et al., 2021). Camerer and Thaler (1995) has indicated that the proposers do not care about the welfare of the other party, but desire equity such as interaction (Jap, 2001).

While in the research of dictatorship games, where the individual has no option but to accept the offer—such as the notice-and-consent protocol—, it is demonstrated that individuals are more likely to share the outcome with strangers in one of the following two scenarios: 1. The relationship is not personal. 2. The proposer (of offers) does not believe that a proposer has earned the right to the outcome (Jap, 2001). This can be observed where SMUs’ benefits, which are just social, are being exchanged with the utilization of personal information for marketing. Thereby, it reflects a deficiency in fairness, as the proposers (SMSPs, companies) do not care about the welfare of the SMUs.

Proposition 3b: fairness has a positive relationship with context

5.3. Equality

According to Jap (2001), the most common principles for outcome sharing are based on the equity rule and the equality rule. The equity rule indicates that each member’s payoffs are a function of its contributions to the collaboration, the greater the contributions, the greater the payoffs. It might be ineffective because each party might debate about its contribution. It is sophisticated to measure each party’s contribution, as SMUs act as consumers or end-users of the product or service, while the other parties are commercial organizations that contribute with idiosyncratic investments or resources (e.g., software, specific expertise, and skill sets). Therefore, each party depends on the other party, to some extent, to achieve its own goals and fulfill the needs (Jap, 2001).

The equality rule demonstrates dividing the outcome into an equal share of payoffs, which can be used with dissension reduction (Deutsch, 1985, Kabanoff, 1991). In such scenario, equality rule has a significant positive effect on relationship quality, especially when both parties value the payoffs of the collaboration similarly (Jap, 2001). Accordingly, when equality is established, comfort should occur.

Proposition 3a: equality sharing has a positive relationship with context.

The conceptual model

With the collaboration of the CPM theory, figure 2 represents the proposed model for the research. It represents the impact of three factors mentioned in the CPM theory on marketing comfort (MKC). The part at the top represents the impact of trust on MKC, and it is following the model of trust proposed by Mayer et al. (1995), in which ability, integrity, benevolence, and propensity to trust, with considering that the propensity to trust is affected by social norms, form trust. Then an individual will engage in risk-taking behavior taking into consideration the perceived risk, which is affected by companies. Finally, it will yield in the outcome that will affect the perceived trustworthiness. The middle part of the model represents the formation of the factor context by the judgements/estimation of the risks and benefits, fairness of the outcome, and equality sharing. The bottom part represents the process of the influence of motivation on marketing comfort. It is based on the research of Afifi (2003), Petronio (1991), Petronio et al. (1998), Petronio (2002), Petronio (2007). It commences with the realization of psychological deficits, which create individuals’ needs, leading the individual to behave in a certain way, in which the causal of motivation plays a significant role. The expected achievable goal, which in this case is social benefits or financial benefits, led to the expected added value.

The presence of social norms is based on two methods, either through imitation or a social representation of privacy. This is due to an interaction of personal preferences and social factors on behavioral choices, in which positive affect plays a role. Eventually, information disclosure and usage is a tool to achieve the expected goal and the achievement of goals will lead to the fulfillment of positive effects.

(Insert Figure 2 of the appendix)

Conclusion and future research direction

The introduction of social media to marketing has enabled companies to leverage the publicly available personal information posted on social media. Furthermore, marketing data practices do not go along with marketing ethics or privacy regulations. The study defined the antecedents of the three factors of CPM theory, the process in which the three factors affect marketing comfort. The model uses the model of trust of Mayer et al. (1995) to define the variables influencing trust. Then, it differentiates between the antecedents of motivation and the factor motivation. Finally, as companies use personal information is exchanged with benefits, the process is viewed as informational exchange, individuals’ estimation of the risk and benefits, fairness, and equality sharing of the outcomes are significant factors forming the context.

Considering that individuals vary in privacy concerns levels, a privacy segmentation index should be used to segment individuals based on their privacy attitudes and behaviors. The establishment of an online privacy segmentation index is crucial, it can rely on the segmentation index of Dolnicar and Jordaan (2007) but with changing the last behavioral variable from shopping via catalogue to shopping via internet, and with considering disqualifying the second behavioral variable––using internet banking–– as individuals who shop online are using internet banking. Therefore, other variables can be introduced such as users’ sensitivity to companies requesting personal information (behavioral variable) and tendency to trust companies (attitudinal variable), because privacy-concerned and privacy unconcerned segments have significant differences in these variables. The topic needs to be studied and tested cross-culturally as several variables might be affected by subjective cultural variances. It is recommended for future research to assess the impact of the overall factors of the theory on each privacy segment. Therefore, collecting a huge sample size is necessary. It is suggested for future research to consider the high correlation between benevolence and integrity by introducing several indicators. Despite that the high correlation is explained by David Schoorman et al. (2016), but it should be considered as it might affect the discriminant validity.

As individuals tend to use each social media platform for specific purposes, it is recommended to consider the relationship between advertisement’s gratifications and the positive affect. Future research can study social commerce platforms that offer watching advertisements for the users but with a small financial benefit. In addition, social commerce platforms are not used just for social reasons. It is critical for future research to define the ethical borderline behind marketing data practices. Furthermore, identify companies’ boundaries to define the appropriateness of marketing data practices.

 

References

AFIFI, T. D. (2003) ‘Feeling caught’in stepfamilies: Managing boundary turbulence through appropriate communication privacy rules. Journal of Social and Personal Relationships, 20 (6), 729-755.

AKTER, S., et al. (2016) How does social media analytics create value? Journal of Organizational and End User Computing (JOEUC), 28 (3), 1-9.

ALMUQRIN, A. (2018) Examining the influences of technology acceptance, self-efficacy, and locus of control on information security behavior of social media users.

ALVANDI, D. (2020) The power of controversy: the allure of controversial brands.

AMERICAN-PSYCHOLOGICAL-ASSOCIATION (2022a) APA Dictionary of Psychology.  American psychological association,  [cited: may 28, 2022]. Available from https://dictionary.apa.org/motivation.

AMERICAN-PSYCHOLOGICAL-ASSOCIATION (2022b) definition of context.   Updated 2022.  Available from https://dictionary.apa.org/context.

ASHWORTH, L. and FREE, C. J. J. O. B. E. (2006) Marketing dataveillance and digital privacy: Using theories of justice to understand consumers’ online privacy concerns. 67 (2), 107-123.

AUXIER, B., et al. (2019) Americans and privacy: Concerned, confused and feeling lack of control over their personal information.

BAROCAS, S. and NISSENBAUM, H. (2014) Big data’s end run around anonymity and consent. Privacy, big data, and the public good: Frameworks for engagement, 1, 44-75.

BELEN SAĞLAM, R., et al. (2022) An Investigation into the Sensitivity of Personal Information and Implications for Disclosure: A UK Perspective. Frontiers in Computer Science.

BLANK, G., et al. (2014) A new privacy paradox.   Citeseer, Vol. 202014.

BOERMAN, S. C., et al. (2021) When is personalized advertising crossing personal boundaries? How type of information, data sharing, and personalized pricing influence consumer perceptions of personalized advertising. Computers in Human Behavior Reports, 4, 100144.

BOERMAN, S. C., et al. (2017) Online behavioral advertising: A literature review and research agenda. Journal of advertising, 46 (3), 363-376.

BOYD, D. and CRAWFORD, K. (2012) Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, communication & society, 15 (5), 662-679.

BRIGHT, L. F., et al. (2021) “Should I Post or Ghost?”: Examining how privacy concerns impact social media engagement in US consumers. Psychology & Marketing, 38 (10), 1712-1722.

CAMERER, C. F. and THALER, R. H. (1995) Anomalies: Ultimatums, dictators and manners. Journal of Economic perspectives, 9 (2), 209-219.

CHOI, B. H. (2014) A Prospect Theory of Privacy. Idaho L. Rev., 51, 623.

CLARKE, R. (1999) Internet privacy concerns confirm the case for intervention. Communications of the ACM, 42 (2), 60-67.

CLULEY, R. (2020) The politics of consumer data. Marketing Theory, 20 (1), 45-63.

CRIDDLE, C. (2020) Facebook sued over Cambridge Analytica data scandal, BBC NEWS.   Updated 28 October 2020. [cited: 29/3/2022]. Available from https://www.bbc.com/news/technology-54722362#:~:text=Facebook%20is%20being%20sued%20for,used%20for%20advertising%20during%20elections.

CULNAN MARY, J. and MILBERG SANDRA, J. (1999) Information Privacy: Looking Forward, Looking Back.), Washington DC: Georgetown University Press.

CURRY, D. (2022) Signal Revenue & Usage Statistics (2022).   Updated JANUARY 11, 2022. [cited: march 12, 2022]. Available from https://www.businessofapps.com/data/signal-statistics/.

DAVID SCHOORMAN, F., et al. (2016) Empowerment in veterinary clinics: The role of trust in delegation. Journal of Trust Research, 6 (1), 76-90.

DE LOOZE, M. P., et al. (2003) Sitting comfort and discomfort and the relationships with objective measures. 46 (10), 985-997.

DEUTSCH, M. (1958) Trust and suspicion. Journal of conflict resolution, 2 (4), 265-279.

DEUTSCH, M. (1985) Distributive justice: A social-psychological perspective.

DINEV, T., et al. (2013) Information privacy and correlates: an empirical attempt to bridge and distinguish privacy-related concepts. European Journal of Information Systems, 22 (3), 295-316.

DOLNICAR, S. and JORDAAN, Y. (2007) A market-oriented approach to responsibly managing information privacy concerns in direct marketing. Journal of advertising, 36 (2), 123-149.

ELSENBROICH, C. and GILBERT, N. (2014) Modelling norms. In) Modelling Norms.     Springer, pp. 143-149.

EPSTEIN, J. M. (2001) Learning to be thoughtless: Social norms and individual computation. Computational economics, 18 (1), 9-24.

FOXMAN, E. R. and KILCOYNE, P. (1993) Information technology, marketing practice, and consumer privacy: Ethical issues. Journal of Public Policy & Marketing, 12 (1), 106-119.

GEFEN, D. (2000) E-commerce: the role of familiarity and trust. Omega, 28 (6), 725-737.

GELFAND, M. J., et al. (2006) On the nature and importance of cultural tightness-looseness. Journal of applied psychology, 91 (6), 1225.

GENESYS (2020) Survey Shows Consumers Very Willing To Trade Personal Data for Financial Benefits.   Updated 08/05/2020. [cited: 5/4/2022]. Available from https://www.genesys.com/press?release=122826.

GIFFIN, K. (1967) The contribution of studies of source credibility to a theory of interpersonal trust in the communication process. Psychological bulletin, 68 (2), 104.

GLAZER, R. (1991) Marketing in an information-intensive environment: strategic implications of knowledge as an asset. Journal of marketing, 55 (4), 1-19.

HAGHIRIAN, P. and MADLBERGER, M. (2005) Consumer attitude toward advertising via mobile devices-An empirical investigation among Austrian users. ECIS 2005 Proceedings, 44.

HALLAM, C. and ZANELLA, G. (2017) Online self-disclosure: The privacy paradox explained as a temporally discounted balance between concerns and rewards. Computers in Human Behavior, 68, 217-227.

HANSEN, M. (2007) Marrying transparency tools with user-controlled identity management. In) IFIP International Summer School on the Future of Identity in the Information Society.     Springer, pp. 199-220.

HENNIG-THURAU, T., et al. (2004) Electronic word-of-mouth via consumer-opinion platforms: what motivates consumers to articulate themselves on the internet? Journal of interactive marketing, 18 (1), 38-52.

HIGGINS, E. T., et al. (2003) Transfer of value from fit. Journal of personality and social psychology, 84 (6), 1140.

HOMANS, G. C. (1958) Social behavior as exchange. American journal of sociology, 63 (6), 597-606.

JACOBSON, J., et al. (2020) Social media marketing: Who is watching the watchers? Journal of Retailing and Consumer Services, 53, 101774.

JAP, S. D. (2001) “Pie sharing” in complex collaboration contexts. Journal of Marketing Research, 38 (1), 86-99.

KABANOFF, B. (1991) Equity, equality, power, and conflict. Academy of management Review, 16 (2), 416-441.

KENNEDY, H. and MOSS, G. (2015) Known or knowing publics? Social media data mining and the question of public agency. Big Data & Society, 2 (2), 2053951715611145.

KOKOLAKIS, S. (2017) Privacy attitudes and privacy behaviour: A review of current research on the privacy paradox phenomenon. Computers & security, 64, 122-134.

LACZNIAK, G. R. and MURPHY, P. E. (2019) The role of normative marketing ethics. Journal of Business Research, 95, 401-407.

LEE, H., et al. (2013) Why do people share their context information on Social Network Services? A qualitative study and an experimental study on users’ behavior of balancing perceived benefit and risk. 71 (9), 862-877.

LEONELLI, S., et al. (2021) From FAIR data to fair data use: Methodological data fairness in health-related social media research. Big Data & Society, 8 (1), 20539517211010310.

LUCAS, M. M. and BORISOV, N. Flybynight: mitigating the privacy risks of social networking. pp. 1-8.

LUTZ, C. and STRATHOFF, P. (2014) Privacy concerns and online behavior–Not so paradoxical after all? Viewing the privacy paradox through different theoretical lenses. Viewing the Privacy Paradox Through Different Theoretical Lenses (April 15, 2014).

MALIK, A., et al. (2016) Uses and gratifications of digital photo sharing on Facebook. Telematics and Informatics, 33 (1), 129-138.

MARKOS, E., et al. (2017) Information sensitivity and willingness to provide continua: a comparative privacy study of the United States and Brazil. 36 (1), 79-96.

MAYER, R. C., et al. (1995) An integrative model of organizational trust. Academy of management review, 20 (3), 709-734.

MILNE, G. R., et al. (2017) Information sensitivity typology: Mapping the degree and type of risk consumers perceive in personal data sharing. 51 (1), 133-161.

MURPHY, P. E. (1999) Character and virtue ethics in international marketing: An agenda for managers, researchers and educators. Journal of Business Ethics, 18 (1), 107-124.

NADLER, A. and MCGUIGAN, L. (2018) An impulse to exploit: the behavioral turn in data-driven marketing. Critical Studies in Media Communication, 35 (2), 151-165.

NGUYEN, T. J. J. O. I. C. (2021) Continuance intention in traffic-related social media: a privacy calculus perspective. 20 (2), 215-245.

PETRONIO, S. (2002) Boundaries of privacy: Dialectics of disclosure.    Suny Press.

PETRONIO, S., et al. (1998) (Mis) communicating across boundaries: Interpersonal and intergroup considerations. Communication Research, 25 (6), 571-595.

PETRONIO, S. J. C. T. (1991) Communication boundary management: A theoretical model of managing disclosure of private information between marital couples. 1 (4), 311-335.

PETRONIO, S. J. J. O. A. C. R. (2007) Translational research endeavors and the practices of communication privacy management. 35 (3), 218-222.

POWELL, A. E., et al. (2017) Developing a scale for the perceived social benefits of sharing. Journal of Consumer Marketing.

SCHNACKENBERG, A. K. and TOMLINSON, E. C. (2016) Organizational transparency: A new perspective on managing trust in organization-stakeholder relationships. Journal of management, 42 (7), 1784-1810.

SIVARAJAH, U., et al. (2020) Role of big data and social media analytics for business to business sustainability: A participatory web context. Industrial Marketing Management, 86, 163-179.

SMITH, K. E. (1997) The Use of Political Conditionality in the EU’s Relations with Third Countries: How Effective?’(1998) 3European. Foreign Affairs Review, 253.

SOLOVE, D. J. (2012) Introduction: Privacy self-management and the consent dilemma. Harv. L. Rev., 126, 1880.

TADDICKEN, M. (2014) The ‘privacy paradox’in the social web: The impact of privacy concerns, individual characteristics, and the perceived social relevance on different forms of self-disclosure. Journal of computer-mediated communication, 19 (2), 248-273.

WEAVER LARISCY, R., et al. (2011) Kids these days: Examining differences in political uses and gratifications, internet political participation, political information efficacy, and cynicism on the basis of age. American Behavioral Scientist, 55 (6), 749-764.

 

 

 

Appendix

Figures

 

Figure 1. The proposed model for the impact of motivation on comfort

 

Figure 2. research model

References:

AFIFI, T. D. (2003) ‘Feeling caught’in stepfamilies: Managing boundary turbulence through appropriate communication privacy rules. Journal of Social and Personal Relationships, 20 (6), 729-755.

AKTER, S., et al. (2016) How does social media analytics create value? Journal of Organizational and End User Computing (JOEUC), 28 (3), 1-9.

ALMUQRIN, A. (2018) Examining the influences of technology acceptance, self-efficacy, and locus of control on information security behavior of social media users.

ALVANDI, D. (2020) The power of controversy: the allure of controversial brands.

AMERICAN-PSYCHOLOGICAL-ASSOCIATION (2022a) APA Dictionary of Psychology.  American psychological association,  [cited: may 28, 2022]. Available from https://dictionary.apa.org/motivation.

AMERICAN-PSYCHOLOGICAL-ASSOCIATION (2022b) definition of context.   Updated 2022.  Available from https://dictionary.apa.org/context.

ASHWORTH, L. and FREE, C. J. J. O. B. E. (2006) Marketing dataveillance and digital privacy: Using theories of justice to understand consumers’ online privacy concerns. 67 (2), 107-123.

AUXIER, B., et al. (2019) Americans and privacy: Concerned, confused and feeling lack of control over their personal information.

BAROCAS, S. and NISSENBAUM, H. (2014) Big data’s end run around anonymity and consent. Privacy, big data, and the public good: Frameworks for engagement, 1, 44-75.

BELEN SAĞLAM, R., et al. (2022) An Investigation into the Sensitivity of Personal Information and Implications for Disclosure: A UK Perspective. Frontiers in Computer Science.

BLANK, G., et al. (2014) A new privacy paradox.   Citeseer, Vol. 202014.

BOERMAN, S. C., et al. (2021) When is personalized advertising crossing personal boundaries? How type of information, data sharing, and personalized pricing influence consumer perceptions of personalized advertising. Computers in Human Behavior Reports, 4, 100144.

BOERMAN, S. C., et al. (2017) Online behavioral advertising: A literature review and research agenda. Journal of advertising, 46 (3), 363-376.

BOYD, D. and CRAWFORD, K. (2012) Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, communication & society, 15 (5), 662-679.

BRIGHT, L. F., et al. (2021) “Should I Post or Ghost?”: Examining how privacy concerns impact social media engagement in US consumers. Psychology & Marketing, 38 (10), 1712-1722.

CAMERER, C. F. and THALER, R. H. (1995) Anomalies: Ultimatums, dictators and manners. Journal of Economic perspectives, 9 (2), 209-219.

CHOI, B. H. (2014) A Prospect Theory of Privacy. Idaho L. Rev., 51, 623.

CLARKE, R. (1999) Internet privacy concerns confirm the case for intervention. Communications of the ACM, 42 (2), 60-67.

CLULEY, R. (2020) The politics of consumer data. Marketing Theory, 20 (1), 45-63.

CRIDDLE, C. (2020) Facebook sued over Cambridge Analytica data scandal, BBC NEWS.   Updated 28 October 2020. [cited: 29/3/2022]. Available from https://www.bbc.com/news/technology-54722362#:~:text=Facebook%20is%20being%20sued%20for,used%20for%20advertising%20during%20elections.

CULNAN MARY, J. and MILBERG SANDRA, J. (1999) Information Privacy: Looking Forward, Looking Back.), Washington DC: Georgetown University Press.

CURRY, D. (2022) Signal Revenue & Usage Statistics (2022).   Updated JANUARY 11, 2022. [cited: march 12, 2022]. Available from https://www.businessofapps.com/data/signal-statistics/.

DAVID SCHOORMAN, F., et al. (2016) Empowerment in veterinary clinics: The role of trust in delegation. Journal of Trust Research, 6 (1), 76-90.

DE LOOZE, M. P., et al. (2003) Sitting comfort and discomfort and the relationships with objective measures. 46 (10), 985-997.

DEUTSCH, M. (1958) Trust and suspicion. Journal of conflict resolution, 2 (4), 265-279.

DEUTSCH, M. (1985) Distributive justice: A social-psychological perspective.

DINEV, T., et al. (2013) Information privacy and correlates: an empirical attempt to bridge and distinguish privacy-related concepts. European Journal of Information Systems, 22 (3), 295-316.

DOLNICAR, S. and JORDAAN, Y. (2007) A market-oriented approach to responsibly managing information privacy concerns in direct marketing. Journal of advertising, 36 (2), 123-149.

ELSENBROICH, C. and GILBERT, N. (2014) Modelling norms. In) Modelling Norms.     Springer, pp. 143-149.

EPSTEIN, J. M. (2001) Learning to be thoughtless: Social norms and individual computation. Computational economics, 18 (1), 9-24.

FOXMAN, E. R. and KILCOYNE, P. (1993) Information technology, marketing practice, and consumer privacy: Ethical issues. Journal of Public Policy & Marketing, 12 (1), 106-119.

GEFEN, D. (2000) E-commerce: the role of familiarity and trust. Omega, 28 (6), 725-737.

GELFAND, M. J., et al. (2006) On the nature and importance of cultural tightness-looseness. Journal of applied psychology, 91 (6), 1225.

GENESYS (2020) Survey Shows Consumers Very Willing To Trade Personal Data for Financial Benefits.   Updated 08/05/2020. [cited: 5/4/2022]. Available from https://www.genesys.com/press?release=122826.

GIFFIN, K. (1967) The contribution of studies of source credibility to a theory of interpersonal trust in the communication process. Psychological bulletin, 68 (2), 104.

GLAZER, R. (1991) Marketing in an information-intensive environment: strategic implications of knowledge as an asset. Journal of marketing, 55 (4), 1-19.

HAGHIRIAN, P. and MADLBERGER, M. (2005) Consumer attitude toward advertising via mobile devices-An empirical investigation among Austrian users. ECIS 2005 Proceedings, 44.

HALLAM, C. and ZANELLA, G. (2017) Online self-disclosure: The privacy paradox explained as a temporally discounted balance between concerns and rewards. Computers in Human Behavior, 68, 217-227.

HANSEN, M. (2007) Marrying transparency tools with user-controlled identity management. In) IFIP International Summer School on the Future of Identity in the Information Society.     Springer, pp. 199-220.

HENNIG-THURAU, T., et al. (2004) Electronic word-of-mouth via consumer-opinion platforms: what motivates consumers to articulate themselves on the internet? Journal of interactive marketing, 18 (1), 38-52.

HIGGINS, E. T., et al. (2003) Transfer of value from fit. Journal of personality and social psychology, 84 (6), 1140.

HOMANS, G. C. (1958) Social behavior as exchange. American journal of sociology, 63 (6), 597-606.

JACOBSON, J., et al. (2020) Social media marketing: Who is watching the watchers? Journal of Retailing and Consumer Services, 53, 101774.

JAP, S. D. (2001) “Pie sharing” in complex collaboration contexts. Journal of Marketing Research, 38 (1), 86-99.

KABANOFF, B. (1991) Equity, equality, power, and conflict. Academy of management Review, 16 (2), 416-441.

KENNEDY, H. and MOSS, G. (2015) Known or knowing publics? Social media data mining and the question of public agency. Big Data & Society, 2 (2), 2053951715611145.

KOKOLAKIS, S. (2017) Privacy attitudes and privacy behaviour: A review of current research on the privacy paradox phenomenon. Computers & security, 64, 122-134.

LACZNIAK, G. R. and MURPHY, P. E. (2019) The role of normative marketing ethics. Journal of Business Research, 95, 401-407.

LEE, H., et al. (2013) Why do people share their context information on Social Network Services? A qualitative study and an experimental study on users’ behavior of balancing perceived benefit and risk. 71 (9), 862-877.

LEONELLI, S., et al. (2021) From FAIR data to fair data use: Methodological data fairness in health-related social media research. Big Data & Society, 8 (1), 20539517211010310.

LUCAS, M. M. and BORISOV, N. Flybynight: mitigating the privacy risks of social networking. pp. 1-8.

LUTZ, C. and STRATHOFF, P. (2014) Privacy concerns and online behavior–Not so paradoxical after all? Viewing the privacy paradox through different theoretical lenses. Viewing the Privacy Paradox Through Different Theoretical Lenses (April 15, 2014).

MALIK, A., et al. (2016) Uses and gratifications of digital photo sharing on Facebook. Telematics and Informatics, 33 (1), 129-138.

MARKOS, E., et al. (2017) Information sensitivity and willingness to provide continua: a comparative privacy study of the United States and Brazil. 36 (1), 79-96.

MAYER, R. C., et al. (1995) An integrative model of organizational trust. Academy of management review, 20 (3), 709-734.

MILNE, G. R., et al. (2017) Information sensitivity typology: Mapping the degree and type of risk consumers perceive in personal data sharing. 51 (1), 133-161.

MURPHY, P. E. (1999) Character and virtue ethics in international marketing: An agenda for managers, researchers and educators. Journal of Business Ethics, 18 (1), 107-124.

NADLER, A. and MCGUIGAN, L. (2018) An impulse to exploit: the behavioral turn in data-driven marketing. Critical Studies in Media Communication, 35 (2), 151-165.

NGUYEN, T. J. J. O. I. C. (2021) Continuance intention in traffic-related social media: a privacy calculus perspective. 20 (2), 215-245.

PETRONIO, S. (2002) Boundaries of privacy: Dialectics of disclosure.    Suny Press.

PETRONIO, S., et al. (1998) (Mis) communicating across boundaries: Interpersonal and intergroup considerations. Communication Research, 25 (6), 571-595.

PETRONIO, S. J. C. T. (1991) Communication boundary management: A theoretical model of managing disclosure of private information between marital couples. 1 (4), 311-335.

PETRONIO, S. J. J. O. A. C. R. (2007) Translational research endeavors and the practices of communication privacy management. 35 (3), 218-222.

POWELL, A. E., et al. (2017) Developing a scale for the perceived social benefits of sharing. Journal of Consumer Marketing.

SCHNACKENBERG, A. K. and TOMLINSON, E. C. (2016) Organizational transparency: A new perspective on managing trust in organization-stakeholder relationships. Journal of management, 42 (7), 1784-1810.

SIVARAJAH, U., et al. (2020) Role of big data and social media analytics for business to business sustainability: A participatory web context. Industrial Marketing Management, 86, 163-179.

SMITH, K. E. (1997) The Use of Political Conditionality in the EU’s Relations with Third Countries: How Effective?’(1998) 3European. Foreign Affairs Review, 253.

SOLOVE, D. J. (2012) Introduction: Privacy self-management and the consent dilemma. Harv. L. Rev., 126, 1880.

TADDICKEN, M. (2014) The ‘privacy paradox’in the social web: The impact of privacy concerns, individual characteristics, and the perceived social relevance on different forms of self-disclosure. Journal of computer-mediated communication, 19 (2), 248-273.

WEAVER LARISCY, R., et al. (2011) Kids these days: Examining differences in political uses and gratifications, internet political participation, political information efficacy, and cynicism on the basis of age. American Behavioral Scientist, 55 (6), 749-764.

Other articles of the issue

Dr.Natasha Hoda, Doc.Dr. Hysen Hoda MNIMUM LIVING AND POVERITY, ALBANIA COMPARED TO THE COUNTRIES OF THE REGION
Download article in PDF680 views
cc-license
About us Journals Books
Publication ethics Terms of use of services Privacy policy
Copyright 2013-2024 Premier Publishing s.r.o.
Praha 8 - Karlín, Lyčkovo nám. 508/7, PSČ 18600, Czech Republic pub@ppublishing.org