Academy of Marketing Studies Journal (Print ISSN: 1095-6298; Online ISSN: 1528-2678)

Research Article: 2021 Vol: 25 Issue: 1

Investigating Privacy Paradox: Consumer Data Privacy Behavioural Intention and Disclosure Behaviour

Dr. Bijay Prasad Kushwaha, Assistant Professor, USB-MBA, Chandigarh University, Mohali, Punjab

Dr. Raj Kumar Singh, Assistant Professor, USB-MBA, Chandigarh University, Mohali, Punjab

Dr. Vikas Tyagi, Associate Professor, USB-MBA, Chandigarh University, Mohali, Punjab


Data privacy has become a serious concern due to the misuse of consumer data by sellers in the recent decade. In the context of the privacy paradox, consumer actual claim regarding data privacy and actual disclosure behaviour has contradictory observation. The objective of this study is to find out the factors that form positive information disclosure behaviour in the mind of consumers. To study the consumer privacy paradox, the researcher has considered risk-benefit, nature of data, trust in the brand, and behavioural intention as variables that play a crucial role in framing a particular type of disclosure behaviour. Through a convenient sampling technique, 376 respondents were selected and interviewed with the help of structured questionnaire. Results suggest that trust in the brand has a major role in forming positive behavioural intention and disclosure behaviour. Similarly, risk-benefit has a positive impact on disclosure intention however nature of data hurts intention building about personal information disclosure.


Data Privacy, Privacy Paradox, Behavioural Intention, Disclosure Behaviour.


Maintaining privacy in today’s Web 2.0 world is very difficult because of various complications. We all are connected to the internet and without this, we feel loneliness. Today, privacy is also a debatable issue because companies want more data of customers however, customers hesitate to provide more data, yet for several requirements, they easily provide their data to companies (Kim et al., 2008; Kelley et al., 2013). It is creating a blurred line among private and public data. People are easily sharing their pictures, videos, hobbies, education background, address, location, card details, etc. on various internet platforms, however, they still show that they are very much concern about their data and claim that it is an important element of online purchase decision process (Simon, 1982; Norberg et al., 2007; Hughes-Roberts, 2012; Wu & Tsai, 2018). In reality, the concern relating to the privacy of data and actual online behaviour is contradictory (Acquisti, 2004; Barnes, 2006; Rathod & Raju, 2020) therefore, the situation of the privacy paradox arises.

The discrepancy among an intention toward data privacy and actual data disclosure behaviour during online shopping is witnessed (Flender & Müller, 2012). Due to increasing advance technology and artificial intelligence algorithms, the fashion to collect, store, and analyse a big amount of consumer data has increased (Culnan & Armstrong, 1999; Culnan & Bies, 2003; Tsai et al., 2011). Results from these data analyses help marketers to target and tailor offerings that are exactly needed and desired by consumer groups (Moon, 2000; Farahat & Bailey, 2012; Kushwaha et al., 2020). However, asking personal data from customers has arisen serious concern on the potential erosion of personal data privacy (Williams, 2002; Zhou, 2012) because many a times, individuals in general sense anxiety about the misuse of their data by private companies (Reis et al., 2020). This raises questions to companies about how to create and cultivate trust in data processing practices (Mitgen et al., 2014). Nevertheless, people express their compassion on personal information but observation of actual consumer behaviour at the marketplace suggests that people are less discerning and often offhand in their data profiles protection (Norberg et al., 2007; Chen & Tsai, 2016). Why consumer claimed privacy seriousness and actual disclosure behaviour varies?

In line with the theoretical scrutiny, there are very limited studies have been conducted on what people say about their data privacy and what they do at the time of online shopping. The objective of this study is to investigate the discrepancy between people proclaimed behavior and actual behaviour regarding personal data privacy during online marketing exchange (Joinson et al., 2010; Pötzsch, 2009). For this investigation, we have taken three independent variables such as privacy risk, nature of data, and trust in the company to decide the behaviour intention of customers. The intention to share information backed by the trust in the company reflects the actual disclose behaviour of consumers regarding the data privacy paradox. The outcomes of this study will alert the sellers to take care of the factors that are important in forming positive personal information disclosure behaviours.

Review of Literature

The success of e-commerce and digital marketing depends on how much consumer data a company has and how effective it can analyse these data (Sharma & Jhamb, 2020; Rathod & Raju, 2020). Jenitzsch et al. (2012) have found 47% of the service providers companies treated customer data as a commercial asset whereas 48% said that they share data with third parties to identify business opportunities. These are the reason customers often think about their data privacy. Concerns of consumers regarding data privacy, government regulations, and cost to the data collection are marketing the e-commerce environment expensive and complex.

Behavioural Intention

Behavioural intention of an individual or consumer is formed based on attitude, subjective norms, and perception bias (Ajzen, 1985). Previous findings have been showing a strong correlation between intention and behaviour whereas intention can be a predictor of behaviour (O,keefe, 2002). User usually states disclosure intention but do state their actual disclosure intention (Keith et al, 2013). Sutanto et al. (2013) cited in their study that users bother privacy concerns more than their willingness to share information even on trusted sites. There may be a possibility that an individual’s indicated intentions are not reflective of their actual behaviour because of other factors that may influence both intention and behaviour independently. The constant and routine information requested by companies could be easily shared by an individual as such information has a low level of realized losses (FTC, 2003).

Risk-Benefit and Behavioural Intention

Risk analysis is an important aspect of information privacy. It is the process of deciding that to whom, how much, in what way, and to what extent personal data is good to share (Li et al., 2010). The risk-benefit calculation is a logical, rule-based, sequential, cause and effect, high effort, conscious process (Novak & Hoffman, 2008). Misuse of data and its consequences are rationally weighted during information exchange. The risk-benefit calculation is done aiming to increase benefits (Peter & Tarpey, 1975) and decrease the risk of information disclosure (Vroom, 1964; Keith et al., 2013). Hence, behavioural intention and disclosure behaviour are positively influenced by benefits and negatively influenced by risk (Deering & Jacoby, 1972; Culnan & Armstrong, 1999).

H1: Risk-Benefit impact behavioural intention to share personal data.

Nature of Data and Behavioural Intention

The sensitivity of data that is asked by the company determines an intention to share personal information (Lwin et al., 2007). The requirement of data that is compulsorily needed for execution of the transaction, in such situation consumer may act normal to share required personal data whereas, in case of irrelevant data requirement, the consumer may act abnormal (White, 2004; Arora et al., 2019). The type of personal data and its quantity might be used by consumers to create intention regarding data sharing (Deering & Jacoby, 1972; Norgberg et al., 2007). The usual data such as address, contact number, postal code, name, etc. has less losses. Such nature of data is easily shared by consumers where the perceived risk is low (Milne, 1997).

H2: Nature of Data Impact Behavioural Intention to Share Personal Data.

Trust in the Brand and Behavioural Intention

Consumer trust in the brand has a positive influence on the intention to share information (Garbarino & Lee, 2003; Norberg et al., 2007). It is the experience of customers and activities of companies, if data privacy is violated by the company then consumers trust the brand decrease, and in the future consumers may hesitate to share their personal information with the company (Motiwala, et al., 2014). Moreover, the company claims the data privacy and protective nature of their service wherein consumers expect high data privacy which may inconsistent in real data privacy practice by the firm (Kehr et al., 2015). The dynamic nature of data privacy practices by companies suggests that the relative consumer perception toward data privacy is an important predictor of consumer intention to share personal information (Altman, 1975; Hoffman et al., 1999; Wakefield, 2013).

H3: Trust in the Brand Impact Behavioural Intention to Share Personal Data.

Trust in the Brand and Disclosure Behaviour

According to Milne & Boza (1999) trust directly affects the behavioural intention and actual behaviour of a consumer while sharing personal information (Kehr et al., 2015). Privacy risk may significantly impact information sharing whereas if an individual has trust in the company then the trust may play environmental cues to show positive disclosure behaviour (Norberg et al., 2007; Kehr et al., 2015). Therefore, if the company has a positive trust image in the market regarding data privacy then consumers may show positive disclosure behaviour and vice versa (Keith et al., 2013).

H4: Trust in the brand impact behavioural intention to share personal data.

Behavioural Intention and Disclosure Behaviour

In Figure 1 Disclosure behaviour is decided upon the intention of consumers to share information. Consumers always evaluate the negative consequence of sharing personal information against the benefits they are going to received and then disclosure behaviour is shown (Keith et al., 2013). Perceived benefit outweighs the perceived risk which eventually neglects the privacy concern and often results in information disclosure (Culnan & Armstrong, 1999; Moon, 2000; Leon et al., 2013). The typical benefits of personal data sharing includes discounts, bonus, convenience, and socialization with the seller (Xie & Kang, 2015). The disclosure behaviours are not stable because the privacy preference of an individual may be malleable. The consumers' disclosure behaviour is the outcome of their interest (Brandimarte et al., 2013).

Figure 1 Conceptual Model of Data Disclosure Behaviour

H5: Behavioural Intention Impact Disclosure Behaviour to Share Personal Data.

Research Methodology

This study has empirically attempted to explain the privacy paradox by considering eminent factors that help to predict privacy such as associated risk-benefits, nature of data, trust in brand, behaviour intention, and disclosure behaviour. These factors have been taken from existing literature. The survey was conducted in two phases through a structured questionnaire. The respondents were the customers of shopping malls outlets. In the first phase, respondents were interviewed before entering to the malls. The first phase of inquiry consists of questions related to risk-benefits, nature of data, trust in the brand, and behavioural intention whereas the second round of inquiry consists of questions relating to disclosure behaviours. Through convenient sampling techniques, 376 respondents were chosen and interviewed with the help of structured questionnaire. More than 300 sample size is considered comfortable and sufficient for data analysis and model validation using structural equation modelling (Tabachnick & Fidell, 2007; Hair et al., 2010). The location of this study was the Mohali city of Punjab, India. The duration of data collection was December 2019 to March 2020. Partial least square structural equation modelling is used to analyse the data (Hair et al., 2014) in Figure 2. The Smart PLS-SEM 3.0 statistical software was used to analyse and verify the relationship among variables under this study (Hair et al., 2020).

Figure 2 Path Relationship Diagram

Data Analysis and Interpretation

Measurement Model: Reliability and Validity

The table 1 contains standardised factor loading values, mean, and standard deviation of manifest variables. The standardized factor loading values of all the statements are above 0.70 (Long 1983; Bollen 1989; Diamantopoulos et al., 2012) therefore all the manifest variables in the above table are reserved whereas two manifest variables under the Risk-Benefits variable were removed as their loading values were less than 0.70. The Variance Inflation Factor (VIF) values are lease than three therefore, there is no situation of Multicollinearity among these items (Mooi & Sarstedt, 2011; Sarstedt et al., 2014). Similarly, the mean values of all manifest variables are ranging from 3.54 to 4.00 and standard deviation results are ranging from 0.97 to 1.316 which are a good range and suitable for further analyses (Hair et al. 1995).

Table 1 Measurement Model Assessment
Latent Variables Manifest Variables
(Measured Variables)
Codes Standardized Factor loading Mean VIF SD
Risk-Benefits Too much Uncertainty about disclosed data RB1 0.8957 3.57 2.42 1.14
High potential for loss associated RB2 0.9231 3.70 1.93 1.22
Likely misused shared data RB3 0.9254 3.78 1.46 1.30
Monetary rewards RB4 0.9432 3.67 2.10 1.32
Nature of Data Demographic data ND1 0.8749 4.00 2.05 1.09
Economic data ND2 0.8865 4.05 2.33 1.09
Education data ND3 0.8939 3.96 1.72 1.06
Payment data ND4 0.8915 3.94 2.39 1.05
Address & Communication data ND5 0.8419 3.78 1.64 1.20
Family members details ND6 0.8553 3.76 2.02 1.06
Trust in the Brand Know and trust the data collector or website TB1 0.7789 3.80 1.66 1.17
Long relationship with company TB2 0.7765 3.70 2.04 1.13
Ethics are on top of company policy TB3 0.7694 3.95 2.99 0.97
Consumer data privacy is on utmost priority TB4 0.8354 3.89 2.12 1.15
Behavioural Intention Vulnerable to identity theft BI1 0.9247 3.86 2.92 1.12
Convenience to instantly access BI2 0.9162 3.90 2.17 1.11
Better service expectation BI3 0.9365 3.86 1.70 1.11
Access better products and services BI4 0.9424 3.81 2.15 1.10
Disclosure Behaviour My personal photos DB1 0.9186 3.58 2.42 1.23
Addresses and cellphone numbers DB2 0.9309 3.64 1.93 1.12
Disclose my income situation DB3 0.9204 3.66 1.46 1.24
Disclose demographic data DB4 0.8995 3.54 2.10 1.18
Disclose payment details DB5 0.8079 3.86 2.05 1.12

The above table 2 reflects the correlation matrix of all the variables under this study. The correlation values show moderate correlations among variables. Likewise, the values of the correlation are not so high therefore, the situation of multicollinearity may not occur (Mooi & Sarstedt, 2011; Sarstedt et al., 2014). The average variance extracted (AVE) score of all variables under this study is more than 0.50. The variables explain more than 50% variance in its items thus, the convergent validity of variables is successfully established (Hair et al., 2012). Similarly, the shared variance of all variable among each others are greater than its AVE therefore, discriminants validity is also successfully proven. Composite reliability values of variables under this study are falling in the range from 0.86 to 0.94 which are more than 0.70 and less than 0.95 therefore internal consistency reliability is also successfully proven (Hair et al., 2014; Sarstedt et al., 2014). Cronbach’s Alpha values are greater than 0.70 for all variables therefore, this data passed the reliability test (Bland & Altman, 1997; DeVellis, 2003).

Table 2 Correlation Coefficients Matrix and Quality Criteria
  Behavioural Intention Disclosure Behaviour Nature of Data Risk-Benefit     Trust in the Brand
Behavioural Intention          
Disclosure Behaviour 0.8524        
Nature of Data 0.4356 0.4997      
Risk-Benefit 0.4657 0.455 0.4351    
Trust in the Brand 0.7976 0.7383 0.3318 0.8043  
Average Variance Extracted (AVE) 0.8649 0.8039 0.7643 0.8501 0.6249
Composite Reliability (CR) 0.9424 0.9334 0.9311 0.9378 0.8694
Cronbach’s Alpha 0.9479 0.9387 0.9383 0.9411 0.8127

Structural Equation Model

The above table-3 indicates the results of the bootstrapping procedure with (5376 cases, 5000 subsamples, and no sign change option). The blindfolding was used to examine the model’s predictive relevance for all endogenous constructs by running the procedure of blindfolding with an omission distance of seven yielded cross-validated redundancy (Q2) values for all the two endogenous constructs are well above zero (Behavioural Intention: 0.726; and Disclosure Behaviours: 0.736), providing support for the model’s predictive relevance. The above results also indicates that all hypotheses of this study have been accepted at p≤0.001 (Hair et al., 2014; Sarstedt et al., 2014). This means all independent variables have a significant impact on dependent variables. Risk-benefits, nature of data, and trust in the brand have a significant impact on behavioural intention to share personal information with the sellers. Similarly, the trust in the brand and behavioural intention have a significant impact on the information disclosure behaviour of customers.

Table 3 Structural Model Assessments
Hypotheses   Beta Estimate S.E. t-values Final Decision
H1 Risk-Benefit Behavioural Intention -0.5022 0.0152 33.00*** Accepted
H2 Nature of Data -0.0505 0.0099 5.10*** Accepted
H3 Trust in the Brand 1.2183 0.0167 72.76*** Accepted
H4 Disclosure Behaviour 0.1605 0.0142 11.31*** Accepted
H5 Behavioural Intention 0.7245 0.0142 50.97*** Accepted

Discussion and Findings

This study aims to provide sufficient evidence that how customers react when they are asked to share their personal information during sales transactions. The results suggest that customers first calculate the risk associated with information sharing against the benefits they are going to get after that they decide whether information should be disclosed or not (Peter & Tarpey, 1975; Novak & Hoffman, 2008; Keith et al., 2013). The nature of data demanded by the sellers also decides whether such data should be shared with the company or not (White, 2004; Norgberg et al., 2007). Common data that have low losses in such cases the customer may not hesitate much to reveal and share such data but when a loss is huge that time customers may hesitate to show a negative attitude in sharing such data. Many a time they avoid to share such information (Deering & Jacoby, 1972; Norgberg et al., 2007). Again, trust in the brand or company that has also greater roles in behavioural intention to share information with the sellers. If a company is very authentic and reliable to which customers also trust that this company does not misuse my information (Garbarino & Lee, 2003; Norberg et al., 2007; Motiwala et al., 2014). For such a company or seller customer attitude and intention to disclose information is positive (Kehr et al., 2015).

Implications of this Study

This study is very much useful for policymakers of the company. Today, data has various roles in the productivity, growth, and success of the company. With this regard, how much data and what type of information the company should ask consumers so that consumer would not hesitate to disclose information. Many a time, we ask some information from our customer and customer may not willing to share such information with the company. As a result, customers may hesitate to visit the store or website. So, this study will assist policymakers to what extent we can force customers to share personal data and how customers will feel comfortable sharing these data. This study is also helpful to provide the elements that could be considered by the customer before revealing personal information to the company. It is useful for digital marketers also where it has become very common to ask basic customer information before allowing them to move the next page of the company's website.


Though data has an important role in satisfying customers and retaining them with the business but many times customers texture hesitation in sharing their personal information. Alike, customers may not feel comfortable all the time with the company's communication. Various calls from the company's executives, SMS, emails, promotions, etc. may annoy customers. Nevertheless, the amount of information and the role of such information in business transactions also play a vital role in disclosure behaviour. The company should ask minimum information in a single transaction so that customers could feel easy to reveal. The relevancy of such information in the business transaction should also be justified to achieve positive disclosure behaviour. Therefore, the company should collect minimum information from consumer that should be more common, and consumer should not feel anxiety in sharing such information. Companies should also improve their data processing practices to gain the trust of consumers.

The data was collected from the shoppers of Shopping Malls that are situated in Mohali city of India only, which may be a limitation of this study. Nevertheless, Mohali is part of Chandigarh Tri-city wherein people from different states and having diversified cultures are residing. Therefore, the outcomes of this study will be also useful for Indian companies. However, generalization from a global perspective may not possible as the respondents of this study largely follow the qualities of Indian consumers. Further, this study can be conducted in other locations and other sectors like tourism, telecom, and insurance sectors. Furthermore, other variables such as situations, transaction requirements, regulatory bodies’ rules, etc. can be taken for future study.


  1. Acquisti, A. (2004). Privacy in electronic commerce and the economics of immediate gratification. EC '04 Proceedings of the 5th ACM Conference on Electronic Commerce, USA, 21-29.
  2. Ajzen, I. (1985). From intentions to actions: A theory of planned behaviour, in: Kuhl, J., Beckman J. (Eds.), Action-control: From cognition to behaviour. Springer, Heidelberg, 11-39.
  3. Altman, I. (1975). The environment and social behaviour. Brooks/Cole, Monterey, CA.
  4. Arora, N., Prashar, S., Parsad, C., & Tata, S.V. (2019). Influence of celebrity factors, consumer attitude and involvement on shoppers’ purchase intention using hierarchical regression. Decision, 46(3), 179-195.
  5. Barnes, S.B. (2006). A privacy paradox: Social networking in the United States. First Monday, 11(9). Retrieved from
  6. Brandimarte, L., Acquisti, A., & Loewenstein, G. (2013). Misplaced Confidences: Privacy and the Control Paradox. Social Psychological and Personality Science, 4(3), 340-347.
  7. Bland, J., & Altman, D. (1997). ‘Statistics notes: Cronbach’s alpha’, British Medical Journal, 314, 572.
  8. Bollen, & Kenneth, A.V. (1989). Structural Equations with Latent Variables. New York: Wiley.
  9. Chen, Kuan-Yu., & Tsai, Sang-Bing. (2016). Service Quality and Competitive Strategies in Online Banking. Advances in Economics, Business and Management Research, 16, 174-180.  10.2991/febm-16.2016.27.
  10. Culnan, M.J., & Armstrong, P.K. (1999). Information privacy concerns, procedural fairness, and impersonal trust: An empirical investigation. Organization Science, 10(1), 340-347.
  11. Deering, Barbara, J., & Jacob Jacoby. (1972). Risk Enhancement and Risk Reduction as Strategies for Handling Perceived Risk. In Proceedings of the Third Annual Conference of the ACR (404-416). Association for Consumer Research.
  12. DeVellis, R. (2003) Scale Development: Theory and Applications, Sage, Thousand Okas, CA.
  13. Diamantopoulos, A., Sarstedt, M., Fuchs, C., Wilczynski, P., & Kaiser, S. (2012) ‘Guide-lines for choosing between multi-item and single-item scales for construct measurement: a predictive validity perspective’, Journal of the Academy of Marketing Science, 40(3), 434-449.
  14. Flender, C., & Müller, G. (2012). Type indeterminacy in privacy decisions: The privacy paradox revisited, in: Busemeyer J., Dubois F., Lambert-Mogiliansky A., Melucci M. (Eds.), and Quantum interaction. Lecture Notes in Computer Science, 7620. Springer- Verlag, Berlin, Heidelberg, pp. 148-159.
  15. Farahat, A., & Bailey, M.C. (2012). “How Effective Is Targeted Advertising?” in Proceedings of the 21st International Conference on World Wide Web, New York: ACM Press, pp. 111-120.
  16. FTC. (2003). ID Theft: When Bad Things Happen to Your Good Name.
  17. Garbarino, Ellen., & Olivia, F., Lee. (2003). Dynamic Pricing in Internet Retail: Effects on Consumer Trust. Psychology and Marketing, 20(June): 495-513.
  18. Hair, J.F., Anderson, R.E., Tatham, R.L., & Black, W.C. (1995) Multivariate Data Analysis with Readings, 4th edition, Prentice-Hall, Englewood Cliffs, NJ.
  19. Hair, J.F., Black, W.C., Babin, B.J., & Anderson, R.E. (2010). Multivariate Data Analysis: A Global Perspective, New Jersey, Pearson Prentice Hall.
  20. Hair, J.F., Jr., Hult, G.T.M., Ringle, C.M., & Sarstedt, M. (2014). A primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks, CA: SAGE Publications Ltd.f
  21. Hair, Joseph, F., Matthew, H., & Christian, N. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101-110.
  22. Hair, J.F., Jr., Sarstedt, M., Ringle, C.M., & Mena, J.A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research, Journal of the Academy of Marketing Science, 40(3), 414-433.
  23. Hoffman, Donna, L., Thomas, P., Novak., & Marcos, A.P. (1999). Building Consumer Trust Online. Communications of the ACM, 42(April), 80-5.
  24. Hughes-Roberts, T. (2012). A cross-disciplined approach to exploring the privacy paradox: Explaining disclosure behaviour using the theory of planned behaviour. UK Academy for Information Systems Conference Proceedings, Paper 7.
  25. Jentzsch, N., Preibusch, S., Harasser, A. (2012). Study on monetising privacy. An economic model for pricing personal information. ENISA, February.
  26. Joinson, A.N., Reips, U.D., Buchanan, T., Paine Schofield, C.B. (2010). Privacy, trust, and self-disclosure online. Human-Computer Interaction, 25, 1-24.
  27. Keith, M.J., Thompson, S.C., Hale, J., Lowry, P.B., Greer, C. (2013). Information disclosure on mobile devices: Re-examining privacy calculus with actual user behaviour. International Journal of Human-Computer Studies, 71, 1163-1173.
  28. Kehr, F., Wentzel, D., & Kowatsch, T. (2014). Privacy paradox revised: Pre-existing attitudes, psychological ownership, and actual disclosure. Thirty-Fifth International Conference on Information Systems, Auckland, New Zealand, 1-12. 
  29. Kim, G.S., Park, S.B., & Oh, J. (2008). An examination of factors influencing consumer adoption of short message service (SMS). Psychology & Marketing, 25(8), 769-786.
  30. Kelley, P.G., Cranor, L.F., Sadeh, N., (2013). Privacy as part of the app decision-making process. CHI 2013, 1-11.
  31. Kushwaha, B., Singh, R.K., Tyagi, V., & Singh, V. (2020). Ethical Relationship Marketing in the Domain of Customer Relationship Marketing. Test Engineering and Management, 83, 16573-16584.
  32. Leon, P.G., Ur, B., Wang, Y., Sleeper, M., Balebako, R., Shay, R., Bauer, L., Christodorescu, M., & Cranor, L.F. (2013). What Matters To Users? Factors That Affect Users‟ Willingness to Share Information with Online Advertisers. Proceedings of the Ninth Symposium on Usable Privacy and Security - SOUPS ’13.
  33. Li, H., Sarathy, R., & Xu, H. (2010). Understanding situational online information disclosure as a privacy calculus. Journal of Computer Information Systems, 51(1), 62-71.
  34. Long, J.S. (1983). Confirmatory Factor Analysis. Newbury Park, Calif.: Sage.
  35. Lwin, M., Wirtz, J., & Williams, J.D. (2007). Consumer Online Privacy Concerns and Responses: A Power– Responsibility Equilibrium Perspective, Journal of the Academy of Marketing Science, 35(4), 572-585.
  36. Milne, G.R. (1997). Consumer Participation in Mailing Lists: A Field Experiment. Journal of Public Policy & Marketing, 16(Fall), 298-310.
  37. Moon, Y. (2000). Intimate Exchanges: Using Computers to Elicit Self-Disclosure from Consumers. Journal of Consumer Research, 26(March), 323-339.
  38. Motiwalla, L.F., Li, X., & Liu, X., (2014). Privacy paradox: Do state privacy concerns translate into the valuation of personal information? Proceeding of the 19th Pacific Asia Conference on Information Systems (PACIS 2014), Paper 281.
  39. Milne, George, R., & Maria-Eugenia, B. (1999). Trust and Concern in Consumers’ Perceptions of Marketing Information Management Practices. Journal of Interactive Marketing, 13(Winter), 5-24.
  40. Miltgen, C., & Peyrat-Guillard, D. (2014). Cultural and generational influences on privacy concerns: A qualitative 
study in seven European countries. European Journal of Information Systems, 23, 103-125.
  41. Mooi, E.A., & Sarstedt, M. (2011). A concise guide to market research: The process, data, and methods using IBM SPSS statistics. Berlin: Springer.
  42. Norberg, P.A., Horne, D.R., & Horne, D.A. (2007). The privacy paradox: Personal information disclosure intentions versus behaviours. The Journal of Consumer Affairs, 41(1), 100-126.
  43. Novak, T.P., & Hoffman, D.L. (2008). The fit of thinking style and situation: New measures of situation-specific experiential and rational cognition. Journal of Consumer Research, 36(6), 56-72.
  44. Nurmartiani, E., Sucherly, H.M., & Komaladewi, R. (2019). Customer Value in Improving Indihome Customer's Trust in West Java. Academy of Marketing Studies Journal, 23(4), 1-13.
  45. O’Keefe, & Daniel, J. (2002). Persuasion Theory and Research, 2nd edition. Thousand Oaks, CA: Sage Publications.
  46. Peter, J., Paul, Lawrence, X., & Tarpey, Sr. (1975). A Comparative Analysis of Three Consumer Decision Strategies. Journal of Consumer Research, 2(June), 29-37.
  47. Pötzsch, S. (2009). Privacy awareness: A means to solve the privacy paradox? in: Vashek, M., Fischer-Hübner, S., Cvrcek, D., Švenda, P. (Eds.), The future of identity in the information society. Springer-Verlag, Berlin Heidelberg, pp. 226-236.
  48. Rathod, J., & Raju, G. (2020). The Determinants of Customer Shop Online: A Case of Study from Indian Context. Academy of Marketing Studies Journal, 24(3), 1-16.
  49. Reis, J., Santo, P., & Melão, N. (2020). Impact of Artificial Intelligence Research on Politics of the European Union Member States: The Case Study of Portugal. Sustainability12(17), 6708.
  50. Sarstedt, M., Ringle, C.M., Smith, D., Reams, R., & Hair, J.F. (2014). Partial least square structural equation modeling (PLS-SEM): A useful tool for family business researchers. Journal of Family Business Strategy, 5, 105-115.
  51. Sharma, A., & Jhamb, D. (2020). Changing Consumer Behaviours towards Online Shopping - An Impact of Covid 19. Academy of Marketing Studies Journal, 24(3), 1-10.
  52. Simon, H.A. (1982). Models of bounded rationality. MIT Press, Cambridge, MA.
  53. Sutanto, J., Palme, E., Tan, C., & Phang, C.W. (2013). Addressing the personalization-privacy paradox: an empirical assessment from a field experiment on smartphone users. MIS Quarterly, 37, 4.
  54. Tabachnick, B.G., & Fidell, L.S. (2007). Using Multivariate Statistics (5th Ed.) New York, HarperCollins.
  55. Tsai, S.B., Lee, Y.C., Wu, C.H., & Lo, K.L. (2011) A Comparison Study on the Evaluation Criteria for Corporate Social Responsibility, 2011 International Conference on Management and Service Science, Wuhan, 1-5. doi: 10.1109/ICMSS.2011.5997938.
  56. Vroom, V.H. (1964). Work and motivation. Wiley, New York.
  57. Williams, E. (2002). The Man Who Knows Too Much. Forbes, November 11, 2002, 68-70.
  58. Wakefield, R. (2013). The influence of user affect in online information disclosure. Journal of Strategic Information Systems, 22, 157-174.
  59. White, T.B. (2004). Consumer Disclosure and Disclosure Avoidance: A Motivational Framework. Journal of Consumer Psychology, 14(1-2), 41-51.
  60. Wu, Chia-Huei, & Sang-Bing, T. (2018). Using DEMATEL-Based ANP Model to Measure the Successful Factors of E-Commerce. Intelligent Systems: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global: 1122-1138.
  61. Xie, W., & Kang, C. (2015). See You, See Me: Teenagers Self-Disclosure and Regret of Posting on Social Network Site. Computers in Human Behaviour, 52, 398-407.
  62. Zhou, T. (2012). Understanding users’ initial trust in mobile banking: An elaboration likelihood perspective. Computers in Human Behaviour, 28(2012), 1518-1525.
Get the App