Summary of selected recent research of OPC in human–computer interactions
| Author (year) | Context | Theoretical underpin | Nomologic network of OPC: A = antecedent ME = mediator MO = moderator O = outcome | Major findings | Journal in abbr. |
|---|---|---|---|---|---|
| Agnihotri and Bhattacharya (2023) | Chatbot | Computer are social actors (CASA) theory | A: Perceived privacy concern, anthropomorphism, perceived empathy ME: Perceived trustworthiness MO: NA O: Consumer forgiveness, WOM | Perceived privacy concern influenced only perceived ability and not benevolence and integrity of the chatbot to influence consumer forgiveness and spread negative word of mouth | Int. J. Inf. Manage |
| Xiong and Zuo (2023) | Medical and senior care service platform | Value-based adoption model | A: Privacy concerns, legal concerns, perceived efforts, outcome expectations, perceived mobility ME: Perceived value MO: NA O: Intention to adopt | Perceived value and legal concerns can predict health care professionals’ intention to adopt. Outcome expectations, perceived mobility, perceived effort and privacy concerns can predict perceived value | Inf. Manage |
| Sandhu et al. (2023) | Video conferencing app | Privacy calculus theory; Social presence theory | A: Mobile user information privacy concerns, ubiquity, social presence ME: Trust, perceived risks, perceived value, perceived benefits MO: Ubiquity, technicality O: Continuance intention | The study emphasizes the promotion of privacy protection at the organizational level, control mechanisms that motivate employees to actively engage in privacy protection behavior and a multi-faceted approach for data transparency within the VC app platforms | Int. J. Inf. Manage |
| Ogbanufe (2023) | Online account | Protection motivation theory | A: Investment size ME: Perceived threat severity, perceived threat vulnerability, self-efficacy, response cost, response efficiency MO: NA O: Protection motivation, use | Investment size influences threat and coping appraisals, which in turn increases protection motivation and use. These results highlight the importance of eliciting individuals’ personal investments to improve their protective security behaviors | Int. J. Inf. Manage |
| Tang and Ning (2023) | Social app | Privacy calculus theory | A: Perceived privacy control, disposition to value privacy, app permission sensitivity, perceived effectiveness of privacy policy ME: Privacy concerns, social rewards, personalized benefits MO: NA O: Misinterpretation behavior | Both privacy concerns and social rewards motivate users to engage in misrepresentation behavior, while personalized benefits discourage users from doing so. Perceived privacy control and app permission sensitivity influence privacy concerns significantly, while disposition to value privacy and perceived effectiveness of privacy policies have nonsignificant effects on privacy concerns | Decis. Support syst |
| Tseng et al. (2022) | Online health community platform | Social support theory | A: Perceived control of information, perceived privacy risk, community engagement ME: Informational support, Emotional support MO: NA O: Intention to participate | Community engagement and privacy concerns can influence certain social support (e.g. information or emotional support), leading to OHC members’ intention to participate | Technol. Forecast. Soc. Change |
| Prakash and Das (2022) | Digital contact tracing apps | Innovation resistance theory; distrust theory | A: Information privacy concern, government surveillance concern, security risk, usage barrier, complexity barrier, value barrier ME: Distrust MO: NA O: Resistance, intention to use | Distrust, value barrier, information privacy concerns and usage barrier predicted the resistance to the DCT app, and resistance, in turn, predicted intention to use. Distrust was a key mediator between innovation barriers and resistance | Int. J. Inf. Manage |
| Shin et al. (2022) | Personalized algorithms | Privacy calculus theory | A: Algorithm awareness (AA) ME: Privacy concerns, efficacy MO: NA O: User self-disclosure | AA leads users to envisage, understand and interact with algorithms depending on their efficacy of understanding. AA influences the trust of algorithmic processes and the way users evaluate privacy concerns and self-disclosures | Int. J. Inf. Manage |
| Liu et al. (2022) | Mobile commerce | Justice theory | A: Perceived justice, privacy invasion experience ME: Perceived privacy MO: Privacy feedback, choice (presence/absence) O: User self-disclosure | Perceived justice determines perceived privacy, which shapes disclosure intentions. Privacy feedback enhances the positive effect of perceived justice on perceived privacy and the effect of trust propensity on disclosure intention and alleviates the negative effect of privacy experience on perceived privacy | Decis. Support syst |
| Ou et al. (2022) | Breach security | Protection motivation theory | A: Security breach, response strategy ME: Perceived risk, perceived severity, response efficacy MO: NA O: Re-transaction intention | The variations in the response strategy of organization after a security breach can lead to significantly different consumers’ reactions | Int. J. Inf. Manage |
| Zhang et al. (2022) | Mobile medical consultation | Social presence theory | A: Social presence of the interface, social presence of the interaction, social validation ME: Privacy concerns, trust in physicians, trust in applications MO: NA O: Intention to disclose, intention to continuously use, intention to follow advice | The social cue design factors influence Two types of trust and decrease privacy concerns. Privacy concerns hinder both types of trust. The impacts of the Two types of trust on patients’ intention to continue using the service, disclose information and follow medical advice are revealed | Comput. Hum. Behav |
| Koohang et al. (2022) | IoT | A: IoT awareness ME: IoT privacy knowledge, IoT security knowledge, trust MO: NA O: Continued intention to use | IoT awareness can positively influence users’ knowledge of IoT privacy and security. The users’ knowledge of IoT privacy and security can positively influence users’ IoT trust and subsequently, the users’ IoT trust can positively influence continued intention to use IoT | Int. J. Inf. Manage | |
| Alraja (2022) | IoT-based health application (HA) | Privacy calculus theory; theory of planned behavior | A: Privacy, security, trust ME: Risk perception, attitude MO: Gender O: Behavioral intention | There were gender differences in gen Y, but there was little evidence that risk perception affects any of the cohort’s behavioral intention towards the use of IoT-enabled HA | Technol. Forecast. Soc. Change |
| Ameen et al. (2022) | Smart shopping mall | Trust-commitment theory; privacy calculus theory | A: Interface design, trust, consumer peer interaction, relationship commitment ME: Personalization MO: Privacy concerns O: Loyalty | There were significant mediating effects of personalization on the positive relationships between interface design, trust, consumer peer interaction and relationship commitment and shopping mall loyalty. Privacy concerns, unlike prior research, do not exert a moderating role | Comput. Hum. Behav |
| Cichy et al. (2021) | IoT-based connected car | Proposed extended model from privacy calculus | A: Psychological ownership, relational trust, data sensitivity, data security ME: Privacy concerns MO: Self-efficacy enhancement, self-image congruency O: Sharing of personal data | Our findings highlight the interplay between virtual and physical risks in shaping drivers’ privacy concerns and data sharing decisions—with information privacy and data security emerging as discrete yet closely interrelated concepts. Psychological ownership is an important addition to established privacy calculus models of data sharing | MIS Q |
| Wagner et al. (2021) | Data-driven service app | Equity theory | A: Users’ net value, providers’ net value ME: Distributive equity MO: Information sensitivity O: Continuance intention; satisfaction | Users balance their own net value (benefits minus risks) as well as providers’ net value from monetizing users’ data The relationship between provider’s net value based on users’ information and distributive equity is moderated by information sensitivity | Int. J. Inf. Manage |
| Zhu and Kanjanamekanant (2021) | Personalized ads | Communication privacy management theory | A: Information co-ownership, personification, internal data source, ad embarrassment ME: Perceived privacy, ad attitude MO: Information co-ownership, personification O: Purchase intention | Personalized ads based on internal data source, perceived personification and co-ownership of facebook are positively related to perceived privacy, which leads to better ad attitude and higher purchase intentions. Perceived personification and co-ownership further moderate the relationship between internal data source and embarrassment to perceived privacy | Inf. Manage |
| Libaque-Sáenz et al. (2021) | Mobile apps | A: Fair information practices (FIPs), automatic data collection (AUTO) ME: Perceived data control, perceived information risks MO: FIPs, AUTO O: Behavioral intention | Both intervention strategies (FIPs and AUTO) have a significant effect on perceived data control and perceived risks and in turn on behavioral intention | Inf. Manage | |
| Cheng et al. (2021) | Ride-sharing platform | Privacy calculus theory | A: Privacy awareness, previous online privacy invasion, mobile payment security, negative media exposure, personal information disclosure requirements, immediate gratification ME: Perceived risks/benefits of information privacy disclosure MO: NA O: Intention to disclosure, disclosure | Privacy awareness, previous online privacy invasion, mobile payment security and negative media exposure influence information disclosure’s perceived risks and that perceived risks and benefits are significantly related to immediate gratification | Inf. Manage |
| Bandara et al. (2021) | E-commerce | Construal level theory; power-responsibility equilibrium framework | A: Privacy concerns ME: Privacy empowerment MO: Psychological distance O: Defensive behavior | Psychological distance moderates the relationship between privacy concerns and privacy behavior. Empowered consumers’ privacy behavior does not vary despite the degree of psychological distance | Inf. Manage |
| Balapour et al. (2020) | Mobile app | Communication privacy management theory | A: Perceived effectiveness of privacy policy ME: Perceived privacy risk MO: Information sensitivity, perceived privacy awareness O: Perceived mobile app security | Perceived privacy risk negatively influences the perceived security of the mobile apps; perceived effectiveness of a privacy policy positively influences user perceptions of mobile app security. Perceived privacy awareness moderates the effect of perceived privacy risk on the perceived security of mobile apps. Users have different privacy-security perceptions based on the information sensitivity of the mobile apps | Int. J. Inf. Manage |
| Ioannou et al. (2020) | Online traveling service | Privacy calculus theory | A: Disposition to privacy, privacy awareness, perceived privacy control, trust, privacy experience, privacy knowledge, privacy protection regulation ME: Online privacy concerns MO: NA O: Willingness to share | Travelers are concerned over their information privacy they are still willing to share their behavioral data. In the case of biometric information, the disclosure decision is dependent upon expected benefits rather than privacy concerns | Int. J. Inf. Manage |
| Degirmenci (2020) | App permission request | Antecedents–privacy concerns–outcomes (APCO) | A: Prior privacy experience, computer anxiety, perceived control, app permission concerns ME: Privacy concerns, trust, privacy calculus MO: NA O: Intention to accept | Prior privacy experience, computer anxiety and perceived control have significant effects on privacy concerns. However, concerns for app permission requests have approximately twice as much predictive value than the other factors put together to explain mobile users’ overall information privacy concerns | Int. J. Inf. Manage |
| Al-Natour et al. (2020) | Mobile apps | Agency theory; signaling theory | A: Informational signals, information asymmetry ME: Privacy uncertainty (collection, use, protection), seller uncertainty, product uncertainty MO: NA O: Intention to use | Privacy uncertainty significantly influences users’ intention to use an app above and beyond their uncertainty about the seller and the product. It also affects the perceived risk associated with using an app and the price consumers are willing to pay | Inf. Syst. Res |
| Zeng et al. (2020) | Online privacy policy | Motivation theory | A: Privacy assurance, personalization declaration ME: Privacy concerns MO: NA O: Purchase responses | Privacy assurance negatively affects customers’ purchase probability and purchase amount. Personalization declaration positively affects customers’ purchase probability and purchase amount. Privacy concerns significantly mediate above relations | J. Bus. Ethics |
| Lin and Wang (2020) | SNS | Social role theory; theory of reasoned action | A: Social presence, privacy risk, social ties, commitment ME: Attitude towards sharing information MO: Gender O: Intention to share information | Privacy risks, social ties and commitment were more important in the formation of attitudes toward information sharing for women than men. Gender significantly moderates the relationship between people’s perceptions of information sharing and their intention to share information | Int. J. Inf. Manage |
| Yang et al. (2020) | Mobile payment | Privacy calculus theory; control agency theory | A: Perceived benefits, perceived effectiveness of privacy setting, perceived effectiveness of privacy policy ME: Perceived value, psychological comfort MO: NA O: Intention to disclose | Perceived benefits, perceived effectiveness of privacy setting, perceived effectiveness of privacy policy and perceived risks together predict perceived value and psychological comfort, which further determine consumers’ self-disclosure | Int. J. Inf. Manage |
| Wu et al. (2020) | Mobile security notification | A: Intrusiveness, app interface usability ME: Perceived security, irritation MO: Disruption O: Continued intention to use | Both app interface usability and the design of MSNs significantly impacted users’ perceived security, which, in turn, has a positive influence on users’ intention to continue using the app | Inf. Manage | |
| Jozani et al. (2020) | Social media app | Privacy calculus theory | A: Privacy risk, privacy control, information sensitivity ME: Institutional privacy concerns, social privacy concerns MO: NA O: Engagement | Both institutional and social privacy concerns decrease engagement. Information sensitivity increases institutional privacy concerns. However, social privacy concerns are influenced by the perception of risk and control | Comput. Hum. Behav |
| Park and Shin (2020) | Health-related IT | A: Privacy attitudes, perception, evaluation ME: Interest in sharing MO: Medical condition, internet reliance for health O: Engagement | Privacy concern and confidence are mediated through One’s interest in sharing information with health professionals and moderated by One’s medical condition and the reliance on internet | Comput. Hum. Behav | |
| Shaw and Sergueeva (2019) | Mobile commerce | UTAUT2 | A: Perceived privacy risk, perceived transaction risk, perceived privacy protection ME: Perceived privacy concerns, perceived value MO: Personal innovativeness O: Intention to use | Both paths (perceived privacy concerns to perceived value and performance expectancy to perceived value) were significant. Perceived value motivates customers to use m-commerce and shapes perceptions as they evaluate the trade-off they are making | Int. J. Inf. Manage |
| Chen et al. (2019) | Personalized ads | Rational choice theory | A: Ownership, vulnerability ME: Privacy concerns, perceived cost of non-personalization, opportunity cost MO: NA O: Reactance | Three rational choice factors from a negative-effect perspective have significant impacts on consumer reactance. Affective factors such as ownership and vulnerability are dominant determinants of these rational choice factors | Int. J. Inf. Manage |
| Wang and Herrand (2019) | S-commerce | Privacy-trust-behavioral intention (PTB) model | A: Perceived effectiveness of privacy policy, perceived effectiveness of industry-self regulation ME: Trust MO: NA O: Intention of purchase | Institutional privacy assurance positively influences institutional-based trust, which, in turn, affects online social interactions and consequently increases the likelihood of product purchases on s-commerce sites | Int. J. Inf. Manage |
| Crossler and Bélanger (2019) | App privacy settings | Self-efficacy theory; information–motivation–behavioral (IMB) skills model | A: Privacy risk awareness, privacy knowledge, technology knowledge, sharing preference, subjective norm ME: Privacy self-efficacy, technology self-efficacy MO: Privacy knowledge O: Privacy behavior | Personal motivation is One of the strongest determinants of utilizing privacy-protective settings, and social motivation is not significant. Privacy knowledge and self-efficacy constructs determine One’s use of privacy-protective settings, but knowledge and self-efficacy about smartphone technology do not. An interaction effect exists between privacy knowledge and privacy self-efficacy on privacy behavior | Inf. Syst. Res |
| Lin and Armstrong (2019) | SNS | Communication privacy management theory | A: Information privacy concerns, territory privacy concerns ME: Information trusting beliefs, information privacy risk beliefs, territory privacy risk beliefs, territory trusting beliefs MO: NA O: Privacy disclosure, territory coordination | Perceptions of trespassing over agreed-upon virtual boundaries within SNSs affects risk beliefs regarding information privacy and territory privacy differently. These distinct privacy risk beliefs, in turn, influence Two privacy management behaviors | J. Assoc. Inf. Syst |
| Kim et al. (2019) | Mobile health | Refined SERVQUAL and SERVPERF | A: Privacy, content quality, engagement, reliability, usability ME: Satisfaction MO: NA O: Continuance intention | The quality dimensions (engagement followed by content quality and reliability) have the most considerable effect on continuance intention. By contrast, the effects of usability and privacy on continuance intention were insignificant | Int. J. Inf. Manage |
| Suen (2018) | SNS screening | Signaling theory | A: Employer use of SNS screening ME: Perception of privacy violation MO: Ability to control SNS information, transparency level of data collection O: Withdraw intention | A candidate who can better control his/her SNS information is less likely to perceive privacy violation during SNS screening by potential employers, thus mitigating his/her perception of procedural unfairness. When SNS screening is more transparent, the candidate is less likely to perceive procedural unfairness, which will reduce his/her intention to withdraw | Comput. Hum. Behav |
| McLean and Osei-Frimpong (2019) | In-home voice assistant | Uses and gratification theory | A: Utilitarian benefits, hedonic benefits, symbolic benefits, social presence, social attraction ME: NA MO: Perceived privacy risk O: Usage | Individuals are motivated by the utilitarian benefits, symbolic benefits and social benefits. Hedonic benefits only motivate the use of in-home voice assistants in smaller households. The research establishes a moderating role of perceived privacy risks in dampening and negatively influencing the use of in-home voice assistants | Comput. Hum. Behav |
| Ketelaar and Van Balen (2018) | Phone-embedded tracking | Psychological ownership theory; innovation diffusion theory | A: Privacy concerns ME: Attitude MO: Position on adoption curve O: Behavior | The more privacy concerns users experience, the more negative their attitudes are towards the collection of location data and that they adjust the settings to prevent being tracked. Users’ earlier position on the adoption curve, and with that their smartphone literacy, decreases the strength of the connection between privacy concerns and attitude | Comput. Hum. Behav |
| Miltgen and Smith (2019) | Commercial website | Privacy calculus theory | A: Perceived relevance ME: Perceived benefits, perceived risks, trust MO: Manipulated context O: Withholding, falsification | Trust is the most important driver of both withholding and falsification decisions. Perceived relevance influenced perceived benefits, risks and trust | Inf. Manage |
| Xiao and Mou (2019) | Social app | Person-environment fit model | A: Anonymity, flexibility, presenteeism ME: Privacy invasion, invasion of life MO: Neuroticism, extraversion O: Social media fatigue | Anonymity and presenteeism significantly influence privacy invasion and invasion of life, both of which are determinants of social media fatigue. Neuroticism strengthens the effect of social media characteristics on privacy invasion and invasion of life, while extraversion weakens these effects | Comput. Hum. Behav |
| Farivar et al. (2018) | Social commerce | Social identity theory | A: Perceived commerce risk, perceived participation risk ME: NA MO: Social identity O: Intention to purchase, intention to post | Perceived commerce risk reduces intentions to purchase, and that perceived participation risk curtails intentions to post comments on social commerce forums. The influence of these risk assessments is reduced when the degree of social identification with the website community increases | Inf. Manage |
| Choi et al. (2018) | SNS | Impression formation theory; privacy calculus theory | A: Network mutuality, profile diagnosticity ME: Privacy risks, expected social capital gains MO: Dispositional privacy concerns O: No-action, acceptance | Individuals utilize Two key types of social information: network mutuality and profile diagnosticity in evaluating privacy risks and expected social capital gains. Privacy risks and expected social capital gains powerfully predict the likelihood of no-action and the likelihood of accepting friend requests on SNS | J. Assoc. Inf. Syst |
| Fox and Connolly (2018) | Mobile health | Protection motivation theory; social cognitive theory | A: Ability to adopt, risk beliefs trust beliefs ME: Health information privacy concerns MO: NA O: Adoption intention | Health digital divide is deepening due to older adults’ perceived inability to adopt and their unwillingness to adopt stemming from mistrust, high risk perceptions and strong desire for privacy | Inf. Syst. J |
| Ortiz et al. (2018) | SNS | Privacy calculus theory | A: Information security awareness ME: Concern for information privacy, consumer alienation, privacy risk belief MO: Perceived privacy empowerment O: Lurking, self-concealment | Information security awareness significantly and positively influences concern for information privacy, consumer alienation and privacy risk belief. Concerns for information privacy and consumer alienation significantly and positively affect privacy risk belief. Privacy risk belief has a significant and positive effect on lurking and self-concealment. Perceived privacy empowerment moderates the relation between privacy risk belief and lurking as well as that between privacy risk belief and self-concealment |
| Author (year) | Context | Theoretical underpin | Nomologic network of OPC: A = antecedent ME = mediator MO = moderator O = outcome | Major findings | Journal in abbr. |
|---|---|---|---|---|---|
| Chatbot | Computer are social actors ( | A: Perceived privacy concern, anthropomorphism, perceived empathy ME: Perceived trustworthiness MO: | Perceived privacy concern influenced only perceived ability and not benevolence and integrity of the chatbot to influence consumer forgiveness and spread negative word of mouth | Int. J. Inf. Manage | |
| Medical and senior care service platform | Value-based adoption model | A: Privacy concerns, legal concerns, perceived efforts, outcome expectations, perceived mobility ME: Perceived value MO: | Perceived value and legal concerns can predict health care professionals’ intention to adopt. Outcome expectations, perceived mobility, perceived effort and privacy concerns can predict perceived value | Inf. Manage | |
| Video conferencing app | Privacy calculus theory; Social presence theory | A: Mobile user information privacy concerns, ubiquity, social presence ME: Trust, perceived risks, perceived value, perceived benefits MO: Ubiquity, technicality O: Continuance intention | The study emphasizes the promotion of privacy protection at the organizational level, control mechanisms that motivate employees to actively engage in privacy protection behavior and a multi-faceted approach for data transparency within the | Int. J. Inf. Manage | |
| Online account | Protection motivation theory | A: Investment size ME: Perceived threat severity, perceived threat vulnerability, self-efficacy, response cost, response efficiency MO: | Investment size influences threat and coping appraisals, which in turn increases protection motivation and use. These results highlight the importance of eliciting individuals’ personal investments to improve their protective security behaviors | Int. J. Inf. Manage | |
| Social app | Privacy calculus theory | A: Perceived privacy control, disposition to value privacy, app permission sensitivity, perceived effectiveness of privacy policy ME: Privacy concerns, social rewards, personalized benefits MO: | Both privacy concerns and social rewards motivate users to engage in misrepresentation behavior, while personalized benefits discourage users from doing so. Perceived privacy control and app permission sensitivity influence privacy concerns significantly, while disposition to value privacy and perceived effectiveness of privacy policies have nonsignificant effects on privacy concerns | Decis. Support syst | |
| Online health community platform | Social support theory | A: Perceived control of information, perceived privacy risk, community engagement ME: Informational support, Emotional support MO: | Community engagement and privacy concerns can influence certain social support (e.g. information or emotional support), leading to | Technol. Forecast. Soc. Change | |
| Digital contact tracing apps | Innovation resistance theory; distrust theory | A: Information privacy concern, government surveillance concern, security risk, usage barrier, complexity barrier, value barrier ME: Distrust MO: | Distrust, value barrier, information privacy concerns and usage barrier predicted the resistance to the | Int. J. Inf. Manage | |
| Personalized algorithms | Privacy calculus theory | A: Algorithm awareness ( | Int. J. Inf. Manage | ||
| Mobile commerce | Justice theory | A: Perceived justice, privacy invasion experience ME: Perceived privacy MO: Privacy feedback, choice (presence/absence) O: User self-disclosure | Perceived justice determines perceived privacy, which shapes disclosure intentions. Privacy feedback enhances the positive effect of perceived justice on perceived privacy and the effect of trust propensity on disclosure intention and alleviates the negative effect of privacy experience on perceived privacy | Decis. Support syst | |
| Breach security | Protection motivation theory | A: Security breach, response strategy ME: Perceived risk, perceived severity, response efficacy MO: | The variations in the response strategy of organization after a security breach can lead to significantly different consumers’ reactions | Int. J. Inf. Manage | |
| Mobile medical consultation | Social presence theory | A: Social presence of the interface, social presence of the interaction, social validation ME: Privacy concerns, trust in physicians, trust in applications MO: | The social cue design factors influence Two types of trust and decrease privacy concerns. Privacy concerns hinder both types of trust. The impacts of the Two types of trust on patients’ intention to continue using the service, disclose information and follow medical advice are revealed | Comput. Hum. Behav | |
| IoT | A: IoT awareness ME: IoT privacy knowledge, IoT security knowledge, trust MO: | IoT awareness can positively influence users’ knowledge of IoT privacy and security. The users’ knowledge of IoT privacy and security can positively influence users’ IoT trust and subsequently, the users’ IoT trust can positively influence continued intention to use IoT | Int. J. Inf. Manage | ||
| IoT-based health application ( | Privacy calculus theory; theory of planned behavior | A: Privacy, security, trust ME: Risk perception, attitude MO: Gender O: Behavioral intention | There were gender differences in gen Y, but there was little evidence that risk perception affects any of the cohort’s behavioral intention towards the use of IoT-enabled | Technol. Forecast. Soc. Change | |
| Smart shopping mall | Trust-commitment theory; privacy calculus theory | A: Interface design, trust, consumer peer interaction, relationship commitment ME: Personalization MO: Privacy concerns O: Loyalty | There were significant mediating effects of personalization on the positive relationships between interface design, trust, consumer peer interaction and relationship commitment and shopping mall loyalty. Privacy concerns, unlike prior research, do not exert a moderating role | Comput. Hum. Behav | |
| IoT-based connected car | Proposed extended model from privacy calculus | A: Psychological ownership, relational trust, data sensitivity, data security ME: Privacy concerns MO: Self-efficacy enhancement, self-image congruency O: Sharing of personal data | Our findings highlight the interplay between virtual and physical risks in shaping drivers’ privacy concerns and data sharing decisions—with information privacy and data security emerging as discrete yet closely interrelated concepts. Psychological ownership is an important addition to established privacy calculus models of data sharing | ||
| Data-driven service app | Equity theory | A: Users’ net value, providers’ net value ME: Distributive equity MO: Information sensitivity O: Continuance intention; satisfaction | Users balance their own net value (benefits minus risks) as well as providers’ net value from monetizing users’ data The relationship between provider’s net value based on users’ information and distributive equity is moderated by information sensitivity | Int. J. Inf. Manage | |
| Personalized ads | Communication privacy management theory | A: Information co-ownership, personification, internal data source, ad embarrassment ME: Perceived privacy, ad attitude MO: Information co-ownership, personification O: Purchase intention | Personalized ads based on internal data source, perceived personification and co-ownership of facebook are positively related to perceived privacy, which leads to better ad attitude and higher purchase intentions. Perceived personification and co-ownership further moderate the relationship between internal data source and embarrassment to perceived privacy | Inf. Manage | |
| Mobile apps | A: Fair information practices (FIPs), automatic data collection ( | Both intervention strategies (FIPs and | Inf. Manage | ||
| Ride-sharing platform | Privacy calculus theory | A: Privacy awareness, previous online privacy invasion, mobile payment security, negative media exposure, personal information disclosure requirements, immediate gratification ME: Perceived risks/benefits of information privacy disclosure MO: | Privacy awareness, previous online privacy invasion, mobile payment security and negative media exposure influence information disclosure’s perceived risks and that perceived risks and benefits are significantly related to immediate gratification | Inf. Manage | |
| E-commerce | Construal level theory; power-responsibility equilibrium framework | A: Privacy concerns ME: Privacy empowerment MO: Psychological distance O: Defensive behavior | Psychological distance moderates the relationship between privacy concerns and privacy behavior. Empowered consumers’ privacy behavior does not vary despite the degree of psychological distance | Inf. Manage | |
| Mobile app | Communication privacy management theory | A: Perceived effectiveness of privacy policy ME: Perceived privacy risk MO: Information sensitivity, perceived privacy awareness O: Perceived mobile app security | Perceived privacy risk negatively influences the perceived security of the mobile apps; perceived effectiveness of a privacy policy positively influences user perceptions of mobile app security. Perceived privacy awareness moderates the effect of perceived privacy risk on the perceived security of mobile apps. Users have different privacy-security perceptions based on the information sensitivity of the mobile apps | Int. J. Inf. Manage | |
| Online traveling service | Privacy calculus theory | A: Disposition to privacy, privacy awareness, perceived privacy control, trust, privacy experience, privacy knowledge, privacy protection regulation ME: Online privacy concerns MO: | Travelers are concerned over their information privacy they are still willing to share their behavioral data. In the case of biometric information, the disclosure decision is dependent upon expected benefits rather than privacy concerns | Int. J. Inf. Manage | |
| App permission request | Antecedents–privacy concerns–outcomes ( | A: Prior privacy experience, computer anxiety, perceived control, app permission concerns ME: Privacy concerns, trust, privacy calculus MO: | Prior privacy experience, computer anxiety and perceived control have significant effects on privacy concerns. However, concerns for app permission requests have approximately twice as much predictive value than the other factors put together to explain mobile users’ overall information privacy concerns | Int. J. Inf. Manage | |
| Mobile apps | Agency theory; signaling theory | A: Informational signals, information asymmetry ME: Privacy uncertainty (collection, use, protection), seller uncertainty, product uncertainty MO: | Privacy uncertainty significantly influences users’ intention to use an app above and beyond their uncertainty about the seller and the product. It also affects the perceived risk associated with using an app and the price consumers are willing to pay | Inf. Syst. Res | |
| Online privacy policy | Motivation theory | A: Privacy assurance, personalization declaration ME: Privacy concerns MO: | Privacy assurance negatively affects customers’ purchase probability and purchase amount. Personalization declaration positively affects customers’ purchase probability and purchase amount. Privacy concerns significantly mediate above relations | J. Bus. Ethics | |
| Social role theory; theory of reasoned action | A: Social presence, privacy risk, social ties, commitment ME: Attitude towards sharing information MO: Gender O: Intention to share information | Privacy risks, social ties and commitment were more important in the formation of attitudes toward information sharing for women than men. Gender significantly moderates the relationship between people’s perceptions of information sharing and their intention to share information | Int. J. Inf. Manage | ||
| Mobile payment | Privacy calculus theory; control agency theory | A: Perceived benefits, perceived effectiveness of privacy setting, perceived effectiveness of privacy policy ME: Perceived value, psychological comfort MO: | Perceived benefits, perceived effectiveness of privacy setting, perceived effectiveness of privacy policy and perceived risks together predict perceived value and psychological comfort, which further determine consumers’ self-disclosure | Int. J. Inf. Manage | |
| Mobile security notification | A: Intrusiveness, app interface usability ME: Perceived security, irritation MO: Disruption O: Continued intention to use | Both app interface usability and the design of MSNs significantly impacted users’ perceived security, which, in turn, has a positive influence on users’ intention to continue using the app | Inf. Manage | ||
| Social media app | Privacy calculus theory | A: Privacy risk, privacy control, information sensitivity ME: Institutional privacy concerns, social privacy concerns MO: | Both institutional and social privacy concerns decrease engagement. Information sensitivity increases institutional privacy concerns. However, social privacy concerns are influenced by the perception of risk and control | Comput. Hum. Behav | |
| Health-related | A: Privacy attitudes, perception, evaluation ME: Interest in sharing MO: Medical condition, internet reliance for health O: Engagement | Privacy concern and confidence are mediated through One’s interest in sharing information with health professionals and moderated by One’s medical condition and the reliance on internet | Comput. Hum. Behav | ||
| Mobile commerce | UTAUT2 | A: Perceived privacy risk, perceived transaction risk, perceived privacy protection ME: Perceived privacy concerns, perceived value MO: Personal innovativeness O: Intention to use | Both paths (perceived privacy concerns to perceived value and performance expectancy to perceived value) were significant. Perceived value motivates customers to use m-commerce and shapes perceptions as they evaluate the trade-off they are making | Int. J. Inf. Manage | |
| Personalized ads | Rational choice theory | A: Ownership, vulnerability ME: Privacy concerns, perceived cost of non-personalization, opportunity cost MO: | Three rational choice factors from a negative-effect perspective have significant impacts on consumer reactance. Affective factors such as ownership and vulnerability are dominant determinants of these rational choice factors | Int. J. Inf. Manage | |
| Wang and Herrand (2019) | S-commerce | Privacy-trust-behavioral intention ( | A: Perceived effectiveness of privacy policy, perceived effectiveness of industry-self regulation ME: Trust MO: | Institutional privacy assurance positively influences institutional-based trust, which, in turn, affects online social interactions and consequently increases the likelihood of product purchases on s-commerce sites | Int. J. Inf. Manage |
| App privacy settings | Self-efficacy theory; information–motivation–behavioral ( | A: Privacy risk awareness, privacy knowledge, technology knowledge, sharing preference, subjective norm ME: Privacy self-efficacy, technology self-efficacy MO: Privacy knowledge O: Privacy behavior | Personal motivation is One of the strongest determinants of utilizing privacy-protective settings, and social motivation is not significant. Privacy knowledge and self-efficacy constructs determine One’s use of privacy-protective settings, but knowledge and self-efficacy about smartphone technology do not. An interaction effect exists between privacy knowledge and privacy self-efficacy on privacy behavior | Inf. Syst. Res | |
| Communication privacy management theory | A: Information privacy concerns, territory privacy concerns ME: Information trusting beliefs, information privacy risk beliefs, territory privacy risk beliefs, territory trusting beliefs MO: | Perceptions of trespassing over agreed-upon virtual boundaries within SNSs affects risk beliefs regarding information privacy and territory privacy differently. These distinct privacy risk beliefs, in turn, influence Two privacy management behaviors | J. Assoc. Inf. Syst | ||
| Mobile health | Refined SERVQUAL and SERVPERF | A: Privacy, content quality, engagement, reliability, usability ME: Satisfaction MO: | The quality dimensions (engagement followed by content quality and reliability) have the most considerable effect on continuance intention. By contrast, the effects of usability and privacy on continuance intention were insignificant | Int. J. Inf. Manage | |
| Signaling theory | A: Employer use of | A candidate who can better control his/her | Comput. Hum. Behav | ||
| In-home voice assistant | Uses and gratification theory | A: Utilitarian benefits, hedonic benefits, symbolic benefits, social presence, social attraction ME: | Individuals are motivated by the utilitarian benefits, symbolic benefits and social benefits. Hedonic benefits only motivate the use of in-home voice assistants in smaller households. The research establishes a moderating role of perceived privacy risks in dampening and negatively influencing the use of in-home voice assistants | Comput. Hum. Behav | |
| Phone-embedded tracking | Psychological ownership theory; innovation diffusion theory | A: Privacy concerns ME: Attitude MO: Position on adoption curve O: Behavior | The more privacy concerns users experience, the more negative their attitudes are towards the collection of location data and that they adjust the settings to prevent being tracked. Users’ earlier position on the adoption curve, and with that their smartphone literacy, decreases the strength of the connection between privacy concerns and attitude | Comput. Hum. Behav | |
| Commercial website | Privacy calculus theory | A: Perceived relevance ME: Perceived benefits, perceived risks, trust MO: Manipulated context O: Withholding, falsification | Trust is the most important driver of both withholding and falsification decisions. Perceived relevance influenced perceived benefits, risks and trust | Inf. Manage | |
| Social app | Person-environment fit model | A: Anonymity, flexibility, presenteeism ME: Privacy invasion, invasion of life MO: Neuroticism, extraversion O: Social media fatigue | Anonymity and presenteeism significantly influence privacy invasion and invasion of life, both of which are determinants of social media fatigue. Neuroticism strengthens the effect of social media characteristics on privacy invasion and invasion of life, while extraversion weakens these effects | Comput. Hum. Behav | |
| Social commerce | Social identity theory | A: Perceived commerce risk, perceived participation risk ME: | Perceived commerce risk reduces intentions to purchase, and that perceived participation risk curtails intentions to post comments on social commerce forums. The influence of these risk assessments is reduced when the degree of social identification with the website community increases | Inf. Manage | |
| Impression formation theory; privacy calculus theory | A: Network mutuality, profile diagnosticity ME: Privacy risks, expected social capital gains MO: Dispositional privacy concerns O: No-action, acceptance | Individuals utilize Two key types of social information: network mutuality and profile diagnosticity in evaluating privacy risks and expected social capital gains. Privacy risks and expected social capital gains powerfully predict the likelihood of no-action and the likelihood of accepting friend requests on | J. Assoc. Inf. Syst | ||
| Mobile health | Protection motivation theory; social cognitive theory | A: Ability to adopt, risk beliefs trust beliefs ME: Health information privacy concerns MO: | Health digital divide is deepening due to older adults’ perceived inability to adopt and their unwillingness to adopt stemming from mistrust, high risk perceptions and strong desire for privacy | Inf. Syst. J | |
| Privacy calculus theory | A: Information security awareness ME: Concern for information privacy, consumer alienation, privacy risk belief MO: Perceived privacy empowerment O: Lurking, self-concealment | Information security awareness significantly and positively influences concern for information privacy, consumer alienation and privacy risk belief. Concerns for information privacy and consumer alienation significantly and positively affect privacy risk belief. Privacy risk belief has a significant and positive effect on lurking and self-concealment. Perceived privacy empowerment moderates the relation between privacy risk belief and lurking as well as that between privacy risk belief and self-concealment |
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