Cited by 786Privacy, Security, and Data ProtectionCustomer Service Quality and LoyaltyInformation and Cyber Security

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Data Privacy: Effects on Customer and Firm Performance

Martin, Kelly D., Borah, Abhishek, Palmatier, Robert W.·2017·Journal of Marketing

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Abstract

Although marketers increasingly rely on customer data, firms have little insight into the ramifications of such data use and do not know how to prevent negative effects. Data management efforts may heighten customers’ vulnerability worries or create real vulnerability. Using a conceptual framework grounded in gossip theory, the authors link customer vulnerability to negative performance effects. Three studies show that transparency and control in firms’ data management practices can suppress the negative effects of customer data vulnerability. Experimental manipulations reveal that mere access to personal data inflates feelings of violation and reduces trust. An event study of data security breaches affecting 414 public companies also confirms negative effects, as well as spillover vulnerabilities from rival firms’ breaches, on firm performance. Severity of the breach hurts the focal firm but helps the rival firm, which provides some insight into mixed findings in prior research. Finally, a field study with actual customers of 15 companies across three industries demonstrates consistent effects across four types of customer data vulnerability and confirms that violation and trust mediate the effects of data vulnerabilities on outcomes.

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Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models
Preacher, K. J., & Hayes, A. F. (2008) · Behavior Research Methods · doi:10.3758/brm.40.3.879 · Cited by 34,117
The need to belong: Desire for interpersonal attachments as a fundamental human motivation.
Baumeister, R. F., & Leary, M. R. (1995) · Psychological Bulletin · doi:10.1037//0033-2909.117.3.497 · Cited by 23,245
CAPITAL ASSET PRICES: A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK*
Sharpe, W. F. (1964) · The Journal of Finance · doi:10.1111/j.1540-6261.1964.tb02865.x · Cited by 19,441
Specification Tests in Econometrics
Hausman, J. A. (1978) · Econometrica · doi:10.2307/1913827 · Cited by 18,820
Ostracism
Williams, K. D. (2007) · Annual Review of Psychology · doi:10.1146/annurev.psych.58.110405.085641 · Cited by 1,828
A Taxonomy of Privacy
Solove, D. J. (2006) · University of Pennsylvania Law Review · doi:10.2307/40041279 · Cited by 1,388
Reactions to discrimination, stigmatization, ostracism, and other forms of interpersonal rejection: A multimotive model
Smart Richman, L., & Leary, M. R. (2009) · Psychological Review · doi:10.1037/a0015250 · Cited by 942
The role of data privacy in marketing
Martin, K. D., & Murphy, P. E. (2017) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-016-0495-4 · Cited by 902 · cited 1×
“Nobody can articulate what it means ” (Solove 2006, p. 476; for a comprehensive review of privacy literature in marketing, see also Martin and Murphy [2016]).”
Self-citation
Building Understanding of the Domain of Consumer Vulnerability
Baker, S. M., Gentry, J. W., & Rittenburg, T. L. (2005) · Journal of Macromarketing · doi:10.1177/0276146705280622 · Cited by 895
Intimate Exchanges: Using Computers to Elicit Self-Disclosure from Consumers
Moon, Y. (2000) · Journal of Consumer Research · doi:10.1086/209566 · Cited by 842

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Research works printed but not linked: 59

Acquisti Alessandro, 2006, Proceedings of the 27th International Conference on Information Systems, 94
10.1509/jmr.09.0215 · DOI · Paper page
10.2307/25148715 · DOI · Paper page
10.1509/jmkg.2005.69.4.133 · DOI · Paper page
10.1037/0033-2909.117.3.497 · DOI · Paper page
10.1037/1089-2680.8.2.111 · DOI · Paper page
10.1111/j.1559-1816.2012.00956.x · DOI
BiesdorfStefan, CourtDavid, and WillmottPaul (2013), “Big Data: What's Your Plan?” McKinsey Quarterly (March), http://www.mckinsey.com/business-functions/business-technology/our-insights/big-data-whats-your-plan.
10.1287/mksc.2013.0818 · DOI
10.1509/jmr.13.0009 · DOI
10.1016/j.jbankfin.2010.07.016 · DOI
10.1007/s10799-005-5879-y · DOI
10.1509/jm.10.0414 · DOI
10.1177/1096348004265027 · DOI
10.1016/j.clsr.2013.07.007 · DOI
10.1509/jmr.11.0136 · DOI
10.1037/1089-2680.8.2.100 · DOI
10.2307/2096270 · DOI
Emler Nicholas, 1994, Good Gossip, 119
10.1016/S0304-405X(98)00026-9 · DOI
10.1037/a0026650 · DOI
Fisher John A., 2013, William & Mary Business Law Review, 215, 217
10.1007/s11002-006-3796-4 · DOI
10.1007/s11747-007-0054-0 · DOI
10.1509/jm.12.0179 · DOI
10.1109/THS.2015.7225301 · DOI
10.1002/(SICI)1097-0266(199902)20:2<195::AID-SMJ13>3.0.CO;2-7 · DOI · Paper page
Iyengar Sheena, 2010, The Art of Choosing
10.1086/656423 · DOI · Paper page
Ko Myung, 2006, Journal of Information Technology Management, 17, 13
10.1509/jmr.13.0169 · DOI
10.1017/CBO9780511770548.003 · DOI
10.1007/s11747-006-0003-3 · DOI
10.1177/1094670510383409 · DOI
10.1287/isre.1040.0032 · DOI · Paper page
McAfee Andrew, 2012, Harvard Business Review, 3
10.2307/257056 · DOI
Mills Colleen, 2010, Group & Organization Management, 35, 371, 10.1177/1059601109360389 · DOI
10.1111/j.1745-6606.2004.tb00865.x · DOI
10.1016/j.jom.2011.01.002 · DOI
10.1177/1094670511424924 · DOI · Paper page
10.1509/jmkg.72.4.076 · DOI
10.1509/jmkg.70.4.136 · DOI · Paper page
Ponemon Institute, 2015, 2015 Cost of Data Breach Study: Global Analysis
10.1509/jmr.09.0317 · DOI
10.1002/pam.20567 · DOI
Scharf S., 2007, Internal Auditor, 64, 23
10.1108/ICS-03-2014-0020 · DOI
10.1509/jmkg.70.2.133 · DOI · Paper page
10.1509/jm.11.0316 · DOI · Paper page
10.1080/07421222.2015.1063315 · DOI
Sharpe William F., 1964, Journal of Finance, 19, 425
10.1177/1088868314530515 · DOI
10.1177/002224299706100301 · DOI
Solove Daniel J., 2003, Hastings Law Journal, 54, 1
10.1509/jmkg.75.3.49 · DOI
10.1509/jmkr.46.3.293 · DOI
10.1080/0363775032000133782 · DOI
10.1207/s15327663jcp1401&2_6 · DOI · Paper page

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“Third, we advance the limited knowl - edge of ‘ how CMOs matter ’ by identifying customer orien - tation as a significant yet partial mechanism driving CMOs’ impact on breach prevention and firm performance (Martin et al., 2017; Whitler et al., 2021 ).”
Personalisation‐Privacy Paradox: Systematic Review and Survey Evidence on Personal Data Stores
Hsu, M.-W., Parry, G., & Ng, I. C. L. (2026) · Expert Systems · doi:10.1111/exsy.70314 · Cited by 1
“Transparency and fairness in data handling also affect emotional responses; Martin et al. (2017) found that companies with transparent practices experience less stock volatility post-data breaches.”
Drivers of augmented reality acceptance in young consumers' online shopping: a cross-cultural analysis
Bartosik-Purgat, M., Rakowska, W., Grzegorczyk, T., Guzek, M., Johnson, R., & Balboa, P. R. (2026) · Central European Management Journal · doi:10.1108/cemj-07-2025-0181
How privacy communication formats shape sensitive data disclosure in AI applications
Gistri, G., Scarpi, D., & Testi, N. (2026) · Management Decision · doi:10.1108/md-10-2025-2959
Personalization vs Privacy: Examining the Paradox in AI-Driven Marketing and Its Impact on Consumer Trust
Duggal, M. (2026) · Minnesota Journal of Business Law and Entrepreneurship · doi:10.52783/mjble.342
“Martin et al. (2017) represent a significant advance in specifying the conditions under which the paradox manifests most strongly.”
Influential
Resonant Risk: A Sociotechnical Model for Understanding Cyber Threat Perception and Amplification in the Age of Artificial Intelligence (AI)
Khan, M. S. (2026) · ICST Transactions on Security and Safety · doi:10.4108/eetss.9915
“The incident illustrates how personalization of risk (“your identity is exposed”) significantly amplifies perception, resulting in long-term reputational damage and erosion of trust in financial institutions [22].”
Le privacy compliance paradox : mieux comprendre les infractions des entreprises au Règlement Général sur la Protection des Données (RGPD)
Roques, P., Vidal, D., & Cases, A. (2026) · Décisions Marketing · doi:10.3917/dm.122.0031
The Impact of E-CRM Dimensions on Repurchase Intention and E-WOM: The Mediating Role of Relationship Quality in Online Shopping Platforms
Du, L. (2026) · Journal of New Media and Economics · doi:10.62517/jnme.202610206
The Strategic Integration of Artificial Intelligence in Digital Marketing: Global Case Studies and Implications for the Albanian Emerging Market
Rista, L., & Biti, T. T. (2026) · Interdisciplinary journal of research and development · doi:10.56345/ijrdv13n120
“The European Union’s GDPR and California’s CCPA are direct legislative responses to the aggressive data - harvesting practices required for AI training (Martin et al., 2017).”
The catalytic power of compliance: evaluating the impact of China’s Personal Information Protection Law on corporate value chain upgrading
Liang, L., Bo, L., & Yan, A. (2026) · Applied Economics · doi:10.1080/00036846.2026.2644560
Research on the Practice Path of Digital Marketing from the Perspective of Consumer Behavior Changes
Li, X. (2026) · Frontiers in Business, Economics and Management · doi:10.54097/sxnwtz40
“…behavior can help brands increase traffic acquisition efficiency by 35%–45%, reduce conversion costs by more than 50%, and the effect improvement of non-standard categories such as clothing and beauty is higher than that of standardized categories, with extensive industry promotion value [9].”
Impact of data breaches on user intentions toward GenAI bots
Rao, A.-N.-C.-V. (2026) · Journal of Computational Information Systems · doi:10.1080/08874417.2026.2638467
Above and beyond compliance: Investigating how non-market privacy activities impact consumer trust, privacy concerns and behavior intentions
Lyons, V., Fox, G., van der Werff, L., & Lynn, T. (2025) · Information and Organization · doi:10.1016/j.infoandorg.2025.100601 · Cited by 3
Consumer adoption of community-focused smart products: the moderating role of political ideology
Du, S.-L., Zhao, M., & Sen, S. (2026) · Journal of business research · doi:10.1016/j.jbusres.2025.115951
Privacy trade-offs in international markets
Chisam, N., Moffett, J. W., Germann, F., & Palmatier, R. W. (2026) · Journal of International Business Studies · doi:10.1057/s41267-025-00837-4 · Cited by 1
“At the industry level, we follow prior research (Lilien, 2016; Martin et al., 2017) and classify firms as operating in either a B2C industry (firm data dependence = 1), for which data have operational roles, or a B2B industry (firm data dependence = 0), in which data typically are less integral.”
Self-citation
The path to social license for data practices in tourism and hospitality: a sufficiency-necessity analysis of key antecedents
Yallop, A. C., Moisescu, O., & Gica, O. (2026) · Journal of Hospitality Marketing & Management · doi:10.1080/19368623.2026.2623416
“Similarly, Yallop et al. (2023) emphasize the value of fairness and equity as critical ethical aspects required from THOs, while Martin et al. (2017) argue that fairness is a key driver of consumer trust in firms.”
Influential
Tourist coping strategies and vulnerability to digital deception along the customer journey: how tourists react
Presti, L. L., Capone, V., & Maggiore, G. (2026) · Current Issues in Tourism · doi:10.1080/13683500.2026.2623536
Cross-border data risk and firm performance: The moderating role of digital resilience and agility
Xu, W., & Zuo, X. (2026) · Journal of International Management · doi:10.1016/j.intman.2026.101350 · Cited by 3
Alert eyes, resistant minds: how privacy sensitivity drives avoidance of persuasive narratives in in-feed advertising
Zheng, Y.-Q., & Li, Y.-H. (2026) · Internet Research · doi:10.1108/intr-10-2024-1580
Digital Boundary Translation Theory: How Cultural Hotwords Navigate Cross-Linguistic Privacy Boundaries in Digital Consumer Behavior
Chan, Y.-F. (2026) · Journal of International Consumer Marketing · doi:10.1080/08961530.2025.2609081
Do data breach notification laws affect accounting conservatism?
Zhu, Y., Zhou, Y., & Gao, X. (2026) · Journal of Applied Accounting Research · doi:10.1108/jaar-02-2025-0093
Artificial Intelligence in Personalisation and Its Impact on Consumer Trust
Arun, K. D., & Varsha, V. (2026) · African Journal of Commercial Studies · doi:10.59413/ajocs/v7.i1.4
Personalization in Customer Relationship Management : Balancing Data Privacy and Customer Experience
Zahran, M., Mikhail, M. H., & Lubis, M. (2025) · Acceleration, Quantum, Information Technology and Algorithm Journal · doi:10.62123/aqila.v2i2.73
“This study investigates how companies can optimize customer experience through personalization while maintaining customer trust through ethical and transparent data privacy practices [5].”
Influential
Exploring Intelligent Connected Vehicles Users’ Privacy Perception and Communicative Resistance Behavior in Cabin
Yu, Z., & Wang, X. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2594150 · Cited by 1
“For example, corporate data vulnerabilities will reduce user trust, which in turn affects the company’s economic benefits (Martin et al., 2017).”
AI-Driven Personalization in Price Comparison Platforms: Balancing Efficiency and Privacy
Sunmola, P. T. (2025) · International journal of business management · doi:10.5539/ijbm.v21n1p15
“Furthermore, data brokers and third-party trackers integrated within PCPs may exploit personal information for targeted advertising beyond the original transactional context (Martin, Borah, & Palmatier, 2017).”
Disruptive Innovation and the Future of Digital Agriculture: A Systematic Review of New Technologies in Agricultural Marketing Channels
Usmiati, Said, L., Rifani, A., Firdaus, M., & Fitriadiansyah, R. (2025) · Archives of Business Research · doi:10.14738/abr.1312.19046
“Data privacy and cybersecurity concerns remain paramount, particularly as agribusinesses collect and manage extensive consumer data to enable personalised marketing strategies (Martin, 2017).”
Online Shopping Experience in Minimarket and The Impact on Sales Figure
Budiman, C. W., & Yohannis, A. (2025) · TEPIAN · doi:10.51967/tepian.v6i4.3445
Towards Responsible Predictive Loyalty: A Bibliometric and Conceptual Mapping Study on AI and Ethics in Marketing
Guermai, R. E., Reda, K., & Jamal, A. (2025) · 2025 IEEE International Conference on Advanced Technologies in Supply Chain Management (ATSCM) · doi:10.1109/atscm67505.2025.11473590
“…machine learning, big data • Marketing objectives: customer loyalty, churn, CRM • Ethical issues: ethics, privacy, trust The triadic convergence matches current demands for ethical personalization because companies need to optimize retention while following ethical guidelines [ 19 ; 20 ].”
Towards Understanding Phygital Experience in Logistic-Retail Networks: A Netnographic Exploration of Connected Commerce
Boudri, R., Bentalha, B., & Tariq, B. (2025) · 2025 IEEE International Conference on Advanced Technologies in Supply Chain Management (ATSCM) · doi:10.1109/atscm67505.2025.11473673
“"While providing customers with control and visibility into their data is a good thing, that goodness is undermined if it's not clear what the data is being attributed to within the system" [13].”
The Impact of Data Breach on Consumer Self-Disclosure, Message Valence and Protection Behavior on Social Media: A Cross-Cultural Comparison Between the USA and Taiwan
Ho, F. N., Huang, S., Ho-Dac, N. N., Chang, C.-C., & Lee, S. (2025) · Journal of Internet Commerce · doi:10.1080/15332861.2025.2588977
“Research shows that consumers have a strong feeling of vulnerability in relation to marketers’ collection of their personal data (Martin, Borah, and Palmatier 2017).”
What Can I Do for You? Amplification and Suppression: The Double‐Edged Sword of Virtual Avatars
Wei, S., Hu, J., & Meng, N. (2025) · Journal of Consumer Behaviour · doi:10.1002/cb.70071
Artificial Intelligence in Online Retail A Review of Consumer Perception and Adoption
Khandelwal, A., & Singh, P. (2025) · International Journal of Academic Excellence and Research · doi:10.62823/ijaer/2025/01/04.111
“Studies by Martin et al. (2017) and Bleier et al. (2019) highlight that perceived privacy risks significantly reduce engagement, especially when consumers are unsure how their data is collected and utilized.”
Consumer perceptions and satisfaction levels towards personalised advertisements
Narcı, M. (2025) · Business & Management Studies: An International Journal · doi:10.15295/bmij.v13i3.2595
“It also supports the study of Martin, Borah and Palmatier (2017), who argue that participants' expectations from marketers, transparent data practices, consent mechanisms, and ethical standards are not only theoretical preferences but also active demands that shape user behaviour.”
Factors Affecting the Adoption of ChatGPT Among Tax Professionals
Patricia, F., Venecia, & Prabowo, I. C. (2025) · IEEE International Symposium on Future Telecommunication Technologies (SOFTT) · doi:10.1109/softt67007.2025.11213066
“When ITU is reinforced by a positive user experience and organizational support, the likelihood of actual adoption increases [34], [35], [36].”
Dijital Pazarlama Sistemlerinde Siber Güvenliğin Rolü ve Önemi
Gündüzyeli, B. (2025) · Girişimcilik İnovasyon ve Pazarlama Araştırmaları Dergisi · doi:10.31006/gipad.1604902

Authors: Martin, K. D., Borah, A., & Palmatier, R. W.
Affiliations: Colorado State University (US)
University of Washington (US)
ORCID: Martin, Kelly D.
Borah, Abhishek
Palmatier, Robert W.
Venue: Journal of Marketing
Publisher: SAGE Publications
Published: 2017-01
Volume: 81
Issue: 1
Pages: 36-58
ISSN: 0022-2429 (ISSN-L)
1547-7185
DOI: 10.1509/jm.15.0497
OpenAlex: W2495772237
Cited by: 786
References: 0
Citations by year: 2026: 123 · 2025: 132 · 2024: 92 · 2023: 99 · 2022: 97 · 2021: 67 · 2020: 82 · 2019: 54 · 2018: 26 · 2017: 13
FWCI: 89.60
Type: journal article
Language: en
Open access: closed
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: Journal of Marketing (publishedVersion)
Griffith Research Online (submittedVersion)
Funders: Colorado State University
Marketing Science Institute
Topics: Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
Social Sciences › Business, Management and Accounting › Marketing › Customer Service Quality and Loyalty
Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
Keywords: data privacy, data management practices, consumer trust, data breaches, firm performance, customer performance, transparency, data control, spillover effects, rival firms

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Data Privacy: Effects on Customer and Firm Performance

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