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The transformative potential of personalization in a data-rich world

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The Transformative Potential of Personalization in a Data Rich World Although personalization has existed long before the advent of Artificial Intelligence (AI) (Koch & Benlian, 2015; Montgomery & Smith, 2009), recent advancement in AI based technologies along with unprecedented access to individual data has enabled marketers to discover insightful patterns and predict consumer behavior more accurately (Tong, Luo, & Xu, 2020). Adoption of these technologies leads to better and more enriched personalized experiences, which are likely to enhance the firm’s transactional outcomes (e.g., customer satisfaction) and the relational outcomes (e.g., customer loyalty) (Ostrom, Fotheringham, & Bitner, 2019). Despite growing attention for increasing customer well-being in service and marketing literature being (Anderson & Ostrom, 2015; Field et al., 2021; Zeithaml, Verleye, Hatak, Koller, & Zauner, 2020), personalization researchers have been relatively silent on the impact of personalization on transformative outcomes (Henkens, Verleye, & Larivière, 2020). While initial research anticipates for positive well-being implications of personalization (e.g., recognition) (e.g. Aguirre, Mahr, Grewal, de Ruyter, & Wetzels, 2015; Guo, Zhang, & Sun, 2016; Wang & Benbasat, 2016), other evidence refers to - among others - the emergence of privacy concerns with ill-being implications at the individual level (Guo et al., 2016; Yu, 2020) and surveillance as an ill-being implication at the societal level (Riegger, Klein, Merfeld, & Henkel, 2021). To better understand how personalization affects well-being/ill-being at customer and societal levels, this research opts for a discovery-oriented abductive approach. Specifically, we relied on in-depth interviews with 30 respondents using the critical incident technique and a document analysis of the ways in which personalized offerings are presented in company and third-party communication. After triangulating the insights that emerged from these analyses with academic evidence on personalization, we propose that personalization goes along with three tensions at the customer level (1: feeling recognized versus exploited, 2: reduced information overload versus loss of control and 3: conscious versus conspicuous consumption) and three tensions at the societal level (1: social inclusion versus discrimination, 2: convenience versus surveillance society, and 3: circular versus linear economy). This study responds to recent calls from transformative service researchers to investigate well-being at individual and societal level (Blocker & Barrios, 2015; Field et al., 2021) while also contributing to personalization literature by exploring its transformative potential. Along with theoretical and managerial implications, the paper concludes with a research agenda that transcends these six tensions and provides suggestions for how researchers might contribute new knowledge to this vital research area. References Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K., & Wetzels, M. (2015). Unraveling the Personalization Paradox: The Effect of Information Collection and Trust-Building Strategies on Online Advertisement Effectiveness. Journal of Retailing, 91(1), 34-49. doi:10.1016/j.jretai.2014.09.005 Anderson, L., & Ostrom, A. L. (2015). Transformative Service Research:Advancing Our Knowledge About Service and Well-Being. Journal of Service Research, 18(3), 243-249. doi:10.1177/1094670515591316 Blocker, C. P., & Barrios, A. (2015). The Transformative Value of a Service Experience. Journal of Service Research, 18(3), 265-283. doi:10.1177/1094670515583064 Field, J. M., Fotheringham, D., Subramony, M., Gustafsson, A., Ostrom, A. L., Lemon, K. N., . . . McColl-Kennedy, J. R. (2021). Service Research Priorities: Designing Sustainable Service Ecosystems. Journal of Service Research. doi:10.1177/10946705211031302 Guo, X., Zhang, X., & Sun, Y. (2016). The privacy–personalization paradox in mHealth services acceptance of different age groups. Electronic Commerce Research and Applications, 16, 55-65. doi:10.1016/j.elerap.2015.11.001 Henkens, B., Verleye, K., & Larivière, B. (2020). The Smarter, the Better?! Customer Well-Being, Engagement, and Perceptions in Smart Service Systems. International Journal of Research in Marketing. doi:https://doi.org/10.1016/j.ijresmar.2020.09.006 Koch, O. F., & Benlian, A. (2015). Promotional Tactics for Online Viral Marketing Campaigns: How Scarcity and Personalization Affect Seed Stage Referrals. Journal of Interactive Marketing, 32, 37-52. doi:10.1016/j.intmar.2015.09.005 Montgomery, A. L., & Smith, M. D. (2009). Prospects for Personalization on the Internet. Journal of Interactive Marketing, 23(2), 130-137. doi:10.1016/j.intmar.2009.02.001 Ostrom, A. L., Fotheringham, D., & Bitner, M. J. (2019). Customer Acceptance of AI in Service Encounters: Understanding Antecedents and Consequences. In Handbook of Service Science, Volume II (pp. 77-103). Riegger, A.-S., Klein, J. F., Merfeld, K., & Henkel, S. (2021). Technology-enabled personalization in retail stores: Understanding drivers and barriers. Journal of Business Research, 123, 140-155. doi:10.1016/j.jbusres.2020.09.039 Tong, S., Luo, X., & Xu, B. (2020). Personalized mobile marketing strategies. Journal of the Academy of Marketing Science, 48(1), 64-78. doi:10.1007/s11747-019-00693-3 Wang, W., & Benbasat, I. (2016). Empirical Assessment of Alternative Designs for Enhancing Different Types of Trusting Beliefs in Online Recommendation Agents. Journal of Management Information Systems, 33(3), 744-775. doi:10.1080/07421222.2016.1243949 Yu, C.-E. (2020). Humanlike robots as employees in the hotel industry: Thematic content analysis of online reviews. Journal of Hospitality Marketing & Management, 29(1), 22-38. doi:10.1080/19368623.2019.1592733 Zeithaml, V. A., Verleye, K., Hatak, I., Koller, M., & Zauner, A. (2020). Three Decades of Customer Value Research: Paradigmatic Roots and Future Research Avenues. Journal of Service Research, 0(0), 1094670520948134. doi:10.1177/1094670520948134

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Citation

Please use this url to cite or link to this publication:

MLA
Mehmood, Khalid, et al. “The Transformative Potential of Personalization in a Data-Rich World.” 12th SERVSIG Conference, Abstracts, 2022.
APA
Mehmood, K., Verleye, K., De Keyser, A., & Larivière, B. (2022). The transformative potential of personalization in a data-rich world. 12th SERVSIG Conference, Abstracts. Presented at the 12th SERVSIG 2022, Glasgow, Scotland.
Chicago author-date
Mehmood, Khalid, Katrien Verleye, Arne De Keyser, and Bart Larivière. 2022. “The Transformative Potential of Personalization in a Data-Rich World.” In 12th SERVSIG Conference, Abstracts.
Chicago author-date (all authors)
Mehmood, Khalid, Katrien Verleye, Arne De Keyser, and Bart Larivière. 2022. “The Transformative Potential of Personalization in a Data-Rich World.” In 12th SERVSIG Conference, Abstracts.
Vancouver
1.
Mehmood K, Verleye K, De Keyser A, Larivière B. The transformative potential of personalization in a data-rich world. In: 12th SERVSIG Conference, Abstracts. 2022.
IEEE
[1]
K. Mehmood, K. Verleye, A. De Keyser, and B. Larivière, “The transformative potential of personalization in a data-rich world,” in 12th SERVSIG Conference, Abstracts, Glasgow, Scotland, 2022.
@inproceedings{8765967,
  abstract     = {{The Transformative Potential of Personalization in a Data Rich World
Although personalization has existed long before the advent of Artificial Intelligence (AI) (Koch & Benlian, 2015; Montgomery & Smith, 2009), recent advancement in AI based technologies along with unprecedented access to individual data has enabled marketers to discover insightful patterns and predict consumer behavior more accurately (Tong, Luo, & Xu, 2020). Adoption of these technologies leads to better and more enriched personalized experiences, which are likely to enhance the firm’s transactional outcomes (e.g., customer satisfaction) and the relational outcomes (e.g., customer loyalty) (Ostrom, Fotheringham, & Bitner, 2019).
Despite growing attention for increasing customer well-being in service and marketing literature being (Anderson & Ostrom, 2015; Field et al., 2021; Zeithaml, Verleye, Hatak, Koller, & Zauner, 2020), personalization researchers have been relatively silent on the impact of personalization on transformative outcomes (Henkens, Verleye, & Larivière, 2020). While initial research anticipates for positive well-being implications of personalization (e.g., recognition) (e.g. Aguirre, Mahr, Grewal, de Ruyter, & Wetzels, 2015; Guo, Zhang, & Sun, 2016; Wang & Benbasat, 2016), other evidence refers to - among others - the emergence of privacy concerns with ill-being implications at the individual level (Guo et al., 2016; Yu, 2020) and surveillance as an ill-being implication at the societal level (Riegger, Klein, Merfeld, & Henkel, 2021).
To better understand how personalization affects well-being/ill-being at customer and societal levels, this research opts for a discovery-oriented abductive approach. Specifically, we relied on in-depth interviews with 30 respondents using the critical incident technique and a document analysis of the ways in which personalized offerings are presented in company and third-party communication.
After triangulating the insights that emerged from these analyses with academic evidence on personalization, we propose that personalization goes along with three tensions at the customer level (1: feeling recognized versus exploited, 2: reduced information overload versus loss of control and 3: conscious versus conspicuous consumption) and three tensions at the societal level (1: social inclusion versus discrimination, 2: convenience versus surveillance society, and 3: circular versus linear economy).
This study responds to recent calls from transformative service researchers to investigate well-being at individual and societal level (Blocker & Barrios, 2015; Field et al., 2021) while also contributing to personalization literature by exploring its transformative potential. Along with theoretical and managerial implications, the paper concludes with a research agenda that transcends these six tensions and provides suggestions for how researchers might contribute new knowledge to this vital research area.

References
Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K., & Wetzels, M. (2015). Unraveling the Personalization Paradox: The Effect of Information Collection and Trust-Building Strategies on Online Advertisement Effectiveness. Journal of Retailing, 91(1), 34-49. doi:10.1016/j.jretai.2014.09.005
Anderson, L., & Ostrom, A. L. (2015). Transformative Service Research:Advancing Our Knowledge About Service and Well-Being. Journal of Service Research, 18(3), 243-249. doi:10.1177/1094670515591316
Blocker, C. P., & Barrios, A. (2015). The Transformative Value of a Service Experience. Journal of Service Research, 18(3), 265-283. doi:10.1177/1094670515583064
Field, J. M., Fotheringham, D., Subramony, M., Gustafsson, A., Ostrom, A. L., Lemon, K. N., . . . McColl-Kennedy, J. R. (2021). Service Research Priorities: Designing Sustainable Service Ecosystems. Journal of Service Research. doi:10.1177/10946705211031302
Guo, X., Zhang, X., & Sun, Y. (2016). The privacy–personalization paradox in mHealth services acceptance of different age groups. Electronic Commerce Research and Applications, 16, 55-65. doi:10.1016/j.elerap.2015.11.001
Henkens, B., Verleye, K., & Larivière, B. (2020). The Smarter, the Better?! Customer Well-Being, Engagement, and Perceptions in Smart Service Systems. International Journal of Research in Marketing. doi:https://doi.org/10.1016/j.ijresmar.2020.09.006
Koch, O. F., & Benlian, A. (2015). Promotional Tactics for Online Viral Marketing Campaigns: How Scarcity and Personalization Affect Seed Stage Referrals. Journal of Interactive Marketing, 32, 37-52. doi:10.1016/j.intmar.2015.09.005
Montgomery, A. L., & Smith, M. D. (2009). Prospects for Personalization on the Internet. Journal of Interactive Marketing, 23(2), 130-137. doi:10.1016/j.intmar.2009.02.001
Ostrom, A. L., Fotheringham, D., & Bitner, M. J. (2019). Customer Acceptance of AI in Service Encounters: Understanding Antecedents and Consequences. In Handbook of Service Science, Volume II (pp. 77-103).
Riegger, A.-S., Klein, J. F., Merfeld, K., & Henkel, S. (2021). Technology-enabled personalization in retail stores: Understanding drivers and barriers. Journal of Business Research, 123, 140-155. doi:10.1016/j.jbusres.2020.09.039
Tong, S., Luo, X., & Xu, B. (2020). Personalized mobile marketing strategies. Journal of the Academy of Marketing Science, 48(1), 64-78. doi:10.1007/s11747-019-00693-3
Wang, W., & Benbasat, I. (2016). Empirical Assessment of Alternative Designs for Enhancing Different Types of Trusting Beliefs in Online Recommendation Agents. Journal of Management Information Systems, 33(3), 744-775. doi:10.1080/07421222.2016.1243949
Yu, C.-E. (2020). Humanlike robots as employees in the hotel industry: Thematic content analysis of online reviews. Journal of Hospitality Marketing & Management, 29(1), 22-38. doi:10.1080/19368623.2019.1592733
Zeithaml, V. A., Verleye, K., Hatak, I., Koller, M., & Zauner, A. (2020). Three Decades of Customer Value Research: Paradigmatic Roots and Future Research Avenues. Journal of Service Research, 0(0), 1094670520948134. doi:10.1177/1094670520948134}},
  author       = {{Mehmood, Khalid and Verleye, Katrien and De Keyser, Arne and Larivière, Bart}},
  booktitle    = {{12th SERVSIG Conference, Abstracts}},
  language     = {{eng}},
  location     = {{Glasgow, Scotland}},
  pages        = {{2}},
  title        = {{The transformative potential of personalization in a data-rich world}},
  year         = {{2022}},
}