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Research Article

The role of marketing analytics in the ethical consumption of online consumers

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  • Agarwal, R., Dugas, M., Gao, G. G., & Kannan, P. K. (2020). Emerging technologies and analytics for a new era of value-centered marketing in healthcare. Journal of the Academy of Marketing Science, 48(1), 9–23. https://doi.org/10.1007/s11747-019-00692-4
  • Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Harvard Business Press.
  • Ajayi, S., Loureiro, S. M. C., & Langaro, D. (2022). Internet of things and consumer engagement on retail: State-of-the-art and future directions. EuroMed Journal of Business, ahead-of-print(ahead-of-print), https://doi.org/10.1108/EMJB-10-2021-0164
  • Aljumah, A. I., Nuseir, M. T., & Alam, M. M. (2021). Traditional marketing analytics, big data analytics and big data system quality and the success of new product development. Business Process Management Journal, 27(4), 1108–1125. https://doi.org/10.1108/BPMJ-11-2020-0527
  • Appel, G., Grewal, L., Hadi, R., & Stephen, A. T. (2020). The future of social media in marketing. Journal of the Academy of Marketing Science, 48(1), 79–95. https://doi.org/10.1007/s11747-019-00695-1
  • Bornschein, R., Schmidt, L., & Maier, E. (2020). The effect of consumers’ perceived power and risk in digital information privacy: The example of cookie notices. Journal of Public Policy & Marketing, 39(2), 135–154. https://doi.org/10.1177/0743915620902143
  • Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1(3), 185–216. https://doi.org/10.1177/135910457000100301
  • Byun, K. A. K., Ma, M., Kim, K., & Kang, T. (2021). Buying a new product with inconsistent product reviews from multiple sources: The role of information diagnosticity and advertising. Journal of Interactive Marketing, 55, 81–103. https://doi.org/10.1016/j.intmar.2021.01.003
  • Chae, I., Bruno, H. A., & Feinberg, F. M. (2019). Wearout or weariness? Measuring potential negative consequences of online ad volume and placement on website visits. Journal of Marketing Research, 56(1), 57–75. https://doi.org/10.1177/0022243718820587
  • Chen, R., & He, F. (2003). Examination of brand knowledge, perceived risk and consumers’ intention to adopt an online retailer. Total Quality Management & Business Excellence, 14(6), 677–693. https://doi.org/10.1080/1478336032000053825
  • Chin, W., Cheah, J. H., Liu, Y., Ting, H., Lim, X. J., & Cham, T. H. (2020). Demystifying the role of causal-predictive modeling using partial least squares structural equation modeling in information systems research. Industrial Management & Data Systems, 120(12), 2161–2209. https://doi.org/10.1108/IMDS-10-2019-0529
  • Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155. https://doi.org/10.1037/0033-2909.112.1.155
  • Cowley, S., Humphrey Jr, W., & Muñoz, C. (2021). Industry certifications in digital marketing and media education: An examination of perceptions and use among educators. Journal of Marketing Education, 43(2), 189–203. https://doi.org/10.1177/0273475320948570
  • Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0
  • Davis, B., Grewal, D., & Hamilton, S. (2021). The future of marketing analytics and public policy. Journal of Public Policy & Marketing, 40(4), 447–452. https://doi.org/10.1177/07439156211042372
  • Dzyabura, D., & Hauser, J. R. (2019). Recommending products when consumers learn their preference weights. Marketing Science, 38(3), 417–441. https://doi.org/10.1287/mksc.2018.1144
  • Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897–904. https://doi.org/10.1016/j.jbusres.2015.07.001
  • Ganguli, D. (2021, June 14). Top 10 Trends in Marketing Analytics to Look out for in 2021. Analytics Insight. https://www.analyticsinsight.net/top-10-trends-in-marketing-analytics-to-look-out-for-in-2021
  • Goldstein, D. G., Suri, S., McAfee, R. P., Ekstrand-Abueg, M., & Diaz, F. (2014). The economic and cognitive costs of annoying display advertisements. Journal of Marketing Research, 51(6), 742–752. https://doi.org/10.1509/jmr.13.0439
  • Grewal, D., Hulland, J., Kopalle, P. K., & Karahanna, E. (2020). The future of technology and marketing: A multidisciplinary perspective. Journal of the Academy of Marketing Science, 48(1), 1–8. https://doi.org/10.1007/s11747-019-00711-4
  • Grewal, L., & Stephen, A. T. (2019). In mobile we trust: The effects of mobile versus nonmobile reviews on consumer purchase intentions. Journal of Marketing Research, 56(5), 791–808. https://doi.org/10.1177/0022243719834514
  • Hayes, A. F. (2015). An index and test of linear moderated mediation. Multivariate Behavioral Research, 50(1), 1–22. https://doi.org/10.1080/00273171.2014.962683
  • Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442–458. https://doi.org/10.1108/IMDS-04-2016-0130
  • Hair Jr, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069
  • Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019a). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
  • Hair, J. F., Sarstedt, M., & Ringle, C. M. (2019b). Rethinking some of the rethinking of partial least squares. European Journal of Marketing, 53(4), 566–584. https://doi.org/10.1108/EJM-10-2018-0665
  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
  • Herhausen, D., Ludwig, S., Grewal, D., Wulf, J., & Schoegel, M. (2019). Detecting, preventing, and mitigating online firestorms in brand communities. Journal of Marketing, 83(3), 1–21. https://doi.org/10.1177/0022242918822300
  • Hildebrand, C., & Schlager, T. (2019). Focusing on others before you shop: exposure to Facebook promotes conventional product configurations. Journal of the Academy of Marketing Science, 47(2), 291–307. https://doi.org/10.1007/s11747-018-0599-0
  • Hoffman, D. L., & Novak, T. P. (2018). Consumer and object experience in the internet of things: An assemblage theory approach. Journal of Consumer Research, 44(6), 1178–1204. https://doi.org/10.1093/jcr/ucx105
  • Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
  • Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9
  • Iacobucci, D., Petrescu, M., Krishen, A., & Bendixen, M. (2019). The state of marketing analytics in research and practice. Journal of Marketing Analytics, 7(3), 152–181. https://doi.org/10.1057/s41270-019-00059-2
  • Kejriwal, M., Wang, Q., Li, H., & Wang, L. (2021). An empirical study of emoji usage on Twitter in linguistic and national contexts. Online Social Networks and Media, 24, 100149. https://doi.org/10.1016/j.osnem.2021.100149
  • Kock, N. (2015). Common method bias in PLS-SEM. International Journal of e-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
  • Kopalle, P. K., & Lehmann, D. R. (2021). Big data, marketing analytics, and public policy: Implications for health care. Journal of Public Policy & Marketing, 40(4), 453–456. https://doi.org/10.1177/0743915621999031
  • Kurtzke, S., & Setkute, J. (2021). Analytics capability in marketing education: A practice-informed model. Journal of Marketing Education, 43(3), 298–316. https://doi.org/10.1177/02734753211042404
  • Langan, R., Cowley, S., & Nguyen, C. (2019). The state of digital marketing in academia: An examination of marketing curriculum’s response to digital disruption. Journal of Marketing Education, 41(1), 32–46. https://doi.org/10.1177/0273475318823849
  • Li, Y., Liu, H., Lee, M., & Huang, Q. (2019). Information privacy concern and deception in online retailing. Internet Research, 30(2), 511–537. https://doi.org/10.1108/INTR-02-2018-0066
  • Liu, X., & Burns, A. C. (2018). Designing a marketing analytics course for the digital age. Marketing Education Review, 28(1), 28–40. https://doi.org/10.1080/10528008.2017.1421049
  • Loureiro, S. M. C. (2022). Using immersive and artificial intelligence technologies to promote different industries. Journal of Promotion Management, 28(2), 111–112. https://doi.org/10.1080/10496491.2021.1987942
  • Loureiro, S. M. C., Guerreiro, J., & Tussyadiah, I. (2021a). Artificial intelligence in business: State of the art and future research agenda. Journal of Business Research, 129, 911–926. https://doi.org/10.1016/j.jbusres.2020.11.001
  • Loureiro, S. M. C., Japutra, A., Molinillo, S., & Bilro, R. G. (2021b). Stand by me: Analyzing the tourist–intelligent voice assistant relationship quality. International Journal of Contemporary Hospitality Management, 33(11), 3840–3859. https://doi.org/10.1108/IJCHM-09-2020-1032
  • Lourenco, C. J., Dellaert, B. G., & Donkers, B. (2020). Whose algorithm says so: The relationships between type of firm, perceptions of trust and expertise, and the acceptance of financial robo-advice. Journal of Interactive Marketing, 49(1), 107–124. https://doi.org/10.1016/j.intmar.2019.10.003
  • MacKenzie, S. B., & Podsakoff, P. M. (2012). Common method bias in marketing: Causes, mechanisms, and procedural remedies. Journal of Retailing, 88(4), 542–555. https://doi.org/10.1016/j.jretai.2012.08.001
  • Mende, M., Scott, M. L., van Doorn, J., Grewal, D., & Shanks, I. (2019). Service robots rising: How humanoid robots influence service experiences and elicit compensatory consumer responses. Journal of Marketing Research, 56(4), 535–556. https://doi.org/10.1177/0022243718822827
  • Mintu-Wimsatt, A., & Lozada, H. R. (2018). Business analytics in the marketing curriculum: A call for integration. Marketing Education Review, 28(1), 1–5. https://doi.org/10.1080/10528008.2018.1436974
  • Mondal, T., Jayadeva, S. M., Pani, R., Subramanian, M., & Sumana, B. (2022). E marketing strategy in health care using IoT and Machine Learning. Materials today: Proceedings 1.
  • Nayyar, V. (2022). Reviewing the impact of digital migration on the consumer buying journey with robust measurement of PLS-SEM and R Studio. Systems Research and Behavioral Science, 39(3), 542–556. https://doi.org/10.1002/sres.2857
  • Nayyar, V., & Batra, R. (2020). Does online media self-regulate consumption behavior of Indian youth? International Review on Public and Nonprofit Marketing, 17(3), 277–288. https://doi.org/10.1007/s12208-020-00248-1
  • Okazaki, S., Eisend, M., Plangger, K., de Ruyter, K., & Grewal, D. (2020). Understanding the strategic consequences of customer privacy concerns: A meta-analytic review. Journal of Retailing, 96(4), 458–473. https://doi.org/10.1016/j.jretai.2020.05.007
  • Petrescu, M., Krishen, A., & Bui, M. (2020). The internet of everything: Implications of marketing analytics from a consumer policy perspective. Journal of Consumer Marketing, 37(6), 675–686. https://doi.org/10.1108/JCM-02-2019-3080
  • Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
  • Presthus, W., & Sørum, H. (2018). Are consumers concerned about privacy? An online survey emphasizing the general data protection regulation. Procedia Computer Science, 138, 603–611. https://doi.org/10.1016/j.procs.2018.10.081
  • Purcarea, V. L., Gheorghe, I. R., & Gheorghe, C. M. (2015). Uncovering the online marketing mix communication for health care services. Procedia Economics and Finance, 26, 1020–1025. https://doi.org/10.1016/S2212-5671(15)00925-9
  • Ramsey, J. B. (1969). Tests for specification errors in classical linear least-squares regression analysis. Journal of the Royal Statistical Society: Series B (Methodological), 31(2), 350–371. https://doi.org/10.1111/j.2517-6161.1969.tb00796.x
  • Rigdon, E. E., Sarstedt, M., & Ringle, C. M. (2017). On comparing results from CB-SEM and PLS-SEM: Five perspectives and five recommendations. Marketing: Zfp–Journal of Research and Management, 39(3), 4–16. https://www.jstor.org/stable/26426850
  • Ringle, C. M., Wende, S., & Becker, J. M. (2015). SmartPLS 3. SmartPLS GmbH.
  • Schlee, R. P., & Karns, G. L. (2017). Job requirements for marketing graduates: Are there differences in the knowledge, skills, and personal attributes needed for different salary levels? Journal of Marketing Education, 39(2), 69–81. https://doi.org/10.1177/0273475317712765
  • Shiu, J. Y. (2017). Investigating consumer confusion in the retailing context: The causes and outcomes. Total Quality Management & Business Excellence, 28(7-8), pp.746-764.https://doi.org/10.1080/14783363.2015.1121094
  • Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of big data challenges and analytical methods. Journal of Business Research, 70, 263–286. https://doi.org/10.1016/j.jbusres.2016.08.001
  • Stocchi, L., Pourazad, N., Michaelidou, N., Tanusondjaja, A., & Harrigan, P. (2022). Marketing research on mobile apps: Past, present and future. Journal of the Academy of Marketing Science, 50(2), 195–225. https://doi.org/10.1007/s11747-021-00815-w
  • Tapscott, D. (1995). Digital economy. Promise and peril in the age of networked intelligence. McGraw-Hill.
  • Thomaz, F., Salge, C., Karahanna, E., & Hulland, J. (2020). Learning from the dark web: Leveraging conversational agents in the era of hyper-privacy to enhance marketing. Journal of the Academy of Marketing Science, 48(1), 43–63. https://doi.org/10.1007/s11747-019-00704-3
  • Tong, S., Luo, X., & Xu, B. (2020). Personalized mobile marketing strategies. Journal of the Academy of Marketing Science, 48(1), 64–78. https://doi.org/10.1007/s11747-019-00693-3
  • Wang, X. S., Ryoo, J. H. J., Bendle, N., & Kopalle, P. K. (2021). The role of machine learning analytics and metrics in retailing research. Journal of Retailing, 97(4), 658–675. https://doi.org/10.1016/j.jretai.2020.12.001
  • Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97–121. https://doi.org/10.1509/jm.15.0413
  • Wong, K. K. K. (2016). Mediation analysis, categorical moderation analysis, and higher-order constructs modeling in partial least squares structural equation modeling (PLS-SEM): A B2B example using SmartPLS. Marketing Bulletin, 26(1), 1–22.
  • Wu, K. W., Huang, S. Y., Yen, D. C., & Popova, I. (2012). The effect of online privacy policy on consumer privacy concern and trust. Computers in Human Behavior, 28(3), 889–897. https://doi.org/10.1016/j.chb.2011.12.008

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