Human–tech synergy: How digital marketing 5.0 shapes customer decisions in Pegadaian’s digital ecosystem
DOI:
https://doi.org/10.61511/crsusf.v3i1.3410Keywords:
contextual marketing, ethical and human-centered technology, consumer decision, marketing 5.0, predictive marketingAbstract
Background: The acceleration of digital transformation has reshaped consumer behavior within financial service ecosystems, particularly through the adoption of advanced technologies integrating predictive intelligence, contextual personalization, ethical algorithms, and customer experience enhancement. Pegadaian Digital represents a significant example of the integration of Marketing 5.0 principles in Indonesia’s financial sector, combining artificial intelligence and human-centered technology to support digital transactions and decision-making processes. However, empirical research assessing how these Marketing 5.0 dimensions influence consumer purchase decisions—especially within state-owned digital financial platforms—remains limited. Methods: This study employed a quantitative approach using multiple linear regression analysis with purposive sampling. A total of 130 respondents participated, consisting of active users of the Pegadaian Digital application who engaged within the last six months. Five independent variables were analyzed: Predictive Marketing, Contextual Marketing, Augmented Reality (AR) Marketing, Ethical and Human-Centered Application of Technology, and Customer Journey. Findings: The regression model demonstrates statistical significance with an F-value of 51.111 and a p-value < 0.001, indicating that the model effectively predicts consumer decisions. The results reveal that Predictive Marketing (B = 0.224, Sig. = 0.038), Contextual Marketing (B = 0.285, Sig. = 0.015), Ethical and Human-Centered Technology (B = 0.262, Sig. = 0.016), and Customer Journey (B = 0.256, Sig. = 0.012) have a positive and significant effect on consumer decisions. Meanwhile, Augmented Reality Marketing (B = 0.148, Sig. = 0.182) does not significantly influence consumer decision-making. The strongest predictor is Customer Journey (β = 0.229), followed by Ethical Technology and Contextual Marketing. Conclusion: Predictive Marketing, Contextual Marketing, Ethical and Human-Centered Technology, and Customer Journey Engagement play a crucial role in shaping consumer purchase decisions in the Pegadaian Digital ecosystem, whereas AR marketing has not yet demonstrated a significant impact. These findings underscore the importance of human–tech synergy in enhancing trust, personalization, and decision effectiveness in digital financial services. Novelty/Originality of this article: This study introduces an integrated empirical model examining Marketing 5.0 strategic dimensions collectively within a digital financial environment—an analytical perspective that remains underexplored, especially in state-owned hybrid service institutions. The research also contributes empirical evidence regarding the limited role of AR marketing in financial decision-making contexts.
References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Azad, M. S., Khan, S. S., Hossain, R., Rahman, R., & Momen, S. (2023). Predictive modeling of consumer purchase behavior on social media: Integrating theory of planned behavior and machine learning for actionable insights. Plos One, 18(12), e0296336. https://doi.org/10.1371/journal.pone.0296336
Bolte, L., Vandemeulebroucke, T., & van Wynsberghe, A. (2022). From an ethics of carefulness to an ethics of desirability: Going beyond current ethics approaches to sustainable AI. Sustainability, 14(8), 4472. https://doi.org/10.3390/su14084472
Brodie, R. J., Hollebeek, L. D., Jurić, B., & Ilić, A. (2011). Customer engagement: Conceptual domain, fundamental propositions, and implications for research. Journal of Service Research, 14(3), 252–271. https://doi.org/10.1177/1094670511411703
Calder, B. J., Malthouse, E. C., & Maslowska, E. (2016). Brand marketing, big data, and social innovation as future research directions for engagement. Journal of Marketing Management, 32(5–6), 579–585. https://doi.org/10.1080/0267257X.2016.1143726
Calvo, R. A., Peters, D., Vold, K., & Ryan, R. M. (2020). Supporting human autonomy in AI systems: A framework for ethical enquiry. Philosophy & Technology, 33(2), 293–313. https://doi.org/10.1007/s13347-019-00377-1
Chaffey, D., & Ellis-Chadwick, F. (2022). Digital marketing: Strategy, implementation and practice (8th ed.). Pearson Education.
Court, D., Elzinga, D., Mulder, S., & Vetvik, O. J. (2009). The consumer decision journey. McKinsey Quarterly.
Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Harper & Row.
DataReportal. (2024). Digital 2024: Indonesia. DataReportal. https://datareportal.com/reports/digital-2024-indonesia
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
de Oliveira, A. S. R. (2023). The effect of Brand Experience on Consumer Satisfaction, Brand Trust and Brand Loyalty-In the Portuguese Skincare Industry. Universidade Nova de Lisboa. http://hdl.handle.net/10362/149808
Dignum, V. (2019). Responsible artificial intelligence: How to develop and use AI in a responsible way (Vol. 2156). Cham: Springer.
Edelman, D., & Singer, M. (2020). The new consumer decision journey. McKinsey & Company. https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-new-consumer-decision-journey
Floridi, L. (2019). The logic of information: A theory of philosophy as conceptual design. Oxford University Press.
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Schafer, B. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
Green, S. B. (1991). How many subjects does it take to do a regression analysis. Multivariate Behavioral Research, 26(3), 499–510. https://doi.org/10.1207/s15327906mbr2603_7
Heller, J., Chylinski, M., de Ruyter, K., Mahr, D., & Keeling, D. I. (2021). Let me imagine that for you: Transforming the retail frontline through augmenting customer mental imagery ability. Journal of Retailing, 97(4), 484–501. https://doi.org/10.1016/j.jretai.2019.03.005
Hilken, T., de Ruyter, K., Chylinski, M., Mahr, D., & Keeling, D. I. (2017). Augmenting the eye of the beholder: Exploring the strategic potential of augmented reality to enhance online service experiences. Journal of the Academy of Marketing Science, 45(6), 884–905. https://doi.org/10.1007/s11747-017-0541-x
Hollebeek, L. D., Glynn, M. S., & Brodie, R. J. (2014). Consumer brand engagement in social media. Journal of Interactive Marketing, 28(2), 149–165. https://doi.org/10.1016/j.intmar.2013.12.002
Irgui, A. (2024). Contextual marketing and information privacy concerns in mobile apps: Their effects on customer loyalty. Asian Journal of Government & Social Responsibility, 42(3), 1150. https://doi.org/10.1108/AGJSR-09-2022-0198
Islam, J. U., & Rahman, Z. (2017). The impact of online brand community characteristics on customer engagement. Telematics and Informatics, 34(4), 96–109. https://doi.org/10.1016/j.tele.2017.01.004
Javornik, A. (2016). ‘It’s an illusion, but it looks real!’ Consumer responses to augmented reality applications. Journal of Marketing Management, 32(9–10), 987–1011. https://doi.org/10.1080/0267257X.2016.1174726
Jessen, A., Hilken, T., Chylinski, M., Mahr, D., Heller, J., Keeling, D. I., & de Ruyter, K. (2020). The playground effect: How augmented reality drives creative customer engagement. Journal of Business Research, 116, 85–98. https://doi.org/10.1016/j.jbusres.2020.05.002
Kasemrat, R., Kraiwanit, T., & Yuenyong, N. (2025). Predictive analytics in customer behavior. Journal of Governance & Regulation, 14(1), 318–331. https://doi.org/10.22495/jgrv14i1siart8
Kotler, P., Kartajaya, H., & Setiawan, I. (2021). Marketing 5.0: Technology for humanity. John Wiley & Sons.
Kumar, V., & Pansari, A. (2016). Competitive advantage through engagement. Journal of Marketing Research, 53(4), 497–514. https://doi.org/10.1509/jmr.15.0044
Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96. https://doi.org/10.1509/jm.15.0420
Martin, K., Shilton, K., & Smith, J. (Eds.). (2022). Business and the Ethical Implications of Technology. Springer.
Martínez-Garmendia, J. (2024). Machine learning for product choice prediction. Journal of Marketing Analytics, 12, 656–667. https://doi.org/10.1057/s41270-023-00217-7
Mcknight, D. H., Carter, M., Thatcher, J. B., & Clay, P. F. (2011). Trust in a specific technology: An investigation of its components and measures. ACM Transactions on management information systems (TMIS), 2(2), 1-25. https://doi.org/10.1145/1985347.1985353
Norman, D. A. (2019). The design of everyday things (Rev. ed.). Basic Books.
Pauwels, K., & Joshi, A. (2016). Selecting predictive metrics for marketing dashboards. Journal of Marketing Behavior, 2(2–3), 195–224. https://doi.org/10.1561/107.00000035
Pegadaian. (2023). Pegadaian annual report 2023. PT Pegadaian.
Pertiwi, M. (2025). Analisis pengaruh strategi promosi, citra merek dan persepsi terhadap keputusan nasabah pengguna tabungan emas. Digital Bisnis.
Pinheiro, P., & Cavique, L. (2025). A machine learning framework for uplift modeling through customer segmentation. Decision Analytics Journal, 100639. http://hdl.handle.net/10400.2/20392
Putri, N. E., & Fadhilah, J. R. (2024). Peran E-Satisfaction sebagai variabel intervening. Universitas Telkom
Raditya, M. R., Utami, S., & Chan, S. (2023). The Impact of Customer Journey and Trust on Purchasing Decisions for Quality Furniture in the Digital Era: A Serial Mediation Analysis. Journal of Economics, Finance and Management Studies, 6(8), 3773-3785. https://doi.org/10.47191/jefms/v6-i8-27
Rauschnabel, P. A., Felix, R., & Hinsch, C. (2019). Augmented reality marketing: How mobile AR-apps can improve brands through inspiration. Journal of Retailing and Consumer Services, 49, 43-53. https://doi.org/10.1016/j.jretconser.2019.03.004
Sarstedt, M., Liu, Y. (2024). Advanced marketing analytics using partial least squares structural equation modeling (PLS-SEM). Journal of Marketing Analytics, 12, 1–5. https://doi.org/10.1057/s41270-023-00279-7
Schmitt, B. (1999). Experiential marketing. The Free Press.
Scholz, J., & Duffy, K. (2018). We ARe at home: How augmented reality reshapes mobile marketing and consumer-brand relationships. Journal of Retailing and Consumer Services, 44, 11-23. https://doi.org/10.1016/j.jretconser.2018.05.004
Sheth, J. (2021). New areas of research in marketing strategy, consumer behavior, and marketing analytics: the future is bright. Journal of Marketing Theory and Practice, 29(1), 3-12. https://doi.org/10.1080/10696679.2020.1860679
Statista. (2024). Digital advertising spending in Indonesia 2019–2024. Statista. https://www.statista.com/statistics/1546701/indonesia-digital-ad-spending-by-format/?srsltid=AfmBOoobWmznkT3URV7uIrnKgKZD0bPbA6Oaxe--v_M2o4SlgR_Ln29l
Suomala, J. (2020). The consumer contextual decision-making model. Frontiers in Psychology, 11, 570430. https://doi.org/10.3389/fpsyg.2020.570430
Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson.
Tiago, M. T. P. M. B., & Veríssimo, J. M. C. (2014). Digital marketing and social media: Why bother?. Business horizons, 57(6), 703-708. https://doi.org/10.1016/j.bushor.2014.07.002
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view1. MIS quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
Verhoef, P. C., Lemon, K. N., Parasuraman, A., Roggeveen, A., Tsiros, M., & Schlesinger, L. A. (2009). Customer experience creation: Determinants, dynamics and management strategies. Journal of retailing, 85(1), 31-41. https://doi.org/10.1016/j.jretai.2008.11.001
Wang, L., Zhang, Z., Wang, D., Cao, W., Zhou, X., Zhang, P., ... & Tian, F. (2023). Human-centered design and evaluation of AI-empowered clinical decision support systems: a systematic review. Frontiers in Computer Science, 5, 1187299. https://doi.org/10.3389/fcomp.2023.1187299
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
Wilson, G., Johnson, O., & Brown, W. (2024). The Influence of Digital Marketing on Consumer Purchasing Decisions. Preprints. https://doi.org/10.20944/preprints202408.0347.v1
Witjaksono, G., & Sentanu, V. A. D. (2024). Analysis of Pegadaian Digital Services based on user satisfaction using D&M IS Success Model. @ is The Best: Accounting Information Systems and Information Technology Business Enterprise, 9(2), 151-165. https://orcid.org/0000-0002-9771-4223
Yim, M. Y. C., Chu, S. C., & Sauer, P. L. (2017). Is augmented reality technology an effective tool for e-commerce? An interactivity and vividness perspective. Journal of interactive marketing, 39(1), 89-103. https://doi.org/10.1016/j.intmar.2017.04.001
Yim, M. Y. C., & Park, S. Y. (2019). “I am not satisfied with my body, so I like augmented reality (AR)”: Consumer responses to AR-based product presentations. Journal of Business Research, 100, 581-589. https://doi.org/10.1016/j.jbusres.2018.10.041
Downloads
Published
Issue
Section
Citation Check
License
Copyright (c) 2026 Chairul Arif
This work is licensed under a Creative Commons Attribution 4.0 International License.
