From 4ps to 4es: Drivers of Phygital Purchase Intention in Generations Y and Z
DOI:
https://doi.org/10.61194/ijjm.v7i3.2448Keywords:
marketing mix, digital natives, pragmatic converters, sceptical advocatesAbstract
Introduction: At the start of 2024, Indonesia’s 185 million internet users and 139 million social media users formed a highly connected landscape where this research explores how the marketing mix’s evolution (from 4Ps to 4Cs to 4Es) associates with purchase intentions across Generation Y and Z in a phygital environment. Methods: Utilizing a quantitative approach, data from 372 valid Indonesian respondents were gathered via non-probability accidental sampling using a 4-point Likert scale. PLS-SEM was employed to analyse direct and total effects within the conceptual model. Results: The structural path analysis confirms that the marketing mix successfully evolves from the 4Ps, through the 4Cs, to the 4Es for both cohorts, identifying two distinct behavioural archetypes based on total effects. Generation Y operates as "Pragmatic Converters," whose purchase intent is significantly associated with the Place–Convenience–Everyplace pathway (Original Sample = 0.16, T Statistic = 3.32, and P Values = 0.00), prioritizing immediate accessibility and seamless utility. Conversely, Generation Z acts as "Sceptical Advocates," whose purchase intent is strongly tied to the Product–Customer–Experience pathway (Original Sample = 0.12, T Statistic = 2.32, and P Values = 0.02), requiring deep product validation and high-value, shareable experiences to overcome baseline marketing scepticism. Conclusion: Generation Y values frictionless utility, whereas Generation Z demands transparency and social currency. Consequently, businesses need cohort-specific strategies, maximizing convenience for Generation Y while delivering experience-driven content for Generation Z. The Pragmatic Converter and Skeptical Advocate archetypes provide a strategic roadmap for localized, experience-centric marketing.
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Addis, M., Batat, W., Atakan, S., & Peterson, L. (2022). Food Experience Design to Prevent Unintended Consequences and Improve Well-being. Journal of Service Research, 25(1), 143–159. https://doi.org/10.1177/10946705211057593
Adelson, J. L., & Mccoach, D. B. (2010). Measuring the Mathematical Attitudes of Elementary Students: The Effects of a 4-Point or 5-Point Likert-Type Scale. https://doi.org/10.1177/0013164410366694
Adila Kasni Astiena, Yudiantri Asdi, & Rika Ampuh Hadiguna. (2026). Beyond the standard : Rethinking Likert scale use in measuring patient satisfaction at public health center in Indonesia. Journal of Public Health Research, 15(2), 1–12. https://doi.org/10.1177/22799036261441329
Agárdi, I., & Alt, M. A. (2022). Do digital natives use mobile payment differently than digital immigrants? A comparative study between generation X and Z. Electronic Commerce Research, 24(3), 1463–1490. https://doi.org/https://doi.org/10.1007/s10660-022-09537-9 Do
Aguirre-Urreta, M. I., & Rönkkö, M. (2018). Statistical Inference with PLSc Using Bootstrap Confidence Intervals. MIS Quarterly, 42(3), 1001–1020. https://doi.org/10.25300/MISQ/2018/13587
Aju, D., Kumar, K. A., & Lal, A. M. (2022). Exploring News-Feed Credibility using Emerging Machine Learning and Deep Learning Models. Journal of Engineering Science and Technology Review, 15(3), 31–37. https://doi.org/10.25103/jestr.153.04
Allcott, H., Braghieri, L., Eichmeyer, S., & Gentzkow, M. (2020). The Welfare Effects of Social Media. American Economic Review, 110(3), 629–676. https://doi.org/10.1257/aer.20190658
Arpaci, I. (2020). The Influence of Social Interactions and Subjective Norms on Social Media Postings. Journal of Information and Knowledge Management, 19(3), 34–48. https://doi.org/10.1142/S0219649220500239
Baig, M. W., Qamar, S., Fatima, T., Khan, A. M., & Ahmed, M. (2020). The Impact of Marketing Mix and Customer Value on Customer Loyalty. MPRA Munich Personal RePEc Archive, (104683), 1–48.
Batat, W. (2022). Why is the traditional marketing mix dead ? Towards the “ experiential marketing mix ” ( 7E ), a strategic framework for business experience design in the phygital age. Journal of Strategic Marketing, 32(1), 101–113. https://doi.org/10.1080/0965254X.2022.2129745
Cao, D., Meadows, M., Wong, D., & Xia, S. (2021). Understanding consumers’ social media engagement behaviour: An examination of the moderation effect of social media context. Journal of Business Research, 122(June), 835–846. https://doi.org/10.1016/j.jbusres.2020.06.025
Casado-Aranda, L. A., Sánchez-Fernández, J., & Ibáñez-Zapata, J. Á. (2022). It is all about our impulsiveness – How consumer impulsiveness modulates neural evaluation of hedonic and utilitarian banners. Journal of Retailing and Consumer Services, 67(January). https://doi.org/10.1016/j.jretconser.2022.102997
Chen, N., & Yang, Y. (2023). The Role of Influencers in Live Streaming E-Commerce: Influencer Trust, Attachment, and Consumer Purchase Intention. Journal of Theoretical and Applied Electronic Commerce Research, 18(3), 1601–1618. https://doi.org/10.3390/jtaer18030081
Chen, Y. H., Chen, M. C., & Keng, C. J. (2020). Measuring online live streaming of perceived servicescape: Scale development and validation on behavior outcome. Internet Research, 30(3), 737–762. https://doi.org/10.1108/INTR-11-2018-0487
Djohan Gunawan, D., & Prihanto, Y. J. N. (2026). Information Sharing Intention to Improve Social Media Content Quality. Review of Integrative Business and Economics Research, 15(1), 682–696.
Ebrahimi, P., Basirat, M., Yousefi, A., Nekmahmud, M., Gholampour, A., & Fekete‐farkas, M. (2022). Social Networks Marketing and Consumer Purchase Behavior: The Combination of SEM and Unsupervised Machine Learning Approaches. Big Data and Cognitive Computing, 6(2). https://doi.org/10.3390/bdcc6020035
Fetherstonhaugh, B. (2009). The4Ps are Out , the 4Es are In. In New York: Ogilvy & Mather.
Garland, R. (1991). The mid-point on a rating scale: Is it desirable? Marketing Bulletin, 2, 66–70.
Gerlich, M. (2023). The Power of Virtual Influencers: Impact on Consumer Behaviour and Attitudes in the Age of AI. Administrative Sciences, 13(8). https://doi.org/10.3390/admsci13080178
González-Serrano, M. H., Alonso-Dos-Santos, M., Crespo-Hervás, J., & Calabuig, F. (2024). Information management in social media to promote engagement and physical activity behavior. International Journal of Information Management, 78(May). https://doi.org/10.1016/j.ijinfomgt.2024.102803
Grewal, L., Stephen, A. T., & Coleman, N. V. (2019). When posting about products on social media backfires: The negative effects of consumer identity signaling on product interest. Journal of Marketing Research, 56(2), 197–210. https://doi.org/10.1177/0022243718821960
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. https://doi.org/https://doi.org/10.1007/978-3-030-80519-7
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). The Results of PLS-SEM Article information. European Business Review, 31(1), 2–24.
Hallock, W., Roggeveen, A. L., & Crittenden, V. (2019). Firm-level perspectives on social media engagement: an exploratory study. Qualitative Market Research, 22(2), 217–226. https://doi.org/10.1108/QMR-01-2017-0025
Hamid, R., Fadzil, F. R. M., Ong, M. H. A., & Azdel, A. A. (2023). A Robotic Concepts: Study of Perceived Brand Reputation and Customers’ Perceived Performance in a Restaurant. Global Business and Finance Review, 28(5), 13–30. https://doi.org/10.17549/gbfr.2023.28.5.13
Haniff, M. (2021). Marketing Mix Elements and Customer Service Satisfaction : Empirical Evidence in the Malaysia Edutainment Theme Park Industry Marketing Mix Elements and Customer Service. Services Marketing Quarterly, 42(1–2), 93–107. https://doi.org/10.1080/15332969.2021.1947087
Indonesia Central Bureau of Statistics / BPS. (2024). Statistical Yearbook of Indonesia 2024. Indonesia Central Bureau of Statistics (BPS).
Janschitz, G., Penker, & Matthias. (2020). How digital are ‘digital natives’ actually? Developing an instrument to measure the degree of digitalisation of university students – the DDS-Index. FIIB Business Review, 153(1), 2–16. https://doi.org/10.1177/07591063211061760
Kemp, S. (2024). Digital 2024: Indonesia. In DataReportal.
Kirk, C. P., Chiagouris, L., Lala, V., & Thomas, J. D. E. (2015). How do digital natives and digital immigrants respond differently to interactivity online: A model for predicting consumer attitudes and intentions to use digital information products. Journal of Advertising Research, 55(1), 81–94. https://doi.org/10.2501/JAR-55-1-081-094
Konhäusner, P., Shang, B., & Dabija, D.-C. (2021). Application of the 4Es in Online Crowdfunding Platforms: A Comparative Perspective of Germany and China. Journal of Risk and Financial Management, 14(2), 49. https://doi.org/10.3390/jrfm14020049
Kozłowski, W. (2024). Exploring Generation Z ’ s Skepticism Towards Cause-Related Marketing : Understanding the antecedents and consequences. 68(2). https://doi.org/10.15611/pn.2024.2.09
Lauterborn, R. F. “Bob.” (1990). New Marketing Litany: Four Ps Passé: C-Words Take Over. Advertising Age, 61(41), 26.
Li, F., Larimo, J., & Leonidou, L. C. (2021). Social media marketing strategy: definition, conceptualization, taxonomy, validation, and future agenda. Journal of the Academy of Marketing Science, 49(1), 51–70. https://doi.org/10.1007/s11747-020-00733-3
Manyanga, W., Kanyepe, J., Chikazhe, L., & Manyanga, T. (2024). The effect of social media marketing on brand loyalty in the hospitality industry in Zimbabwe: the moderating role of age. Cogent Business and Management, 11(1). https://doi.org/10.1080/23311975.2024.2302311
McCarthy, E. J. (1968). Basic Marketing. Journal of Marketing.
Mertala, P., López-Pernas, S., Vartiainen, H., Saqr, M., & Tedre, M. (2024). Digital natives in the scientific literature: A topic modeling approach. Computers in Human Behavior, 152(October 2023). https://doi.org/10.1016/j.chb.2023.108076
Miah, M. R., Hossain, A., Shikder, R., Saha, T., & Neger, M. (2022). Evaluating the impact of social media on online shopping behavior during COVID-19 pandemic: A Bangladeshi consumers’ perspectives. Heliyon, 8(9), e10600. https://doi.org/10.1016/j.heliyon.2022.e10600
Moon, S., & Iacobucci, D. (2022). Social Media Analytics and its Applications in Marketing. In Foundations and Trends in Marketing (Vol. 15, Number 4, pp. 213–292). https://doi.org/10.1561/1700000073
Munsch, A. (2021). Millennial and generation Z digital marketing communication and advertising effectiveness: A qualitative exploration. Journal of Global Scholars of Marketing Science: Bridging Asia and the World, 31(1), 10–29. https://doi.org/10.1080/21639159.2020.1808812
Nagajayanthi, B. (2022). Decades of Internet of Things Towards Twenty-first Century: A Research-Based Introspective. Wireless Personal Communications, 123(4), 3661–3697. https://doi.org/10.1007/s11277-021-09308-z
Nasir, M., Adil, M., & Dhamija, A. (2021). The synergetic effect of after sales service, customer satisfaction, loyalty and repurchase intention on word of mouth. International Journal of Quality and Service Sciences, 13(3), 489–505. https://doi.org/10.1108/IJQSS-01-2021-0015
Olsson, J., Hellström, D., & Vakulenko, Y. (2023). Customer experience dimensions in last-mile delivery: an empirical study on unattended home delivery. International Journal of Physical Distribution and Logistics Management, 53(2), 184–205. https://doi.org/10.1108/IJPDLM-12-2021-0517
Prensky, M. (2001). Digital Natives, Digital Immigrants Part 2: Do They Really Think Differently? On the Horizon, 9(6), 1–6. https://doi.org/10.1108/10748120110424843
Purnamaningsih, P., & Rizkalla, N. (2020). The Role of Parasocial Interaction on Consumers’ Intention to Purchase Beauty Products. Revista CEA, 6(12), 13–27. https://doi.org/https://doi.org/10.22430/24223182.1617
Rajagopal. (2009). Arousal and merriment as decision drivers among young consumers. Journal of International Consumer Marketing, 21(4), 271–283. https://doi.org/10.1080/08961530802282190
Shahbaznezhad, H., Dolan, R., & Rashidirad, M. (2021). The Role of Social Media Content Format and Platform in Users’ Engagement Behavior. Journal of Interactive Marketing, 53, 47–65. https://doi.org/10.1016/j.intmar.2020.05.001
Silalahi, N., & Heryjanto, A. (2023). Influence Social Media Marketing and Marketing Mix of Repurchase Decision Mediated By Repurchase Intention. Jurnal Indonesia Sosial Sains, 4(11), 1223–1236. https://doi.org/10.59141/jiss.v4i11.926
Terziyska, I. (2024). Drivers of memorable wine tourism experiences – a netnography study. Wine Economics and Policy, 13(1), 17–31. https://doi.org/10.36253/wep-14433
Tham, A., Mair, J., & Croy, G. (2020). Social media influence on tourists’ destination choice: importance of context. Tourism Recreation Research, 45(2), 161–175. https://doi.org/10.1080/02508281.2019.1700655
Tran, A. V., & Bui Thanh Khoa. (2025). Generation Z Customers’ Online Outbound Tourism Booking Intention in Vietnam: Extending the Technology Acceptance Model with Intercultural Competence. Geojournal of Tourism and Geosites I, 60(2), 1119–1127. https://doi.org/10.30892/gtg.602spl09-1485
Vazquez, D., Cheung, J., Nguyen, B., Dennis, C., & Kent, A. (2020). Examining the influence of user-generated content on the fashion consumer online experience. Journal of Fashion Marketing and Management, 25(3), 528–547. https://doi.org/10.1108/JFMM-02-2020-0018
Wongkitrungrueng, A., & Assarut, N. (2020). The role of live streaming in building consumer trust and engagement with social commerce sellers. Journal of Business Research, 117(November 2017), 543–556. https://doi.org/10.1016/j.jbusres.2018.08.032
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Copyright (c) 2026 Djohan Gunawan, Y. Johny Natu Prihanto , Purnamaningsih Purnamaningsih
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This work is licensed under a Creative Commons Attribution 4.0 International License.
