Journals / İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi / 2026 / Cilt: 25 Sayı: 56

Intention to Use Phygital Banking Services: The Roles of Perceived Value and Trust

Fijital Bankacılık Hizmetlerini Kullanma Niyeti: Algılanan Değer ve Güvenin Rolü

Pages
914–951
Publication Date
2026-09-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings indicate that all four service characteristics positively contribute to customers’ perceived value.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The convergence of physical and digital service channels has transformed the delivery of banking services by enabling seamless customer experiences across multiple touchpoints.
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Key Findings & Data:Drawing on Means–End Theory, the proposed research model was tested using data collected through an online survey of 362 individuals who regularly use both mobile banking applications and physical bank branches.
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Conclusion & Impact:The findings indicate that all four service characteristics positively contribute to customers’ perceived value.
🔬Design:Survey & Empirical Study
📍Scope:Comprehensive Sample
⚙️Variables:Fijital bankacılık · fijital hizmet özellikleri · algılanan müşteri değeri

Abstract

The convergence of physical and digital service channels has transformed the delivery of banking services by enabling seamless customer experiences across multiple touchpoints. This study investigates how four key phygital service characteristics—integration, consistency, flexibility, and personalization—influence customers’ intentions to use phygital banking services. In addition, it examines the parallel and sequential mediating roles of perceived value and trust within these relationships. Drawing on Means–End Theory, the proposed research model was tested using data collected through an online survey of 362 individuals who regularly use both mobile banking applications and physical bank branches. The data were analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 4. The findings indicate that all four service characteristics positively contribute to customers’ perceived value. While integration, flexibility, and personalization significantly strengthen customer trust, consistency does not exhibit a significant direct effect on trust. Furthermore, both perceived value and trust positively influence customers’ intentions to adopt phygital banking services. The mediation analyses reveal that these two constructs explain the influence of phygital service characteristics on usage intention through both independent and sequential mediation mechanisms. Overall, the study extends Means–End Theory within the context of phygital banking and offers practical implications for financial institutions aiming to enhance customer value perceptions, reinforce trust, and promote greater adoption of integrated banking services.

Özet

Fiziksel ve dijital temas noktalarının bütünleşmesiyle ortaya çıkan fijital hizmet sistemleri, bankacılık sektöründe müşteri deneyimini yeniden şekillendirmektedir. Bu araştırmanın amacı, fijital hizmet özelliklerinin (entegrasyon, tutarlılık, esneklik ve kişiselleştirme) fijital bankacılık hizmetlerini kullanma niyeti üzerindeki etkilerini incelemek ve bu ilişkilerde algılanan müşteri değeri ile müşteri güveninin aracılık rollerini ortaya koymaktır. Araştırma modeli Araç-Amaç Teorisi temel alınarak geliştirilmiştir. Veriler, hem mobil bankacılık hem de fiziksel şube hizmetlerini kullanan 362 katılımcıdan çevrimiçi anket yöntemiyle toplanmış ve SmartPLS 4 programı kullanılarak PLS-SEM yöntemiyle analiz edilmiştir. Bulgular, entegrasyon, tutarlılık, esneklik ve kişiselleştirmenin algılanan müşteri değerini anlamlı biçimde artırdığını göstermektedir. Ayrıca entegrasyon, esneklik ve kişiselleştirme müşteri güvenini olumlu etkilerken, tutarlılığın güven üzerindeki etkisi anlamlı bulunmamıştır. Algılanan müşteri değeri ve güvenin kullanım niyeti üzerinde pozitif etkileri olduğu belirlenmiştir. Aracılık analizleri, algılanan değer ve güvenin hem bağımsız hem de ardışık biçimde kullanım niyetinin oluşumunda önemli rol oynadığını ortaya koymuştur. Çalışma, Araç-Amaç Teorisi’ni fijital bankacılık bağlamında genişleterek literatüre katkı sağlamakta ve bankalara müşteri değeri ile güveni güçlendirmeye yönelik stratejik öneriler sunmaktadır.

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Cite This Article

Özatar, A. B., Varan, A. (2026). Intention to Use Phygital Banking Services: The Roles of Perceived Value and Trust. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, Cilt: 25 Sayı: 56, 914–951. https://doi.org/10.46928/iticusbe.1969645

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References (56)

  1. Abdennebi, H. B. (2023). User satisfaction and trust in mobile banking adoption: A study of Tunisian customers. Digital Business, 3(1), 100065. https://doi.org/10.1016/j.digbus.2023.100065
  2. Ahn, S. J., & Lee, S. H. (2019). The effect of consumers’ perceived value on acceptance of an internet-only bank service. Sustainability, 11(17), 4599. https://doi.org/10.3390/su11174599
  3. Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99–110. https://doi.org/10.1016/j.ijinfomgt.2016.04.004
  4. Alrawad, M., Lutfi, A., Almaiah, M. A., Alsyouf, A., Al-Khasawneh, A. L., & Al-Qudah, A. A. (2023). The influence of perceived risk and trust on the intention to use NFC mobile payment. Journal of Open Innovation: Technology, Market, and Complexity, 9(1), 100070. https://doi.org/10.1016/j.joitmc.2023.100070
  5. Appiah, T., & Agblewornu, V. V. (2025). The role of trust, privacy concerns, and perceived risk-related factors in fintech adoption in Sub-Saharan Africa. Heliyon, 11(1), e41992. https://doi.org/10.1016/j.heliyon.2025.e41992
  6. Batat, W. (2022). What does phygital really mean? A conceptual introduction to the phygital customer experience (PH-CX) framework. Journal of Strategic Marketing, 32(1), 1–24. https://doi.org/10.1080/0965254X.2022.2059775
  7. Banik, S. (2021). Exploring the involvement–patronage link in the phygital retail experiences. Journal of Retailing and Consumer Services, 63, 102739. https://doi.org/10.1016/j.jretconser.2021.102739
  8. Becker, J.-M., Cheah, J.-H., Gholamzadehmir, M., Ringle, C. M., & Sarstedt, M. (2023). PLS-SEM’s most wanted guidance. International Journal of Contemporary Hospitality Management, 35(1), 321–346. https://doi.org/10.1108/IJCHM-04-2022-0474
  9. Cheah, J.-H., Lim, X.-J., Ting, H., Liu, Y., & Quach, S. (2022). Are privacy concerns still relevant? Revisiting consumer behaviour in omnichannel retailing. Journal of Retailing and Consumer Services, 65, 102242. https://doi.org/10.1016/j.jretconser.2020.102242
  10. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
  11. Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of price, brand, and store information on buyers’ product evaluations. Journal of Marketing Research, 28(3), 307–319. https://doi.org/10.1177/002224379102800305
  12. Dwivedi, Y. K., Ismagilova, E., Sarker, P., Jeyaraj, A., Jadil, Y., & Hughes, L. (2021). A meta-analytic structural equation model for understanding social commerce adoption. Information Systems Frontiers, 23(1), 1–21. https://doi.org/10.1007/s10796-021-10172-2
  13. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
  14. Franke, G. R., & Sarstedt, M. (2019). Heuristics versus statistics in discriminant validity testing: A comparison of four procedures. Internet Research, 29(3), 430–447. https://doi.org/10.1108/IntR-12-2017-0515
  15. Gao, W., Fan, H., Li, W., & Wang, H. (2021). Crafting the customer experience in omnichannel contexts: The role of channel integration. Journal of Business Research, 126, 12–22. https://doi.org/10.1016/j.jbusres.2020.12.056
  16. Gao, W., & Fan, H. (2021). Omni-channel customer experience (in)consistency and service success: A study based on polynomial regression analysis. Journal of Theoretical and Applied Electronic Commerce Research, 16(6), 1997–2013. https://doi.org/10.3390/jtaer16060112
  17. Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519
  18. Gutman, J. (1982). A means-end chain model based on consumer categorization processes. Journal of Marketing, 46(2), 60–72. https://doi.org/10.1177/002224298204600207
  19. Hair, J. F., Jr., Babin, B. J., Anderson, R. E., & Black, W. C. (2018). Multivariate data analysis (8th ed.). Cengage Learning.
  20. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). 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
  21. Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2020). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springer. https://doi.org/10.1007/978-3-030-80519-7
  22. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage.
  23. Hamouda, M. (2019). Omni-channel banking integration quality and perceived value as drivers of consumers’ satisfaction and loyalty. Journal of Enterprise Information Management, 32(4), 608–625. https://doi.org/10.1108/JEIM-12-2018-0279
  24. 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
  25. Jafri, J. A., Amin, S. I. M., Rahman, A. A., & Nor, S. M. (2024). A systematic literature review of the role of trust and security on fintech adoption in banking. Heliyon, 10(1), e22980. https://doi.org/10.1016/j.heliyon.2023.e22980
  26. Klaus, P. (2021). Viewpoint: Phygital—The emperor’s new clothes? Journal of Strategic Marketing, 29(5), 1–8. https://doi.org/10.1080/0965254X.2021.1976252
  27. Kline, R. B., & Little, T. D. (2023). Principles and practice of structural equation modeling (5th ed.). The Guilford Press.
  28. Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
  29. Kock, N., & Lynn, G. S. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546–580. https://doi.org/10.17705/1jais.00302
  30. Lee, Z. W. Y., Chan, T. K. H., Chong, A. Y.-L., & Thadani, D. R. (2019). Customer engagement through omnichannel retailing: The effects of channel integration quality. Industrial Marketing Management, 77, 90–101. https://doi.org/10.1016/j.indmarman.2018.12.004
  31. Liengaard, B. D., Sharma, P. N., Hauff, J. C., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2021). Prediction: Coveted, yet forsaken? Introducing a cross-validated predictive ability test in partial least squares path modeling. Decision Sciences, 52(2), 362–392. https://doi.org/10.1111/deci.12445
  32. Mainardes, E. W., de Moura Rosa, C. A., & Nossa, S. N. (2020). Omnichannel strategy and customer loyalty in banking. International Journal of Bank Marketing, 38(4), 799–822. https://doi.org/10.1108/IJBM-07-2019-0272
  33. McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, 13(3), 334–359. https://doi.org/10.1287/isre.13.3.334.81
  34. Mele, C., & Russo-Spena, T. (2022). The architecture of the phygital customer journey: A dynamic interplay between systems of insights and systems of engagement. European Journal of Marketing, 56(1), 72–91. https://doi.org/10.1108/EJM-04-2019-0308
  35. Memon, M. A., Ting, H., Cheah, J.-H., Thurasamy, R., Chuah, F., & Cham, T. H. (2020). Sample size for survey research: Review and recommendations. Journal of Applied Structural Equation Modeling, 4(2), i–xx. https://doi.org/10.47263/JASEM.4(2)01
  36. Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. Journal of Marketing, 58(3), 20–38. https://doi.org/10.1177/002224299405800302
  37. Nitzl, C., Roldan, J. L., & Cepeda, G. (2016). Mediation analysis in partial least squares path modeling: Helping researchers discuss more sophisticated models. Industrial Management & Data Systems, 116(9), 1849–1864. https://doi.org/10.1108/IMDS-07-2015-0302
  38. Picot-Coupey, K., Huré, E., & Piveteau, L. (2016). Channel design to enrich customers’ shopping experiences: Synchronizing clicks with bricks in an omni-channel perspective—The Direct Optic case. International Journal of Retail & Distribution Management, 44(3), 336–368. https://doi.org/10.1108/IJRDM-04-2015-0056
  39. 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
  40. Rahman, S. M., Carlson, J., Gudergan, S. P., Wetzels, M., & Grewal, D. (2022). Perceived omnichannel customer experience (OCX): Concept, measurement, and impact. Journal of Retailing, 98(4), 611–632. https://doi.org/10.1016/j.jretai.2022.03.003
  41. Ringle, C. M., Hair, J. F., Sarstedt, M., Becker, J.-M., & Cheah, J.-H. (2023). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. In R. Sinkovics (Ed.), New challenges to international marketing (Advances in International Marketing, Vol. 34, pp. 1–25). Emerald Publishing Limited. https://doi.org/10.1108/S1474-797920230000034001
  42. Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). Editor’s comments: A critical look at the use of PLS-SEM in MIS Quarterly. MIS Quarterly, 36(1), iii–xiv. https://doi.org/10.2307/41410402
  43. Ringle, C. M., & Sarstedt, M. (2016). Gain more insight from your PLS-SEM results: The importance-performance map analysis. Industrial Management & Data Systems, 116(9), 1865–1886. https://doi.org/10.1108/IMDS-10-2015-0449
  44. Saghiri, S., Wilding, R., Mena, C., & Bourlakis, M. (2017). Toward a three-dimensional framework for omni-channel. Journal of Business Research, 77, 53–67. https://doi.org/10.1016/j.jbusres.2017.03.025
  45. Saif, M. A. M., Hussin, N., Husin, M. M., Muneer, A., & Alwadain, A. (2024). Beyond conventions: Unravelling perceived value’s role in shaping digital-only banks’ adoption. Technological Forecasting and Social Change, 203, 123337. https://doi.org/10.1016/j.techfore.2024.123337
  46. Sharma, N., & Fatima, J. K. (2024). Omnichannel integration quality and customer perceived value. Journal of Retailing and Consumer Services, 77, 103627. https://doi.org/10.1016/j.jretconser.2023.103627
  47. Shen, X.-L., Li, Y.-J., Sun, Y., & Wang, N. (2018). Channel integration quality, perceived fluency and omnichannel service usage: The moderating roles of internal and external usage experience. Decision Support Systems, 109, 61–73. https://doi.org/10.1016/j.dss.2018.01.006
  48. Shi, S., Wang, Y., Chen, X., & Zhang, Q. (2020). Conceptualization of omnichannel customer experience and its impact on shopping intention: A mixed-method approach. International Journal of Information Management, 50, 325–336. https://doi.org/10.1016/j.ijinfomgt.2019.09.001
  49. Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189
  50. Sirdeshmukh, D., Singh, J., & Sabol, B. (2002). Consumer trust, value, and loyalty in relational exchanges. Journal of Marketing, 66(1), 15–37. https://doi.org/10.1509/jmkg.66.1.15.18449
  51. Thaler, R. (1980). Toward a positive theory of consumer choice. Journal of Economic Behavior & Organization, 1(1), 39–60. https://doi.org/10.1016/0167-2681(80)90051-7
  52. Tran Xuan, Q., Truong, H. T. H., & Vo Quang, T. (2023). The impacts of omnichannel retailing properties on customer experience and brand loyalty: A study in the banking sector. Cogent Business & Management, 10(1), 2244765. https://doi.org/10.1080/23311975.2023.2244765
  53. Tyrväinen, O., Karjaluoto, H., & Saarijärvi, H. (2020). Personalization and hedonic motivation in creating customer experiences and loyalty in omnichannel retail. Journal of Retailing and Consumer Services, 57, 102233. https://doi.org/10.1016/j.jretconser.2020.102233
  54. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
  55. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
  56. Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22. https://doi.org/10.1177/002224298805200302