Examining Online Shopping Intentions Based on HEXACO Personality Traits: A Theoretical and Empirical Assessment Within The Framework of TpB, ECM, and TAM

Year 2025, Volume: 3 Issue: 2, 6 - 26, 16.11.2025

Emre Harorlı

Abstract

The increasing demand of consumers for online platforms causes competition to become increasingly intense, and this situation results in businesses increasing their efforts to improve their services by analyzing consumer behavior in more detail. Accordingly, businesses are looking for different methods to develop strategies by understanding the factors affecting consumers' online shopping in more detail to create personalized shopping experiences and increase customer satisfaction. This research combines the Technology Acceptance Model (TAM), Theory of Planned Behavior (TpB), and Expectation-Confirmation Model (ECM) as common variables to evaluate the factors that affect participants' purchases through websites and mobile applications. In addition, the research model is added with the exogenous variable "HEXACO Personality Inventory Personality Traits" to understand the personality traits of consumers and to determine whether there is a difference in the factors that affect purchasing behavior in different personality traits. It aims to examine the effect of personality traits on online purchasing behavior.
The research includes the responses of 400 of the 423 participants, which were analyzed using Structural Equation Modeling with SPSS AMOS. It was determined that Intention, Perceived Ease of Use, Perceived Behavioral Control, Subjective Norm, and Expectation had statistically significant effects on Attitude Towards Behavior and Perceived Usefulness. Furthermore, it was proven that the variables in the research model had distinct effects on each other for participants with varying personality traits within the HEXACO personality inventory.

Keywords

HEXACO , Theory of Planned Behavior , Technology Acceptance Model , Expectation-Confirmation Model , Online Shopping , Consumer Behavior

HEXACO Kişilik Özelliklerine Göre Çevrimiçi Alışveriş Niyetlerinin İncelenmesi: PDT, BOM ve TKM Çerçevesinde Teorik ve Ampirik Bir Değerlendirme

Abstract

Tüketicilerin çevrimiçi platformlara olan talebinin artması, rekabetin giderek yoğunlaşmasına neden olmaktadır. Bu durum, işletmelerin tüketici davranışlarını daha ayrıntılı analiz ederek hizmetlerini iyileştirme çabalarını artırmalarına yol açmaktadır. İşletmeler, kişiselleştirilmiş alışveriş deneyimleri sunmak ve müşteri memnuniyetini artırmak amacıyla, tüketicilerin çevrimiçi alışveriş kararlarını etkileyen faktörleri daha iyi anlamaya çalışmakta ve bu doğrultuda stratejiler geliştirmek için farklı yöntemler aramaktadır.
Bu araştırma, katılımcıların web siteleri ve mobil uygulamalar aracılığıyla gerçekleştirdikleri satın alma kararlarını etkileyen faktörleri değerlendirmek için Teknoloji Kabul Modeli (TAM), Planlı Davranış Teorisi (TpB) ve Beklenti-Onay Modeli'ni (ECM) ortak değişkenler çerçevesinde birleştirmektedir. Ayrıca, tüketicilerin kişilik özelliklerini anlamak ve farklı kişilik profillerinin satın alma davranışlarını nasıl etkilediğini belirlemek amacıyla modele dışsal değişken olarak "HEXACO Kişilik Envanteri" eklenmiştir. Bu kapsamda, kişilik özelliklerinin çevrimiçi satın alma davranışı üzerindeki etkileri incelenmektedir.
Araştırma, SPSS AMOS ile Yapısal Eşitlik Modellemesi (SEM) kullanılarak analiz edilen 423 katılımcının 400'ünün yanıtlarını içermektedir. Analizler sonucunda, niyet, algılanan kullanım kolaylığı, algılanan davranışsal kontrol, öznel norm ve beklentinin, davranışa yönelik tutum ve algılanan fayda üzerinde istatistiksel olarak anlamlı etkileri olduğu tespit edilmiştir. Ayrıca, araştırma modelindeki değişkenlerin, HEXACO kişilik envanterinde farklı kişilik özelliklerine sahip katılımcılar için değişen etkiler gösterdiği kanıtlanmıştır.

Keywords

HEXACO , Planlı Davranış Teorisi , Teknoloji Kabul Modeli , Beklenti-Onaylama Modeli , Online Alışveriş , Tüketici Davranışı

References

• Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes. https://doi.org/10.1016/0749-5978(91)90020-T
• Ajzen, I. (2011). The Theory of Planned Behaviour: Reactions and Reflections. Psychology and Health, 26(9), 1113–1127. https://doi.org/10.1080/ 08870446.2011.613995
• Ajzen, I. (2015). Consumer Attitudes and Behavior. Handbook of Consumer Psychology, January 2008. https://doi.org/10.4324/9780203809570.ch20
• Anglim, J., & O’Connor, P. (2019). Measurement and research using the Big Five, HEXACO, and narrow traits: A primer for researchers and practitioners. Australian Journal of Psychology, 71(1), 16–25. https://doi.org/10.1111/ajpy.12202
• Ashraf, A. R., Thongpapanl, N., & Auh, S. (2014). The application of the technology acceptance model under different cultural contexts: The case of online shopping adoption. Journal of International Marketing, 22(3), 68-93. https://doi.org/10.1509/jim.14.0065
• Ashton, M. C., & Lee, K. (2001). A theoretical basis for the major dimensions of personality. European Journal of Personality, 15(5), 327–353. https://doi.org/10.1002/per.417
• Ashton, M. C., & Lee, K. (2007). Empirical, Theoretical, and Practical Advantages of the HEXACO Model of Personality Structure. Personality and Social Psychology Review, 11(2), 150–166. https://doi.org/10.1177/1088868306294907
• Ashton, M. C., & Lee, K. (2009). The HEXACO-60: A short measure of the major dimensions of personality. Journal of Personality Assessment, 91(4), 340–345. https://doi.org/10.1080/00223890902935878
• Baharum, A., & Jaafar, A. (2015). User interface design: A study of expectation-confirmation theory, Proceedings of the 5th International Conference on Computing and Informatics, ICOCI 2015, 064, 17–24.
• Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351–370.
• Cheung, C. M., Chan, G. W., & Limayem, M. (2005). A critical review of online consumer behavior: Empirical research. Journal of electronic commerce in organizations (JECO), 3(4), 1-19. https://doi.org/10.4018/978-1-59904-813-0.ch017
• Chou, S. F., Horng, J. S., Sam Liu, C. H., & Lin, J. Y. (2020). Identifying the critical factors of customer behavior: An integration perspective of marketing strategy and components of attitudes. Journal of Retailing and Consumer Services, 55(September 2019), 102113. https://doi.org/10.1016/j.jretconser.2020.102113
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008
• Eroğlu, E. (2015). Geçmişten Günümüze Sosyal Normlar. Akademik Bakış Uluslararası Hakemli Sosyal Bilimler Dergisi, 299–308.
• Fayad, R., & Paper, D. (2015). The Technology Acceptance Model E-Commerce Extension: A Conceptual Framework. Procedia Economics and Finance, 26(961), 1000–1006. https://doi.org/10.1016/s2212-5671(15)00922-3
• Forsythe, S., Liu, C., Shannon, D., & Gardner, L. C. (2006). Development of a scale to measure the perceived benefits and risks of online shopping. Journal of interactive marketing, 20(2), 55-75. https://doi.org/10.1002/dir.20061
• Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in Online Shopping: An Integrated Model. MIS Quarterly: Management Information Systems, 27(1), 51–90.
• Ha, N. T., Nguyen, T. L. H., Nguyen, T. P. L., & Nguyen, T. D. (2019). The effect of trust on consumers’ online purchase intention: An integration of TAM and TPB. Management Science Letters, 9(9), 1451-1460. https://doi.org/10.5267/j.msl. 2019.5.006
• Ha, N. T., Nguyen, T. L. H., Pham, T. Van, & Nguyen, T. H. T. (2021). Factors Influencing Online Shopping Intention: An Empirical Study in Vietnam. Journal of Asian Finance, Economics and Business, 8(3), 1257–1266. https://doi.org/10.13106/jafeb.2021.vol8.no3.1257
• Ha, S., & Stoel, L. (2009). Consumer e-shopping acceptance: Antecedents in a technology acceptance model. Journal of Business Research, 62(5), 565–571. https://doi.org/10.1016/j.jbusres.2008.06.016
• Harorlı, E. (2021). Personalization is possible. In Mehmed Zahid Çögenli (Ed.), Digitalization in Organizations, Cambridge Scholars Publishing, 44-60.
• Hossain, M. A., & Quaddus, M. (2011). Expectation–confirmation theory in information system research: A review and analysis. Information systems theory: Explaining and predicting our digital society, Vol. 1, 441-469. https://doi.org/10.1007/978-1-4419-6108-2_21
• Hozhabri, A., Raeesi, R., Nor, K. M., Salimianrizi, H., & Tayebiniya, J. (2014, April). Online re-purchase intention: Testing expectation confirmation model ECM on online shopping context in Iran. In 8th International Conference on e-Commerce in Developing Countries: With Focus on e-Trust, 1-7. https://doi.org/ 10.1109/ECDC.2014.6836757
• Hsu, M. H., Yen, C. H., Chiu, C. M., & Chang, C. M. (2006). A longitudinal investigation of continued online shopping behavior: An extension of the theory of planned behavior. International journal of human-computer studies, 64(9), 889-904. https://doi.org/10.1016/j.ijhcs.2006.04.004
• Huda, M. N. (2023). Analysis the critical factors of M-government service acceptance: An integrating theoretical model between TAM and ECM. Policy & Governance Review, 7(2), 109-124.
• Lee, K., & Ashton, M. C. (2009). Hexaco Personality Inventory. https://hexaco.org/hexaco-inventory
• Lee, M. C. (2009). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic commerce research and applications, 8(3), 130-141. https://doi.org/10.1016/j.elerap.2008. 11.006
• Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation-confirmation model. Computers and Education, 54(2), 506–516. https://doi.org/10.1016/j.compedu.2009.09.002
• Lee, W. O., Huam, H. T., & Raja Yusof, R. N. (2024). Determinants of Mobile Shopping Apps Continuance Intention: A Review and Proposed Conceptual Model. Journal of Entrepreneurship and Business, 12(1), 77–90. https://doi.org/10.17687/jeb.v12i1. 1193
• Liao, C., Lin, H. N., Luo, M. M., & Chea, S. (2017). Factors influencing online shoppers’ repurchase intentions: The roles of satisfaction and regret. Information and Management, 54(5), 651–668. https://doi.org/10.1016/j.im.2016.12.005
• Luo, Y., Ye, Q., & Meng, L. (2017). An Empirical Research on Influence Factor of College Students’ Continued Intentions of Online Self-Regulated Learning Based on the Model of ECM and TAM. 99, 521–528. https://doi.org/10.2991/icsshe-17.2017.132
• MacCann, C., Todd, J., Mullan, B. A., & Roberts, R. D. (2015). Can Personality Bridge the Intention-behavior Gap to Predict Who Will Exercise? American Journal of Health Behavior, 39(1), 140–147. https://doi.org/10.5993/AJHB.39.1.15
• McCrae, R. R., & Costa, P. T. (2008). The five-factor theory of personality. In O. P. John, R. W. Robins, & L. A. Pervin (Eds.), Handbook of personality: Theory and research (3rd ed., pp. 159–181). The Guilford Press.
• Monds, L. A., MacCann, C., Mullan, B. A., Wong, C., Todd, J., & Roberts, R. D. (2016). Can personality close the intention-behavior gap for healthy eating? An examination with the HEXACO personality traits. Psychology, Health & Medicine, 21(7), 845–855. https://doi.org/10.1080/13548506.2015.1112416
• Moshrefjavadi, M. H., Rezaie Dolatabadi, H., Nourbakhsh, M., Poursaeedi, A., & Asadollahi, A. (2012). An Analysis of Factors Affecting on Online Shopping Behavior of Consumers. International Journal of Marketing Studies, 4(5). https://doi.org/10.5539/ijms.v4n5p81
• Mothersbaugh, D. L., Hawkins, D., Kleiser, S. B., Mothersbaugh, L. L., & Watson, C. (Casey) F. (2019). Consumer Behavior: Building Marketing Strategy. McGraw-Hill Education.
• Mustafa, A. S., & Garcia, M. B. (2021). 2021 1st Conference on Online Teaching for Mobile Education, OT4ME 2021.
• Oliver, R. L. (1980). A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions. Journal of Marketing Research, 17(4), 460. https://doi.org/ 10.2307/3150499
• Petrosyan, A. (2024). Number of Internet and Social Media Users Worldwide as of October 2024. https://www.statista.com/statistics/617136/digital-population-worldwide/
• Qin, M. (2007). Consumer behavior towards continued use of online shopping: An extend expectation disconfirmation model. In Integration and Innovation Orient to E-Society Volume 1: Seventh IFIP International Conference on e-Business, e-Services, and e-Society (I3E2007), October 10–12, Wuhan, China (pp. 400-407). https://doi.org/10.1007/978-0-387-75466-6_45
• Rehman, S. U., Bhatti, A., Mohamed, R., & Ayoup, H. (2019). The Moderating Role of Trust and Commitment Between Consumer Purchase Intention and Online Shopping Behavior in the Context of Pakistan. Journal of Global Entrepreneurship Research, 9(1). https://doi.org/10.1186/s40497-019-0166-2
• Rogers, E. M. (2003). Diffusion of innovation. Free Press.
• Rouibah, K., Thurasamy, R., & May, O. S. (2009). User Acceptance of Internet Banking In Malaysia: Test of Three Competing Models. International Journal of E-Adoption (IJEA), 1(1), 1–19. https://doi.org/10.4018/jea.2009010101
• Ruangkanjanases, A., Khan, A., Sivarak, O., Rahardja, U., & Chen, S.-C. (2024). Modeling the Consumers’ Flow Experience in E-commerce: The Integration of ECM and TAM with the Antecedents of Flow Experience. Sage Open, 14(2). https://doi.org/10.1177/21582440241258595
• Rudolph, T., Rosenbloom, B., & Wagner, T. (2004). Barriers to Online Shopping in Switzerland. Journal of International Consumer Marketing, 16(3), 55–74. https://doi.org/10.1300/J046v16n03_04
• Samaradiwakara, G. D. M. N., & Gunawardena, C. G. (2014). Comparison of existing technology acceptance theories and models to suggest a well improved theory/model. International Technical Sciences Journal, 1(1), 21–36.
• Sarkar, S., & Khare, A. (2019). Influence of Expectation Confirmation, Network Externalities, and Flow on Use of Mobile Shopping Apps. International Journal of Human-Computer Interaction, 35(16), 1449–1460. https://doi.org/10.1080/ 10447318.2018.1540383
• Sheth, J. N., Sethia, N. K., & Srinivas, S. (2011). Mindful Consumption: A Customer-Centric Approach to Sustainability. Journal of the Academy of Marketing Science, 39(1), 21–39. https://doi.org/10.1007/s11747-010-0216-3
• Singh, D. P. (2015). Integration of TAM, TPB, and Self-image to Study Online Purchase Intentions in an Emerging Economy. International Journal of Online Marketing, 5(1), 20–37. https://doi.org/10.4018/ijom.2015010102
• Song, H. G., & Jo, H. (2023). Understanding the Continuance Intention of Omnichannel: Combining TAM and TPB. Sustainability, 15(4), 3039. https://doi.org/10.3390/su15043039
• Statista. (2024). e - Commerce - Worldwide. https://www.statista.com/outlook/emo/ ecommerce/worldwide
• Tong, X. (2010). A cross‐national investigation of an extended technology acceptance model in the online shopping context. International Journal of Retail & Distribution Management, 38(10), 742–759. https://doi.org/10.1108/09590551011076524
• Tsao, W., & Chang, H. (2010). Exploring the Impact of Personality Traits on Online Shopping Behavior. African Journal of Business Management, 4(9), 1800–1812.
• Uşaklı, H. (2020). A Study on Personality Traits and Shopping Online. Zeszyty Naukowe Uniwersytetu Przyrodniczo-Humanistycznego w Siedlcach. Seria: Administracja i Zarządzanie, 48(48). https://doi.org/10.34739/zn.2019.48.05
• Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions Subject Areas: Design Characteristics, Interventions. Decision Sciences, 39(2), 273–315. http://www.vvenkatesh.com/wp-content/uploads/2015/11/Venkatesh_Bala_DS_2008.pdf
• Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204. https://www.jstor.org/stable/2634758
• Vijayasarathy, L. R. (2004). Predicting consumer intentions to use on-line shopping: the case for an augmented technology acceptance model. Information & Management, 41(6), 747–762. https://doi.org/10.1016/j.im.2003.08.011
• Wu, J. H., & Wang, S. C. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information and Management, 42(5), 719–729. https://doi.org/10.1016/j.im.2004.07.001
• Wu, W. Y., & Ke, C. C. (2015). An Online Shopping Behavior Model Integrating Personality Traits, Perceived Risk, and Technology Acceptance. Social Behavior and Personality, 43(1), 85–98. https://doi.org/10.2224/sbp.2015.43.1.85
• Yılmaz, Ö. (2018). Tüketicilerin Online Alışveriş Niyetlerinin Teknoloji Kabul Modeli Bağlamında İncelenmesi. Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi, December, 331–346. https://doi.org/10.32709/akusosbil.478718