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Predictors of investment intention in real estate: Extending the theory of planned behavior

    Akshita Singh Affiliation
    ; Shailendra Kumar Affiliation
    ; Utkarsh Goel Affiliation
    ; Amar Johri Affiliation

Abstract

This paper explores the factors affecting the investment intention of individual real estate investors utilizing the extended theory of planned behavior. With the help of self-administered questionnaire, data from 366 individual investors from India was collected. This data was analysed using two-step structural equation modelling. While significant direct effect of attitude, external influence, financial self-efficacy and perceived financial return was found, interpersonal influence, perceived financial risk, facilitating conditions and financial awareness had no significant direct impact on investment intention. Upon checking the mediating effect of attitude on the factors, all factors influenced investment intention through attitude except facilitating condition and financial awareness. It was also observed that attitude stands out as the most important aspect due to strongest influence on intention directly and also providing mediation to all variables except two. The study guides policymakers and investment institutions to develop strategies and utilize resources in a direction that can bring out a positive outcome by strengthening real estate investors’ investment intentions. It brings out the fact that financial confidence should be boosted by enabling investors to handle and manage their finances which can bring in a positive attitude for investing.

Keyword : real estate, investment intention, theory of planned behavior, perceived financial risk, perceived financial return, financial awareness

How to Cite
Singh, A., Kumar, S., Goel, U., & Johri, A. (2024). Predictors of investment intention in real estate: Extending the theory of planned behavior. International Journal of Strategic Property Management, 28(6), 349–368. https://doi.org/10.3846/ijspm.2024.22234
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Oct 22, 2024
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References

Abbasi, G. A., Tiew, L. Y., Tang, J., Goh, Y. N., & Thurasamy, R. (2021). The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis. PLoS ONE, 16(3), 1–26. https://doi.org/10.1371/journal.pone.0247582

Abroud, A., Choong, Y. V., & Muthaiyah, S. (2015). A conceptual framework for online stock trading service adoption. In I. Management Association (Ed.), Banking, finance, and accounting: Concepts, methodologies, tools, and applications (pp. 467–483). IGI Global. https://doi.org/10.4018/978-1-4666-6268-1.ch024

Adil, M., Singh, Y., & Ansari, M. S. (2022). Does financial literacy affect investor’s planned behavior as a moderator? Managerial Finance, 48(9–10), 1372–1390. https://doi.org/10.1108/MF-03-2021-0130

Ajjan, H., & Hartshorne, R. (2008). Investigating faculty decisions to adopt Web 2.0 technologies: Theory and empirical tests. The Internet and Higher Education, 11(2), 71–80. https://doi.org/10.1016/J.IHEDUC.2008.05.002

Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action control: From cognition to behavior (pp. 11–39). Springer. https://doi.org/10.1007/978-3-642-69746-3_2

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

Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology, 32(4), 665–683. https://doi.org/10.1111/J.1559-1816.2002.TB00236.X

Ajzen, I., & Driver, B. L. (1992). Application of the theory of planned behavior to leisure choice. Journal of Leisure Research, 24(3), 207–224. https://doi.org/10.1080/00222216.1992.11969889

Akhtar, F., & Das, N. (2019). Predictors of investment intention in Indian stock markets: Extending the theory of planned behaviour. International Journal of Bank Marketing, 37(1), 97–119. https://doi.org/10.1108/IJBM-08-2017-0167

Akhter, T., & Hoque, M. (2022). Moderating effects of financial cognitive abilities and considerations on the attitude–intentions nexus of stock market participation. International Journal of Financial Studies, 10(1), Article 5. https://doi.org/10.3390/IJFS10010005

Ali, M., Raza, S. A., Khamis, B., Puah, C. H., & Amin, H. (2021). How perceived risk, benefit and trust determine user Fintech adoption: A new dimension for Islamic finance. Foresight, 23(4), 403–420. https://doi.org/10.1108/FS-09-2020-0095

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411

Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi.org/10.1007/BF02723327

Bagozzi, R. P., & Yi, Y. (2012). Specification, evaluation, and interpretation of structural equation models. Journal of the Academy of Marketing Science, 40(1), 8–34. https://doi.org/10.1007/S11747-011-0278-X

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191

Bandura, A. (1986). The explanatory and predictive scope of self-efficacy theory. Journal of Social and Clinical Psychology, 4(3), 359–373. https://doi.org/10.1521/JSCP.1986.4.3.359

Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588–606. https://doi.org/10.1037/0033-2909.88.3.588

Browne, M., & Cudeck, R. (1993). Alternative ways of assessing model fit. Sociological Methods & Research, 21(2), 230–258. https://doi.org/10.1177/0049124192021002005

Byrne, B. M. (2001). Structural equation modeling with AMOS, EQS, and LISREL: Comparative approaches to testing for the factorial validity of a measuring instrument. International Journal of Testing, 1(1), 55–86. https://doi.org/10.1207/S15327574IJT0101_4

Chandon, P., Wansink, B., & Laurent, G. (2018). A benefit congruency framework of sales promotion effectiveness. Journal of Marketing, 64(4), 65–81. https://doi.org/10.1509/JMKG.64.4.65.18071

Chau, P. Y. K., & Hu, P. J. H. (2001). Information technology acceptance by individual professionals: A model comparison approach. Decision Sciences, 32(4), 699–719. https://doi.org/10.1111/J.1540-5915.2001.TB00978.X

Cherodian, R., & Thirlwall, A. P. (2015). Regional disparities in per capita income in India: Convergence or divergence? Journal of Post Keynesian Economics, 37(3), 384–407. https://doi.org/10.1080/01603477.2015.1000109

Cheung, G. W., & Lau, R. (2008). Testing mediation and suppression effects of latent variables: Bootstrapping with structural equation models. Organizational Research Methods, 11(2), 296–325. https://doi.org/10.1177/1094428107300343

Choi, Y. J., & Park, J. W. (2020). Investigating factors influencing the behavioral intention of online duty-free shop users. Sustainability, 12(17), Article 7108. https://doi.org/10.3390/SU12177108

Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://doi.org/10.1037/0033-2909.112.1.155

Daiyabu, Y. A., Manaf, N. A. A., & Mohamad Hsbollah, H. (2023). Extending the theory of planned behaviour with application to renewable energy investment: The moderating effect of tax incentives. International Journal of Energy Sector Management, 17(2), 333–351. https://doi.org/10.1108/IJESM-11-2021-0011

Dakhan, S., Sohu, J., Jabeen, A., Mirani, M., Shaikh, J., & Iqbal, S. (2020). Impact of Green HRM on employees pro-environmental behavior: Mediating role of women environmental knowledge at higher education institutions. IJCSNS International Journal of Computer Science and Network Security, 20(12), 202–208.

Dash, G., Kiefer, K., & Paul, J. (2021). Marketing-to-Millennials: Marketing 4.0, customer satisfaction and purchase intention. Journal of Business Research, 122, 608–620. https://doi.org/10.1016/J.JBUSRES.2020.10.016

Dholakia, U. M. (2001). A motivational process model of product involvement and consumer risk perception. European Journal of Marketing, 35(11/12), 1340–1362. https://doi.org/10.1108/EUM0000000006479

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/10.2307/3151312

Fox, J., Bartholomae, S., & Lee, J. (2005). Building the case for financial education. Journal of Consumer Affairs, 39(1), 195–214. https://doi.org/10.1111/J.1745-6606.2005.00009.X

Fu, J. R., Farn, C. K., & Chao, W. P. (2006). Acceptance of electronic tax filing: A study of taxpayer intentions. Information and Management, 43(1), 109–126. https://doi.org/10.1016/j.im.2005.04.001

Georgiev, G., Gupta, B., & Kunkel, T. (2003). Benefits of real estate investment. The Journal of Portfolio Management, 29(5), 28–33. https://doi.org/10.3905/jpm.2003.319903

Gopi, M., & Ramayah, T. (2007). Applicability of theory of planned behavior in predicting intention to trade online: Some evidence from a developing country. International Journal of Emerging Markets, 2(4), 348–360. https://doi.org/10.1108/17468800710824509

Goyal, K., & Kumar, S. (2020). Financial literacy: A systematic review and bibliometric analysis. International Journal of Consumer Studies, 45(1), 80–105. https://doi.org/10.1111/ijcs.12605

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

Gulseven, O., & Ekici, O. (2021). The role of real estate and gold as inflation hedges: The Islamic influence. International Journal of Islamic and Middle Eastern Finance and Management, 14(2), 391–408. https://doi.org/10.1108/IMEFM-01-2019-0038

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, T. L. (2006). Multivariate data analysis (6th ed.). Pearson Prentice Hall.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202

Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. The Guilford Press.

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

Hoque, M. E., Kabir Hassan, M., Hashim, N. M. H. N., & Zaher, T. (2019). Factors affecting Islamic banking behavioral intention: The moderating effects of customer marketing practices and financial considerations. Journal of Financial Services Marketing, 24, 44–58. https://doi.org/10.1057/S41264-019-00060-X

Hsieh, P. J. (2015). Physicians’ acceptance of electronic medical records exchange: An extension of the decomposed TPB model with institutional trust and perceived risk. International Journal of Medical Informatics, 84(1), 1–14. https://doi.org/10.1016/J.IJMEDINF.2014.08.008

Hsu, M.-H., & Chiu, C.-M. (2004). Predicting electronic service continuance with a decomposed theory of planned behaviour. Behaviour & Information Technology, 23(5), 359–373. https://doi.org/10.1080/01449290410001669969

Hudson-Wilson, S., Gordon, J. N., Fabozzi, F. J., Anson, M. J. P., & Giliberto, S. M. (2005). Why real estate? Journal of Portfolio Management, 31(5), 12–21. https://doi.org/10.3905/jpm.2005.593883

Huhmann, B. A., & McQuitty, S. (2009). A model of consumer financial numeracy. International Journal of Bank Marketing, 27(4), 270–293. https://doi.org/10.1108/02652320910968359

Hung, S. Y., Ku, Y. C., & Chien, J. C. (2012). Understanding physicians’ acceptance of the Medline system for practicing evidence-based medicine: A decomposed TPB model. International Journal of Medical Informatics, 81(2), 130–142. https://doi.org/10.1016/J.IJMEDINF.2011.09.009

IBEF. (2023). IBEF. https://www.ibef.org/

Jabeen, F., Dhir, A., Islam, N., Talwar, S., & Papa, A. (2023). Emotions and food waste behavior: Do habit and facilitating conditions matter? Journal of Business Research, 155, Article 113356. https://doi.org/10.1016/j.jbusres.2022.113356

Jariyapan, P., Mattayaphutron, S., Gillani, S. N., & Shafique, O. (2022). Factors influencing the behavioural intention to use cryptocurrency in emerging economies during the COVID-19 pandemic: Based on technology acceptance model 3, perceived risk, and financial literacy. Frontiers in Psychology, 12, Article 814087. https://doi.org/10.3389/fpsyg.2021.814087

Jia, D., Li, R., Bian, S., & Gan, C. (2021). Financial planning ability, risk perception and household portfolio choice. Emerging Markets Finance and Trade, 57(8), 2153–2175. https://doi.org/10.1080/1540496X.2019.1643319

Kapoor, K., Dwivedi, Y., Piercy, N. C., Lal, B., & Weerakkody, V. (2014). RFID integrated systems in libraries: Extending TAM model for empirically examining the use. Journal of Enterprise Information Management, 27(6), 731–758. https://doi.org/10.1108/JEIM-10-2013-0079

Kaur, I., & Kaushik, K. P. (2016). Determinants of investment behaviour of investors towards mutual funds. Journal of Indian Business Research, 8(1), 19–42. https://doi.org/10.1108/JIBR-04-2015-0051

Khan, Y., Hameed, I., & Akram, U. (2023). What drives attitude, purchase intention and consumer buying behavior toward organic food? A self-determination theory and theory of planned behavior perspective. British Food Journal, 125(7), 2572–2587. https://doi.org/10.1108/BFJ-07-2022-0564

Kling, L., König-Kersting, C., & Trautmann, S. T. (2023). Investment preferences and risk perception: Financial agents versus clients. Journal of Banking & Finance, 154, Article 106489. https://doi.org/10.1016/j.jbankfin.2022.106489

Kock, N. (2021). Harman’s single factor test in PLS-SEM: Checking for common method bias. Data Analysis Perspectives Journal, 2(2), 1–6.

Kundu, A., & Saraswati, L. R. (2012). Migration and exclusionary urbanisation in India. Economic and Political Weekly, 47(26–27), 219–227. https://www.jstor.org/stable/23251688

Leavell, J. P. (2015). Contolling and informational planned behavior: Self-determination theory and the theory of planned behavior. Atlantic Marketing Journal, 5(3), 2165–3879.

Li, C., Khaliq, N., Chinove, L., Khaliq, U., Popp, J., & Oláh, J. (2023). Cryptocurrency acceptance model to analyze consumers’ usage intention: Evidence from Pakistan. SAGE Open, 13(1), 1–19. https://doi.org/10.1177/21582440231156360

Li, W., Yuan, K., Yue, M., Zhang, L., & Huang, F. (2021). Climate change risk perceptions, facilitating conditions and health risk management intentions: Evidence from farmers in rural China. Climate Risk Management, 32, Article 100283. https://doi.org/10.1016/j.crm.2021.100283

Li, Z. (2021). Role of affective mediators in the effects of media use on proenvironmental behavior. Science Communication, 43(1), 64–90. https://doi.org/10.1177/1075547020971646

Lim, T. S., Mail, R., Abd Karim, M. R., Ahmad Baharul Ulum, Z. K., Jaidi, J., & Noordin, R. (2018). A serial mediation model of financial knowledge on the intention to invest: The central role of risk perception and attitude. Journal of Behavioral and Experimental Finance, 20, 74–79. https://doi.org/10.1016/j.jbef.2018.08.001

Liu, M. T., Brock, J. L., Shi, G. C., Chu, R., & Tseng, T. H. (2013). Perceived benefits, perceived risk, and trust: Influences on consumers’ group buying behaviour. Asia Pacific Journal of Marketing and Logistics, 25(2), 225–248. https://doi.org/10.1108/13555851311314031

Lowry, P. B., & Gaskin, J. (2014). Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it. IEEE Transactions on Professional Communication, 57(2), 123–146. https://doi.org/10.1109/TPC.2014.2312452

Ma, K. V., Nguyen, P. V., & Ahmed, Z. U. (2023). The role of government policy, social infrastructure and Fengshui in intending to buy tourism real estate. PLoS ONE, 18(7), Article e0281436. https://doi.org/10.1371/journal.pone.0281436

Mäder, S. J., Thoma, M. V., Salas Castillo, A. N., Dorigo, M., & Rohner, S. L. (2024). Intra- and interpersonal influences on child adjustment and resilience in welfare care: A qualitative study with former caregivers in Switzerland. Children and Youth Services Review, 161, Article 107653. https://doi.org/10.1016/J.childyouth.2024.107653

Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91. https://doi.org/10.2307/2975974

Mazambani, L., & Mutambara, E. (2020). Predicting FinTech innovation adoption in South Africa: The case of cryptocurrency. African Journal of Economic and Management Studies, 11(1), 30–50. https://doi.org/10.1108/AJEMS-04-2019-0152

Melser, D., & Hill, R. J. (2019). Residential real estate, risk, return and diversification: Some empirical evidence. Journal of Real Estate Finance and Economics, 59(1), 111–146. https://doi.org/10.1007/s11146-018-9668-x

Meng, B., & Choi, K. (2018). An investigation on customer revisit intention to theme restaurants: The role of servicescape and authentic perception. International Journal of Contemporary Hospitality Management, 30(3), 1646–1662. https://doi.org/10.1108/IJCHM-11-2016-0630

Mishra, A. K., Bansal, R., Maurya, P. K., Kar, S. K., & Bakshi, P. K. (2023). Predicting the antecedents of consumers’ intention toward purchase of mutual funds: A hybrid PLS‐SEM‐neural network approach. International Journal of Consumer Studies, 47(2), 563–587. https://doi.org/10.1111/ijcs.12850

Nadeem, M. A., Qamar, M. A. J., Nazir, M. S., Ahmad, I., Timoshin, A., & Shehzad, K. (2020). How investors attitudes shape stock market participation in the presence of financial self-efficacy. Frontiers in Psychology, 11, Article 553351. https://doi.org/10.3389/fpsyg.2020.553351

Nair, P. S., Shiva, A., Yadav, N., & Tandon, P. (2022). Determinants of mobile apps adoption by retail investors for online trading in emerging financial markets. Benchmarking, 30(5), 1623–1648. https://doi.org/10.1108/BIJ-01-2022-0019

Newell, G., & Kamineni, R. (2007). The significance and performance of real estate markets in India. Journal of Real Estate Portfolio Management, 13(2), 161–172. https://doi.org/10.1080/10835547.2007.12089769

Nur Aini, N. S., & Lutfi, L. (2019). The influence of risk perception, risk tolerance, overconfidence, and loss aversion towards investment decision making. Journal of Economics, Business & Accountancy Ventura, 21(3), 401–413. https://doi.org/10.14414/jebav.v21i3.1663

Nyasulu, C., & Dominic Chawinga, W. (2019). Using the decomposed theory of planned behaviour to understand university students’ adoption of WhatsApp in learning. E-Learning and Digital Media, 16(5), 413–429. https://doi.org/10.1177/2042753019835906

Padmavathy, C., Swapana, M., & Paul, J. (2019). Online second-hand shopping motivation – Conceptualization, scale development, and validation. Journal of Retailing and Consumer Services, 51, 19–32. https://doi.org/10.1016/j.jretconser.2019.05.014

Pai, F. Y., & Huang, K. I. (2011). Applying the technology acceptance model to the introduction of healthcare information systems. Technological Forecasting and Social Change, 78(4), 650–660. https://doi.org/10.1016/j.techfore.2010.11.007

Paul, J., Modi, A., & Patel, J. (2016). Predicting green product consumption using theory of planned behavior and reasoned action. Journal of Retailing and Consumer Services, 29, 123–134. https://doi.org/10.1016/J.jretconser.2015.11.006

Podsakoff, P., MacKenzie, S., Lee, J., & Podsakoff, N. (1990). 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

Rana, J., & Paul, J. (2017). Consumer behavior and purchase intention for organic food: A review and research agenda. Journal of Retailing and Consumer Services, 38, 157–165. https://doi.org/10.1016/j.jretconser.2017.06.004

Rana, K., Poudel, P., & Chimoriya, R. (2023). Qualitative methodology in translational health research: Current practices and future directions. Healthcare, 11(19), Article 2665. https://doi.org/10.3390/healthcare11192665

Raut, R. K., & Kumar, S. (2024). An integrated approach of TAM and TPB with financial literacy and perceived risk for influence on online trading intention. Digital Policy, Regulation and Governance, 26(2), 135–152. https://doi.org/10.1108/DPRG-07-2023-0101

Rimal, R. N., & Real, K. (2003). Perceived risk and efficacy beliefs as motivators of change. Human Communication Research, 29(3), 370–399. https://doi.org/10.1111/J.1468-2958.2003.tb00844.x

Roldán, J. L. & Sánchez-Franco, M. J. (2012). Variance-based structural equation modeling: Guidelines for using partial least squares in information systems research. In M. Mora, O. Gelman, A. Steenkamp, & M. Raisinghani (Eds.), Research methodologies, innovations and philosophies in software systems engineering and information systems (pp. 193–221). IGI Global. https://doi.org/10.4018/978-1-4666-0179-6.ch010

Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information and Management, 44(1), 90–103. https://doi.org/10.1016/j.im.2006.10.007

Schneider, B., Ehrhart, M. G., Mayer, D. M., Saltz, J. L., & Niles-Jolly, K. (2005). Understanding organization-customer links in service settings. Academy of Management Journal, 48(6), 1017–1032. https://doi.org/10.5465/AMJ.2005.19573107

Seyal, A. H., Rahman, Mohd. N. Abd., & Rahim, Md. M. (2002). Determinants of academic use of the Internet: A structural equation model. Behaviour & Information Technology, 21(1), 71–86. https://doi.org/10.1080/01449290210123354

Shaw, D., Shiu, E., & Clarke, I. (2000). The contribution of ethical obligation and self-identity to the theory of planned behaviour: An exploration of ethical consumers. Journal of Marketing Management, 16(8), 879–894. https://doi.org/10.1362/026725700784683672

Shi, J., Xu, K., Si, H., Song, L., & Duan, K. (2021). Investigating intention and behaviour towards sorting household waste in Chinese rural and urban–rural integration areas. Journal of Cleaner Production, 298, Article 126827. https://doi.org/10.1016/j.jclepro.2021.126827

Shim, G., Lee, S., & Kim, Y. (2008). How investor behavioral factors influence investment satisfaction, trust in investment company, and reinvestment intention. Journal of Business Research, 61(1), 47–55. https://doi.org/10.1016/j.jbusres.2006.05.008

Sivaramakrishnan, S., Srivastava, M., & Rastogi, A. (2017). Attitudinal factors, financial literacy, and stock market participation. International Journal of Bank Marketing, 35(5), 818–841. https://doi.org/10.1108/IJBM-01-2016-0012

Soomro, B. A., Shah, N., & Abdelwahed, N. A. A. (2024). Intention to adopt cryptocurrency: A robust contribution of trust and the theory of planned behavior. Journal of Economic and Administrative Sciences, 40(2), 419–433. https://doi.org/10.1108/JEAS-10-2021-0204

Soon, H. T., & Sharifah, S. L. (2017). The drivers for cloud-based virtual learning environment: Examining the moderating effect of school category. Internet Research, 27(4), 942–973. https://doi.org/10.1108/INTR-08-2016-0256

Sridharan, U., Mansour, F., Ray, L., & Huning, T. (2023). Effect of risk attitude on cryptocurrency adoption for compensation and spending. Journal of Financial Economic Policy, 15(4–5), 337–350. https://doi.org/10.1108/JFEP-04-2023-0099

Tajurahim, N. N. S., Bakar, E. A., Jusoh, Z. M., Ahmad, S., & Arif, A. M. M. (2020). The effect of intensity of consumer education, self-efficacy, personality traits and social media on consumer empowerment. International Journal of Consumer Studies, 44(6), 511–520. https://doi.org/10.1111/ijcs.12585

Tarkiainen, A., & Sundqvist, S. (2005). Subjective norms, attitudes and intentions of Finnish consumers in buying organic food. British Food Journal, 107(11), 808–822. https://doi.org/10.1108/00070700510629760

Taylor, S., & Todd, P. (1995). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International Journal of Research in Marketing, 12(2), 137–155. https://doi.org/10.1016/0167-8116(94)00019-K

Tingchi Liu, M., Brock, J. L., Cheng Shi, G., Chu, R., & Tseng, T. H. (2013). Perceived benefits, perceived risk, and trust: Influences on consumers’ group buying behaviour. Asia Pacific Journal of Marketing and Logistics, 25(2), 225–248. https://doi.org/10.1108/13555851311314031

Triandis, H. (1979). Values, attitudes, and interpersonal behavior. Nebraska Symposium on Motivation, 27, 195–259.

Vaidyanathan, R., Aggarwal, P., Stem, D. E., Muehling, D. D., & Umesh, U. N. (2000). Deal evaluation and purchase intention: The impact of aspirational and market-based internal reference prices. Journal of Product & Brand Management, 9(3), 179–192. https://doi.org/10.1108/10610420010332449

van Rooij, M. C. J., Lusardi, A., & Alessie, R. J. M. (2012). Financial literacy, retirement planning and household wealth. The Economic Journal, 122(560), 449–478. https://doi.org/10.1111/j.1468-0297.2012.02501.x

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

Venkatesh, V., Thong, J., & 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

Vörös, Z., Szabó, Z., Kehl, D., Kovács, O. B., Papp, T., & Schepp, Z. (2021). The forms of financial literacy overconfidence and their role in financial well‐being. International Journal of Consumer Studies, 45(6), 1292–1308. https://doi.org/10.1111/ijcs.12734

Walton, A. J., & Johnston, K. A. (2018). Exploring perceptions of bitcoin adoption: The South African virtual community perspective. Interdisciplinary Journal of Information, Knowledge, and Management, 13, 65–182. https://doi.org/10.28945/4080

Wang, S., Lin, S., & Li, J. (2018). Exploring the effects of non-cognitive and emotional factors on household electricity saving behavior. Energy Policy, 115(32), 171–180. https://doi.org/10.1016/j.enpol.2018.01.012

Xiao, J. J., Ahn, S. Y., Serido, J., & Shim, S. (2014). Earlier financial literacy and later financial behaviour of college students. International Journal of Consumer Studies, 38(6), 593–601. https://doi.org/10.1111/ijcs.12122

Xiao, J. J., Tang, C., Serido, J., & Shim, S. (2011). Antecedents and consequences of risky credit behavior among college students: Application and extension of the theory of planned behavior. Journal of Public Policy and Marketing, 30(2), 239–245. https://doi.org/10.1509/jppm.30.2.239

Xie, K., Vongkulluksn, V. W., Heddy, B. C., & Jiang, Z. (2023). Experience sampling methodology and technology: An approach for examining situational, longitudinal, and multi-dimensional characteristics of engagement. Educational Technology Research and Development, 1–31. https://doi.org/10.1007/S11423-023-10259-4

Xu, Y., Du, J., Khan, M. A. S., Jin, S., Altaf, M., Anwar, F., & Sharif, I. (2022). Effects of subjective norms and environmental mechanism on green purchase behavior: An extended model of theory of planned behavior. Frontiers in Environmental Science, 10, Article 779629. https://doi.org/10.3389/fenvs.2022.779629

Xu, Z., Li, J., Shan, J., & Zhang, W. (2021). Extending the theory of planned behavior to understand residents’ coping behaviors for reducing the health risks posed by haze pollution. Environment, Development and Sustainability, 23(2), 2122–2142. https://doi.org/10.1007/S10668-020-00666-5

Yadav, R., & Pathak, G. S. (2017). Determinants of consumers’ green purchase behavior in a developing nation: Applying and extending the theory of planned behavior. Ecological Economics, 134, 114–122. https://doi.org/10.1016/j.ecolecon.2016.12.019

Yang, M., Li, H., Shao, Z., & Shang, W. (2017). Influencing lenders’ repeat investment intention in P2P lending platforms in China through signaling [Conference presentation]. Proceedings of the 21st Pacific Asia Conference on Information Systems (PACIS 2017), Langkawi. https://aisel.aisnet.org/pacis2017/72

Yousaf, I., & Ali, S. (2020). Integration between real estate and stock markets: New evidence from Pakistan. International Journal of Housing Markets and Analysis, 13(5), 887–900. https://doi.org/10.1108/IJHMA-01-2020-0001

Zhang, Y., Wang, C., Tian, W., & Zhang, G. (2020). Determinants of purchase intention for real estate developed on industrial brownfields: Evidence from China. Journal of Housing and the Built Environment, 35(4), 1261–1282. https://doi.org/10.1007/S10901-020-09741-9

Zhao, H., & Zhang, L. (2021). Financial literacy or investment experience: Which is more influential in cryptocurrency investment? International Journal of Bank Marketing, 39(7), 1208–1226. https://doi.org/10.1108/IJBM-11-2020-0552

Zhu, T., & Xiao, J. J. (2022). Consumer financial education and risky financial asset holding in China. International Journal of Consumer Studies, 46(1), 56–74. https://doi.org/10.1111/ijcs.12643