Examining Taxpayers’ Behavior in Phnom Penh to Use Cambodia Road Tax Mobile Payment Application
Main Article Content
Abstract
Purpose: Cambodia Road Tax Mobile Payment Application (CRTMPA) was newly introduced in 2021, and the system requires a large adoption among its citizen. Hence, this study investigates the behavioral intention and use behavior of taxpayers in Phnom Penh to use CRTMPA. The conceptual framework is constructed with perceived usefulness, ease of use, trust, social influence, facilitating condition, behavioral intention, and use behavior. Research design, data, and methodology: 500 taxpayers in Phnom Penh who have experienced using road tax mobile payment applications were investigated. The sample techniques are judgmental and convenience sampling. The Item Objective Congruence (IOC) Index and the pilot test (n=50) conducted the content validity and internal consistency. Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were applied to analyze the data and test hypotheses. Results: All hypotheses are supported in this study. Perceived usefulness, perceived ease of use, social influence, and facilitating conditions significantly impact behavioral intention. Perceived ease of use also significantly impacts perceived usefulness. In addition, trust and behavioral intention significantly impact the use behavior of CRTMPA among Cambodian taxpayers. Conclusions: This study contributes to the General Department of Taxation, tax branch directors, ICT policymakers, and related businesses to improve the adoption rate of CRTMPA.
Downloads
Article Details

This work is licensed under a Creative Commons Attribution 4.0 International License.
The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data, or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution License (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
References
Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Journal of Psychological bulletin, 82(2), 261-277.
Alam, M. Z., Hu, W., Hoque, M. R., & Kaium, M. A. (2020). Adoption intention and usage behavior of mHealth services in Bangladesh and China: A cross-country analysis. International Journal of Pharmaceutical and Healthcare Marketing, 14(1), 37-60. https://doi.org/10.1108/IJPHM-03-2019-0023
Ali, F., Nair, P. K., & Hussain, K. (2016). An assessment of students' acceptance and usage of computer supported collaborative classrooms in hospitality and tourism schools. Journal of Hospitality, Leisure, Sport & Tourism Education, 18, 51-60.
Al Mansoori, K. A., Sarabdeen, J., & Tchantchane, A. L. (2018). Investigating Emirati citizens’ adoption of e-government services in Abu Dhabi using modified UTAUT model. Journal of Information Technology People, 31(2), 455-481. https://doi.org/10.1108/ITP-12-2016-0290
Alsaif, M. (2014). Factors affecting citizens’ adoption of e-government moderated by socio-cultural values in Saudi Arabia [Master's Thesis]. University of Birmingham.
Bahrini, R., & Qaffas, A. A. (2019). Impact of information and communication technology on economic growth: Evidence from developing countries. Journal of Economies, 7(1), 21.
Bélanger, F., & Carter, L. (2008). Trust and risk in e-government adoption. The Journal of Strategic Information Systems, 17(2), 165-176.
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. https://doi.org/10.1037/0033-2909.107.2.238
Carter, L., & Bélanger, F. (2005). The utilization of e‐government services: citizen trust, innovation and acceptance factors. Information systems journal, 15(1), 5-25.
Chiu, J. L., Bool, N. C., & Chiu, C. L. (2017). Challenges and factors influencing initial trust and behavioral intention to use mobile banking services in the Philippines. Asia Pacific Journal of Innovation and Entrepreneurship, 11(2), 246-278. https://doi.org/10.1108/APJIE-08-2017-029
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319-340.
El-Masri, M., & Tarhini, A. (2017). Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Educational Technology Research and Development, 65(3), 743-763.
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
Gupta, K., & Arora, N. (2020). Investigating consumer intention to accept mobile payment systems through unified theory of acceptance model: An Indian perspective. South Asian Journal of Business Studies, 9(1), 88-114. https://doi.org/10.1108/SAJBS-03-2019-0037
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate Data Analysis (6th ed.). Pearson Education.
Horst, M., Kuttschreuter, M., & Gutteling, J. M. (2007). Perceived usefulness, personal experiences, risk perception and trust as determinants of adoption of e-government services in The Netherlands. Computers in Human Behavior, 23(4), 1838-1852.
Kijsanayotin, B., Pannarunothai, S., & Speedie, S. M. (2009). Factors influencing health information technology adoption in Thailand's community health centers: applying the UTAUT model. International journal of medical informatics, 78(6), 404–416. https://doi.org/10.1016/j.ijmedinf.2008.12.005
Lean, O. K., Zailani, S., Ramayah, T., & Fernando, Y. (2009). Factors influencing intention to use e-government services among citizens in Malaysia. International Journal of Information Management, 29(6), 458-475.
Liébana-Cabanillas, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2014). Antecedents of the adoption of the new mobile payment systems: The moderating effect of age. Journal of Computers in Human Behavior, 35(4), 464-478.
Magsamen-Conrad, K., Upadhyaya, S., Joa, C. Y., & Dowd, J. (2015). Bridging the divide: Using UTAUT to predict multigenerational tablet adoption practices. Computers in Human Behavior, 50, 186-196.
Mathwick, C., Malhotra, N., & Rigdon, E. (2001). Experiential value: conceptualization, measurement and application in the catalog and Internet shopping environment. Journal of retailing, 77(1), 39-56.
Molinillo, S., Liébana-Cabanillas, F., Anaya-Sánchez, R., & Buhalis, D. (2018). DMO online platforms: Image and intention to visit. Journal of Tourism management, 65, 116-130.
Moshagen, M. (2012). The model size effect in SEM: Inflated goodness-of-fit statistics are due to the size of the covariance matrix. Structural Equation Modeling: A Multidisciplinary Journal, 19(1), 86-98.
Park, E., & Kim, K. J. (2014). An integrated adoption model of mobile cloud services: exploration of key determinants and extension of technology acceptance model. Journal of Telematics Informatics, 31(3), 376-385.
Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International journal of electronic commerce, 7(3), 101-134.
Pedroso, R., Zanetello, L., Guimaraes, L., Pettenon, M., Goncalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry, 43(3), 37-40.
Radner, R., & Rothschild, M. (1975). On the allocation of effort. Journal of Economic Theory, 10(3), 358-376.
Raman, A., Don, Y., Khalid, R., Hussin, F., Fauzee, O., Sofian, M., & Ghani, M. (2014). Technology acceptance on smart board among teachers in Terengganu using UTAUT model. Asian Social Science, 10(11), 84-91.
Rotter, J. B. (1980). Interpersonal Trust, Trustworthiness, and Gullibility. American Psychologist, 35(1), 1-7. https://doi.org/10.1037/0003-066X.35.1.1
Saeed, R., & Al-Emran, M. (2018). Students Acceptance of Google Classroom: An Exploratory Study using PLS-SEM Approach. International Journal of Emerging Technologies in Learning (iJET), 13(6), 112-123. https://doi.org/10.3991/ijet.v13i06.8275
Samnang, A., Daengdej, J., & Vongurai, R. (2021). Factors Affecting Acceptance and Use of E-Tax Services among Medium Taxpayers in Phnom Penh, Cambodia. The Journal of Asian Finance, Economics Business research methods, 8(7), 79-90.
Samsudeen, S. N., Selvaratnam, G., & Hayathu Mohamed, A. H. (2022). Intention to use mobile banking services: an Islamic banking customers’ perspective from Sri Lanka. Journal of Islamic Marketing, 13(2), 410-433. https://doi.org/10.1108/JIMA-05-2019-0108
Sang, S., Lee, J., & Lee, J. (2009). E‐government adoption in ASEAN: the case of Cambodia. Internet Research, 19(5), 517-534. https://doi.org/10.1108/10662240910998869
Sarmah, R., Dhiman, N., & Kanojia, H. (2021). Understanding intentions and actual use of mobile wallets by millennial: an extended TAM model perspective. Journal of Indian Business Research, 13(3), 361-381. https://doi.org/10.1108/JIBR-06-2020-0214
Sekaran, U., & Bougies, R. (2013). Research Methods for Business (6th ed.). Wiley.
Sharma, S., Mukherjee, S., Kumar, A., & Dillon, W. (2005). A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models. Journal of Business Research, 58(7), 935-943. https://doi.org/10.1016/j.jbusres.2003.10.007
Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. In M. A. Lange (Ed.), Leading-edge psychological tests and testing research (pp. 27–50). Nova Science Publishers.
Sobti, N. (2019). Impact of demonetization on diffusion of mobile payment service in India: Antecedents of behavioral intention and adoption using extended UTAUT model. Journal of Advances in Management Research, 16(4), 478. https://doi.org/10.1108/JAMR-09-2018-0086
Soper, D. S. (2022, May 24). A-priori Sample Size Calculator for Structural Equation Models. Danielsoper. www.danielsoper.com/statcalc/default.aspx
Tan, P. J. B. (2013). Applying the UTAUT to understand factors affecting the use of English e-learning websites in Taiwan. Sage Open, 3(4), 1-7.
Teicher, J., Hughes, O., & Dow, N. (2002). E‐government: a new route to public sector quality. Managing Service Quality: An International Journal, 12(6), 384-393. https://doi.org/10.1108/09604520210451867
Thakur, R., & Srivastava, M. (2015). A study on the impact of consumer risk perception and innovativeness on online shopping in India. International Journal of Retail & Distribution Management, 43(2), 148-166. https://doi.org/10.1108/IJRDM-06-2013-0128
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.
Vongurai, R. (2020). Factors affecting customer brand preference toward electric vehicle in Bangkok, Thailand. The Journal of Asian Finance, Economics, Business, 7(8), 383-393. https://doi.org/10.13106/JAFEB.2020.VOL7.NO8.383
Wallace, C. (2012). Can information and communications technology enhance social quality? The International Journal of Social Quality, 2(2), 98-117.
Wang, Y.-S., & Shih, Y.-W. (2009). Why do people use information kiosks? A validation of the Unified Theory of Acceptance and Use of Technology. Journal of Government information quarterly, 26(1), 158-165.
Warkentin, M., Gefen, D., Pavlou, P. A., & Rose, G. M. (2002). Encouraging citizen adoption of e-government by building trust. Electronic markets, 12(3), 157-162.
Wu, J. H., & Wang, Y. M. (2006). Measuring KMS Success: A Respecification of the DeLone and McLean’s Model. Journal of Information & Management, 43, 728-739. http://dx.doi.org/10.1016/j.im.2006.05.002
Zhang, L., Zhu, J., & Liu, Q. (2012). A meta-analysis of mobile commerce adoption and the moderating effect of culture. Computers in Human Behavior, 28(5), 1902-1911.