Measuring Behavioral Intention and Use Behavior of Medium & Large Enterprise Customers Towards Accounting Information System In Dazhou, China
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Abstract
Purpose: This paper investigates the intention and influencing factors of using computerized accounting information systems in Dazhou enterprises in China. The key variables are perceived ease of use, perceived usefulness, attitude, social influence, perceived risk, facilitating conditions, behavioral intention, and use behavior. Research design, data, and methodology: Researchers collected questionnaires from 500 target medium & large corporate clients. The Index of Item-Objective Congruence (IOC) was determined to indicate the validity of the research content. The researcher opted for a pilot test of 50 respondents from the target population for this preliminary assessment. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) indicates convergent validity, composite reliability, Cronbach α reliability, factor load, mean square extraction analysis and discriminant validity. Results: It shows that perceived ease of use significantly affects perceived usefulness. Additionally, perceived usefulness and percived ease of use significantly influence attitude. Furthermore, attitude, social influence and perceived risk have a significant effect on behavioral intention. Additionally, behavioral intention significantly affects usage behavior. However, facilitating conditions has no significant effect on behavioral intention Conclusions: This study has important theoretical significance and practical value for Chinese enterprises to realize modernization in financial accounting methods.
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References
Alleyne, P., & Lavine, M. (2013). Factors influencing accountants' behavioural intentions to use and actual usage of enterprise resource planning systems in a global development agency. Journal of Financial Reporting and Accounting, 11(2), 179-200. https://doi.org/10.1108/JFRA-11-2011-0017
Awwad, M., & Al-Majali, S. (2015). Electronic library services acceptance and use: an empirical validation of unified theory of acceptance and use of technology. The Electronic Library, 33(6), 1-10.
Bashir, I., & Madhavaiah, C. (2015). Consumer attitude and behavioural intention towards Internet banking adoption in India. Journal of Indian Business Research, 7(1), 67-102.
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
Brown, T. A. (2015). Confirmatory factor analysis for applied research. Guilford Publications.
Byrne, B. M. (2016). Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming (3rd ed.). Routledge
Carlsson, C., Carlsson, J., Hyvonen, K., Puhakainen, J., & Walden, P. (2006, January). Adoption of Mobile Devices/Services-Searching for Answers with the UTAUT. Proceedings of the 39th Annual Hawaii International Conference on System Sciences, 132a. https://doi.org/10.1109/HICSS.2006.38
Chen, C. (2019). Research Summary of Health and Wellness Tourism at Home and Abroad. Journal of Panzhihua University, 36, 43-47.
Cheung, S. K. (2000). Psychometric properties of the Chinese version of the Parental Stress Scale. PSYCHOLOGIA, 43(4), 253-261.
Choy, C. K., Benzie, I. F., & Cho, P. (2004). Is Ascorbate in Human Tears from Corneal Leakage or from Lacrimal Secretion? Clinical and Experimental Optometry, 87, 24-27. https://doi.org/10.1111/j.1444-0938.2004.tb03142.x
Cudjoe, A. G., Anim, P., & Nyanyofio, J. (2015). Determinants of Mobile Banking Adoption in the Ghanaian Banking Industry: A Case of Access Bank Ghana Limited. Journal of Computer and Communications, 3(1), 1-19. https://doi.org/10.4236/jcc.2015.32001
Davis, F. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
Davis, F. D. (1986). A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results. Sloan School of Management, Massachusetts Institute of Technology.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 25(8), 982-1003.
Eagly, A. H., & Chaiken, S. (1993). The Psychology of Attitudes. Harcourt Brace Jovanovich College Publishers.
Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Addison-Wesley.
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50.
Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate Data Analysis (6th ed.). Pearson Prentice Hall.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th ed.). Pearson.
Hanafizadeh, P., Behboudi, M., Koshksaray, A. A., & Tabar, M. J. S. (2014). Mobile-Banking Adoption by Iranian Bank Clients. Telematics and Informatics, 31, 62-78. https://doi.org/10.1016/j.tele.2012.11.001
Harsano, I. L. D., & Suryana, L. A. (2014). Factors Affecting the Use Behavior of social media Using UTAUT2 Model. Proceedings of the First Asia-Pacific Conference on Global Business.
Hsu, C. L., & Lu, H. P. (2004). Why Do People Play On-Line Games? An Extended TAM with Social Influences and Flow Experience. Information & Management, 41, 853-868. http://dx.doi.org/10.1016/j.im.2003.08.014
Jones, G., Hanton, S., & Connaughton, D. (2002). What Is This Thing Called Mental Toughness? An Investigation of Elite Sport Performers. Journal of Applied Sport Psychology, 14, 205-218. http://dx.doi.org/10.1080/10413200290103509
Kesharwani, A., & Bisht, S. (2012). The impact of trust and perceived risk on Internet banking adoption in India. International Journal of Bank Marketing, 30(4), 303-322. https://doi.org/10.1108/02652321211236923
Kim, J., & Lennon, S. J. (2013). Effects of Reputation and Website Quality on Online Consumers’ Emotion, Perceived Risk and Purchase Intention. Journal of Research in Interactive Marketing, 7, 33-56. https://doi.org/10.1108/17505931311316734
Koksal, M. H. (2016). The intentions of Lebanese consumers to adopt mobile banking. International Journal of Bank Marketing, 34(3), 327-346. https://doi.org/10.1108/IJBM-03-2015-0025
Lin, C., & Lin, M. (2019). The determinants of using cloud supply chain adoption. Industrial Management & Data Systems, 119(2), 351-366. https://doi.org/10.1108/IMDS-12-2017-0589
Lu, R. (2020). Genomic Characterisation and Epidemiology of 2019 Novel Coronavirus: Implications for Virus Origins and Receptor Binding. The Lancet, 395, 565-574. https://doi.org/10.1016/s0140-6736(20)30251-8
Luo, C. (2010). A Study on the Biological Characteristic of Phallus impudicus. Journal of Xinyang Normal University Natural Science, 23, 242-244.
Makanyeza, C. (2017). Determinants of consumers intention to adopt mobile banking services in Zimbabwe. International Journal of Bank Marketing, 35(6), 997-1017. https://doi.org/10.1108/ijbm-07-2016-0099
Mathieson, K. (1991). Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior. Information Systems Research, 2, 173-191. http://dx.doi.org/10.1287/isre.2.3.173
Menozzi, D., Sogari, G., Veneziani, M., Simoni, E., & Mora, C. (2017). Eating Novel Foods: An Application of the Theory of Planned Behaviour to Predict the Consumption of an Insect-Based Product. Food Quality and Preference, 59.
Nawaz, S. S., & Sheham, A. N. (2015). Evaluating the Intention to use Accounting Information Systems by Small and Medium Sized Enterpreneur. Research Journal of Finance and Accounting, 6(22), 38-48.
Ndubisi, N., & Sinti, Q. (2006). Consumer attitudes, system's characteristics, and internet banking adoption in Malaysia. Management Research News, 29(1/2), 16-27. https://doi.org/10.1108/01409170610645411
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). McGraw-Hill.
Pavlou, P. A. (2003). Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model. International Journal of Electronic Commerce, 7, 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. https://doi.org/10.1590/0101-60830000000081
Püschel, J., Mazzon, J. A., & Hernandez, J. M. C. (2010). Mobile Banking: Proposition of an Integrated Adoption Intention Framework. International Journal of Bank Marketing, 28, 389-409. https://doi.org/10.1108/02652321011064908
Rotchanakitumnuai, S., & Speece, M. (2009). Modeling electronic service acceptance of an e-securities trading system. Industrial Management & Data Systems, 109(8), 1069-1084. https://doi.org/10.1108/02635570910991300
Schierz, P., Schilke, O., & Wirtz, B. (2010). Understanding consumer acceptance of mobile payment services: An empirical analysis. Electronic Commerce Research and Applications, 9(3), 209-216. https://doi.org/10.1016/j.elerap.2009.07.005
Shambare, R. (2013). Barriers to Student Entrepreneurship in South Africa. Journal of Economics and Behavioral Studies, 5(7), 449-459. https://doi.org/10.22610/jebs.v5i7.419
Sharma, G. P., Verma, R. C., & Pathare, P. (2005). Mathematical modeling of infrared radiation thin layer drying of onion slices. Journal of Food Engineering, 71(3), 282-286.
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.
Stevens, J. P. (1992). Applied multivariate statistics for the social sciences (2nd ed.). Erlbaum.
Stone, R. N., & Gronhaug, K. (1993). Perceived Risk: Further Considerations for the Marketing Discipline. European Journal of Marketing, 27, 39-50. http://dx.doi.org/10.1108/03090569310026637
Triandis, H. C. (1980). Values, Attitudes, and Interpersonal Behavior. Nebraska Symposium on Motivation, University of Nebraska Press, Lincoln.
Venkatesh, V. (2000). Determinants of Perceived Ease of Use: Integrating Perceived Behavioral Control, Computer Anxiety and Enjoyment into the Technology Acceptance Model. Information Systems Research, 11, 342-365. https://doi.org/10.1287/isre.11.4.342.11872
Venkatesh, V., Morris, M. G., Davis, F. D., & Davis, G. B. (2003). User acceptance of information technology: Towards a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
Wang, S., Li, J., & Zhao, D. (2017). Understanding the intention to use medical big data processing technique from the perspective of medical data analyst. Information Discovery and Delivery, 45(4), 194-201. https://doi.org/10.1108/IDD-03-2017-0017
Wiafe, I., Koranteng, F. N., Tettey, T., Kastriku, F. A., & Abdulai, J.-D. (2020). Factors that affect acceptance and use of information systems within the Maritime industry in developing countries: The case of Ghana. Journal of Systems and Information Technology, 22(1), 21-45. https://doi.org/10.1108/JSIT-06-2018-0091
Wu, J. H., & Wang, Y. M. (2006). Measuring KMS success: A respecification of the DeLone and McLean’s model. Information and Management, 43(6), 728-739. https://doi.org/10.1016/j.im.2006.05.002
Yu, C. S. (2012). Factors Affecting Individuals to Adopt Mobile Banking: Empirical Evidence from the UTAUT Model. Journal of Electronic Commerce Research, 13, 104-121.