The Assessment on Factors Impacting Small & Micro Corporate Clients’ Behavioral Intention and Use Behavior of Accounting Information System in Dazhou, China

Authors

  • Gou Congcong

Keywords:

Perceived Ease of Use, Perceived Usefulness, Behavioral Intention, Use Behavior, Accounting Information System

Abstract

Purpose: This study delves into the determinants of behavioral intention and use behavior of small & micro corporate clients towards accounting information systems among enterprises in Dazhou, 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 gathered questionnaires from small & micro corporate clients, yielding 500 valid responses. The Index of Item-Objective Congruence (IOC) was utilized to assess the validity of the research content, with a pilot test involving 50 respondents from the target population conducted for this purpose. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were employed to evaluate various aspects of validity and goodness of fit. Results: This study's findings are that perceived ease of use significantly impacts perceived usefulness. Attitude is significantly affected by perceived ease of use, but not by perceived usefulness. Moreover, Attitudes and perceived risk significantly impact behavioral intention. Nevertheless, social influence and facilitating conditions significantly affect behavioral intention. Finally, behavioral intention has a significant effect on use behavior. Conclusions: The findings hold both theoretical significance and practical value, providing insights for Chinese enterprises seeking to modernize their financial accounting practices.

Author Biography

Gou Congcong

Ph. D. Candidate in Technology, Education and Management, Graduate School of Business and Advance Technology Management, Assumption University, Thailand

References

Agarwal, R., & Prasad, J. (1999). Are Individual Differences Germane to the Acceptance of New Information Technologies? Decision Sciences, 30, 361-391. http://dx.doi.org/10.1111/j.1540-5915.1999.tb01614.x

Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50, 179-211.

http://dx.doi.org/10.1016/0749-5978(91)90020-T

Ajzen, I. (2006). Constructing a Theory of Planned Behavior Questionnaire. Conceptual and Methodological Consideration.

Ali, A., & Abd Rahman, M. S., & Wan Ismail, W. N. S. (2012). Predicting Continuance Intention to use Accounting Information Systems among SMEs in Terengganu, Malaysia. International Journal of Services Economics and Management, 6(2), 295-320.

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), 1100-1120. https://doi.org/10.1108/el-03-2014-0057

Bashir, I., & Madhavaiah, C. (2015). Consumer attitude and behavioral intention towards Internet banking adoption in India. Journal of Indian Business Research, 7(1), 67-102. https://doi.org/10.1108/jibr-02-2014-0013

Bauer, R. A. (1960). Consumer Behavior as Risk Taking. Conference of the American Marketing Association, 389-398.

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

Bettman, J. R. (1973). Perceived risk and its components: A model and empirical test. Journal of Marketing Research, 10(2), 184-190. https://doi.org/10.2307/3149824

Bhattacherjee, A., & Premkumar, G. (2004). Understanding Changes in Belief and Attitude toward Information Technology Usage: A Theoretical Model and Longitudinal Test. MIS Quarterly, 28, 229-254. https://doi.org/10.2307/25148634

Bollen, K. A. (1989). Structural Equations with Latent Variables. Wiley

Brown, T. A. (2015). Confirmatory factor analysis for applied research. Guilford Publications.

Chua, P. Y., Rezaei, S., Gu, M.-L., Oh, Y., & Jambulingam, M. (2018). Elucidating social networking apps decisions: Performance expectancy, effort expectancy and social influence. Nankai Business Review International, 9(2), 118-142.

https://doi.org/10.1108/NBRI-01-2017-0003

Connor, H., Dewson, S., Tyers, C., Eccles, J., Regan, J., & Aston, J. (2001). Social Class and Higher Education: Issues Affecting Decisions on Participation by Lower Social Class Groups. Research Gate.

Cox, W. E. (1967). Product Life Cycles as Marketing Models. Journal of Business, 40, 375-384. https://doi.org/10.1086/295003

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., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 25(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982

Dowling, G. R., & Staelin, R. (1994). A Model of Perceived Risk and Intended Risk-Handling Activity. Journal of Consumer Research, 21, 119-134. http://dx.doi.org/10.1086/209386

Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50.

Goodhue, D., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19, 213-236.

http://dx.doi.org/10.2307/249689

Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate Data Analysis (6th ed.). Pearson Prentice Hall.

Hu, P. J., Chau, P. Y. K., & Sheng, O. R. L. (1999). Examining The Technology Acceptance Model Using Physician Acceptance of Telemedicine Technology. Journal of Management Information Systems, 16, 91-112. https://doi.org/10.1080/07421222.1999.11518247

Juwaheer, D., Pudaruth, S., & Noyaux, M. (2012). Analysing the impact of green marketing strategies on consumer purchasing patterns in Mauritius. World Journal of Entrepreneurship, Management and Sustainable Development, 8(1), 36-59. https://doi.org/10.1108/20425961211221615

Karahanna, E., Agarwal, R., & Angst, C. (2006). Reconceptualizing Compatibility Beliefs in Technology Acceptance. MIS Quarterly, 30, 781. https://doi.org/10.2307/25148754

Kim, M., & Lennon, S. (2000). Television shopping for apparel in the United States: effects of perceived amount of information on perceived risks and purchase intentions. Family and Consumer Sciences Research Journal, 28, 301-331.

https://doi.org/10.1177/1077727x00283002

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

Lee, G.-G., & Lin, H.-F. (2005). Customer Perceptions of E-Service Quality in Online Shopping. International Journal of Retail & Distribution Management, 33, 161-176. https://doi.org/10.1108/09590550510581485

Lu, H., While, A. E., & Barriball, K. L. (2005). Job Satisfaction among Nurses: A Literature Review. International Journal of Nursing Studies, 42, 211-227. https://doi.org/10.1016/j.ijnurstu.2004.09.003

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

Marti, E., & Gond, J.-P. (2018). When do theories become self-fulfilling? Exploring the boundary conditions of performativity. Academy of Management Review, 43, 487-508.

Martins, C., Oliveira, T., & Popovic, A. (2014). Understanding the Internet Banking Adoption: A Unified Theory of Acceptance and Use of Technology and Perceived Risk Application. International Journal of Information Management, 34, 1-13.

http://dx.doi.org/10.1016/j.ijinfomgt.2013.06.002

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., & Muhamad, J. (2003). Evaluating IS usage in Malaysian small and medium-sized firms using the technology acceptance model. Logistics Information Management, 16(6), 440-450.

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). McGraw-Hill.

Nysveen, H., Pedersen, P. E., & Thorbjørnsen, H. (2005). Intentions to use mobile services: antecedents and cross-service comparisons. Journal of the Academy of Marketing Science, 33(3), 330-346. https://doi.org/10.1177/0092070305276149

Pastorella, F., Giacovelli, G., De Meo, I., & Paletto, A. (2017). People’s preferences for Alpine forest landscapes: Results of an internet-based survey. Journal of Forest Research, 22(1), 36-43. https://doi.org/10.1080/13416979.2017.1279708

Patch, C. S., Tapsell, L. C., & Williams, P. G. (2005). Attitudes and Intentions toward Purchasing Novel Foods Enriched with Omega-3 Fatty Acids. Journal of Nutrition Education and Behavior, 37(5), 235-241. https://doi.org/10.1016/s1499-4046(06)60277-7

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

Roselius, T. (1971). Consumer Rankings of Risk Reduction Methods. Journal of Marketing, 35, 56-61.

https://doi.org/10.1177/002224297103500110

Saade, R., Nebebe, F., & Tan, W. (2007). Viability of the "Technology Acceptance Model" in Multimedia Learning Environments: A Comparative Study. Journal of eLearning and Learning Objects, 3, 175-184. https://doi.org/10.28945/392

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. https://doi.org/10.1016/j.jfoodeng.2005.02.010

Shih, Y.-N., Huang, R.-H., & Chiang, H.-S. (2009). Correlation between work concentration level and background music: A pilot study. Work, 33(3), 329-333. https://doi.org/10.3233/wor-2009-0880

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), 472-497.

https://doi.org/10.1108/jamr-09-2018-0086

Stevens, J. P. (1992). Applied multivariate statistics for the social sciences (2nd ed.). Erlbaum.

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

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

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

Wu, B., & Chen, X. (2017). Continuance Intention to Use MOOCs: Integrating the Technology Acceptance Model (TAM) and Task Technology Fit (TTF) Model. Computers in Human Behavior, 67, 221-232. https://doi.org/10.1016/j.chb.2016.10.028

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

Zhang, J., Anderson, R. C., & Nguyen, K. (2013). Language-rich discussions for English language learners. International Journal of Educational Research, 58(1), 44-60. https://doi.org/10.1016/j.ijer.2012.12.003

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Published

2025-06-24

How to Cite

Congcong, G. (2025). The Assessment on Factors Impacting Small & Micro Corporate Clients’ Behavioral Intention and Use Behavior of Accounting Information System in Dazhou, China . Scholar: Human Sciences, 17(2), 316-324. Retrieved from https://assumptionjournal.au.edu/index.php/Scholar/article/view/8091