Behavioral Factors and Successful Implementation of Mandatory Management Information Systems in Indonesia
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Abstract
Information systems help local governments present quality financial statements. An understanding of the factors that influence successful implementation is crucial. This study examines antecedents of a management information system implementation in Indonesia. Data, using a questionnaire, was obtained 453 operators in East Java, one of the largest provinces in Indonesia. Validity and reliability testing showed that the questionnaire matched the common consensus criteria (each item greater than .07). Path analysis test results for usage behavior caused by self-efficacy (β = .69, p = .05), perceived complexity (β = -.34, p = .01), and intention to use (β = .39, p = .00). The determinants of usefulness are self-efficacy (β = .52, p = .00), and ease of use (β = .53, p = .00). One of the variables that affect attitude is usefulness (β = .18, p = .26). Intention of use influenced by self-efficacy (β = .69, p = .00), and attitude toward using (β = .46, p = .00). Self-efficacy affects perceived usefulness and behavioral intention to use, and ultimately affects actual system use. The Sobel test was utilized to examine the mediation function and the results shows that intention to use significantly mediates the relationship between the self-efficacy and usage behavior (Z = 3.9, p < .001). This study contributes to developing a new simple model to explain usage behavior in a mandatory information system where the intention to use could be important mediating variable.
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Abu-Bader, S., & Jones, T. V. (2021). Statistical mediation analysis using the Sobel test and Hayes SPSS process macro. International Journal of Quantitative and Qualitative Research Methods, 9(1), 42–61. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3799204
Al Kurdi, B., Alshurideh, M., Salloum, S. A., Obeidat, Z. M., & Al-Dweeri, R. M. (2020). An empirical investigation into examination of factors influencing university students’ behavior towards elearning acceptance using SEM approach. International Journal of Interactive Mobile Technologies (IJIM), 14(02), 19-41. https://doi.org/10.3991/ijim.v14i02.11115
Al Rahmi, W. M., Yahaya, N., Aldraiweesh, A. A., Alamri, M. M., Aljarboa, N. A., Alturki, U., & Aljeraiwi, A. A. (2019). Integrating technology acceptance model with innovation diffusion theory: an empirical investigation on students’ intention to use e-learning systems. IEEE Access, 7(February), 26797–26809. https://doi.org/10.1109/ACCESS.2019.2899368
Aswar, K. (2020). Assessing the impact of influencing factors on the quality of local government financial statements. Pressacademia, 7(1), 1–8. https://doi.org/10.17261/pressacademia. 2020.1176
Awofala, A. O. A., Olabiyi, O. S., Awofala, A. A., Arigbabu, A. A., Fatade, A. O., & Udeani, U. N. (2019). Attitudes toward computer, computer anxiety and gender as determinants of pre-service science, technology, and mathematics teachers’ computer self-efficacy. Digital Education Review, 36, 51–67. https://doi.org/10.1344/der.2019.36.51-67
Brusso, R. C. (2015). Employee behavioral intention and technology use: Mediating processes and individual difference moderators. [Doctoral dissertation, Old Dominion University]. ProQuest Dissertations and Theses. https://doi.org/10.25777/hjsr-0x64
Cigdem, H., & Ozturk, M. (2016). Factors affecting students’ behavioral intention to use LMS at a Turkish post-secondary vocational school. International Review of Research in Open and Distance Learning, 17(3), 276–295. https://doi.org/10.19173/irrodl.v17i3.2253
Echchabi, A., Al-Hajri, S., & Nazier Tanas, I. (2019). Analysis of e-banking acceptance in oman: the case of islamic banks’ customers. International Journal of Islamic Economics and Finance (IJIEF), 1(2), 145–164. https://doi.org/10.18196/ijief.128
Elshafey, A., Saar, C. C., Aminudin, E. B., Gheisari, M., & Usmani, A. (2020). Technology acceptance model for augmented reality and building information modeling integration in the construction industry. Journal of Information Technology in Construction, 25(August 2018), 161–172. https://doi.org/10.36680/j.itcon.2020.010
Ferdousi, B. (2019). The effect of computer self-efficacy and attitude on undergraduate students’ intention to use emerging technology in classroom learning. Journal of Computer Sciences and Applications, 7(1), 50–55. https://doi.org/10.12691/jcsa-7-1-8
Gbongli, K., Xu, Y., & Amedjonekou, K. M. (2019). Extended technology acceptance model to predict mobile-based money acceptance and sustainability: A multi-analytical structural equation modeling and neural network approach. Sustainability (Switzerland), 11(13), 1–33. https://doi.org/10.3390/su11133639
Gomes, V. M., Paiva, J. R. B., Reis, M. R. C., Wainer, G. A., & Calixto, W. P. (2019). Mechanism for measuring system complexity applying sensitivity analysis. Complexity, Article ID 1303241. https://doi.org/10.1155/2019/1303241
Hanif, A., Jamal, F. Q., & Ahmed, N. (2018). Behavioral intention for adopting technology enhanced learning initiatives in universities. Journal of Behavioural Sciences, 28(1), 1689–1699. https://doi.org/10.1017/CBO9781107415324.004
Ho, K. F., Ho, C. H., & Chung, M. H. (2019). Theoretical integration of user satisfaction and technology acceptance of the nursing process information system. PLoS ONE, 14(6), 1–14. https://doi.org/10.1371/journal.pone.0217622
Ji, Z., Yang, Z., Liu, J., & Yu, C. (2019). Investigating users’ continued usage intentions of online learning applications. Information (Switzerland), 10(6), 1–13. https://doi.org/10.3390 /info10060198
Jimenez, I. A. C., García, L. C. C., Violante, M. G., Marcolin, F., & Vezzetti, E. (2021). Commonly used external tam variables in e-learning, agriculture and virtual reality applications. Future Internet, 13(1), 1–21. https://doi.org/10.3390/fi13010007
Kahar, A., Wardi, Y., & Patrisia, D. (2019). The Influence of perceived of usefulness, perceived ease of use, and perceived security on repurchase intention at Tokopedia.com. Advances in Economics, Business and Management Research, 64, 429–438. https://doi.org/10.2991/ piceeba2-18.2019.20
Kang, Y., Choi, N., & Kim, S. (2021). Searching for new model of digital informatics for human-computer interaction: testing the institution-based technology acceptance model (ITAM). International Journal of Environmental Research and Public Health, 18(11), Article ID 5593. https://doi.org/10.3390/ijerph18115593
Karsten, R., Mitra, A., & Schmidt, D. (2014). Computer self-efficacy. Journal of Organizational and End User Computing, 24, 54–80. https://doi.org/10.4018/joeuc.2012100104
Kustono, A. S. (2020). How Total quality management mediates antecedent variables of employee performance? The Journal of Asian Finance, Economics and Business, 7(12), 523–534. https://doi.org/10.13106/jafeb.2020.vol7.no12.523
Kustono, A. S., & Valencia, Z. G. (2017). How the effectiveness knowledge sharing affect enterprise resource planning system case in East Java—Indonesia. Advanced Science Letters, 23(5), 4295–4297. https://doi.org/10.1166/asl.2017.8263
Kustono, A. S., Winarno, W. A., & Nanggala, A. Y. A. (2021). Effect of accounting lecturer behavior on the level of online learning outcomes achievement. International Journal of Learning, Teaching and Educational Research, 20(3), 169–187. https://doi.org/10.26803/ijlter.20.3.11
Larasati, G. R. (2017). Hubungan antara self acceptance dan self efficacy dengan konformitas pada siswa kelas vii di SMP Negeri 2 Kalasan Sleman Yogyakarta (Relationship between self acceptance and self efficacy with conformity to students of SMP Negeri 2 Kalasan Sleman Yogyakarta). [Master's thesis, Yogyakarta State University]. http://eprints.uny.ac.id/id/eprint/56558
Le, O. T. T., & Cao, Q. M. (2020). Examining the technology acceptance model using cloud-based accounting software of Vietnamese enterprises. Management Science Letters, 10(12), 2781–2788. https://doi.org/10.5267/j.msl.2020.4.032
Liao, S., Hong, J.-C., Wen, M.-H., Pan, Y.-C., & Wu, Y.-W. (2018). Applying technology acceptance model (TAM) to explore users’ behavioral intention to adopt a performance assessment system for e-book production. Eurasia Journal of Mathematics, Science and Technology Education, 14(10), 1-12. https://doi.org/10.29333/ejmste/93575
Ma, Y. J., Gam, H. J., & Banning, J. (2017). Perceived ease of use and usefulness of sustainability labels on apparel products: application of the technology acceptance model. Fashion and Textiles, 4(1), 1–20. https://doi.org/10.1186/s40691-017-0093-1
Mankad, A., & Loechel, B. (2020). Perceived competence, threat severity and response efficacy: key drivers of intention for area wide management. Journal of Pest Science, 93(3), 929–939. https://doi.org/10.1007/s10340-020-01225-7
Maulina, E., & Natakusumah, K. (2020). Determinants of supply chain operational performance. Uncertain Supply Chain Management, 8(1), 117–130. https://doi.org/10.5267/j.uscm. 2019.8.001
Merawati, L. K. (2019). Determinants of auditor perfomance: : Case of government auditor in Bali province. International Journal of Applied Business & International Management, 4(2), 17–24. https://ejournal.aibpm.org/index.php/IJABIM/article/view/562
Muñoz-Leiva, F., Climent-Climent, S., & Liébana-Cabanillasa, F. (2017). Determinants of intention to use the mobile banking apps: An extension of the classic TAM model. Spanish Journal of Marketing - ESIC, 21(1), 25–38. https://doi.org/10.1016/j.sjme.2016.12.001
Nagy, J. T. (2018). Evaluation of online video usage and learning satisfaction: An extension of the technology acceptance model. International Review of Research in Open and Distance Learning, 19(1), 160–185. https://doi.org/10.19173/irrodl.v19i1.2886
Nanggala, A. Y. A. (2020). Use of fintech for payment : Approach to technology acceptance model modified. Journal of Contemporary Information Technology, Management, and Accounting, 1(1), 1–8. http://journal.p2ai.or.id/index.php/JCITMA/article/view/6/2
Nawangsih, E. (2020). Internet self-efficacy dan psychological well-being: Studi meta-analisis (Internet self-efficacy and psychological well-being: Meta-analysis). Jurnal Psikologi, 13(2), 133–145. https://ejournal.gunadarma.ac.id/index.php/psiko/article/view/3130
Novariana, N. W., & Andrianto, S. (2020). Entrepreneurial self-efficacy dan intensi kewirausahaan: peran mediasi perilaku inovatif pada mahasiswa di Yogyakarta (Entrepreneurial self-efficacy and entrepreneurial intention: The mediating role of innovative behavior among college students in Yogyakarta). Motiva: Journal of Psychology, 3(1), 26–34. http://ejurnal.untag-smd.ac.id/index.php/MV/ article/view/4803/4630
Ordiyasa, I. W. (2015). Kegagalan penerapan e-government di negara-negara berkembang (Failure of the e-goverment implementation in developing countries). Journal Seminar Nasional Informatika, 3(1), 6–8. https://ojs.amikom.ac.id/index.php/semnasteknomedia/article/viewFile /1033/995
Pishchenko, V., & Myriounis, A. (2016). Consumer’s acceptance of new technology : A netnographic study on self-driving automobiles. (Master’s thesis, Uppsala University). http://www.diva-portal.org/smash/get/diva2:939140/FULLTEXT01.pdf
Saadé, R. G., Kira, D., Mak, T., & Nebebe, F. (2017, July 31 - August 5). Anxiety & performance in online learning [Conference session]. The Informing Science and Information Technology Education Conference, Vietnam. https://doi.org/10.28945/3736
Sebetci, Ö. (2015). A TAM-based model for e-government: A case for Turkey. International Journal of Electronic Governance, 7(2), 113-135. https://doi.org/10.1504/IJEG.2015.069503
Shahmohammadi, N. (2017). The evaluation of teachers’ job performance based on total quality management (TQM). International Education Studies, 10(4), 58-64. https://doi.org/10.5539/ies.v10n4p58
Suana, W. (2018). Students’ internet access, internet self-efficacy, and internet for learning physics: gender and grade differences. Journal of Technology and Science Education, 8(4), 281–290. https://doi.org/10.3926/jotse.399
Sukwatjanee, A. (2014). Mechanisms of motivational program to increase perceived self-efficacy of healthy eating among Thai elderly with hypertension and hyperlipidemia. International Journal of Behavioral Science, 9(2), 45–52. https://so06.tci-thaijo.org/index.php/IJBS/article/view/ 20103
Sunny, P., & George, A. (2018). Determinants of behavioral intention to use mobile wallets-a conceptual model. Journal of Management, 5(5), 52–62. http://www.iaeme.com/JOM/issues.asp?JType=JOM&VType=5&IType=5
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
Wong, W. H., & Mo, W. Y. (2019). A study of consumer intention of mobile payment in Hongkong, based on perceived risk, perceived trust, perceived security and technological acceptance model. Journal of Advanced Management Science, 7(2), 33–38. https://doi.org/10.18178/joams.7.2.38
Zarei, N., Nazari, F., & FarhadPoor, M. R. (2019). Internet self-efficacy and the use of electronic information services acceptance among university students. International Journal of Information Science and Management, 17(2), 55–70. https://ijism.ricest.ac.ir/index.php/ijism/article/view/1519