The Role of Attitude, Subjective Norms, And Perceived Behavioral Control in Shaping the Intention to Implement Asset Management
1Martua Haposan Echo Sihite, 2Ribhan, 3Keumala Hayati
1,2,3Faculty of Economics and Business, University of Lampung, Lampung city
https://doi.org/10.47191/jefms/v8-i4-46ABSTRACT:
This study aims to analyze the influence of the Theory of Planned Behavior by examining the roles of attitude, subjective norms, and perceived behavioral control on the intention to implement asset management. Employing a quantitative approach through a survey method, data were collected from 130 respondents working at PT PLN Nusantara Power UP Sebalang. The research gap lies in the scarcity of studies that explicitly link knowledge with the intention to implement asset management within Indonesia's power generation industry. Using a quantitative method, this study investigates the relationship between attitude, subjective norms, and perceived behavioral control and their effect on the intention to implement asset management. Data analysis was conducted using Structural Equation Modeling–Partial Least Squares (SEM-PLS). The results indicate that attitude and perceived behavioral control significantly influence the intention to implement asset management. However, subjective norms do not significantly affect implementation intention, suggesting that social pressure does not directly motivate individuals to apply asset management practices. These findings highlight that attitude and behavioral control strengthen psychological factors that effectively drive asset management implementation. The study provides policy recommendations to enhance training and education in asset management to support more optimal implementation.
KEYWORDS:
Attitude, Subjective Norms, Perceived Behavioral Control, Implementation Intention, Asset Management, Theory of Planned Behavior
REFERENCES:
1) Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179– 211.
2) Ajzen, I., Joyce, N., Sheikh, S., & Cote, N. G. (2011). Knowledge and the prediction of behavior: The role of information accuracy in the theory of planned behavior. Basic and Applied Social Psychology, 33(2), 101–117. https://doi.org/10.1080/01973533.2011.568834
3) Anderson, L. W., & Krathwohl, D. R. (2001). Taxonomy for learning, teaching, and assessing: A revision of Bloom’s
4) taxonomy of educational objectives. Longman.
5) ARC Advisory Group. (2020). The state of asset management in manufacturing industries. ARC Advisory Group.
6) Bloom, B. S. (1956). Taxonomy of educational objectives: Cognitive domain (Vol. 1, No. 20, p. 24). McKay.
7) Bloom, B. S., Engelhart, M. D., Furst, E. J., Hill, W. H., & Krathwohl, D. R. (1956). Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive domain. David McKay.
8) Brookhart, S. M. (2010). How to assess higher-order thinking skills in your classroom. ASCD.
9) Conner, M. (2020). Theory of planned behavior. In Handbook of sport psychology (pp.1–18) https://doi.org/10.1002/9781119568124.ch1 10) Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
11) Gartner. (2020). Adopting IoT and predictive analytics for better asset management. Gartner.
12) Ghozali, I. (2012). Aplikasi analisis multivariate dengan program IBM SPSS. Universitas Diponegoro.
13) Grant, R. M. (1996). Toward a knowledge-based theory of the firm. Strategic Management Journal, 17(S2), 109–122.
14) Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson Prentice Hall.
15) Hina Amin, & Munawar Sultana Mirza. (2020). Comparative study of knowledge and use of Bloom’s digital taxonomy by teachers and students in virtual and conventional universities. Asian Association of Open Universities Journal, 15(2), 223–238. https://doi.org/10.1108/AAOUJ-01-2020-0005
16) Krathwohl, D. R., Bloom, B. S., & Masia, B. B. (1964). Taxonomy of educational objectives: Handbook II, Affective domain. David McKay.
17) McKinsey & Company. (2020). How industry leaders can leverage digital tools to improve asset management. McKinsey & Company.
18) Nonaka, I., & Konno, N. (1998). The concept of “Ba”: Building a foundation for knowledge creation. California Management Review, 40(3), 40–54. https://doi.org/10.2307/41165951
19) Ong, A. K. S., Prasetyo, Y. T., Salazar, J. M. L. D., Erfe, J. J. C., Abella, A. A., Young, M. N., … Redi, A. A. N. P. (2021). Investigating the acceptance of the reopening Bataan Nuclear Power Plant: Integrating Protection Motivation Theory and extended Theory of Planned Behavior. Nuclear Engineering and Technology. https://doi.org/10.1016/j.net.2021.08.032
20) PricewaterhouseCoopers (PwC). (2021). The future of asset management in Industry 4.0. PwC.
21) Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
22) Smith, J. R., Terry, D. J., Manstead, A. S. R., Louis, W. R., Kotterman, D., & Wolfs, J. (2007). Interaction effects in the theory of planned behavior: The interplay of self-identity and past behavior. Journal of Applied Social Psychology, 37(11), 2726–2750. https://doi.org/10.1111/j.1559-1816.2007.00278.x
23) Sugiyono. (2007). Metode penelitian untuk penelitian kuantitatif. PT Bumi Aksara.
24) Sugiyono. (2008). Metode penelitian bisnis. Alfabeta.
25) Sugiyono. (2016). Metode penelitian kuantitatif, kualitatif, dan R&D. Alfabeta.
26) Taylor, S., & Todd, P. A. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 19(4), 561–570.
27) Tim PT PJB. (2022). Panduan implementasi manajemen aset pembangkit PT Pembangkitan Jawa Bali 2022. Surabaya: PT PJB.
