Industry 4.0 And Accounting Transparency: A Theoretical Framework For Decision-Usefulness In Smart Manufacturing
Article Sidebar
Main Article Content
Abstract
This study examines the implications of Industry 4.0 technologies for accounting transparency and the decision-usefulness of financial information in smart manufacturing environments. Drawing on theoretical foundations from the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB), the study develops a framework linking technological transparency, information quality, and stakeholder confidence. The article explores how stakeholder perceptions of smart factories affect the purchase intention through a combination of constructs of the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB). The quantitative, cross-sectional design was adopted, whereby 312 respondents were utilized and PLS-SEM was utilized to analyses the data. The results indicate that perceived quality of the products is greatly promoted through the perceived smart factory adoption and that transparency of technology ensures stakeholder trust. Both trusts, as well as perceived quality, have constructive impacts on purchase intention with perception being a major mediating factor. The study contributes to accounting theory by demonstrating how Industry 4.0 technologies enhance accounting transparency, improve information quality, and strengthen the decision-usefulness of accounting information for stakeholders. The proposed framework is further supported through empirical validation using PLS-SEM.
Article Details
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
1.Abdul Latip, M. S., Mohamad, N. H., Gannasin, S. P., Tan, H. L., & Yong, R. Y. M. (2026). Drivers of purchase intention for novel culinary 3D-printed foods: an integrated KAB–UGT model. British Food Journal, 1-21.
2.Abreu-Ledón, R., Luján-García, D. E., Garrido-Vega, P., Machuca, J. A., & Borroto-Pentón, Y. (2026). Evaluating the use of structural equation modeling in online purchase intention research: A comprehensive review (2000− 2023). Technological Forecasting and Social Change, 225, 124512.
3.Ahmed, R. R., Streimikiene, D., Qadir, H., & Streimikis, J. (2023). Effect of green marketing mix, green customer value, and attitude on green purchase intention: evidence from the USA. Environmental Science and Pollution Research, 30(5), 11473-11495.
4.Al Hafizi, N. A., & Ali, H. (2021). Purchase intention and purchase decision model: Multi channel marketing and discount on Medcom. Id online news portal. Dinasti International Journal of Digital Business Management, 2(3), 460-470.
5.Al-Adwan, A. S., Jafar, R. M. S., & Sitar-Tăut, D. A. (2024). Breaking into the black box of consumers’ perceptions on metaverse commerce: an integrated model of UTAUT 2 and dual-factor theory. Asia Pacific Management Review, 29(4), 477-498.
6.Ammar, M., Haleem, A., Javaid, M., Walia, R., & Bahl, S. (2021). Improving material quality management and manufacturing organizations system through Industry 4.0 technologies. Materials Today: Proceedings, 45, 5089-5096.
7.Anvari, F., Edwards, R., & Yuniarto, H. A. (2020). Lean six sigma in smart factories based on industry 4.0. International Journal of Emerging Trends in Energy and Environment (IJETEE), 1(1), 1-26.
8.Arden, N. S., Fisher, A. C., Tyner, K., Yu, L. X., Lee, S. L., & Kopcha, M. (2021). Industry 4.0 for pharmaceutical manufacturing: Preparing for the smart factories of the future. International journal of pharmaceutics, 602, 120554.
9.Bagherian, A., Srivastav, A. L., & Mukherjee, S. (2026). Barriers and strategies of smart factory evolution in industry 4.0: an empirica
10.Barari, A., de Sales Guerra Tsuzuki, M., Cohen, Y., & Macchi, M. (2021). Intelligent manufacturing systems towards industry 4.0 era. Journal of Intelligent Manufacturing, 32(7), 1793-1796.
11.Briem, T., & Schober, D. (2026). Design for Function, not Feeling: How Data-Driven Product Design Influences Consumer Perception.
12.Büchi, G., Cugno, M., & Castagnoli, R. (2020). Smart factory performance and Industry 4.0. Technological forecasting and social change, 150, 119790.
13.Busalim, A., Fox, G., & Lynn, T. (2022). Consumer behavior in sustainable fashion: A systematic literature review and future research agenda. International journal of consumer studies, 46(5), 1804-1828.
14.Camilleri, M. A., Cricelli, L., Mauriello, R., & Strazzullo, S. (2023). Consumer perceptions of sustainable products: A systematic literature review. Sustainability, 15(11), 8923.
15.Chen, J., & Chang, Y. W. (2023). How smart technology empowers consumers in smart retail stores? The perspective of technology readiness and situational factors. Electronic Markets, 33(1), 1.
16.Dam, T. C. (2020). Influence of brand trust, perceived value on brand preference and purchase intention. The Journal of Asian Finance, Economics and Business, 7(10), 939-947.
17.Du, S., Zhao, M., & Sen, S. (2026). Consumer adoption of community-focused smart products: the moderating role of political ideology. Journal of Business Research, 206, 115951.
18.Fortoul-Diaz, J. A., Carrillo-Martinez, L. A., Centeno-Tellez, A., Cortes-Santacruz, F., Olmos-Pineda, I., & Flores-Quintero, R. R. (2023). A smart factory architecture based on industry 4.0 technologies: open-source software implementation. IEEE access, 11, 101727-101749.
19.Gidaković, P., Koklic, M. K., Zečević, M., & Zabkar, V. (2026). Explaining consumer reactions to brand activism with the ABC model of brand stereotypes. Psychology & Marketing, 43(3), 556-574.
20.Gomez-Camara, I., Aramendia-Muneta, M. E., & Panditharathna, R. (2026). Consumers’ perceptions of the genuine significance of sustainable tourism: a systematic literature review. Strategy & Leadership, 54(1), 61-87.
21.Gu, J., Xie, W., Chen, Y., & Yu, J. (2026). A dynamic model considering consumer smart preferences and financial subsidy. International Journal of Innovation Science, 1-32.
22.Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial management & data systems, 117(3), 442-458.
23.Han, M. S., Hampson, D. P., Wang, Y., & Wang, H. (2022). Consumer confidence and green purchase intention: An application of the stimulus-organism-response model. Journal of Retailing and Consumer Services, 68, 103061.
24.Hewei, T., & Youngsook, L. (2022). Factors affecting continuous purchase intention of fashion products on social E-commerce: SOR model and the mediating effect. Entertainment computing, 41, 100474.
25.Huo, C., Hameed, J., Zhang, M., Bin Mohd Ali, A. F., & Nik Hashim, N. A. A. (2022). Modeling the impact of corporate social responsibility on sustainable purchase intentions: insights into brand trust and brand loyalty. Economic research-Ekonomska istraživanja, 35(1), 4710-4739.
26.Indust Soori, M., Arezoo, B., & Dastres, R. (2023). Internet of things for smart factories in industry 4.0, a review. Internet of Things and Cyber-Physical Systems, 3, 192-204.ry 4.0 and smart factories
27.Jain, P., Sapkal, L. D., Singh, S., & Gaikwad, K. K. (2026). Smart packaging and traceability: ensuring quality and safety of functional food products. In Artificial Intelligence in Food Science (pp. 409-422). Academic Press.
28.Kabadurmus, O., Kayikci, Y., Demir, S., & Koc, B. (2023). A data-driven decision support system with smart packaging in grocery store supply chains during outbreaks. Socio-Economic Planning Sciences, 85, 101417.
29.Kamble, S., Gunasekaran, A., & Dhone, N. C. (2020). Industry 4.0 and lean manufacturing practices for sustainable organisational performance in Indian manufacturing companies. International Journal of Production Research, 58(5), 1319–1337. https://doi.org/10.1080/00207543.2019.1630772
30.Khan, S., Tomar, S., Fatima, M., & Khan, M. Z. (2022). Impact of artificial intelligent and industry 4.0 based products on consumer behaviour characteristics: A meta-analysis-based review. Sustainable Operations and Computers, 3, 218-225.
31.Khang, A., Rath, K. C., Satapathy, S. K., Kumar, A., Das, S. R., & Panda, M. R. (2023). Enabling the future of manufacturing: integration of robotics and IoT to smart factory infrastructure in industry 4.0. In Handbook of Research on AI-Based Technologies and Applications in the Era of the Metaverse (pp. 25-50). IGI Global Scientific Publishing.
32.Krestyanpol, L. (2023). Simulation modeling of consumer behavior within the concept of smart consumption. Procedia Computer Science, 217, 774-783.
33.Kumar, A., Prakash, G., & Kumar, G. (2021). Does environmentally responsible purchase intention matter for consumers? A predictive sustainable model developed through an empirical study. Journal of retailing and consumer services, 58, 102270.
34.Liao, S. H., Hu, D. C., Chung, Y. C., & Huang, A. P. (2021). Risk and opportunity for online purchase intention–A moderated mediation model investigation. Telematics and Informatics, 62, 101621.
35.Lou, X., Han, W., & Xu, Y. (2025). Navigating the AI customization landscape: an exploratory study of consumer perceptions. Journal of Internet Commerce, 24(1-2), 33-55.
36.Lu, X., Sheng, T., Zhou, X., Shen, C., & Fang, B. (2022). How does young consumers’ greenwashing perception impact their green purchase intention in the fast fashion industry? An analysis from the perspective of perceived risk theory. Sustainability, 14(20), 13473.
37.Mortimer, G., Fazal-e-Hasan, S. M., Grimmer, M., & Grimmer, L. (2020). Explaining the impact of consumer religiosity, perceived risk and moral potency on purchase intentions. Journal of Retailing and Consumer Services, 55, 102115.
38.Mosleuzzaman, M., Arif, I., & Siddiki, A. (2024). Design and development of a smart factory using Industry 4.0 technologies. Academic journal on business administration, innovation & sustainability, 4(4).
39.Olszewska, J. M., Falkowski, A., Conway, S., Jablonska, M., & Mackiewicz, R. (2026). Exploring the impact of color-coded labeling on consumer perception: the role of positive and negative information in food choice. Current Psychology, 45(3), 246.
40.Othman, N. I., Syed, H. J., Ramly, A. M., binti Suhaimi, N. H. S., Ali, A., Abdulnabi, M., & Ismail, A. F. (2026). Beyond 5G: PHWAN–A secure, low-latency, and cost-effective framework for Industry 4.0 smart manufacturing. Egyptian Informatics Journal, 33, 100859.
41.Peña-García, N., Gil-Saura, I., Rodríguez-Orejuela, A., & Siqueira-Junior, J. R. (2020). Purchase intention and purchase behavior online: A cross-cultural approach. Heliyon, 6(6).
42.Pentina, I., Xie, T., Hancock, T., & Bailey, A. (2023). Consumer–machine relationships in the age of artificial intelligence: Systematic literature review and research directions. Psychology & Marketing, 40(8), 1593-1614.
43.Rahmani, M., Romsdal, A., Sgarbossa, F., Strandhagen, J. O., & Holm, M. (2022). Towards smart production planning and control; a conceptual framework linking planning environment characteristics with the need for smart production planning and control. Annual Reviews in Control, 53, 370-381.
44.Rehman, K. U., Andleeb, S., Ashfaq, M., Akram, N., & Akram, M. W. (2023). Blockchain-enabled smart agriculture: Enhancing data-driven decision making and ensuring food security. Journal of Cleaner Production, 427, 138900.
45.Roy, P., & Datta, D. D. (2022). Theory and models of consumer buying behaviour: A descriptive study. Available at SSRN 5349983.
46.Roy, S. K., Singh, G., Hope, M., Nguyen, B., & Harrigan, P. (2022). The rise of smart consumers: role of smart servicescape and smart consumer experience co-creation. In The role of smart technologies in decision making (pp. 114-147). Routledge.
47.Sarstedt, M., Ringle, C. M., Henseler, J., & Hair, J. F. (2014). On the emancipation of PLS-SEM: A commentary on Rigdon (2012). Long range planning, 47(3), 154-160.ndustry 4.0 and smart factories
48.Shi, Z., Xie, Y., Xue, W., Chen, Y., Fu, L., & Xu, X. (2020). Smart factory in Industry 4.0. Systems Research and Behavioral Science, 37(4), 607-617.
49.Singh, A., Madaan, G., Hr, S., & Kumar, A. (2023). Smart manufacturing systems: a futuristics roadmap towards application of industry 4.0 technologies. International Journal of Computer Integrated Manufacturing, 36(3), 411-428.
50.Song, M., Du, J., Xing, X., & Mou, J. (2022). Should the chatbot “save itself” or “be helped by others”? The influence of service recovery types on consumer perceptions of recovery satisfaction. Electronic Commerce Research and Applications, 55, 101199.
51.Thirupathi Vasuki, M., Kadirvel, V., & Pejavara Narayana, G. (2023). Smart packaging—An overview of concepts and applications in various food industries. Food Bioengineering, 2(1), 25-41.
52.Vetrivel, S. C., Saravanan, T. P., & Maheswari, R. (2026). Smart factories and energy efficiency in industry 4.0. Distributed Time‐Sensitive Systems, 63-103.
53.Yang, F., Ren, L., Zheng, W., Sun, J., & Gu, C. (2026). Extending the stimulus organism response model with exercise habits to explain consumer purchase intentions for alternative foods. Scientific Reports.
54.Zhang, H., Li, B., Hu, B., & Ai, P. (2026). Exploring the role of personal innovativeness on purchase intention of artificial intelligence products: An investigation using social influence theory and value-based adoption model. International Journal of Human–Computer Interaction, 42(2), 752-766.
55.Zhang, Y., & Wang, S. (2023). The influence of anthropomorphic appearance of artificial intelligence products on consumer behavior and brand evaluation under different product types. Journal of Retailing and Consumer Services, 74, 103432.
Zhu, L., Li, H., Wang, F. K., He, W., & Tian, Z. (2020). How online reviews affect purchase intention: a new model based on the stimulus-organism-response (S-O-R) framework. Aslib Journal of Information Management, 72(4), 463