Can Generative AI Improve Corporate Values, Employee Perceptions, and Organizational Practices? A Human-Centric Case Study of Project Management SMEs in Indonesia
生成式人工智能能否改善企业价值观、员工认知与组织实践?一项关于印度尼西亚项目管理型中小企业的人本导向案例研究
DOI:
https://doi.org/10.65967/siss.v44i2.138Keywords:
Generative artificial intelligence, GenAI, ChatGPT, Project management, Organizational adoption, Employee perceptionsAbstract
This study investigates the growing role of generative AI tools, particularly ChatGPT, in the context of Indonesian SMEs, with a focus on their influence on SMEs values, employee perceptions, and organizational practices. Adopting a mixed-methods approach, the study combines a literature review, two industry workshops, and a survey involving 52 professionals from various sectors. The analysis integrates thematic interpretation of qualitative data with an exploratory quantitative assessment. The findings reveal that 74% of respondents reported mixed or negative attitudes toward AI adoption, mainly due to concerns related to job security and data privacy, even though many acknowledged its potential to enhance productivity and support business automation. Furthermore, 42% of participants perceived positive changes in corporate values following the introduction of AI tools, while strong agreement emerged regarding the usefulness of generative AI in business planning, monitoring and control, and organizational integration. Although several statistically significant relationships were identified, including differences in AI use across types of organizations and work functions, these results should be interpreted cautiously because of the limited sample size. Overall, the findings underscore the need for Indonesian SMEs to balance the efficiency gains offered by generative AI with ethical considerations, human supervision, and targeted capability development. This study contributes to the growing discussion on digital transformation by offering early empirical evidence on how generative AI is beginning to reshape managerial practices, employee responses, and organizational culture within SMEs in Indonesia.
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References
Abbas, Q., Younus, W., Malik, S., & Hassan, M. H. (2023). Incorporating ChatGPT in software project management, 11(9).
Al Naqbi, H., Bahroun, Z., & Ahmed, V. (2024). Enhancing work productivity through generative artificial intelligence: A comprehensive literature review. Sustainability, 16(3), Article 1166. https://doi.org/10.3390/su16031166
Ali Mohamed, A., Mohammed, A., Saif Al Busaeedi, N., Saud, S., & Salem Al Sayari, A. (2023). A conceptual model to maximize project efficiency through automated scheduling using generative AI models. ADIPEC. https://doi.org/10.2118/216487-MS
Allioui, H., & Mourdi, Y. (2023). Unleashing the potential of AI: Investigating cutting-edge technologies that are transforming businesses. International Journal of Computer Engineering and Data Science (IJCEDS), 3(2).
AlYahmady, H. H., & Al Abri, S. S. (2013). Using NVivo for data analysis in qualitative research. International Interdisciplinary Journal of Education, 2(2), 181–186. https://doi.org/10.12816/0002914
Aramali, V., Cho, N., & Mahdi M, N. (2023, December 14). Multi-chapter meeting | Empowering project management with AI: The ChatGPT advantage (Virtual). PMI Los Angeles.
Aramali, V., Cho, N., & Nashar, S. (2024a). Preliminary study: Use of large generative artificial intelligence models in integrated project management. In Construction Research Congress 2024. Iowa, United States.
Aramali, V., Gibson, G. E., & Sanboskani, H. (2024b). Enhancing project success: The impact of sociotechnical integration on project and program management using earned value management systems. International Journal of Managing Projects in Business, 17(8), 1–21. https://doi.org/10.1108/IJMPB-07-2023-0160
Aramali, V., Gibson, G. E., El Asmar, M., & Cho, N. (2021). Earned value management system state of practice: Identifying critical subprocesses, challenges, and environment factors of a high-performing EVMS. Journal of Management in Engineering, 37(4), Article 04021031. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000925
Aramali, V., Gibson, G. E., Asmar, M. E., & Sanboskani, H. (2024c). An effective earned value management system (EVMS) is a team sport. Project Management Journal. Advance online publication. https://doi.org/87569728231226226
Auth, G., Johnk, J., & Wiecha, D. A. (2021). A conceptual framework for applying artificial intelligence in project management. Scopus, 1, 161–170. https://doi.org/10.1109/CBI52690.2021.00027
Ayinde, L., Wibowo, M. P., Ravuri, B., & Emdad, F. B. (2023). ChatGPT as an important tool in organizational management: A review of the literature. Business Information Review, 40(3), 137–149. https://doi.org/10.1177/02663821231187991
Balali, A., Valipour, A., Antucheviciene, J., & Saparauskas, J. (2020). Improving the results of the earned value management technique using artificial neural networks in construction projects. Symmetry, 12(10), Article 1745. https://doi.org/10.3390/sym12101745
Bankins, S., & Formosa, P. (2023). The ethical implications of artificial intelligence (AI) for meaningful work. Journal of Business Ethics, 185(4), 725–740.
Barcaui, A., & Monat, A. (2023). Who is better in project planning: Generative artificial intelligence or project managers? Project Leadership and Society, 4, Article 100101. https://doi.org/10.1016/j.plas.2023.100101
Batselier, J., & Vanhoucke, M. (2017). Improving project forecast accuracy by integrating earned value management with exponential smoothing and reference class forecasting. International Journal of Project Management, 35(1), 28–43. https://doi.org/10.1016/j.ijproman.2016.10.003
Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R., Boselie, P., Cooke, F. L., Decker, S., DeNisi, A., Dey, P. K., Guest, D., Knoblich, A. J., Malik, A., Paauwe, J., Papagiannidis, S., Patel, C., Pereira, V., Ren, S., et al. (2023). Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT. Human Resource Management Journal, 33(3), 606–659. https://doi.org/10.1111/1748-8583.12524
Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P. S., & Sun, L. (2023). A comprehensive survey of AI-generated content (AIGC): A history of generative AI from GAN to ChatGPT. arXiv. http://arxiv.org/abs/2303.04226
Cardon, P. W., Getchell, K., Carradini, S., Fleischmann, C., & Stapp, J. (2023). Generative AI in the workplace: Employee perspectives of ChatGPT benefits and organizational policies. https://doi.org/10.31235/osf.io/b3ezy
Dacre, N., Baxter, D., Al-Mhdawi, M. K. S., Dong, H., Shen, Y., & Abeysooriya, R. (2025). Digital transformation and the AI imperative in public and private sector projects. APM.
Dasgupta, D., Venugopal, D., & Gupta, K. D. (2023). A review of generative AI from historical perspectives. https://doi.org/10.36227/techrxiv.22097942
Davis, F. D. (1989). Technology acceptance model: TAM. In M. N. Al-Suqri & A. S. Al-Aufi (Eds.), Information seeking behavior and technology adoption (pp. 205–219).
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., AlBusaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., et al. (2023). Opinion paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, Article 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Felicetti, A. M., Cimino, A., Mazzoleni, A., & Ammirato, S. (2024). Artificial intelligence and project management: An empirical investigation on the appropriation of generative chatbots by project managers. Journal of Innovation & Knowledge, 9(3), Article 100545.
Fui-Hoon Nah, F., Zheng, R., Cai, J., Siau, K., & Chen, L. (2023). Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. Journal of Information Technology Case and Application Research, 25(3), 277–304. https://doi.org/10.1080/15228053.2023.2233814
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90.
Gefen, D., & Straub, D. W. (1997). Gender differences in the perception and use of e-mail: An extension to the technology acceptance model. MIS Quarterly, 21(4), 389–400.
Goldberg, B., Spain, R., Owens, K., Lanman, J., Kwon, C. P., Gupton, K., & Butler, P. A. (2023). A data strategy for data-driven training management: Artificial intelligence and the Army’s synthetic training environment. Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC).
Goyal, T., Li, J. J., & Durrett, G. (2023). News summarization and evaluation in the era of GPT-3. arXiv. https://doi.org/10.48550/arXiv.2209.12356
Gupta, R., Nair, K., Mishra, M., Ibrahim, B., & Bhardwaj, S. (2024). Adoption and impacts of generative artificial intelligence: Theoretical underpinnings and research agenda. International Journal of Information Management Data Insights, 4(1), Article 100232. https://doi.org/10.1016/j.jjimei.2024.100232
Han, X., Zhang, Z., Ding, N., Gu, Y., Liu, X., Huo, Y., Qiu, J., Yao, Y., Zhang, A., Zhang, L., Han, W., Huang, M., Jin, Q., Lan, Y., Liu, Y., Liu, Z., Lu, Z., Qiu, X., Song, R., et al. (2021). Pre-trained models: Past, present and future. AI Open, 2, 225–250. https://doi.org/10.1016/j.aiopen.2021.08.002
Haslwanter, T. (2022). An introduction to statistics with Python: With applications in the life sciences (2nd ed.). Springer.
Iorliam, A., & Ingio, J. A. (2024). A comparative analysis of generative artificial intelligence tools for natural language processing. Journal of Computing Theories and Applications, 1(3), 311–325. https://doi.org/10.62411/jcta.9447
Joshi, A., Kale, S., Chandel, S., & Pal, D. K. (2015). Likert scale: Explored and explained. British Journal of Applied Science & Technology, 7(4), Article 4. https://doi.org/10.9734/BJAST/2015/14975
Joskowicz, J., & Slomovitz, D. (2023). Engineers’ perspectives on the use of generative artificial intelligence tools in the workplace. IEEE Engineering Management Review, 1–6. https://doi.org/10.1109/EMR.2023.3333794
Kim, H. (2017). Statistical notes for clinical researchers: Chi-squared test and Fisher’s exact test. Restorative Dentistry & Endodontics, 42(2), 152–155. https://doi.org/10.5395/rde.2017.42.2.152
Kim, H., Jang, Y., Kang, H., Son, J., & Yi, J.-S. (2021). A suggestion of the direction of construction disaster document management through text data classification model based on deep learning. Korean Journal of Construction Engineering and Management, 22(5), 73–85. https://doi.org/10.6106/KJCEM.2021.22.5.073
Ma, C. (2023). Applied project final report, 2023.
Min, B., Ross, H., Sulem, E., Veyseh, A. P. B., Nguyen, T. H., Sainz, O., Agirre, E., Heintz, I., & Roth, D. (2024). Recent advances in natural language processing via large pre-trained language models: A survey. ACM Computing Surveys, 56(2), 1–40. https://doi.org/10.1145/3605943
Mohamed, M. A. H., Al-Mhdawi, M. K. S., Ojiako, U., Dacre, N., Qazi, A., & Rahimian, F. (2025a). Generative AI in construction risk management: A bibliometric analysis of the associated benefits and risks. Urbanization, Sustainability and Society, 2(1), 196–228.
Mohamed, M. A. H., Al-Mhdawi, M. K. S., Rahimian, F. P., Ojiako, U., O’Connor, A., & Mahammedi, C. (2025b). Exploring the risks of integrating generative artificial intelligence into construction risk management: Insights from a systematic literature review. In 6th International Conference on Civil and Building Engineering Informatics (ICCBEI 2025).
Morgan, S. D., Zeng, F.-G., & Clark, J. (2022). Adopting change and incorporating technological advancements in audiology education, research, and clinical practice. American Journal of Audiology, 31(3S), 1052–1058. https://doi.org/10.1044/2022_AJA-21-00215
National Defense Industrial Association. (2018). Earned value management systems EIA-748-D intent guide. Integrated Program Management Division.
Nguyen, H., & Scheff, D. (2023). “Hey Siri, will AI replace project managers?” Navigating the AI era: Impact of machine learning on project manager’s core competencies.
Noé Chavez, H. (2024). Adoption and adaptation of generative artificial intelligence in organizations: Actions for efficient and responsible use in interaction with collaborators. International Journal of Current Science Research and Review, 7(3). https://doi.org/10.47191/ijcsrr/V7-i3-56
Ooi, K.-B., Tan, G. W.-H., Al-Emran, M., Al-Sharafi, M. A., Capatina, A., Chakraborty, A., Dwivedi, Y. K., Huang, T.-L., Kar, A. K., Lee, V.-H., Loh, X.-M., Micu, A., Mikalef, P., Mogaji, E., Pandey, N., Raman, R., Rana, N. P., Sarker, P., Sharma, A., et al. (2023). The potential of generative artificial intelligence across disciplines: Perspectives and future directions. Journal of Computer Information Systems, 1–32. https://doi.org/10.1080/08874417.2023.2261010
Prasad Agrawal, K. (2023). Towards adoption of generative AI in organizational settings. Journal of Computer Information Systems, 1–16. https://doi.org/10.1080/08874417.2023.2240744
Project Management Institute. (2021). A guide to the project management body of knowledge (PMBOK® guide) and the standard for project management.
Rane, N. (2023). Role and challenges of ChatGPT and similar generative artificial intelligence in business management (SSRN Scholarly Paper 4603227). https://doi.org/10.2139/ssrn.4603227
Ray, P. P. (2023). ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems, 3, 121–154. https://doi.org/10.1016/j.iotcps.2023.04.003
Roumeliotis, K. I., & Tselikas, N. D. (2023). ChatGPT and OpenAI models: A preliminary review. Future Internet, 15(6), Article 192. https://doi.org/10.3390/fi15060192
Seo, W., & Kang, Y. (2024). Auto-summarization for the texts of construction dispute precedents. In Construction Research Congress 2024 (pp. 176–185). https://doi.org/10.1061/9780784485286.018
Simon, J., Rieder, G., & Branford, J. (2024). The philosophy and ethics of AI: Conceptual, empirical, and technological investigations into values. Digital Society, 3(10). https://doi.org/10.1007/s44206-024-00094-2
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd
The University of Iowa Information Technology Services. (2024). Generative AI | Information technology services. The University of Iowa.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.
Weng, J. C. (2023). Putting intellectual robots to work: Implementing generative AI tools in project management. NYU SPS Applied Analytics Laboratory.
Yardley, L., Spring, B. J., Riper, H., Morrison, L. G., Crane, D. H., Curtis, K., Merchant, G. C., Naughton, F., & Blandford, A. (2016). Understanding and promoting effective engagement with digital behavior change interventions. American Journal of Preventive Medicine, 51(5), 833–842. https://doi.org/10.1016/j.amepre.2016.06.015
Zamawe, F. C. (2015). The implication of using NVivo software in qualitative data analysis: Evidence-based reflections. Malawi Medical Journal, 27(1), 13–15.
Zheng, Z., Chen, K.-Y., Cao, X.-Y., Lu, X.-Z., & Lin, J.-R. (2023). LLM-FuncMapper: Function identification for interpreting complex clauses in building codes via LLM. arXiv. https://doi.org/10.48550/arXiv.2308.08728
Zhou, H., Gao, B., Tang, S., Li, B., & Wang, S. (2023). Intelligent detection on construction project contract missing clauses based on deep learning and NLP. Engineering, Construction and Architectural Management. Advance online publication. https://doi.org/10.1108/ECAM-02-2023-0172
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