Drivers and Inhibitors Determining Government-Enabled Digital Platform Adoption for MSMEs in West Papua Province: PLS-SEM and IPMA Analysis
DOI:
https://doi.org/10.38204/tematik.v12i1.2291Keywords:
Keywords: MSMEs, Rumahekraf, Government-supported digital platform, Technology Readiness Index, PLS-SEMAbstract
Digital transformation plays a vital role in enhancing the competitiveness of Micro, Small, and Medium Enterprises (MSMEs) in developing regions. A case in point is Rumahekraf in West Papua Province, which faces infrastructure challenges such as limited internet access, inadequate technological devices, and insufficient digital training for MSME actors. In addition to infrastructure challenges, external factors such as economic conditions, local culture, and the digital divide also influence the adoption rate of this platform. This study aims to investigate the factors that drive and hinder its adoption. By combining the Technology Acceptance Model (TAM), Technology Readiness Index (TRI), and Performance Importance Map Analysis (IPMA). Particularly, this study examines the role of optimism, innovativeness, discomfort, and insecurity in shaping behavioral intention (BI) that might lead to usage behavior (UB). With a total of 157 respondents, and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), the results showed that perceived ease of use (PEOU) and perceived usefulness (PU) have a significant effect on BI (R2=56.3%), while BI influenced UB (R2=58%). Optimism affects PEOU but not PU, this can be explained by the nature of optimism, which tends to reinforce confidence in one's ability to master technology rather than directly evaluating the platform's perceived benefits. While Innovativeness positively affects both. The findings emphasize that in areas with limited infrastructure, such as West Papua, prioritizing easy-to-use design and useful features is key to effective platform adoption. This research provides insights for policymakers and developers to improve strategies in promoting digital platform adoption among MSMEs.
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E. Kurniawati, I. Idris, P. Handayati, and S. Osman, “Digital transformation of MSMEs in Indonesia during the pandemic,” Entrepreneurship and Sustainability Issues, vol. 9, no. 2, pp. 316–331, Dec. 2021, doi: 10.9770/jesi.2021.9.2(21).
M. M. H. Shahadat, M. Nekmahmud, P. Ebrahimi, and M. Fekete-Farkas, “Digital Technology Adoption in SMEs: What Technological, Environmental and Organizational Factors Influence SMEs’ ICT Adoption in Emerging Countries?,” Global Business Review, pp. 1–27, 2023, doi: 10.1177/09721509221137199.
L. Farida Panduwinata, W. T. Subroto, and N. C. Sakti, “Digitalization on Micro, Small, and Medium Enterprises (MSMEs) : A Systematic Literature Review,” International Journal of Economics, vol. 2, no. 1, pp. 397–409, Jan. 2025, doi: 10.62951/ijecm.v1i4.435.
S. A. Hendrawan, Afdhal Chatra, Nurul Iman, Soemarno Hidayatullah, and Degdo Suprayitno, “Digital Transformation in MSMEs: Challenges and Opportunities in Technology Management,” Jurnal Informasi dan Teknologi, vol. 6, no. 2, pp. 141–149, Jun. 2024, doi: 10.60083/jidt.v6i2.551.
M. S. Aziz et al., “Optimalisasi Desa Berdaulat Dalam Pengembangan Desa Wisata Berbasis Kearifan Lokal Di Era Digital,” Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN), vol. 4, no. 3, pp. 2841–2847, 2023, doi: 10.55338/jpkmn.v4i2.1587.
M. Sarstedt, C. M. Ringle, and J. F. Hair, “Partial Least Squares Structural Equation Modeling,” in Handbook of Market Research, Springer International Publishing, 2021, pp. 1–47. doi: 10.1007/978-3-319-05542-8_15-2.
A. Parasuraman, “Technology Readiness Index (TRI) A Multiple-Item Scale to Measure Readiness to Embrace New Technologies,” J Serv Res, vol. 2, no. 4, pp. 307–320, 2000.
V. Venkatesh and F. D. Davis, “Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies,” Manage Sci, vol. 46, no. 2, pp. 186–204, 2000, doi: 10.1287/mnsc.46.2.186.11926.
U. Chotijah and F. Retrialisca, “Analysis of Information Technology Readiness in Furniture Business in Indonesia,” Indonesian Journal of Information Systems (IJIS), vol. 3, no. 1, pp. 14–22, Aug. 2020.
K. Adiyarta, D. Napitupulu, H. Nurdianto, R. Rahim, and A. Ahmar, “User acceptance of E-Government Services Based on TRAM model,” in IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing, May 2018, pp. 1–11. doi: 10.1088/1757-899X/352/1/012057.
N. Larasati, “Technology Readiness and Technology Acceptance Model in New Technology Implementation Process in Low Technology SMEs,” International Journal of Innovation, Management and Technology, vol. 8, no. 2, pp. 113–117, Apr. 2017, doi: 10.18178/ijimt.2017.8.2.713.
A. Parasuraman and C. L. Colby, “An Updated and Streamlined Technology Readiness Index: TRI 2.0,” J Serv Res, vol. 18, no. 1, pp. 59–74, Feb. 2015, doi: 10.1177/1094670514539730.
C. Fornell and D. F. Larcker, “Evaluating Structural Equation Models with Unobservable Variables and Measurement Error,” Source: Journal of Marketing Research, vol. 18, no. 1, pp. 39–50, Feb. 1981.
P. Kengue Mayamou and S. Michel, “Mobile Money : décryptage d’une succes story africaine,” Management & Data Science, vol. 13, no. 3, pp. 319–340, Sep. 2020, doi: 10.36863/mds.a.14027.
C. M. Ringle and D. W. Straub, “EDITOR’S COMMENTS A Critical Look at the Use of PLS-SEM in MIS Quarterly,” MIS Quarterly, vol. 36, no. 1, pp. iii–xiv, 2012, [Online]. Available: http://ssrn.com/abstract=2176426
J. Shahreki, A. L. L. Chin, H. Jamaluddin, A. Shahraki, T. T. Nguyen, and Ali Afzalian Mand, “The effect of technology readiness on technology acceptance in the electronic human resources management field,” Int J Bus Inf Syst, vol. 43, no. 1, pp. 74–86, 2023, doi: 10.1504/IJBIS.2023.131085.
G. Bansal, F. M. Zahedi, and D. Gefen, “The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online,” Decis Support Syst, vol. 49, no. 2, pp. 138–150, May 2010, doi: 10.1016/j.dss.2010.01.010.
R. Roberts, R. Flin, D. Millar, and L. Corradi, “Psychological factors influencing technology adoption: A case study from the oil and gas industry,” Technovation, vol. 102, pp. 1–18, Apr. 2021, doi: 10.1016/j.technovation.2020.102219.
V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User acceptance of information technology: Toward a unified view,” MIS Q, vol. 27, no. 3, pp. 425–478, 2003, doi: 10.2307/30036540.
I. Ajzen, “The Theory of Planned Behavior,” OrganizationalBehaviourAndHumanDecisionProcesses, vol. 50, no. 2, pp. 179–211, Dec. 1991.
Y. Zhang and V. Aryadoust, “A Systematic Review of the Validity of Questionnaires in Second Language Research,” Oct. 01, 2022, MDPI. doi: 10.3390/educsci12100723.
A. Ghanad, “An Overview of Quantitative Research Methods,” INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH AND ANALYSIS, vol. 06, no. 08, pp. 1–10, Aug. 2023, doi: 10.47191/ijmra/v6-i8-52.
J. J. Baroudi and W. J. Orlikowski, “The Problem of Statistical Power in MIS The Problem of Statistical Power in MIS Research,” Source: MIS Quarterly, vol. 13, no. 1, pp. 87–106, Mar. 1989.
FRANZ FAUL, EDGAR ERDFELDER, and ALBERT-GEORG LANG AND AXEL BUCHNER, “G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences,” Behav Res Methods, vol. 39, no. 2, pp. 175–191, 2007.
M. Firmansyah, I. Dewa, and K. Yudha, “The essence of the difference between qualitative and quantitative methods,” Jurnal Ekonomi Pembangunan, vol. 3, no. 2, pp. 156–159, Sep. 2021.
D. I. Inan et al., “Service quality and self-determination theory towards continuance usage intention of mobile banking,” Journal of Science and Technology Policy Management, vol. 14, no. 2, pp. 303–328, Mar. 2023, doi: 10.1108/JSTPM-01-2021-0005.
J. F. Hair, G. T. M. Hult, C. M. Ringle, M. Sarstedt, N. P. Danks, and S. Ray, An Introduction to Structural Equation Modeling. 2021. doi: 10.1007/978-3-030-80519-7_1.
N. Mirantika, F. Yusuf, and T. S. Syamfithriani, “Analysis of M-Commerce Technology Acceptance Using the Technology Acceptance Model (TAM) Method on Retail Sales in Kuningan Regency,” JURNAL NUANSA INFORMATIKA, vol. 16, no. 1, pp. 161–171, Jan. 2022, [Online]. Available: https://journal.uniku.ac.id/index.php/ilkom
G. Ursachi, I. A. Horodnic, and A. Zait, “How Reliable are Measurement Scales? External Factors with Indirect Influence on Reliability Estimators,” Procedia Economics and Finance, vol. 20, pp. 679–686, 2015, doi: 10.1016/s2212-5671(15)00123-9.
H. M. S. El-gharbawy, A. A. Ragab, M. A. Ragheb, and Mahmoud. Farouk, “The Effect of Information Technology on Innovative Performance with Mediation Role of Process Innovation Capability: Evidence from Egyptian SMEs,” Journal of Business and Management Sciences, vol. 12, no. 2, pp. 67–88, Mar. 2024, doi: 10.12691/jbms-12-2-1.
D. I. Inan et al., “Technology anxiety and social influence towards intention to use of ride-hailing service in Indonesia,” Case Stud Transp Policy, vol. 10, no. 3, pp. 1591–1601, Sep. 2022, doi: 10.1016/j.cstp.2022.05.017.
N. Huda Mahmud, D. Iskandar Inan, and I. Yusuf, “Development and Evaluation of the Utilization of Augmented Reality to Enhance the Physics Teaching and Learning Process Using the Design Science Research Method,” Jurnal Riset Sistem Informasi Dan Teknik Informatika (JURASIK, vol. 9, no. 1, pp. 223–234, 2024, [Online]. Available: https://tunasbangsa.ac.id/ejurnal/index.php/jurasik
D. I. Inan et al., “How personal, technical, social environments affecting generation Z to utilise video-based sharing platform in learning process during crisis?,” Res Pract Technol Enhanc Learn, vol. 19, no. 3, pp. 1–20, 2024.
M. Patabang, Dedi Iskandar Inan, Amestina Matualage, and M. Indra, “The Moderation Effect of Technology Anxiety on Digital Transformation Readiness in Public Universities: An Organizational Readiness Approach,” JUSIFO (Jurnal Sistem Informasi), vol. 10, no. 2, pp. 59–70, Dec. 2024, doi: 10.19109/jusifo.v10i2.23969.
D. I. Inan et al., “Factors influencing mobile tourism recommender systems adoption by smart travellers: Perceived value and parasocial interaction perspectives,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2020, pp. 52–62. doi: 10.1007/978-3-030-50350-5_5.
J. H. Kandami, D. Iskandar Inan, R. Juita, L. Yertas Baisa, M. Sanglise, and M. Indra, “Development and Evaluation of Android-based Infrastructure Rental Application: A Design Science Research Approach,” Jurnal Teknologi dan Manajemen Informatika, vol. 10, no. 1, pp. 37–47, Jun. 2024, [Online]. Available: http://http://jurnal.unmer.ac.id/index.php/jtmi
A. D. Oktavia, D. I. Inan, R. N. Wurarah, and O. A. Fenetiruma, “Analysis of Determinants of E-Wallet Adoption in West Papua: Extended UTAUT 2 and Perceived Risk,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, no. 2, pp. 587–600, Mar. 2024, doi: 10.57152/malcom.v4i2.1277.
R. K. Julian and A. Dhini, “The Role of Technology Readiness towards Technology Acceptance on Smartwatch Use,” pp. 1990–1999, Jun. 2022.
