An Adaptability Index and Key Obstacle Identification Model for SMEs’ Digital Transformation: Based on Perceived Utility Analytical Framework

Hongyuan Li

doi:10.62051/ajmse.v1n3.07

Authors

Hongyuan Li School of Business, Lingnan Normal University, Zhanjiang, Guangdong, 524048, China; Guangdong Coastal Economic Belt Development Research Center, Zhanjiang, Guangdong, 524048, China

DOI:

https://doi.org/10.62051/ajmse.v1n3.07

Keywords:

Small and Medium-sized Enterprises, Digital Transformation, Perceived Utility, Digital Adaptability, Obstacle Identification

Abstract

To address the dilemma of "daring not to transform, unwilling to transform, and unable to transform" faced by small and medium-sized enterprises (SMEs) in digital transformation, this paper applies perceived utility theory to construct a digital adaptability index measurement model for SMEs. The model defines positive utility as the weighted sum of belief values and attribute weights of perceived attributes, and negative utility as the product of perceived cost and cost weight. The net utility is defined as the adaptability index. Hierarchical thresholds are set to classify SMEs into three levels: high, medium, and low adaptability. Furthermore, based on importance-performance analysis (IPA), an obstacle identification model is constructed, where attribute weights serve as importance indicators and belief values serve as performance indicators. Key obstacles are identified and improvement priority scores are calculated to achieve ordered ranking. This paper aims to provide an operable model tool for the diagnosis and tiered intervention of digital transformation in SMEs.

Downloads

Download data is not yet available.

References

[1] Xu, Y., & Zhang, Y. (2026). A configurational study on antecedents of digital transformation in small and medium-sized manufacturing enterprises: Dynamic QCA panel data analysis based on TOE theory. Journal of Systems Science and Mathematical Sciences. Advance online publication.

[2] Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading digital: Turning technology into business transformation. Harvard Business Review Press. https://doi.org/10.4135/9781473958542.

[3] Chen, J., & Han, M. (2023). A configurational study on antecedents of digital transformation in manufacturing enterprises: Based on TOE theoretical framework. Science-Technology and Management, 25(2), 89–98.

[4] Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901. https://doi.org/10.1016/j.jbusres.2020.09.041.

[5] Chen, D., Wang, L., & Chen, A. (2020). Digitalization and strategic management theory: Review, challenges and prospects. Journal of Management World, 36(5), 220–236, 20.

[6] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008.

[7] Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.

[8] Zhu, K., Dong, S., Xu, S. X., & Kraemer, K. L. (2006). Innovation diffusion in global contexts: Determinants of post-adoption digital transformation of European companies. European Journal of Information Systems, 15(6), 601–616. https://doi.org/10.1057/palgrave.ejis.3000650.

[9] Schumacher, A., Erol, S., & Sihn, W. (2016). A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises. Procedia CIRP, 52, 161–166. https://doi.org/10.1016/j.procir.2016.07.040.

[10] Zhang, T., & Bai, F. (2026). Maturity evaluation and policy suggestions for digital transformation of tax collection and administration in China. Taxation Research, (4), 138–145.

[11] Wei, J., Huang, Q., Luo, S., et al. (2026). Digital maturity evaluation model for nuclear power construction based on multi-objective optimization. Journal of Shanghai University of Electric Power, 42(1), 47–53.

[12] Martilla, J. A., & James, J. C. (1977). Importance-performance analysis. Journal of Marketing, 41(1), 77–79. https://doi.org/10.1177/002224297704100113.

[13] Bao, Z. (2025). Evaluation of homestay tourist satisfaction in Anhui Province based on IPA analysis. Journal of Anhui University of Technology (Social Sciences), 42(3), 16–21.

[14] Du, Y., Chang, Q., & Xue, Y. (2016). Design of customer satisfaction support system in hotel management information system. Computer Technology and Development, 26(10), 151–155, 159.

[15] Tang, J., Shi, Y., & Zhou, Y. (2025). Research on digital quality evaluation of industrial heritage tourism based on modified IPA. Journal of Central South University of Forestry & Technology (Social Sciences), 19(3), 61–72.

[16] Zhao, M., Zhang, C., Hu, Y., et al. (2020). Multi-attribute group decision making method based on vision satisfaction function. Chinese Journal of Management Science, 28(2), 220–230.

[17] Tong, S., Sun, B., Zhao, M., et al. (2023). Multi-attribute group decision making model based on BWM and vision utility function. Journal of Systems Science and Mathematical Sciences, 43(10), 2573–2597.