With the acceleration of digital transformation, enhancing the digital capability of small and micro enterprises (SMEs) has become a critical factor for their sustainable development. To scientifically evaluate the level of enterprise digitalization, this study constructs a digital capability evaluation model that integrates the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE). The model includes four dimensions: resource collaboration, digital technology, operational management, and environmental awareness, with indicator weights determined through expert interviews. Empirical analysis of 20 SMEs in Guizhou Province reveals that digital technology is the most influential factor (weight = 0.3957), while many enterprises perform poorly in resource coordination and technology application. The average score of digital capability is 60.53, indicating a moderate level overall, with only 5% of enterprises showing strong digital capability and about 20% demonstrating high environmental awareness. The results show that the proposed AHP-FCE model outperforms traditional methods in robustness, reliability, and adaptability, offering an effective tool and practical guidance for the digital transformation of SMEs.
HamidHAAndréME. Migrant entrepreneurs from an advanced economy in a developing country: the case of Korean entrepreneurs in Malaysia. J Entrep Emerg Econ2022; 14: 595–615.
2.
Rupeika-ApogaRPetrovskaKBuleL. The effect of digital orientation and digital capability on digital transformation of SMEs during the COVID-19 pandemic. J Theor Appl Electron Commer Res2022; 17: 669–685.
3.
HanayshaJRAl-ShaikhMEJogheeS,et al.Impact of innovation capabilities on business sustainability in small and medium enterprises. FIIB Bus Rev2022; 11: 67–78.
4.
LoonamJO’ReganN. Global value chains and digital platforms: implications for strategy. Strateg Change2022; 31: 161–177.
5.
TavanaMSoltanifarMSantos-ArteagaFJ. Analytical hierarchy process: revolution and evolution. Ann Oper Res2023; 326: 879–907.
6.
MamatzhonovichODKhamidovichOMEsonalio’g’liMY. Digital economy: essence, features and stages of development. Acad Globe: Indersci Res2022; 3: 355–359.
7.
ZhangTShiZZShiYR,et al.Enterprise digital transformation and production efficiency: mechanism analysis and empirical research. Econ Res-Ekonomska Istraživanja2022; 35: 2781–2792.
8.
EniolaAAKenzhinZKairliyevaG. Entrepreneur digital business strategy and efficiency: intervening role of firm’s IT capabilities. J Sci Ind Res (JSIR)2022; 81: 137–147.
9.
PrakasaY. Digital capability and digital innovation: the impact of small business sustainability during pandemic COVID 19. J-MAS (Jurnal Manajemen dan Sains)2022; 7: 189–197.
10.
ChengW. The impact of enterprise digital transformation on service innovation performance–taking the construction enterprises in the Yangtze river Delta as the research object. Int J Sci Bus2022; 14: 155–172.
11.
De Bem MachadoASecinaroSCalandraD,et al.Knowledge management and digital transformation for industry 4.0: a structured literature review. Knowl Manag Res Pract2022; 20: 320–338.
12.
NuseirMRefaeG. The effect of digital marketing capabilities on business performance enhancement: mediating the role of customer relationship management (CRM). Int J Data Netw Sci2022; 6: 295–304.
13.
NovikovSVSazonovAA. Approaches to developing a strategic program for high-tech enterprise in the context of digital transformation. J Appl Eng Sci2022; 20: 41–52.
14.
ChenSKingJL. Policy and imprecise concepts: the case of digital transformation. J Assoc for Inf Syst2022; 23: 401–407.
15.
ShenLZhangXLiuH. Digital technology adoption, digital dynamic capability, and digital transformation performance of textile industry: moderating role of digital innovation orientation. MDE Manage Decis Econ2022; 43: 2038–2054.
16.
IshfaqRDavis-SramekBGibsonB. Digital supply chains in omnichannel retail: a conceptual framework. J Bus Logist2022; 43: 169–188.
17.
FernandoESiagianP. Proposal to use the analytic hierarchy process method evaluate bank credit submissions. Procedia Comput Sci2021; 179: 232–241.
18.
SjödinDParidaVVisnjicI. How can large manufacturers digitalize their business models? A framework for orchestrating industrial ecosystems. Calif Manage Rev2022; 64: 49–77.
19.
ZdravkovićMPanettoHWeichhartG. AI-enabled enterprise information systems for manufacturing. Enterpr Inf Syst2022; 16: 668–720.
20.
HeimHHopperC. Dress code: the digital transformation of the circular fashion supply chain. International J Fashion Des, Technol Educ2022; 15: 233–244.
21.
LiuC. Risk prediction of digital transformation of manufacturing supply chain based on principal component analysis and backpropagation artificial neural network. Alexandria Eng J2022; 61: 775–784.
22.
ZhangY. Digitalization and enterprise innovation: research hotspots and frontier trends. Ind Eng Innov Manage2022; 5: 13–22.
23.
JagtapMKarandeP. The m-polar fuzzy set ELECTRE-I with revised Simos’ and AHP weight calculation methods for selection of non-traditional machining processes. Decis Making: Appl Manage Eng2023; 6: 240–281.
24.
SivaprakasamPAngamuthuM. Generalized Z-fuzzy soft β-covering based rough matrices and its application to MAGDM problem based on AHP method. Decis Making: Appl Manage Eng2023; 6: 134–152.
25.
XuXZhangY. Price forecasts of ten steel products using Gaussian process regressions. Eng Appl Artif Intell2023; 126: 106870.
26.
JinBXuX. Pre-owned housing price index forecasts using Gaussian process regressions. J Mod Manage2024; 19: 1927–1958.
27.
TalebTSHashimNZakariaN. Mediating effect of innovation capability between entrepreneurial resources and micro business performance. Bottom Line2023; 36: 77–100.
28.
JinBXuX. Contemporaneous causality among price indices of ten major steel products. Ironmak Steelmak2024; 51: 515–526.
29.
JreigeCSKimuraRNSegundoÂRTC, et al.Esthetic treatment planning with digital animation of the smile dynamics: a technique to create a 4-dimensional virtual patient. J Prosthet Dent2022; 128: 130–138.
30.
PangarsoASisiliaKSetyoriniR, et al.The long path to achieving green economy performance for micro small medium enterprise. J Innov Entrep2022; 11: 1–19.
31.
BulturbayevichMB. The role of small business entities in the development of the republic of Uzbekistan. International Journal of Research in Commerce2022; 16: 17–22.
32.
WangPHanW. Construction of a new financial E-commerce model for small and medium-sized enterprise financing based on multiple linear logistic regression. J Org End User Comput (JOEUC)2021; 33: 1–18.
33.
DwivediRPrasadKMandalN, et al.Performance evaluation of an insurance company using an integrated balanced scorecard (BSC) and best-worst method (BWM). Decis Making: Appl Manage Eng2021; 4: 33–50.
34.
AbdulDWenqiJ. Evaluating appropriate communication technology for smart grid by using a comprehensive decision-making approach fuzzy TOPSIS. Soft COmput2022; 26: 8521–8536.
35.
RobsonAGFrishmanLJGriggJ, et al.ISCEV Standard for full-field clinical electroretinography (2022 update). Doc Ophthalmol2022; 144: 165–177.
36.
RanjiAParashkoohiMZamaniD,et al.Evaluation of agronomic, technical, economic, and environmental issues by analytic hierarchy process for rice weeding machine. Energy Reports2022; 8: 774–783.
37.
AguarónJEscobarMTMoreno-JiménezJM. Reducing inconsistency measured by the geometric consistency index in the analytic hierarchy process. Eur J Oper Res2021; 288: 576–583.
38.
FamSFChuanZLWahjonoSI,et al.Fuzzy analytical hierarchy process (f-ahp) method in evaluating e-wallet payment system in Malaysia. Math Stat Eng Appl2022; 71: 742–749.
39.
LiN. A fuzzy evaluation model of college English teaching quality based on analytic hierarchy process. Int J Emerg Technol Learn (iJET)2021; 16: 17–30.
40.
CamposPJCoriaLNCazarez-CastroNR. Model-free design of speed tracking controller via fuzzy lyapunov synthesis for a surface-mounted PMSM. Electr Eng2022; 104: 1565–1572.
