In order to enhance the competitiveness of the manufacturing industry in the face of the great turning point of the 4th Industrial Revolution, governments around the world are making great efforts to spread smart factories to small and medium enterprises (SMEs). The purpose of this study was to explore the factors that influence SME executives’ intentions to adopt a smart factory. To this end, a research model was developed based on the technology acceptance model (TAM), the technology-organization-environment (TOE) framework, and resource dependence theory (RDT). Data from 175 valid questionnaires were collected from SME executives in Korea and analyzed using structural equation modeling. The results of the analysis indicated that perceived usefulness did not influence SME executives’ intentions to adopt a smart factory. However, the perceived importance extracted from the RDT had a strong impact on SME executives’ intention to adopt a smart factory. As a result of the study, we identified practical implications for SMEs’ adoption of smart factories.
AlshamailaYPapagiannidisSLiF (2013) Cloud computing adoption by SMEs in the north east of England: a multi-perspective framework. Journal of Enterprise Information Management26(3): 250–275.
2.
AminiMJahanbakhsh JavidN (2023) A multi-perspective framework established on Diffusion of Innovation (DOI) theory and Technology, Organization and Environment (TOE) framework toward supply chain management system based on cloud computing technology for small and medium enterprises. International Journal of Information Technology and Innovation Adoption11(8): 1217–1234.
3.
AwaHOOjiaboOUEmechetaBC (2015) Integrating TAM, TPB and TOE frameworks and expanding their characteristic constructs for e-commerce adoption by SMEs. Journal of Science6(1): 76–94.
4.
AwiagahRKangJLimJI (2016) Factors affecting e-commerce adoption among SMEs in Ghana. Information Development32(4): 815–836.
5.
BagSRahmanMSGuptaS, et al. (2022) Understanding and predicting the determinants of blockchain technology adoption and SMEs’ performance. The International Journal of Logistics Management. Ahead-of-print.
6.
BagozziRPYiY (1988) On the evaluation of structural equation models. Journal of the Academy of Marketing Science16(1): 74–94.
ChatterjeeSRanaNPDwivediYK, et al. (2021) Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model. Technological Forecasting and Social Change170: 120880.
9.
ChauPYTamKY (1997) Factors affecting the adoption of open systems: an exploratory study. MIS Quarterly21(1): 1–24.
10.
DavisFD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly13(3): 319–340.
11.
ETRI Standards Research Center (2018) Smart manufacturing standards and technologies. ETRI Insight.
12.
GangwarHDateHRamaswamyR (2015) Understanding determinants of cloud computing adoption using an integrated TAM-TOE model. Journal of Enterprise Information Management28(1): 107–130.
13.
GefenDStraubD (2005) A practical guide to factorial validity using PLS-graph: tutorial and annotated example. Communications of the Association for Information Systems16(1): 5.
14.
GrandonEEPearsonJM (2004) Electronic commerce adoption: an empirical study of small and medium US businesses. Information & Management42(1): 197–216.
15.
HairJFHultGTMRingleC, et al. (2013) A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks, CA: Sage Publications.
16.
IacovouCLBenbasatIDexterAS (1995) Electronic data interchange and small organizations: adoption and impact of technology. MIS Quarterly19(4): 465–485.
17.
IfinedoP (2011) An empirical analysis of factors influencing Internet/e-business technologies adoption by SMEs in Canada. International Journal of Information Technology & Decision Making10(4): 731–766.
18.
JeonBNHanKSLeeMJ (2006) Determining factors for the adoption of e-business: the case of SMEs in Korea. Applied Economics38(16): 1905–1916.
19.
KuanKKChauPY (2001) A perception-based model for EDI adoption in small businesses using a technology–organization–environment framework. Information & Management38(8): 507–521.
20.
LutfiAAlshira’hAFAlshirahMH, et al. (2022) Antecedents and impacts of enterprise resource planning system adoption among Jordanian SMEs. Sustainability14(6): 3508.
21.
MaroufkhaniPIsmailWKWGhobakhlooM (2020) Big data analytics adoption model for small and medium enterprises. Journal of Science and Technology Policy Management11(4): 483–513.
22.
MehrtensJCraggPBMillsAM (2001) A model of Internet adoption by SMEs. Information & Management39(3): 165–176.
23.
MoghavvemiSMohd SallehNAZhaoW, et al. (2011) An empirical study of IT innovation adoption among small and medium sized enterprises in Klang Valley, Malaysia. Socialinės Technologijos/Social Technologies1(2): 267–282.
24.
OliveiraTMartinsMF (2011) Literature review of information technology adoption models at firm level. Electronic Journal of Information Systems Evaluation14(1): 110–121.
25.
OliveiraTThomasMEspadanalM (2014) Assessing the determinants of cloud computing adoption: an analysis of the manufacturing and services sectors. Information & Management51(5): 497–510.
26.
PfefferJSalancikGR (1978) The External Control of Organizations: A Resource Dependence Perspective. New York: Harper & Row.
27.
PfefferJSalancikGR (2003) The External Control of Organizations: A Resource Dependence Perspective. Stanford, CA: Stanford University Press.
28.
PodsakoffPMMacKenzieSBLeeJY, et al. (2003) Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology88(5): 879–903.
29.
PremkumarGRobertsM (1999) Adoption of new information technologies in rural small businesses. Omega27(4): 467–484.
30.
RamdaniBKawalekPLorenzoO (2009) Predicting SMEs’ adoption of enterprise systems. Journal of Enterprise Information Management22(1/2): 10–24.
31.
RogersEM (2003) Diffusion of Innovations, 5th ed.New York, NY: Free Press.
32.
RyalatMElMoaqetHAlFaouriM (2023) Design of a smart factory based on cyber-physical systems and Internet of things towards industry 4.0. Applied Sciences13(4): 2156.
33.
SanchezGTrincheraLRussolilloG (2013) plspm: tools for Partial Least Squares Path Modeling (PLS-PM). R package version 0.4, 1.
34.
SooriMArezooBDastresR (2023) Internet of things for smart factories in industry 4.0, a review. Internet of Things and Cyber-Physical Systems.
35.
StjepićAMSušacLVugecDS (2019) Technology, organizational and environmental determinants of business intelligence systems adoption in Croatian SME: a case study of medium-sized enterprise. International Journal of Economics and Management Engineering13(5): 725–730.
36.
SusantyAHandokoAPuspitasariNB (2020) Push-pull-mooring framework for e-commerce adoption in small and medium enterprises. Journal of Enterprise Information Management33(2): 381–406.
37.
ThongJY (1999) An integrated model of information systems adoption in small businesses. Journal of Management Information Systems15(4): 187–214.
38.
TornatzkyLFleischerM (1990) The Process of Technology Innovation. Lexington, MA: Lexington Books.
39.
WangYMWangYSYangYF (2010) Understanding the determinants of RFID adoption in the manufacturing industry. Technological Forecasting and Social Change77(5): 803–815.
40.
WonJYParkMJ (2020) Smart factory adoption in small and medium-sized enterprises: empirical evidence of manufacturing industry in Korea. Technological Forecasting and Social Change157: 120117.
41.
YoonCLimD (2020) An empirical study on factors affecting customers’ acceptance of Internet-only banks in Korea. Cogent Business7(1): 1–23.
42.
YoonCLimDParkC (2020) Factors affecting adoption of smart farms: the case of Korea. Computers in Human Behavior108: 106309.
