Factors Influencing SMEs' Digital Transformation
Factors Influencing SMEs' Digital Transformation
Factors Influencing SMEs' Digital Transformation
Le The Phiet
Tay Nguyen University
ltphiet@ttn.edu.vn
Abstract. Digital transformation (DT) has become critical for the success and sustainability
of small and medium-sized enterprises (SMEs). This study aimed to examine the factors
influencing DT in SMEs, with a focus on technological, organizational, and environmental
contexts. A survey was conducted with 380 SMEs in the Central Highlands region of Vietnam.
The results showed that technological capability, organizational factors like structure and
resources, and environmental elements including government regulations and competitive
pressure positively influenced SMEs’ digital transformation. Additionally, the study found
that the leader’s age and entrepreneurial experience moderated the relationships between
these factors and digital transformation. The findings provide valuable insights for SME
owners, managers, and policymakers in implementing strategies and initiatives to support the
digital transformation of SMEs.
Keywords: Digital Transformation, SMEs, Factors influencing SMEs digitalization.
175
Phiet, Journal of System and Management Sciences, Vol. 14 (2024) No. 1, pp. 175-187
1. Introduction
International Labour Organization (2019) showed that smaller economic units play a substantial role in
the creation of job possibilities. In fact, small businesses, newly established businesses, and individuals
working for themselves account for more than two-thirds, or 70%, of the total workforce. In addition,
the self-employed and micro-enterprises, when viewed separately from one another, make a sizeable
contribution to the overall employment picture. Their combined share of the whole labour market
accounts for between 80 – 90% of the total workforce in low-and middle-income nations. This suggests
that the bulk of people who are working in these nations are either self-employed or work in enterprises
that are relatively tiny in scale. South Asia stands out among other areas as having the greatest
employment proportion that can be attributable to the self-employed and micro-enterprises. This lends
credence to the notion that a significant proportion of the working population in South Asia is either
self-employed or employed by a smaller-scale business of income in the region (International Labour
Organization, 2019).
Thanks to the connectivity platforms made possible by Industry 4.0, established industries are
undergoing a shift to a digital era. Today's machines, devices, and commodities can readily
communicate, learn from one another, and quickly adjust to market shifts (Frank et al., 2019). This
means that by adopting new technologies, SMEs can boost their production capacities and global
competitiveness (Kraft et al., 2022; Malodia et al., 2023). In this light, Industry 4.0 technologies may
provide a potent resource for companies seeking to sustain or improve their competitive standing in
both domestic and international markets. Digital technologies have been shown in studies to improve
enterprises' cost-efficiency and product differentiation strategies which can lead to expanded
opportunities in the market and higher profits for SMEs. SMEs can benefit by customizing products
and services to individual consumers, creating new services to meet unmet consumer needs, and better-
controlling manufacturing processes to minimize costs. However, many SMEs continued to lag in
adoption (OECD, 2021). Telukdarie et al. (2023) argued that SMEs have a hard time adopting digital
technology because of a lack of management resources and financial constraints. Furthermore, Benitez
et al. (2020) showed that despite efforts to remove financial barriers, SMEs may be slow to adopt digital
technologies due to uncertainty about their return on investment or an inability to fully capitalize on the
digital opportunities presented by Industry 4.0. It is important to note, therefore, that despite the fact
that the accelerated adoption of digital tools may be a silver lining to the cloud that the crisis has cast,
there is still a continuous need for advice, support, and guidance from reliable sources in order to cement
the transition, address risks, and exploit the potential of the new tools.
The Central Highlands in Vietnam faced challenges regarding human resource quality, with the
region experiencing the highest poverty rate and slowest economic development (General Statistics
Office, 2022). The economic growth in this area was largely extensive and relied heavily on capital,
cheap labor, and the exploitation of natural resources such as forests and hydropower, while
advancements in technology and institutions played a minor role in contributing to overall productivity
(Ngoc et al., 2021). Recognizing the need for change, localities, and businesses in Central Vietnam and
Central Highlands have been developing strategies and action plans to promote green growth and
facilitate digital transformation, seeking more environmentally friendly development models. To tackle
these issues, Central Highlands' digital transformation requires essential IT infrastructure upgrades, the
modernization and expansion of trade infrastructure to foster connections with domestic and
international markets, and the promotion of e-commerce and logistics (Anh Huyen, 2022). With the
emergence of Industry 4.0, science and technology have become crucial drivers of economic growth. It
is an empirical gap in the context of digitalization in Central Highlands, Vietnam that need to be
addressed.
176
Phiet, Journal of System and Management Sciences, Vol. 14 (2024) No. 1, pp. 175-187
177
Phiet, Journal of System and Management Sciences, Vol. 14 (2024) No. 1, pp. 175-187
The organizational context: When deploying ICT and digital transformation, SMEs confront several
problems, including financial, human, organizational structure, and capital constraints (Kraft et al.,
2022). In addition, SMEs appear skeptical when it comes to the point when they must trust the
fundamental technologies and instruments of the digital economy, such as issues over security and
privacy. According to Leavitt (1965), industrial organizations are regarded to be complex systems that
consist of at least four interdependent components. These aspects include people, technology, structure,
and task. Leavitt believed that changes in any one of these four dimensions would have an effect on the
other three. For workers to carry out tasks such as delivering services and manufacturing goods, they
need to have the appropriate qualifications. Consequently, individuals find themselves embedded inside
systems that govern aspects such as the communication process.
Although digital transformation is defined by technical features, the success of this process is
contingent on the ability of corporate executives to modify their business models to take advantage of
disruptive innovations in information and communications technology. In parallel, The technology-
push innovation strategy is the primary foundation on which the Industry 4.0 movement is built, as it
originates from direct competitors operating inside the same industry as the product firms themselves
(Frank et al., 2019).
H2: The organizational factor will positively influence digital transformation
The environmental context: The final group (Environmental context in TOE framework) represents
the outside influence on the digital transformation process in SMEs. It comprises the industry's structure,
the pressures, and the regulatory setting. Firstly, government regulation may have either a positive or
negative impact on innovation (Baker, 2012). Government rules and institutions, such as competence
centers and research institutions, play a significant role in the success of the industry (Gašperlin et al.,
2021). In addition, the level of pressure from competitors inside the environment in which the
enterprises operate is referred to as competitive pressure (Lutfi et al., 2016). An effective business
strategy aims to increase market competitiveness which has been regularly recognized as one of the
elements influencing DT, as demonstrated by numerous research (Oliveira et al., 2014; Ramdani et al.,
2013; Wong et al., 2020). In other words, when competitors begin to embrace DT, enterprises will be
compelled to use it more widely as part of their efforts to maintain competitiveness. Thirdly, the
preparedness of a company's suppliers and business partners is essential to the smooth rollout of digital
technologies (Gutierrez et al., 2015). This is because there are crucial drivers for adopting inter-
organizational systems within partner relationships. Businesses are more likely to adopt new
technologies if their suppliers and partners have a high level of competence with these technologies
(Abed, 2020). Adopting cutting-edge technology is heavily influenced by the expectations of one's
commercial partners. Finally, encouragement, dedication, and pressure from customers, as well as trust
between an organization and its customers, are key factors in technology adoption (Yoon & George,
2013). It has been shown that providing electronic customer services, which improve customer
interaction, drives technology adoption in firms (Abed, 2020). Because customers expect it, companies
are adopting new innovative technologies. Consumer pressure on technology adoption has been shown
to be significant in several studies.
H3: The environment factor will positively influence digital transformation.
The TOE framework was put through a great amount of testing by researchers from a variety of
countries and settings. Fig. 1 provides both a comprehensive list of factors as well as a list of significant
variables based on their findings.
178
Phiet, Journal of System and Management Sciences, Vol. 14 (2024) No. 1, pp. 175-187
179
Phiet, Journal of System and Management Sciences, Vol. 14 (2024) No. 1, pp. 175-187
3. Research Methodology
The research utilized a stratified random sampling technique based on data obtained from the Ministry
of Planning and Investment of Vietnam's website, which involved a fee. The size of each firm was
categorized as micro, small, or medium, determined by the number of employees and turnover volume
(Oliveira et al., 2014).
A preliminary pilot study with 88 companies at five Central Highlands provinces, namely Lam
Dong, Gia Lai, Kon Tum, Dak Lak, and Dak Nong was conducted to assess the constructs, and these
companies were excluded from the main survey. The objects to be interviewed included business
managers, owners, and specialists who perceived and grasped the firm's level of digital transformation.
The results of the pilot study showed that the measurement scales used in the research were reliable and
valid. Out of the 480 distributed questionnaires, 380 responses were considered valid for analysis. The
Technology-Organization-Environment (TOE) framework was employed, and the questionnaire's items
were directly drawn from existing research (see Appendix A). Each construct (technology, organization,
environment) was measured using a five-point Likert scale, ranging from "strongly disagree" to
"strongly agree." The data collection period spanned from January 2023 to May 2023.
To strengthen the validity of the findings and minimize self-reporting bias, Podsakoff et al. (2003)
showed that Harman's one-factor (or single-factor) test is one of the most valuable strategies. The study
will use this technique to determine the number of factors necessary to account for the variance in the
variables.
Using covariance-based structural equation modeling (CB-SEM) was deemed appropriate based on
the size of the sample and the soundness of the assumptions for performing multivariate analysis. Then,
we created a measurement model using confirmatory factor analysis (CFA). The suggested study
framework's path relations were then tested using SEM (Fornell, C., & Larcker, 1981). The CFA was
used to examine the reliability and validity of the study framework's constructs. The SEM, on the other
hand, was used to assess the strength and importance of the structural routes proposed in the research
framework.
180
Phiet, Journal of System and Management Sciences, Vol. 14 (2024) No. 1, pp. 175-187
(19%) with fewer than 10 workers. In total, 32.6% of them had been performing for three to five years,
while 35.3% had been performing for five to ten years, 11.6% had been acting for less than three years,
and 20.5% had been performing for more than ten years.
Table 1. Respondents’ descriptive statistics.
Frequency Percentage
< 3 years 44 11.6
3-less than 5 years 124 32.6
Age of Firm
5-less than 10 years 134 35.3
>10 years 78 20.5
<30 years 90 23.7
Age of Leader 30-less than 50 171 45.0
> 50 119 31.3
Expert 32 8.4
Job Owners 139 36.6
Managers 209 55.0
Agriculture 34 8.9
Service 133 35.0
Industry Transportation 87 22.9
Construction 99 26.1
Others 27 7.1
<10 72 18.9
Number of
10-99 162 42.6
Employees
100-199 146 38.4
Common method bias (CMB): The Harman single factor test was also used to calculate the
explained variance and reveal common method bias. A single component explained just 23.815% of
the variance. Because the explained variation was less than 50% (Podsakoff et al., 2003), there was no
CMB.
Discriminant validity: The results of Table 2 show that every construct CR was higher than 0.7,
which indicates that every measure may be regarded reliable. The AVEs value, the standardized
loadings, and the t-values of the item loadings are what we look at in order to determine the convergent
validity of the data. According to the findings of our investigation, each and every AVE calculated for
a factor that loaded on its own independent construct was valid (it was greater than 0.5). In addition to
this, the square root of the average variance extracted (AVE) for each construct that was included in the
model was higher than the correlations that were discovered between that specific construct and the
constructs of any other models. This was the case regardless of which models were being compared
(Fornell, C., & Larcker, 1981).
Table 2: Result of Reliability Test
Cronbach's Alpha C.R AVE TECH ORG EN DT
Technology 0.830 0.832 0.623 0.789
Organization 0.868 0.868 0.524 0.089 0.724
Environment 0.825 0.825 0.541 -0.053 0.098 0.735
Digital transformation 0.821 0.822 0.606 0.487 0.386 0.453 0.778
Model Fitness & Structural Model: Indicators were utilized in order to assess the level of model
fitness as follows: CMIN/df, CFI, SRMR, RMSEA and Pclose. As shown in Table 3, CMIN/df = 1.076
< 3; CFI = 0.997 > 0.95; SRMR = 0.035 < 0.08; RMSEA = 0.014<0.06, Pclose=1>0.05. Thus, the
model matches the research hypothesis (Hu, L.T. & Bentler, 1999).
181
Phiet, Journal of System and Management Sciences, Vol. 14 (2024) No. 1, pp. 175-187
182
Phiet, Journal of System and Management Sciences, Vol. 14 (2024) No. 1, pp. 175-187
183
Phiet, Journal of System and Management Sciences, Vol. 14 (2024) No. 1, pp. 175-187
entrepreneurial skills among SME leaders could yield positive outcomes in facilitating their digital
transformation journey.
5.2. Limitations and future research
The research has certain limitations that need to be acknowledged. The first, the study's sample is
confined to the Central Highlands region of Vietnam, which means that the findings may not fully
represent the entire reality of Vietnam. Moreover, it would be interesting to compare the DT of SMEs
in various socioeconomic contexts or to perform a cross-cultural study to compare and evaluate the
results in other cultural situations (Zoppelletto et al., 2023).
The second, the speed of digitalization varies across industries due to their distinct characteristics,
and this aspect should be considered in future studies. It is suggested that separate models be developed
for each industry rather than a comprehensive one combining innovation characteristics. This would
require additional research to estimate models similar to those developed for the manufacturing and
services sectors (Oliveira et al., 2014).
Finally, besides two moderators (age of firm and age of leader), adding new moderating variables
to the suggested framework, such as ownership structure (family-owned vs. professionally controlled)
and business model (manufacturer vs. original equipment manufacturer) (Malodia et al., 2023), might
be the new approach in the future.
6. Conclusion
Despite these limitations, the proposed model in this research serves as a strong foundation for future
endeavors.
This study makes a contribution to individual differences among SME entrepreneurs as antecedents
of digital transformation in their respective SMEs and outcomes that result from such transformation
by empirically examining the relationships between digital transformation and the individual
characteristics of SME entrepreneurs and enterprises themselves. Specifically, this study looks at the
relationships between digital transformation and the likelihood that an SME will adopt new digital
technologies in Vietnam. According to the findings of the study, the favorable influence of technology,
organization, and environment on the digital transformation of SMEs was identified, the same result
(Abed, 2020; Malodia et al., 2023; Wong et al., 2020).
Finance, structure, culture, people, communication, and readiness were discovered to be
fundamental elements for fostering digital transformation in SMEs. In addition, the government
regulatory, external pressures, and external support all have a favorable impact on DT as environment
aspects. Specifically, this study focuses on the correlation between digital transformation and the level
of innovation that SME entrepreneurs are able to bring to their companies. Based on the findings, it
appears that technological capabilities contribute to the DT on of SMEs. Furthermore, the age of the
firm and the leaders moderate the correlation between factors and digital transformation.
References
Abed, S. S. (2020). Social commerce adoption using TOE framework: An empirical investigation of
Saudi Arabian SMEs. International Journal of Information Management, 53(October 2019), 102118.
https://doi.org/10.1016/j.ijinfomgt.2020.102118
Afriliana, N., & Ramadhan, A. (2022). The Trends and Roles of Robotic Process Automation
Technology in Digital Transformation: A Literature Review. Journal of System and Management
Sciences, 12(3), 51–73. https://doi.org/10.33168/JSMS.2022.0303
Altarawneh, H., & Tarawneh, M. M. (2023). Business Intelligence and Information System
Management: A Conceptual View. Journal of System and Management Sciences, 13(2), 31–44.
https://doi.org/10.33168/JSMS.2023.0203
184
Phiet, Journal of System and Management Sciences, Vol. 14 (2024) No. 1, pp. 175-187
Benitez, G. B., Ayala, N. F., & Frank, A. G. (2020). Industry 4.0 innovation ecosystems: An
evolutionary perspective on value cocreation. International Journal of Production Economics,
228(March). https://doi.org/10.1016/j.ijpe.2020.107735
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables
and measurement error. Journal of Marketing Research, 18(1), 39–50.
Fosso Wamba, S., Gunasekaran, A., Bhattacharya, M., & Dubey, R. (2016). Determinants of RFID
adoption intention by SMEs: an empirical investigation. Production Planning and Control, 27(12),
979–990. https://doi.org/10.1080/09537287.2016.1167981
Frank, A. G., Mendes, G. H. S., Ayala, N. F., & Ghezzi, A. (2019). Servitization and Industry 4.0
convergence in the digital transformation of product firms: A business model innovation perspective.
Technological Forecasting and Social Change, 141(July 2018), 341–351.
https://doi.org/10.1016/j.techfore.2019.01.014
Gašperlin, B., Pucihar, A., & Kljajić Borštnar, M. (2021). Influencing Factors of Digital
Transformation in SMEs – Literature Review. 231–244. https://doi.org/10.18690/978-961-286-442-
2.17
General Statistics Office. (2022). Statistical yearbook of Vietnam 2021. Statistical Publishing House.
https://www.gso.gov.vn/en/data-and-statistics/2022/08/statistical-yearbook-of-2021/
Gutierrez, A., Boukrami, E., & Lumsden, R. (2015). Technological, organisational and environmental
factors influencing managers’ decision to adopt cloud computing in the UK. Journal of Enterprise
Information Management, 28(6), 788–807. https://doi.org/10.1108/JEIM-01-2015-0001
Hu, L.T. & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis:
conventional criteria versus new alternatives. Structural Equation Modeling, 6(July 2012), 1–55.
International Labour Organization. (2019). Small matters (Vol. 45, Issue 3).
https://doi.org/10.1353/aph.2001.0076
Kraft, C., Lindeque, J. P., & Peter, M. K. (2022). The digital transformation of Swiss small and medium-
sized enterprises: insights from digital tool adoption. Journal of Strategy and Management, 15(3), 468–
494. https://doi.org/10.1108/JSMA-02-2021-0063
Leavitt, H.J. (1965). Applied organizational change in industry: structural, technological and humanistic
approaches. in March, J.G. (Ed.), Handbook of Organizations. Routledge Library Editions:
Organizations: Theory & Behaviour, Routledge, London and New York, pp. 1144-1170.
Low, C., Chen, Y., & Wu, M. (2011). Understanding the determinants of cloud computing adoption.
Industrial Management and Data Systems, 111(7), 1006–1023.
https://doi.org/10.1108/02635571111161262
Lutfi, A., Kamil, I., & Rosli, M. (2016). The influence of technological, organizational and
environmental factors on the information technology adoption by SMEs. International Journal of
Economics and Financial Issues, 6(7), 240–248.
Malodia, S., Mishra, M., Fait, M., Papa, A., & Dezi, L. (2023). To digit or to head? Designing digital
transformation journey of SMEs among digital self-efficacy and professional leadership. Journal of
Business Research, 157(February 2022), 113547. https://doi.org/10.1016/j.jbusres.2022.113547
185
Phiet, Journal of System and Management Sciences, Vol. 14 (2024) No. 1, pp. 175-187
Ngoc, H. H., Tuan, B. Q., & Hue, N. K. (2021). Situation of Innovation and Technology Application in
Central Highlands Enterprises. April, 54–70.
OECD. (2021). The Digital Transformation of SMEs: Executive Summary. OECD Studies on SMEs
and Entrepreneurship. https://www.oecd.org/industry/smes/PH-SME-Digitalisation-final.pdf
Oliveira, T., Thomas, M., & Espadanal, M. (2014). Assessing the determinants of cloud computing
adoption: An analysis of the manufacturing and services sectors. Information and Management, 51(5),
497–510. https://doi.org/10.1016/j.im.2014.03.006
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common Method Biases in
Behavioral Research: A Critical Review of the Literature and Recommended Remedies. Journal of
Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Ramdani, B., Chevers, D., & Williams, D. A. (2013). SMEs’ adoption of enterprise applications: A
technology-organisation-environment model. Journal of Small Business and Enterprise Development,
20(4), 735–753. https://doi.org/10.1108/JSBED-12-2011-0035
Rogers, E. (1995). Diffusion of innovations (4th ed.). New York: The Free Press.
Tarute, A., Duobiene, J., Kloviene, L., Vitkauskaite, E., & Varaniute, V. (2018). Identifying factors
affecting digital transformation of SMEs. Proceedings of the International Conference on Electronic
Business (ICEB), 2018-Decem, 373–381.
Tornatzky, L. G., & Fleischer, M. (1990). The process of technological innovation. Lexington, MA:
Lexington Books.
Telukdarie, A., Dube, T., Matjuta, P., & Philbin, S. (2023). The opportunities and challenges of
digitalization for SME’s. Procedia Computer Science, 217(2022), 689–698.
https://doi.org/10.1016/j.procs.2022.12.265
Wang, Y. S., & Shih, Y. W. (2009). Why do people use information kiosks? A validation of the Unified
Theory of Acceptance and Use of Technology. Government Information Quarterly, 26(1), 158–165.
https://doi.org/10.1016/j.giq.2008.07.001
Wong, L. W., Leong, L. Y., Hew, J. J., Tan, G. W. H., & Ooi, K. B. (2020). Time to seize the digital
evolution: Adoption of blockchain in operations and supply chain management among Malaysian SMEs.
International Journal of Information Management, 52(August), 101997.
https://doi.org/10.1016/j.ijinfomgt.2019.08.005
Yoon, T. E., & George, J. F. (2013). Why aren’t organizations adopting virtual worlds? Computers in
Human Behavior, 29(3), 772–790. https://doi.org/10.1016/j.chb.2012.12.003
Zoppelletto, A., Orlandi, L. B., Zardini, A., Rossignoli, C., & Kraus, S. (2023). Organizational roles in
the context of digital transformation: A micro-level perspective. Journal of Business Research,
157(February 2022), 113563. https://doi.org/10.1016/j.jbusres.2022.113563
186
Phiet, Journal of System and Management Sciences, Vol. 14 (2024) No. 1, pp. 175-187
APPENDIX
Variables Scale Description Source
It is easy to incorporate new technologies
Compatibility
into your firm.
Does state-of-the-art of technologies deal
Complexity
Technology with many objectives in your firm?
Your firm has the capacity to build and
Characteristics market innovative solutions in digital
transformation.
Can your firm spend on IT and Web-
Financial resources
based?
Adopting digital transformation is
Structure
consistent with your business strategy.
Adopting digital transformation fits your
Culture Tornatzky &
organizational culture.
Fleischer (1990);
Adopting digital transformation, does
Organization Yoon & George
Communication Processes your firm improve access to information
(2013);
and communication processes?
Wong et al.
The number of employees at your
Human capital (2020).
company is high compared to the industry.
To seek sales growth, your firm is willing
Organizational readiness to execute risky digital transformation
projects.
The government policies encourage your
Government regulations
firm to adopt digital transformation.
You believe your firm will lose our market
Competitive pressure share if we do not adopt digital
transformation.
Environment More partner firms in your industry have
Trading partner pressure
adopted in digital transformation
Does the government provide government
External support: procurements and contracts such as
Infrastructure technical support, training, and funding for
firms?
Does your firm motivate to adopt e-
Adoption of e-commerce
commerce for successful digital
platforms
transformation?
Digital Can your firm successfully connect with
Malodia et al.
Transformation Adoption of digital broad audiences via digital marketing
(2023)
in SMEs marketing channels such as social media, mobile
applications and digital platforms?
Does your firm increase the collection and
Use of big data
use of dedicated big data?
187