Factors Influencing Cross-Border E-Commerce Adoption of Thai MSMEs: A Fuzzy DEMATEL Approach

Abstract

This research investigates the factors influencing the adoption of cross-border e-commerce (CBEC) among manufacturing micro, small, and medium enterprises (MSMEs) in Thailand by integrating the Diffusion of Innovation (DOI), Resource-Based View (RBV), and Technology–Organization–Environment (TOE) frameworks with the Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) method. The findings reveal that knowledge of e-commerce, international marketing capabilities, and security and risk concerns are primary drivers of CBEC adoption, while socio-cultural factors and cost-related issues are secondary enablers. This study contributes to the e-commerce adoption literature by developing a context-specific, integrated conceptual framework and empirically validating the causal interrelationships among technological, organizational, and environmental factors in CBEC adoption using Fuzzy DEMATEL. The results provide actionable insights for both MSMEs and policymakers to strengthen Thailand’s participation in the digital economy and advance Sustainable Development Goals (SDGs) 8 and 17.

1. Introduction

Between 2018 and 2021, the total number of online consumers rose from 1.4 billion to 2.3 billion people [1,2]. Collectively, the e-commerce market across 43 developed and developing countries alone accounts for approximately USD 27 T, which is nearly one-third of the world’s GDP [3]. Thus, the widespread use of the internet, technological advancements, and smartphones has driven consumers and producers to enter digital markets for broader choices. Particularly, developing countries have witnessed rapid growth in cross-border e-commerce (CBEC) due to advancements in technology and globalization, and changes in global consumer behavior [4]. CBEC involves online selling and buying of products, services, and information that connect buyers and sellers across several countries [4].

Thailand, despite having 39.23 million e-commerce consumers and being the second largest e-commerce market in ASEAN, receives 30% of its sales share from inbound CBEC [5]. Thus, CBEC plays an important role in both business-to-business (B2B) and business-to-consumer (B2C) e-commerce markets. During the pandemic period, small- and medium-sized enterprises (SMEs) in Thailand were forced to shift towards digital transformation in order to attract overseas markets as a survival option. This was due to several reasons, such as limitations in tourism activities and loss of purchasing power in the domestic market. Thus, in the post-pandemic scenario, CBEC has become highly advantageous and one of the best resilience strategies for SMEs. This approach eliminates the intermediaries in the supply chain, making it highly profitable for SMEs [6]. However, to build a successful digital economy, outbound CBEC initiatives must be led by Thai companies, either in the public or private sector [7]. Hence, the Department of International Trade Promotion (DITP) under the Ministry of Commerce in Thailand has launched Thaitrade.com, a B2B e-market platform aimed at connecting SMEs with global customers, electronic payment gateways, and electronic logistics service providers, which serve both B2B and B2B2C markets [8,9,10]. Thaitrade.com is expected to expand its operations as a cross-border platform to facilitate SMEs and electronic marketplace vendors to increase the country’s competitiveness [9]. Further, during 2021 and 2022, Thailand experienced significant growth in cross-border trade despite the pandemic [11]. This trend continued into the post-pandemic period, with a no 64.9 percent increase (71,409 million Thai Baht) in 2023, involving trade with countries such as China, Vietnam, and Singapore [12].

Given these developments, it is advantageous for MSMEs to move from traditional cross-border trade to cross-border e-commerce, as Thailand progresses toward a digital economy. This shift allows them to benefit from reduced paperwork and fewer regulatory requirements. There are limited studies on CBEC involving SMEs in Thailand [13] such as the CBEC between Thailand and China [14,15,16,17], and studies addressing policy implications [18,19,20]. Overall, most of the previous research has concentrated on cross-border e-commerce between Thailand and China, with a significant focus on business-to-consumer (B2C) markets. Thus, there are limited studies on Thailand’s outbound CBEC and a gap in the literature regarding factors influencing CBEC adoption among manufacturing MSMEs in Thailand. Therefore, the primary aim of this study is to identify the factors influencing CBEC adoption among manufacturing MSMEs in Thailand. Technology–Organization–Environment (TOE) [4,21,22,23,24,25] remains a major framework for assessing the influencing factors of MSMEs’ CBEC. But, due to the need for sustainable growth [26], recent studies have integrated the Diffusion of Innovation (DOI) theory with the TOE framework [27] and the Resource-Based View (RBV) theory with the TOE framework [28] for studying the influencing factors of CBEC. However, these studies are primarily conceptual or reviews rather than empirical research.

To address these gaps, this study contributes to the literature by developing and empirically validating an integrated DOI-RBV-TOE model using the Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy DEMATEL) method. The contribution of the study is threefold. First, it advances theoretical integration by combining three innovation adoption frameworks with a structured causal analysis technique to build a comprehensive model for CBEC adoption. Second, it empirically examines the causal and dependent relationships among technological, organizational, and environmental factors affecting CBEC adoption. Third, it offers practical insights for policy design and managerial decision-making to support digital trade strategies, enhance MSMEs’ competitiveness, and contribute to sustainable development goals, particularly SDG 8 (Decent Work and Economic Growth) and SDG 17 (Partnerships for the Goals).

The research objectives of this study are as follows:

• To develop an integrated DOI-RBV-TOE model for assessing MSMEs’ CBEC adoption.
• To examine the applicability of the integrated DOI-RBV-TOE model for assessing CBEC adoption in the context of manufacturing MSMEs.
• To analyze the factors influencing the adoption of CBEC among manufacturing MSMEs in Thailand using an integrated DOI-RBV-TOE model.
• To provide practical recommendations for Thai MSMEs and policymakers on transitioning from traditional trade to CBEC to enhance competitiveness and profitability in the digital economy.

The remaining sections of this manuscript are organized as follows: Section 2 reviews the literature, summarizes the technology adoption models, and identifies and presents the factors that influence the CBEC of MSMEs; Section 3 reports the methodology, statistical tool, steps, and analysis adopted in this study; Section 4 reports the findings; and Section 5 and Section 6 presents the discussion, and conclusion, limitations and directions for future research.

2. Literature Review

This literature review is divided into three sections: technology adoption models and TOE framework, The Diffusion of Innovation (DOI) and Resource-Based View (RBV) theory, and an Integrated DOI-RBV-TOE theory for identifying CBEC variables.

2.1. Technology Adoption Models and TOE Framework

Since the 1980s, numerous interdisciplinary studies on technology adoption have emerged [29]. Various theoretical models across different industries and sectors were developed and applied by researchers, leading to thousands of publications in research databases. Despite this extensive work, the research remains fragmented [29]. Figure 1 highlights the notable models that made a significant research impact, with researchers replicating and testing them. Early models, rooted in sociology and psychology, were later built with variables to reflect changing trends and practical applications. The TAM model, initially derived from the TRA model, was later extended to fit various research contexts. From Figure 1, it is notable that all TAMs were based on social psychology and focused on the concepts of “stimulus”, “organism”, and “response”. These models have been tested in various contexts, both with and without the “Attitude” construct as a mediator and have been derived and extended from other models. In particular, the TAM, TOE, and UTAUT models have been widely used, replicated, and extended in numerous studies. Among those, the TOE framework [30] provides a comprehensive approach to understanding the factors that influence the adoption and implementation of technological innovations within an organization. It considers three key contexts: “technological”, “organizational”, and “environmental”. Hence, the TOE framework was widely used by researchers [21,22,23,24] for analyzing e-commerce adoption and to identify [4,25] the influencing factors of CBEC adoption.

2.2. The Diffusion of Innovation (DOI) and Resource-Based View (RBV) Theories

The Diffusion of Innovation (DOI) theory includes five perceived characteristics that influence the adoption of innovations: “relative advantage”, “compatibility”, “complexity”, “trialability”, and “observability” [32]. The Resource-Based View (RBV) theory focuses on a firm’s “internal resources and capabilities” for achieving and sustaining a competitive advantage. According to RBV, firms can exploit opportunities and neutralize threats by exploiting “unique, valuable, rare, inimitable, and non-substitutable resources of the firm” [42,43].

2.3. An Integrated DOI-RBV-TOE Theory for Identifying CBEC Variables

Based on the above literature, this study developed an integrated framework to identify the factors influencing the CBEC of Thai MSMEs. For MSMEs, it is crucial to possess the knowledge, international marketing capabilities, resources, and product standards to integrate CBEC into their organizational operations [13,28,44,45]. Additionally, external environmental factors, such as socio-cultural, political, economic, legal, and regulatory, market pressure, and e-commerce platforms are driving firms to adopt CBEC [4,24,25,27,45,46,47,48,49]. The technology factors are key to CBEC adoption, which mainly include relative advantage, security and risks, and associated costs [4,22,23]. Therefore, this study identified potential variables (

Table 1. Summary of factors/variables influencing CBEC of MSMEs.

S.No.	Factors/Variables	Study Type	Authors
1	Owners and their management, industry sectors and relationships, anticipation and realization of benefits, lack of knowledge, lack of resources, external factors, and technology factors.	Qualitative study (mixed method—literature, case studies, stakeholder participation, and seminars)	[50]
2	TOE framework (Characteristics of technology (relative advantage, perceived compatibility, complexity and innovation risk), organization (resources such as HR, financial, technology, and knowledge), environment (government incentives and support, industry support, intensity of competition, and buyer and supplier behavior), and managers (attitude, and new knowledge of IT)).	Quantitative study (logistic regression)	[21]
3	Technology, organization, environment, and management (internal).	Quantitative study (AHP)	[22]
4	Intensity of pressure, perceived benefits, technological competence, and barriers to e-commerce use.	Quantitative study (used SEM tools)	[51]
5	Perceived organizational factors (awareness, management support, human resources, lack of ICT expertise, technology resources, governance, business resources, and mobile phone tech) and perceived environmental factors (lack of market e-readiness, lack of institutional e-readiness, lack of industrial support, and socio-cultural norms).	Qualitative research (thematic analysis)	[46]
6	In the context of technology (relative advantage, compatibility, technology cost, security, and risk), organization (technology readiness, strategic orientation, and firm characteristics), environmental (external pressure, government support, support from technology supplier), and individual (innovativeness of the owner, characteristics, experience, and top management support).	Review and systematic qualitative approach for grouping variables and clustering factors	[23]
7	Country infrastructure, technology infrastructure, management support, Diffusion of Innovation theory factors, environmental factors (external pressures from stakeholders – competitor, supplier, customer, government, industry), organizational readiness, IT skills, and capabilities.	Literature review	[27]
8	Political, economic, social, and technology (PEST) from a macroeconomic perspective along with the stakeholders of CBEC.	Review	[52]
9	Information flow, financial flow, and logistics flow.	Quantitative research (Kano Model)	[13]
10	Resource-Based View (RBV) (capabilities and resources), Diffusion of Innovation (innovation characteristics and IT), and TOE (cost, top management support, government support, and competitive pressure).	Review analysis	[28]
11	Regulatory support, competitor pressure, and the IT infrastructure.	Quantitative research (PLS-SEM)	[47]
12	The study conceptualized and assessed the links between export capabilities (IT, international marketing, export operations) and CBEC’s strategic and financial performance.	Quantitative research (PLS-SEM)	[44]
13	Customs clearance efficiency, logistics costs and risks, customs declaration and tax refunds, payment risks, and management and talents.	Systematic literature review	[53]
14	Technological factors (internal e-commerce tools such as websites, smartphones, and PCs), organizational factors (owner characteristics such as age, gender, education, and IT skills), firm characteristics (firm size, age, e-commerce experience), and external environment (external e-commerce platforms).	Quantitative research technique (used the Tobit regression model and one-sample Wilcoxon signed-rank test)	[24]
15	TOE—technology (perceived relative advantage, cost, complexity, and compatibility), organization (top management support, technology resources, human resources, firm size, level of decentralization and formalization), and environmental factors (pressure from competitors, suppliers, and customers, regulatory and legal environment in home and host country, and e-readiness of the home and host country).	Quantitative research (SEM using SmartPLS 3 software)	[4]
16	TOE—technology (level), organization (resources, internationalization level), and environment (pressure of competition and policy support).	Quantitative study (linear regression)	[25]
17	Internal enterprise factors (e-commerce platforms, e-commerce logistics, foreign trade, payment security, certification, and cooperation among firms), government support (subsidies, tax incentives, infrastructure investment, degree of supervision), economic foundation (GDP, standard of living, per capita income), and external environment (culture, demand, competitiveness, scale of CBEC transactions, and investment in information technology).	Quantitative research (DEMATEL and ISM analysis)	[48]
18	Five capabilities (product, marketing, cross-border potential, knowledge, and cross-border start-up) and CBEC platforms (economic, technological, social, and legal). The study found that firms’ capabilities affect CBEC platforms.	Interviews and case study approach	[45]

) for the Integrated DOI-RBV-TOE framework to assess factors influencing the CBEC adoption of Thai MSMEs.

3. Methodology

• Step 1: The Statistical Tool, Construct, Scale Development and Questionnaire Format

Studies [22,48] have tested several multi-criteria decision-making (MCDM) techniques and found them suitable for identifying and assessing the factors influencing CBEC adoption. However, the DEMATEL method [54] is advantageous in its capability to excel with limited amounts of data [55] and evaluate interdependencies among factors to distinguish causal variables from effect variables through visualization and prioritization. Furthermore, integrating fuzzy logic [56] into the DEMATEL method is widely used [57] to handle uncertainty, vagueness, and subjective human judgments in decision-making. Additionally, the Fuzzy DEMATEL method has been tested to analyze similar cases and found suitable for studying factors influencing international trade [58]. Therefore, this research adopts the Fuzzy DEMATEL approaches proposed by earlier studies [56,58,59] and by computing using R programming language [60]. The complete process of the methodology is presented in Figure 2.

In the first step, four academic experts in the field of international trade and economics were recruited, and a Delphi approach was utilized to identify the key variables of CBEC adoption of MSMEs. The research aims, objectives, available literature, and initial set of variables were presented to the experts individually, and they were asked to identify suitable factors based on the research theme. All four experts were independent in their judging process, and after a brainstorming session, the final set of variables was concluded based on the results of the experts. The final variables included in the study (bold text in Table 1), along with the initial source variables, are presented in Table 1. Consequently, based on the literature review and expert opinion, this study identified the links between DOI-RBV-TOE and suggested an integrated framework under three factors: “technological”, “organizational including knowledge, capability, and resources”, and “external environmental factors” that influence the CBEC of MSMEs.

The construct with 13 variables—consisting of the relative advantage of cross-border e-commerce (in terms of expanding market share, profits, and cost reduction) (T1); security and risk concerns in using technology (business information and financial transactions) (T2); cost of e-business (adoption cost, maintenance, and support costs) (T3); knowledge of e-commerce (O1); firm’s product standards (domestic and international certification, uniqueness, and competitive aspects) (O2); firm’s international marketing capability (digital marketing tools, websites, social media, etc.) (O3); availability of financial resources (O4); socio-cultural (population, social class, education, lifestyle, attitude, culture, and language in the host country) (E1); political (political stability, government policies and support, international cooperation, and trade agreements) (E2); economic (gross domestic product, trade, currency, and exchange rates) (E3); legal and regulatory environment (consumer protection, personal data protection, and cybersecurity in the home and host country) (E4); market pressure (competitors and customers) (E5); and e-commerce platforms (government e-commerce websites, private websites: Alibaba/Aliexpress/Lazada/Shopee) (E6)—was developed through expert review and was transformed into a pairwise comparison matrix questionnaire with a scale of 0–4, in which ‘0’ represents ‘no influence’ and ‘4’ represents ‘very high influence’. The annotated direct-relation matrix (DRM) for respondent 1 can be represented as Equation (1).

• Step 2: Data Collection

The purposive sampling approach was employed to identify and select respondents from the target population of manufacturing micro, small, and medium enterprises (MSMEs) in Thailand. The list of potential respondents was obtained from two primary sources: the Department of Industrial Works (DIW) website and a Thai trade e-commerce platform. The method ensures the sample is representative based on inclusion criteria (Thai manufacturing MSMEs involved in cross-border e-commerce) and exclusion criteria (traders and exporters without manufacturing units but registered as MSMEs). Researchers made calls, scheduled appointments with owners/managers, and personally visited MSMEs, selecting only those who agreed to participate and met the study’s criteria. Thus, the questionnaire was distributed to 60 MSMEs involved in the e-commerce business in six regions: the central, eastern, western, northern, northeastern, and southern regions. The details of the research project along with the participant consent form and the ethical committee-approved documents were also included in the questionnaire set. The respondents were clearly informed about the anonymous nature of the results and encouraged to provide honest responses. Out of 60, 18 agreed to complete the questionnaire. Among the 18 responses received, 14 were complete and included in this study. A sample of one respondent’s results in DRM is provided in

Table 2. Initial individual DRM (response 1).

	T1	T2	T3	O1	O2	O3	O4	E1	E2	E3	E4	E5	E6
T1	NA	3	2	3	2	3	4	1	3	3	2	4	4
T2	4	NA	3	3	2	4	3	2	4	4	4	4	2
T3	3	3	NA	3	3	4	2	2	1	4	3	4	4
O1	3	3	4	NA	2	3	4	4	4	4	3	4	3
O2	3	3	3	3	NA	2	3	3	2	4	3	3	2
O3	3	3	3	3	3	NA	4	4	2	4	4	4	4
O4	4	2	4	4	4	4	NA	2	3	2	4	4	4
E1	3	2	2	3	3	4	3	NA	1	3	3	4	4
E2	3	3	4	3	3	4	3	3	NA	3	2	3	2
E3	4	3	3	3	4	4	4	4	4	NA	2	2	3
E4	4	3	3	3	3	3	3	3	3	3	NA	4	3
E5	3	2	2	3	3	3	3	3	3	3	2	NA	4
E6	3	3	3	3	3	3	3	2	2	3	2	4	NA

.

• Step 3: Fuzzification

The crisp values of the responses were converted into triangular (l, m, u) fuzzy linguistics values of (0, 0, 0.25) for 0 (no influence), (0, 0.25, 0.5) for 1 (very low influence), (0.25, 0.5, 0.75) for 2 (low influence), (0.5, 0.75, 1) for 3 (high influence), and (0.75, 1, 1) for 4 (very high influence). The results of the first fuzzy triangular array are presented in

Table 3. Fuzzy triangular array.

Component 1 (l)														Component 2 (m)														Component 3 (u)
	T1	T2	T3	O1	O2	O3	O4	E1	E2	E3	E4	E5	E6		T1	T2	T3	O1	O2	O3	O4	E1	E2	E3	E4	E5	E6		T1	T2	T3	O1	O2	O3	O4	E1	E2	E3	E4	E5	E6
T1	0	0.75	0.5	0.5	0.5	0.5	0.75	0.5	0.5	0.75	0.75	0.5	0.5	T1	0	1	0.75	0.75	0.75	0.75	1	0.75	0.75	1	1	0.75	0.75	T1	0.25	1	1	1	1	1	1	1	1	1	1	1	1
T2	0.5	0	0.5	0.5	0.5	0.5	0.25	0.25	0.5	0.5	0.5	0.25	0.5	T2	0.75	0	0.75	0.75	0.75	0.75	0.5	0.5	0.75	0.75	0.75	0.5	0.75	T2	1	0.25	1	1	1	1	0.75	0.75	1	1	1	0.75	1
T3	0.25	0.5	0	0.75	0.5	0.5	0.75	0.25	0.75	0.5	0.5	0.25	0.5	T3	0.5	0.75	0	1	0.75	0.75	1	0.5	1	0.75	0.75	0.5	0.75	T3	0.75	1	0.25	1	1	1	1	0.75	1	1	1	0.75	1
O1	0.5	0.5	0.5	0	0.5	0.5	0.75	0.5	0.5	0.5	0.5	0.5	0.5	O1	0.75	0.75	0.75	0	0.75	0.75	1	0.75	0.75	0.75	0.75	0.75	0.75	O1	1	1	1	0.25	1	1	1	1	1	1	1	1	1
O2	0.25	0.25	0.5	0.25	0	0.5	0.75	0.5	0.5	0.75	0.5	0.5	0.5	O2	0.5	0.5	0.75	0.5	0	0.75	1	0.75	0.75	1	0.75	0.75	0.75	O2	0.75	0.75	1	0.75	0.25	1	1	1	1	1	1	1	1
O3	0.5	0.75	0.75	0.5	0.25	0	0.75	0.75	0.75	0.75	0.5	0.5	0.5	O3	0.75	1	1	0.75	0.5	0	1	1	1	1	0.75	0.75	0.75	O3	1	1	1	1	0.75	0.25	1	1	1	1	1	1	1
O4	0.75	0.5	0.25	0.75	0.5	0.75	0	0.5	0.5	0.75	0.5	0.5	0.5	O4	1	0.75	0.5	1	0.75	1	0	0.75	0.75	1	0.75	0.75	0.75	O4	1	1	0.75	1	1	1	0.25	1	1	1	1	1	1
E1	0	0.25	0.25	0.75	0.5	0.75	0.25	0	0.5	0.75	0.5	0.5	0.25	E1	0.25	0.5	0.5	1	0.75	1	0.5	0	0.75	1	0.75	0.75	0.5	E1	0.5	0.75	0.75	1	1	1	0.75	0.25	1	1	1	1	0.75
E2	0.5	0.75	0	0.75	0.25	0.25	0.5	0	0	0.75	0.5	0.5	0.25	E2	0.75	1	0.25	1	0.5	0.5	0.75	0.25	0	1	0.75	0.75	0.5	E2	1	1	0.5	1	0.75	0.75	1	0.5	0.25	1	1	1	0.75
E3	0.5	0.75	0.75	0.75	0.75	0.75	0.25	0.5	0.5	0	0.5	0.5	0.5	E3	0.75	1	1	1	1	1	0.5	0.75	0.75	0	0.75	0.75	0.75	E3	1	1	1	1	1	1	0.75	1	1	0.25	1	1	1
E4	0.25	0.75	0.5	0.5	0.5	0.75	0.75	0.5	0.25	0.25	0	0.25	0.25	E4	0.5	1	0.75	0.75	0.75	1	1	0.75	0.5	0.5	0	0.5	0.5	E4	0.75	1	1	1	1	1	1	1	0.75	0.75	0.25	0.75	0.75
E5	0.75	0.75	0.75	0.75	0.5	0.75	0.75	0.75	0.5	0.25	0.75	0	0.75	E5	1	1	1	1	0.75	1	1	1	0.75	0.5	1	0	1	E5	1	1	1	1	1	1	1	1	1	0.75	1	0.25	1
E6	0.75	0.25	0.75	0.5	0.25	0.75	0.75	0.75	0.25	0.5	0.5	0.75	0	E6	1	0.5	1	0.75	0.5	1	1	1	0.5	0.75	0.75	1	0	E6	1	0.75	1	1	0.75	1	1	1	0.75	1	1	1	0.25

. The remaining steps and equations for normalization, computing the total relationship matrix, calculating the direct and indirect influences, and defuzzification are presented in

Table 4. Steps and equations.

Steps	Label	Equation	Equation Number
1.	Direct relationship matrix	${Matrix}_A=\left(\begin{array}{c}\begin{array}{cccc}NA& a_{21}& \dots & a_{n1}\\ a_{12}& NA& \dots & a_{n2}\\ ⋮& ⋮& \ddots & ⋮\\ a_{1n}& a_{2n}& \dots & NA\end{array}\end{array}\right)$	(1)
2.	Fuzzification	${\mu }_{\tilde{N}}\left(x\right)=\left\{\begin{array}{c}0, x<1\\ {\displaystyle \frac{x-l}{m-l}}, l\le x\le m\\ {\displaystyle \frac{u-x}{u-m}}, m\le x\le u\\ 0, x>u\end{array}\right.$	(2)
		$B_{ij}^k=\left(l_{ij},m_{ij}, u_{ij}\right)$	(3)
		$B^k=\begin{array}{c}Items\\ T1\\ T2\\ ⋮\\ E6\end{array}\left[\begin{array}{cccc}T1& T2& \cdots & E6\\ \left[0,0,0\right]& ⨂a_{12}^k& \cdots & ⨂a_{1n}^k\\ ⨂a_{21}^k& \left[0,0,0\right]& \cdots & ⨂a_{2n}^k\\ ⋮& ⋮& \ddots & ⋮\\ ⨂a_{n1}^k& ⨂a_{n2}^k& \cdots & \left[0,0,0\right]\end{array}\right]$	(4)
3.	Normalization	$C_n={\displaystyle \frac{B^k}{ma{x}_{1\le i\le n}{\sum }_{j=1}^n{a}_{ij}}}, i,j=\mathrm{1,2}\dots ,n$	(5)
4.	Normalized fuzzy direct relation array	${\tilde{C}}_{ij}=\left({\displaystyle \frac{\sum l_{ij}}{n}},{\displaystyle \frac{\Sigma m_{ij}}{n}},{\displaystyle \frac{\sum u_{ij}}{n}}\right)$	(6)
5.	Compute total relationship	$\tilde{C}{\left(I-\tilde{C}\right)}^{-1}$	(7)
6.	Calculating the direct and indirect influences	$D_{il}={\displaystyle \sum _{j=1}^n}{l}_{ij} \left(i=\mathrm{1,2},\dots ,n\right)$	(8)
		$D_{im}={\displaystyle \sum _{j=1}^n}{m}_{ij} \left(i=\mathrm{1,2},\dots ,n\right)$	(9)
		$D_{iu}={\displaystyle \sum _{j=1}^n}{u}_{ij} \left(i=\mathrm{1,2},\dots ,n\right)$	(10)
		$R_{jl}={\displaystyle \sum _{i=1}^n}{l}_{ij} \left(j=\mathrm{1,2},\dots ,n\right)$	(11)
		$R_{jm}={\displaystyle \sum _{i=1}^n}{m}_{ij} \left(j=\mathrm{1,2},\dots ,n\right)$	(12)
		$R_{ju}={\displaystyle \sum _{i=1}^n}{u}_{ij} \left(j=\mathrm{1,2},\dots ,n\right)$	(13)
7.	Defuzzification	$BNP=l+{\displaystyle \frac{\left(u-l\right)+\left(m-l\right)}{3}}$	(14)

.

4. Findings

The results of the Fuzzy DEMATEL analysis (Figure 3) reveal the causal relationships and prominence of technological, organizational, and environmental factors influencing CBEC adoption. The factors in the upper-right quadrant of the graph (Figure 3) have high prominence, positive net causal effects, and significantly influence other factors. Thus, it can be inferred from the findings of this study that ‘knowledge of e-commerce (O1)’, ‘firm’s international marketing capability (digital marketing tools, websites, social media, etc.) (O3)’, and ‘security and risk concerns in using technology (business information and financial transactions) (T2)’ are the primary driving factors of CBEC adoption within the framework.

Factors in the upper-left quadrant have positive net causal effects but are relatively less prominent within the CBEC framework.

Thus, it can be inferred from the findings of this study that ‘cost of e-business (adoption cost, maintenance, and support costs) (T3)’ and ‘socio-cultural (population, social class, education, lifestyle, attitude, culture, and language in the host country) (E1)’ are the secondary driving factors of CBEC adoption within the framework.

The factors loadings in the lower-right quadrant are of high prominence but are negatively causal. Thus, it can be inferred from the results that the ‘relative advantage of cross-border e-commerce (in terms of expanding market share, profits, and cost reduction) (T1)’, ‘firm’s product standard (domestic and international certification, uniqueness, and competitive aspects) (O2)’, and ‘availability of financial resources (O4)’ were highly influenced by the primary and secondary factors within the CBEC framework. So, T1, O2, and O4 are the outcomes reflecting the changes due to the adoption of CBEC. The factors loadings in the lower-left quadrant of the graph are of low prominence and are negatively causal.

The findings reveal that all environmental factors, except socio-cultural factors (E1) and political factors (E2), are autonomous within the CBEC framework. Specifically, economic factors (E3), the legal and regulatory environment (E4), market pressure (E5), and e-commerce platforms (E6) neither influence nor are influenced by other factors.

5. Discussion

This study identified five key factors (T2, T3, O1, O3, and E1) influencing the adoption of cross-border e-commerce (CBEC) by manufacturing MSMEs in Thailand using an integrated DOI-RBV-TOE framework. These findings highlight four factors that MSMEs should prioritize and one factor for policymakers to address to ensure successful CBEC adoption. First, MSMEs must enhance their knowledge of e-commerce (O1) through targeted training programs and capacity-building initiatives, enabling firms to better navigate CBEC operations and expand into global markets. Second, improving firms’ international marketing capabilities (O3) is essential, including investing in digital marketing tools, optimizing websites, and adopting social media for global outreach. Third, MSMEs must enhance their knowledge of security and risk concerns (T2) by learning cybersecurity protocols, such as secure payment gateways and data encryption, for building trust with international customers. Fourth, aligning strategies with socio-cultural factors (E1) in target markets, such as language, education, and cultural preferences, is necessary to enhance cultural adaptability and improve market acceptance. These findings are in line with earlier findings from different parts of the world. For example, Gomez-Herrera et al. (2014) [61] found that linguistic costs significantly impact CBEC in linguistically diverse markets like the European Union. Ding et al. (2017) [62] also found that cultural differences and payment conditions are major obstacles to CBEC development in China. In another study, Liu et al. (2022) [53] found that “insufficient talent” and “payment risk” are the major challenges of CBEC development in China. Further, Valarezo et al. (2018) [63] identified that “developing digital skills” is one of the major measures for CBEC development in the Spanish market. Thus, these actionable priorities from our findings enable MSMEs to enhance CBEC adoption and increase their competitiveness in the digital economy. Furthermore, policymakers play an essential role in enabling an environment suitable for CBEC adoption. To address the primary driving factors, government-led initiatives such as e-commerce training programs can significantly improve MSMEs’ knowledge of e-commerce (O1). Cost of e-business adoption (T3) has been identified as a secondary driving factor in CBEC adoption. Thus, financial incentives, including tax reductions and subsidies, can motivate firms to enhance their knowledge of e-commerce (O1), international marketing capabilities (O3), and knowledge of security and risk concerns (T2). Furthe policymakers should promote research into socio-cultural dynamics (E1) to help MSMEs adapt to diverse international markets. The findings regarding the autonomous nature of environmental factors, which neither influence nor are influenced by other factors in CBEC adoption, align with earlier studies [52] in the European Union.

6. Conclusion

This research provides empirically grounded insights into the factors influencing CBEC adoption among Thai manufacturing MSMEs by applying an integrated DOI-RBV-TOE framework along with the Fuzzy DEMATEL technique. The analysis identified that internal organizational competencies—namely, e-commerce knowledge, international marketing capabilities, and security risk awareness—serve as critical driving factors, while socio-cultural factors and cost concerns act as secondary enablers. Environmental dimensions such as economic, legal, and market pressure were found to be autonomous, thereby requiring minimal intervention for CBEC adoption.

The main contribution of this study lies in its development of an integrated, empirically tested model that maps the direct and indirect influences of various factors on CBEC adoption using a novel methodological approach. This framework enables a deeper understanding of how different internal and external elements interact within the Thai MSME ecosystem. The findings offer practical implications for policymakers to tailor supportive programs—such as targeted training, marketing capability development, cybersecurity support, and financial subsidies—that directly address the most influential factors. These contributions support Thailand’s goal of digital trade advancement and align with international development targets, particularly SDGs 8 (Decent Work and Economic Growth) and 17 (Partnerships for the Goals).

Limitations and Directions for Future Research

This study has several limitations. The study’s findings are confined to manufacturing MSMEs in Thailand, and they may not be generalizable to larger enterprises or those in other countries. The technology adoption status of MSMEs in other countries can vary significantly, limiting the applicability of the results beyond the specific context of this study. Additionally, this study focuses on manufacturing MSMEs, which may not fully capture the experiences of MSMEs in other sectors. Future research may consider larger and more diverse samples to validate and extend the findings. Further, a cross-country analysis can explore the CBEC adoption factors in different sectors and regions to provide a more comprehensive understanding. A mixed longitudinal qualitative and quantitative research approach could also offer insights into how CBEC adoption evolves over time and the long-term impacts on business practices and performance. Moreover, integrating additional variables such as digital infrastructure and exploring individual sub-dimensions of various environmental factors could enhance the understanding of CBEC adoption.