Navigating the Digital Landscape: An Exploration of the Relationship Between Technology-Organization-Environment Factors and Digital Transformation Adoption in SMEs

Abstract

The aim of this research is to examine the factors that affect the digital transformation (DT) adoption in small and medium-sized enterprises (SMEs) in Danang, Vietnam, using the Technology-Organization-Environment (TOE) framework and PLS-SEM approach. Notwithstanding the growing attention towards the adoption of DT, there is a dearth of comprehensive research that examines the combined impact of environmental, organizational, and technological factors on DT adoption within SMEs. Therefore, this study aims to fill this gap by building upon the existing literature to provide quantitative evidence on the adoption of DT in SMEs. The findings of our study indicate that implementing DT in SMEs is impacted by a confluence of factors, including environmental, organizational, and technological considerations. Specifically, technological factors such as cybersecurity and IT integration capabilities, environmental factors such as clients and partnerships, and organizational capabilities such as top management support and governance, have a significant positive impact on DT adoption. The study also uncovers that technological factors play a mediating role in the relationship between organizational and environmental factors in the adoption of DT by SMEs. The present study enhances the existing knowledge on the adoption of DT in SMEs by conducting a thorough examination of the factors that affect DT adoption. The study underscores the significance of considering both technical and organizational factors while executing DT initiatives and identifies potential areas for further investigation.

Keywords

digital transformation adoption TOE framework SMEs mediating effect Vietnam

Introduction

Digital transformation (DT) refers to a complex and multidimensional phenomenon that involves the integration of new digital technologies, such as mobile devices, social media platforms, and smart embedded devices, into both business and social environments. These technologies are characterized by their ability to process data in real-time, access information intelligently, and provide stakeholders with the knowledge required to improve their products (Gray & Rumpe, 2017; Li et al., 2023; Valdez-de-Leon, 2016). While many research on DT have been undertaken in recent years, the most of them have been descriptive, providing practical overviews and summaries of the phenomena. There is a lack of agreement on the relationships between the factors influencing DT adoption among SMEs, as well as a lack of large-scale empirical studies to validate the ubiquitous nature of important determinants and shed light on the workings of their underlying mechanisms, and explore their interrelationships in depth.

For instance, Kraft et al. (2022) examine the relationship between Swiss small and medium-sized firm (SME) managers’ understanding of DT and their actual use of digital technologies for managerial and operational duties. The findings show opportunities for SMEs to fully exploit the benefits of DT. Using a combination of Partial Least Squares Structural Equation Modelling and Need Condition Analysis, Battistoni et al. (2023) evaluate the implementation pathways followed by Italian SMEs for DT. The study outlines four hierarchical levels of digital technologies that contribute to the collecting, combining, processing, and utilization of organizational data: Sensor, Integration, Intelligence, and Reaction. Cichosz et al. (2020) refine the success elements for DT by utilizing case study methodologies. By doing a literature review, Osmundsen et al. (2018) explain the primary motivations and elements that contribute to the success of enterprise DT. Regrettably, the absence of extensive empirical investigations with large samples prevents the capacity to ascertain whether relevant factors are universal and to provide light on the underlying mechanisms of relevant factors, and explore their interrelationships. Furthermore, there is a dearth of consensus regarding the nature of these relationships.

SMEs are widely recognized as the driving force behind economic growth and prosperity in developing countries. These enterprises account for 60% to 70% of formal employment, making them a significant source of job creation. In ASEAN member nations, SMEs represent between 88.8% and 99.9% of total establishments and contribute up to 97.2% of the total employment. SMEs make up between 30% and 53% of these nations’ GDP (Bala & Feng, 2019). The success of SMEs is crucial for maintaining stability and promoting growth in developing countries.

SMEs have experienced a significant transformation in their business performance because of technological innovation. In the current digital economy landscape, DT has developed as an innovative strategy for businesses to achieve a competitive edge in today’s fast-paced business environment (Martinez-Gomez et al., 2022). While DT has been adopted by firms across different sectors, the process of digitalization among SMEs has been relatively slow due to limited resources and capabilities. Considering this, gaining an understanding of the underlying drivers and routes that impact the adoption of DT among SMEs is absolutely necessary in order to improve the distribution of resources. On the other hand, despite the significance of this topic, there is a paucity of study on implementing DT among SMEs.

In recent years, there have been many helpful investigations carried out on DT, and the number of studies on the components that drive or contribute to the success of DT and its mechanisms, pathways, and impacts is gradually growing. On the other hand, the majority of studies are merely descriptive overviews and operational introductions to the DT phenomenon according to the findings presented in the Zhang et al.’s (2022) research. The lack of extensive empirical research makes it challenging to establish the universality of significant elements. Furthermore, the internal mechanism of these elements remains unclear, and their relationship is rarely explored, making it challenging to draw conclusions about their interaction. More in-depth and analytical research is needed to better understand the mechanisms and processes driving DT and their implications for companies and society at large, despite the growing interest in the DT phenomenon. As a result of these gaps in the previous research, we conducted this investigation to identify the critical factors and explore their interaction mechanisms in SMEs. From a holistic perspective, the study identifies six first-order constructs across three scopes of technology-organization-environment from TOE framework. The study constructs an action mechanism model by drawing on the resource dependence theory and the capabilities-perspective theory. The goals of the study are as follows: (1) to comprehensively identify the essential factors that impact the adoption of DT in SMEs, and (2) to investigate how technological, organizational, and environmental factors interact to affect the adoption of DT in SMEs; and (3) to examine the intermediary effect of factors in the research framework.

This study’s significance lies in its expansion of our understanding and knowledge of the potential of SMEs to promote DT. Consequently, this can enhance their competitiveness and optimize their crucial resources, thereby making a significant contribution to the domain of DT research. Furthermore, it offers a systematic approach for DT and a strategic roadmap for SME managers seeking to integrate DT within their respective firms.

The current investigation is structured into five primary segments. Firstly, the literature review is presented in order to provide a framework for the development of the research hypotheses. Secondly, the methodology section explains the quantitative research process, including data collecting and analysis procedures. Thirdly, the results section summarizes the important findings of the analysis. Following that, the fourth section provides the discussions of these findings, with an emphasis on placing them within the context of the relevant literature. Finally, a summary of the most important findings and some recommendations for further research in this field are presented as the conclusion of the study.

Literature Review

Digital Transformation of SMEs

McKinsey’s research indicates that DT of conventional businesses fails at a rate of 70% to 80% (Li et al., 2023). This noteworthy rate of failure raises significant concerns among SMEs regarding their own DT efforts. One of the primary reasons for the low success rate of DT among SMEs is their insufficient capacity to manage both internal and external knowledge management. To successfully carry out DT, SMEs must integrate their knowledge of productive resources and business systems internally and leverage external knowledge across different subjects and fields.

Despite the considerable attention paid to DT, there is not yet a universally accepted definition in either the academic or professional worlds of business (Kraus et al., 2021; Vial, 2019) and this may also pose a challenge for SMEs when implementing DT. DT has been described as a strategy, a method, and even a business model by a variety of academics; nevertheless, the primary focus of these descriptions is on the utilization of emerging digital technology to enable major advances in company operations. As both the theory and practice of DT have progressed, it has come to the attention of researchers that the process of DT in businesses requires not only the expenditure of a significant amount of capital on technological advancements, but also the concerted efforts of the entire organization as adjustments to its strategies, procedures, and structures, as well as the development of a culture that is innovative and open to new ideas (Tabrizi et al., 2019). Creating value is the goal of DT, and this can be accomplished in several ways, including: increasing operational efficiency; bettering the customer experience; enhancing the business model; achieving strategic differentiation; gaining a competitive advantage; enhancing stakeholder relationships; reducing costs; and so on (Lanzolla & Anderson, 2008). In this research, the current situation and distinguishing features of DT in Vietnamese SMEs, in addition to the shared transformational attributes of various industries, are taken into consideration in this study to argue that DT involves enhancing the digital capacity of SMEs to improve the efficiency of their operations, processes, and products. This is achieved through the application of the latest digital technologies, such as connected networks, internal cloud computing, artificial intelligence, and the construction of big data warehouses for in-depth data analysis into the business.

Factors Influencing Digital Transformation Adoption

The adoption of DT in SMEs is a complicated and multi-step process that is impacted by a wide variety of external and internal variables (Vial, 2019). These factors can considerably influence the success or failure of SMEs’ adoption of DT (Matt et al., 2015). Hence, it is imperative to identify the factors that are likely to improve the probability of success, along with the associated leadership practices, and comprehend their nature and underlying causes to achieve DT.

Numerous academics have discussed various underlying factors that could influence the acceptance of DT. Since the introduction of new technology, academics have gained a greater understanding of the effects that IT has on both society and individual life. The integration of communication and information technology has emerged as a highly impactful force on enterprise models along with operations strategies. DT is a crucial factor for businesses that seek to incorporate technological advancements (Zaoui & Souissi, 2020). Along with this, organizational structure and the ability to innovate have been essential elements of DT. For the purpose of accomplishing DT, customer-centricity and technology-related considerations are also considered to be key fundamentals (Gölzer & Fritzsche, 2017). In addition, preparation for organizational risks and cybersecurity is also an important factor, as has been highlighted in previous research.

Current research shows that in order for organizations to achieve DT, they need to make sure that their modifications connect with their strategy (Peng & Tao, 2022; Tabrizi et al., 2019). In addition, digital management can facilitate organizational change and contribute to the accomplishment of DT’s intended objectives. The DT process necessitates innovative top management, effective integration within the organizations, and other factors such as pressures from customers and suppliers, as indicated by numerous studies (Malodia et al., 2023; Zhang et al., 2022).

Table 1 presents a compilation of various studies examining the factors that affect organizational digital transformation adoption (DTA). Although many of these studies provide descriptive analysis and qualitative discussions on DTA practices, the quantity of empirical research with large samples that study the underlying elements that have an effect on DTA is quite limited. Furthermore, the inadequate research that does exist does not always identify the same factors uniformly. Most of the existing research has also focused on larger enterprises or those in the manufacturing sector, disregarding the unique circumstances of SMEs. Considering the crucial role that SMEs hold in the social economy and their relatively slower pace of DTA, we are of the opinion that there is tremendous value in both the academic and practical contexts in researching the important aspects and mechanisms that drive the DTA of SMEs.

Table 1.

Prominent Scholarly Literature Pertaining to Digital Transformation.

Authors	Method	Objectives of the study	Context/countries	Key factors	Mediating analysis
Wrede et al. (2020)	In-depth interview	Investigates the involvement and enabling strategies of senior executives in addressing the digital transformation	Large firms (Germany)	Three suggestions: developing a deep understanding of digitalization, creating a formal context that supports digitalization initiatives, leading change efforts	No
Ghobakhloo and Iranmanesh (2021)	MIMAC analysis	Provides manufacturing SMEs with a guideline for digital transformation under Industry 4.0	Not available	Eleven determinants: business partner digital maturity, cybersecurity maturity…	No
Annarelli et al. (2021)	co-citation analysis	Uses co-citation to understand digitalization capabilities	Not available	Integration capabilities of digital technologies, platform capabilities of digital technologies, and innovation capabilities of digital technologies make up the micro-foundation of digitalization capabilities.	No
Eller et al. (2020)	Quantitative research	Examines SME resources and digitization and business performance	SMEs (Austria)	Information technology, digital strategy, employee skills, financial performance	Yes
Matarazzo et al. (2021)	Case study	Knows how dynamic capabilities help digital transformation	SMEs (Italy)	Capabilities in sensing and learning are the bedrock upon which digital transformation is built	No
Troise et al. (2022)	Quantitative method	Identifies that agility positively impacts organizational performance and digital technologies play a central role	SMEs (Italy)	Digital technologies capabilities, relational capability, Innovation capacity, financial performance, product innovation, process innovation	No
This study	Mix-method	Explores the mechanisms that influence the adoption of digital transformation among SMEs	SMEs (Vietnam)	Customer centricity, supplier centricity, top management support, governance, cybersecurity, technology integration	Yes

Theoretical Fundamentals and Hypotheses Development

Theory of Resource Dependence (RDT)

The theory of resource dependence (RDT) guides organizations on how to navigate external limitations (Hillman et al., 2009). According to RDT, corporations are open systems that rely on external circumstances and it’s essential to examine their environment to understand their behavior. RDT recognizes the influence that factors in the environment have on the behavior of organizations, such as the need for companies to interact and compete with one another (Pfeffer & Salancik, 2003). In the case of enterprises studying DT, it’s necessary to consider external resource acquisition and adjust dependence on external resources to improve DT levels. By embracing the RDT perspective that focuses on external factors and building upon prior research (Maduku et al., 2016; Yadegaridehkordi et al., 2020), we introduce customer centricity (CUS) and supplier centricity (SUP) as two constructs that exemplify external influences that affect the process of SMEs’ adoption of DT. The aforementioned constructs operate as primary factors of first-order within the realm of environmental factors.

Capabilities-Based Theory

The concept of capabilities-based theory was first introduced in the early 1990s by management scholars such as Gary Hamel, C. K. Prahalad, and Richard Rumelt (Prahalad & Hamel, 1997). The core competencies of a corporation, as expounded in their seminal paper titled “The Core Competence of the Corporation,” form the foundation for its unique competitive edge in the marketplace. These authors defined core competencies as unique and valuable organizational capabilities that are difficult to imitate or duplicate by competitors.

The theory of capabilities-based approach underscores the significance of a company’s internal resources and capabilities in attaining a competitive edge. According to this strategy, a company’s prolonged competitive advantage can be attributed to the unique combination of resources and competencies that exist within the company, which can provide value that is difficult for other businesses to duplicate. The fundamental idea of capabilities-based theory is that an organization’s achievement is not only determined by its resources, but rather by its ability to effectively leverage and integrate those resources into a coherent set of capabilities. Capabilities are the vital drivers of competitive advantage, as they allow organizations to effectively respond to changing market conditions, innovate and create new products, and efficiently allocate resources.

The notion of “complementary assets” is a significant element of capabilities-based theory, denoting the essential resources and capabilities necessary for the complete actualization of a given capability. For example, possessing a cutting-edge technology may not be enough to generate a sustained competitive advantage, as it also requires complementary assets such as proficient personnel, effective organizational structures, and supportive culture (Eisenhardt & Martin, 2000). Subsequently, a multitude of studies have further expanded the capabilities-based theory and explored its implications in various contexts (Badrinarayanan et al., 2022; Prasad et al., 2010; Trainor, 2012). Utilizing this viewpoint and drawing from pertinent previous research (Chan & Chong, 2013; Ghobakhloo & Iranmanesh, 2021; Qalati et al., 2021), we include four first-order factors: Top management support (TMS), Governance (MAN), Cybersecurity (CBS), and Technology integration (INT).

Technology-Organization-Environment (TOE) Framework

Tornatzky and Fleischer (1990) are credited with originating the Technology-Organization-Environment (TOE) framework, which comprehensively explains of the behavioral intentions and implementation of innovation at the organizational level. Compared to other models of behavior, the TOE framework stands out for its ability to capture the influences of various factors, both internal and external, on adoption decisions. The framework contextualizes these factors into three groups: technology, organization, and environment. Current and emerging technologies relevant to each firm are part of the technological context. The organizational context relates to the scope, size, and resources of the firm. Finally, the environmental context depicts the broader business arena in which the firm operates.

Past studies have employed theoretical frameworks to examine the factors affecting decision-making for adopting new technology. The TOE model has been frequently used in various researches on technology adoption, such as in E-commerce (Abed, 2020), cloud computing (AlBar & Hoque, 2019; Christiansen et al., 2022), Big data (Sun et al., 2018), and artificial intelligence (Yang et al., 2022). According to the analysis of existing literature, a comprehensive model has been formulated based on the TOE framework to enhance the understanding of the adoption of DT by SMEs. This model places significant emphasis on the interconnections among four key constructs, namely environmental factors, technological factors, organizational factors, and the adoption of DT.

Environmental Factors (ENV)

SMEs often lack the resources and capabilities to compete with larger enterprises using only their own resources. So SMEs must build a dynamic partner ecosystem to compete with bigger competitors through DT. Theory of resource dependence suggests that forming inter-organizational ties can assist companies in acquiring resources, thereby lowering their reliance on one another and the amount of risk they face and improve business performance. Several prior studies have included customers and suppliers as part of the environmental factors (San-Martín et al., 2016; Tsou & Hsu, 2015). Customers and suppliers can also influence the competitive landscape. If customers demand a particular type of product, organizations that can meet the demand will have a competitive advantage. Similarly, if suppliers only offer products particularly, organizations that can work with those suppliers will have a competitive advantage (Ahmed et al., 2020; Bala & Feng, 2019). The adoption of DT can be notably influenced by external partnerships, including customers and suppliers. However, according to the theory of resource dependence, external resources must be converted into distinctive capabilities to generate value. Previous research has confirmed the influence of environmental factors on the intention to adopt technology (Tripopsakul, 2018; Xu et al., 2017). Furthermore, organizations operate within complex environments that are influenced by a wide range of factors, including the industry, competition, and government regulations. These external factors can have significant effects on the organization’s strategic decision-making, resource allocation, and overall performance. Previous research has also indicated that the environment has an impact on both the organization and the technology employed within that organization. This phenomenon has been acknowledged in several prior studies (Hoque, 2004; Malik et al., 2021; Tornatzky & Fleischer, 1990). Thus, it can be inferred that external environmental factors exert an impact on the adoption of DT by influencing organizational factors. Consequently, the following hypotheses are formulated:

H1: ENV has a positive impact on ORG.

H2: ENV has a positive impact on TEC.

H3: ENV has a positive impact on DTA.

Organizational Factors (ORG)

Governance and top management support are crucial for the successful implementation of DT in enterprises, as highlighted by both researchers and practitioners (Gangwar et al., 2015). Empirical research and practical observations have suggested that digital managerial capabilities play a critical role in the success of DT initiatives, while the motivation and resource recombination abilities of top managers are critical to overall enterprise success (Maroufkhani et al., 2022; Salleh & Janczewski, 2016). Strong top management support reduces internal resistance, encourages greater investment in DT, enhances the ability to reap its benefits, and enables more accurate predictions of future developments. Moreover, the success of DT can be attributed to the managerial leadership of the company. Managers are expected to engage in constant monitoring of market trends, identifying and exploiting technological opportunities, and converting them into profitable business prospects. As change coordinators, they must encourage stakeholder participation in the process of DT and allocate resources efficiently to ensure its seamless implementation. Hitt et al. (1998) argue that organizational factors, such as culture, structure, and leadership play a crucial role in shaping a firm’s technological capabilities and competitive advantage. Furthermore, adoption intention can be influenced by organizational factors (Maroufkhani et al., 2022, 2020). Based on these insights, we propose the following:

H4: ORG has a positive impact on TEC.

H5: ORG has a positive impact on DTA.

Technological Factors (TEC)

From a technical environment perspective, technology infrastructure includes the broader technological context in which an organization operates, such as the availability of compatible systems, software, and networks (Garzoni et al., 2020). Integrating information technology is crucial for companies in their adoption of DT, facilitating the optimization of business processes and ultimately creating value for both customers and the company (S. Chen et al., 2021; Zaoui & Souissi, 2020). However, the mere possession of IT infrastructure does not provide a sustainable competitive advantage, as it can be easily replicated by competitors. Rather, it is the strategic deployment and utilization of these technologies within the constraints of a particular organizational setting that enables organizations to innovate and create value. Digital technologies interact with organizational antecedents, such as managerial capacities and top management support, to initiate DT (Qalati et al., 2021). The adoption of digital technologies requires a coordinated strategy to ensure a successful outcome, and a defined goal, that means technology integration. Significant shifts have occurred in the economic value of corporations, the innovation of businesses, and the patterns of competition as a direct result of advances in technology. Technology has the potential to provide skills that are unique to a business and improve overall performance if it is combined with strong leadership at that organization. In the context of IT strategic choices, technology integration is typically considered crucial. Moreover, our research suggests that the level of preparedness for cybersecurity will also be a significant factor in technology considerations, as supported by previous studies (Möller, 2020; Sandhu, 2021). The reason for incorporating two first-order construct factors, technology integration (INT) and cyber-security (CBS), into the development of the second-order construct, Technological factors (TEC), is clarified by this statement. In addition, digital technologies play a crucial part in the production and intensification of societal and industry-level disruptions, the induction of new kinds of corporate strategies, and the modification of old pathways for the generation of value. Several prior studies have demonstrated that the availability or restriction of essential resources, as determined by the technical environment, can impact an organization’s capacity to adopt and utilize new technologies (Ainin et al., 2015; Tripopsakul, 2018). Consequently, we proceed to propose the following hypothesis:

H6a: TEC has a positive impact on DTA

In addition, technological considerations may serve as a mediator between environmental factors and the acceptance of DT by SMEs, as well as organizational aspects and digital transformation adoption (DTA). The hypothesis implies that technological considerations play a major role in promoting the adoption of DT by SMEs, and that this link may be indirect and complex. Environmental variables, such as market competition, customer demand, and technical breakthroughs, might motivate SMBs to invest in new IT infrastructure and improve their technological preparedness and knowledge. Similarly, organizational elements such as managerial support, innovation capabilities, and human resource development can help SME adoption of DT by boosting their technology competence and preparedness. Studying the mediating impact of technological elements can provide significant insights into the underlying mechanisms that drive the adoption of DT by SMEs and can aid in the development of effective strategies to promote this adoption. Two hypotheses were therefore added on the moderating effect of technological factors:

H6b: TEC acts as an intermediary between ENV and DTA.

H6c: TEC acts as an intermediary between ORG and DTA.

Figure 1 presents the research framework and shows the hypotheses of this study.

Figure 1.

Proposed research framework.

Methodology

Data Collection

This study employed a mixed-methods approach to examine the adoption of DT by SMEs. A comprehensive literature analysis was conducted throughout the framework development phase to identify the important elements that influence the adoption of DT by SMEs. On the basis of the findings of the literature study, a research framework was developed that included a total of seven variables and 22 items. Ultimately, the content validity of each measurement item was assessed by two academic experts to determine its applicability and representativeness. Based on the results of this review, a significant proportion of the items were categorized as moderately to highly representative/applicable. Following the expert panel’s evaluation, a pilot study was conducted with a sample of 20 SMEs to refine the phrasing of measurement items, the linguistic style utilized, and the overall structure of the questionnaire. The objective of the pilot study was to validate the comprehensibility of the measuring items and the soundness of the questionnaire structure, thereby facilitating the acquisition of data of superior quality from SMEs.

The researchers utilized the guidelines presented by Hair et al. (2006) to determine an appropriate sample size for their study. As per the prescribed guideline, it is recommended that the sample size should be at least five times greater than the number of items. With 22 measurement items in our study, this requires a sample size of at least 110 to ensure sufficient statistical power and validity. The survey questionnaire was intended to be completed by the owner/manager of each SME, as they were considered being the primary decision-makers for their own businesses. The survey was administered to 500 SMEs through an online platform using a convenience sampling method. In order to guarantee the accuracy and dependability of our findings, we conducted a thorough analysis of all responses, scrutinizing each one meticulously to eliminate any invalid data resulting from missing or duplicate responses. We kept just the most useful responses from our thorough screening process. Following a rigorous review process to remove invalid responses, we acquired 186 valid responses (yielding a response rate of 37.2%), which exceeded the minimum sample size requirement of 110. It is worth mentioning that all the SMEs that participated in the study were situated in Danang City.

We focused on obtaining participants’ trust and cooperation throughout the survey procedure by clearly stating our study’s goals. To protect their privacy and prevent potential breaches of confidentiality, we also implemented strict measures to ensure that all respondents remained anonymous. This comprehensive and rigorous methodology allowed us to obtain a wide range of relevant insights and data, which we hope will help us comprehend the subject under examination.

Survey Instrument

The survey conducted for this study incorporated a wide range of variables and indicators, which are elaborated in detail in Appendix A. The adoption of an inclusive approach yielded a comprehensive and detailed framework for our research. The framework was designed to capture the complex interplay among the various factors and dimensions that impact our chosen topic. A Likert scale from 1 to 5 was used to quantify participants’ replies, with 1 representing complete disagreement and 5 indicating complete agreement. This well-known measurement technique captured participants’ complex thoughts and attitudes, creating a rich and detailed dataset that will further our research.

Data Analysis

The present investigation aimed to examine the complex and diverse interconnections among seven fundamental constructs: Customer centricity (CUS) and Supplier centricity (SUP) would be first-order constructs of Environmental factors (ENV), governance (MAN) and top management support (TMS) would be first-order constructs of Organizational factors (ORG), technology integration (INT), and cyber-security (CBS) would be first-order constructs of Technological factors (TEC). The dependent variable in this study is digital transformation adoption (DTA). In order to attain this objective, we proposed six distinct hypotheses, each of which was designed to shed light on the various causal relationships that exist among these key constructs.

The present study has employed the second-order construct of the Technology-Organization-Environment (TOE) model, given its perceived superiority over the first-order construct. This is attributed to several reasons. Firstly, the second-order construct exhibits greater complexity by integrating multiple first-order constructs, thus results a more comprehensive and nuanced understanding of the factors that shape technology adoption. Secondly, the second-order construct facilitates enhanced theoretical clarity by linking the first-order constructs and their theoretical foundations, in alignment with the overarching research objective. Finally, it is suggested that the second-order construct has the potential to offer improved practical utility, particularly in guiding technology adoption decisions within organizations. Its capacity to provide a more holistic view of the inter-relationships among the various factors involved in technology adoption renders it a valuable tool for organizational decision-makers (Pattnaik, 2019).

We used structural equation modelling (SEM) to map complex causal linkages between multiple dependent variables (Hair et al., 2013). Through utilization of this approach, we have successfully conducted an analysis of all the various paths and dependencies among latent variables within our research model, providing a comprehensive and detailed understanding of the complex relationships that exist within these constructs (Gefen et al., 2000).

It is essential to mention that SEM is available in two principal forms: Covariance-based SEM (CB-SEM) and Partial Least Squares SEM (PLS-SEM). CB-SEM is frequently applied to assess the extent to which established theories align with empirical data, while PLS-SEM is commonly employed for the purpose of conducting exploratory analysis and testing of emerging theories. Considering the exploratory nature of our study and the intricate relationships we aimed to investigate, PLS-SEM was the ideal approach to our research objectives (Fornell & Bookstein, 1982).

It has been acknowledged that the presence of outliers may impact to the validity of the analysis. In response to this concern, measures have been taken to mitigate this issue. SPSS was utilized, as suggested by Roni and Djajadikerta (2021) to conduct a comprehensive examination of the data for the purpose of detecting and dealing with outliers. According to our findings, there were no indications of any potential issues that could compromise the integrity of the statistical analyses.

The data analysis undertaken in this study comprised a two-stage procedure utilizing SmartPLS. Prior to analyzing the proposed linkages among our theoretical constructs, commonly referred to as the structural model, it was necessary to establish the convergent and discriminant validity of our measurement instruments, also known as the measurement model (Gefen et al., 2000). This provided us with a strong and all-encompassing basis for our subsequent analysis of the intricate relationships that exist among these crucial constructs (Al-Darras & Tanova, 2022).

Results

Characteristics of Respondents

After screening the data, a total of 186 respondents were included in the study. Table 2 presents a diverse range of demographic information, including gender, age group, position, and SME revenue and size. The vast majority of these respondents held positions of managerial responsibility, representing a diverse range of firms that varied in terms of revenue and staff numbers. A significant proportion of these SMEs reported revenues of less than $500,000 and boasted workforces comprising between 10 to less than 50 employees. Moreover, the age range of the respondents suggests that many of these individuals were early to mid-career professionals, a common characteristic of SME employees. Overall, the sample appears to represent a diverse group of individuals with varying characteristics in terms of gender, age, position, firm revenue, and firm size.

Table 2.

Demographic Information.

Characteristic/group	Frequency	Percentage
Gender
Male	75	59.7
Female	111	40.3
Age
Less than 30	49	26.3
From 30 to less than 40	82	44.1
40 and over	55	29.6
Position
Owner	80	43
Manager	106	57
Firms’ revenues
Less than $500,000	108	58.1
From $500,000 to $5,000,000	54	29.0
From $5,000,000 to $15,000,000	10	5.4
Above $15,000,000	14	7.5
Firms’ size
Less than 10 employees	46	24.7
From 10 to less than 50 employees	97	52.2
From 50 to less than 100 employees	25	13.4
From 100 to less than 250 employees	18	9.7

Utilizing partial least squares structural equation modeling (PLS-SEM), a statistical method for analyzing complex relationships among multiple latent variables, the present study follows the guidelines of Hair et al. (2019). The aims is to evaluate the internal consistency and construct validity of the measurement factors that have been employed in the study.

Measurement Model Evaluation: First-Order Construct

The first-order measurement model, consisting of 22 measurement items that formed seven distinct factors, was subjected to testing. The evaluation of the reflective measurement model was carried out based on the criteria outlined in Table 3. In order to evaluate the reliability and validity of our measurement scale, we used various criteria. The criteria considered included internal consistency reliability, convergent validity, and discriminant validity.

Table 3.

Reliability and Validity of Measurement Items.

Dimension/items	Outer loadings	Cronbach’s alpha	Composite reliability	AVE	VIF
CBS
CBS1	.741	.802	.884	.720	1.470
CBS2	.918				2.796
CBS3	.876				2.287
CUS
CUS1	.836	.847	.897	.686	2.092
CUS2	.859				2.291
CUS3	.831				2.016
CUS4	.785				1.548
DTA
DTA1	.821	.808	.887	.723	1.572
DTA2	.866				1.995
DTA3	.864				1.882
FNC
FNC1	.847	.855	.912	.776	2.035
FNC2	.919				2.633
FNC3	.873				2.048
MAN
MAN1	.797	.739	.851	.657	1.469
MAN2	.838				1.523
MAN3	.796				1.418
SUP
SUP1	.805	.747	.855	.663	1.608
SUP2	.845				1.733
SUP3	.793				1.337
TMS
TMS1	.825	.794	.878	.706	1.895
TMS2	.869				1.978
TMS3	.827				1.460

Composite reliability, a measure of internal consistency reliability, was employed, and a score higher than .7 was recommended (Hair et al., 2006). Our results showed that all constructs had composite reliability values ranging from .851 to .912, indicating that they were statistically acceptable. In addition, we evaluated the convergent validity of the model by using outer loadings and values for the average variance extracted (AVE). All outer loadings of measurement items exceeded the recommended threshold of .7 (Hair et al., 2006), and all constructs had AVE values greater than .5, ranging from .657 to .776, indicating high levels of convergent validity. To evaluate discriminant validity, we used the HTMT criterion, as recommended by Hair et al. (2021). If the HTMT value is less than .90, discriminant validity has been established between variables. Our results in Table 4 satisfied this criterion, indicating that the discriminant validity of measurement items was met. Overall, our measurement scale demonstrated high levels of reliability and validity, ensuring the quality of our data analysis and results interpretation.

Table 4.

Discriminant Validity (HTMT Criterion).

	CBS	CUS	DTA	FNC	MAN	SUP
CBS
CUS	.765
DTA	.865	.807
FNC	.864	.679	.800
MAN	.803	.741	.889	.778
SUP	.714	.842	.765	.669	.779
TMS	.580	.682	.663	.677	.762	.852

After assessing the measurement constructs, it was determined that Environmental factors (ENV), Organizational factors (ORG), and Technological factors (TEC) were second-order constructs (Table 5). ENV consisted of two first-order components, namely customer centricity (CUS) and supplier centricity (SUP), while ORG consisted of two components, governance (MAN) and top management support (TMS). The study employed first-order factors as indicators to test a second-order measurement model. The aim was to ascertain the hierarchical relationships between the constructs and their components. Table 5 presents the results of the study, indicating that the composite reliability values for ENV, ORG, and TEC were .911, .886, and .923, respectively. Considering that these values were higher than the minimum requirement of .7, this suggests that the higher-order constructs have a high level of reliability. The loading values of the indicators were all above the recommended value of .7 for convergent validity, and the AVE values of ENV, ORG and TEC were .837, .796 and .857, respectively, which exceeded the recommended threshold of .5. Overall, the results provided empirical evidence that the environmental factors, organizational factors and technological factors constructs were best fit using the first-order model.

Table 5.

Assessment of Second-Order Factor.

Second order	Outer loadings	Cronbach’s alpha	CR	AVE
ENV (environmental factors)
CUS	.920	.805	.911	.837
SUP	.910	.805	.911	.837
ORG (organizational factors)
MAN	.907	.744	.886	.796
TMS	.877	.744	.886	.796
TEC (technological factors)
CBS	.927	.833	.923	.857
FNC	.925	.833	.923	.857

Structural Model Evaluation

The assessment of the structural model involved a comprehensive evaluation of diverse factors, including the examination of its fit, the predictive capabilities, and the complex interplay among its constructs. The initial stage of our investigation focused on examining the adequacy of the structural model’s fit, which was done by utilizing the standardized root mean square residual (SRMR) measure. The findings revealed that the SRMR value was .077, which demonstrated that the model’s fit was satisfactory. This outcome aligns with the guideline proposed by Henseler, Ringle, and Sarstedt, who recommended a cutoff value of .08 for the SRMR measure. Therefore, our findings indicate that the structural model can be deemed adequate and reliable, and is capable of capturing the underlying relationships between the variables with a high degree of accuracy (Henseler et al., 2016).

Evaluation of Predictive Capability

To assess the predictive capacity of the proposed model, the research team utilized a two-fold methodology that involved an assessment of both the predictive precision and the predictive significance. The former was measured using the coefficient of determination (R²), which provides an insight into the extent to which each of the endogenous latent variables can be accounted for by the model constructs under consideration, following the methodology recommended by Hair et al. (2013). Notably, the R² values obtained in this study, as shown in Table 6, reveal that the proposed model accounts for a remarkable 63.9% of the variance in the adoption of digital transformation (DTA), 52.6% of the variance in organizational factors (ORG), and 54.5% of the variance in technological factors (TEC), thus attesting to the model’s efficacy in the prediction of these key outcomes. According to the general rule of thumb that Henseler et al. (2016) proposed, these R² values suggested a substantial level of predictive accuracy. In addition, to further strengthen the predictive power of the extended model, the researchers utilized the blindfolding technique within the SmartPLS 3.3.2 software, and determined the value of Q², which serves as a reliable metric for evaluating the predictive relevance of the endogenous variable in question. As per the seminal work of Hair et al. (2013), which provides invaluable guidance on the evaluation of predictive relevance in structural equation modeling, a threshold value of .35 was determined to indicate a significant level of predictive relevance for a specific endogenous construct. Based on this criterion, the current study examined the predictive relevance of the key endogenous constructs, namely DTA, organizational factors and technological factors. Using the Q² metric, which measures the ability of the model to make accurate predictions for novel data samples, the study found that these constructs exhibited an impressive degree of predictive relevance, as reflected by Q² values of .449, .413, and .455, respectively, all of which significantly surpassed the zero benchmark. Notably, these results are consistent with the findings derived from the coefficient of determination (R²), which demonstrated that the proposed model could account for a considerable portion of the variance in these constructs. Taken together, the high values of both R² and Q² obtained in this study offer compelling evidence for the predictive capability of the proposed model.

Table 6.

R Squared and R Squared Adjusted.

	R ²	R² adjusted
DTA	.639	.633
ORG	.526	.523
TEC	.545	.540

Evaluation of Direct Effects

In this study, we utilized a bootstrapping procedure comprising of 10,000 resamples, with a sample size of 186 cases, to analyze the path relationships among different constructs in the proposed model. The regression coefficient (β) and its statistical significance were deemed crucial indicators for evaluating the strength of the path relationships. In line with the guidelines provided by Hair et al. (2021), we considered an empirical t-value exceeding 1.96 at a significant level of 5% as an indicator of a significant path coefficient. Upon analyzing the data and referring to Table 7, we found that all paths within the proposed model exhibited a significant level of statistical significance.

Table 7.

Test Hypotheses.

Hypothesis	Path	β	SD	t Statistics	p Values	Conclusion
H1	ENV → ORG	.725	0.057	12.754	.000	Supported
H2	ENV → TEC	.379	0.086	4.398	.000	Supported
H3	ENV → DTA	.248	0.084	2.944	.003	Supported
H4	ORG → TEC	.416	0.098	4.259	.000	Supported
H5	ORG → DTA	.228	0.082	2.783	.006	Supported
H6a	TEC → DTA	.416	0.079	5.247	.000	Supported

Interestingly, our findings also revealed that technological factors (TEC) had a more pronounced impact on digital transformation adoption (DTA) than environmental (ENV) and organizational factors (ORG), with a β value of .416 compared to .248 and .228, respectively. Moreover, organizational factors were identified as having the stronger influence to technological factors (β = .416) in comparison to the effects of environmental factors (β = .379). These findings contribute to the understanding of the relationships between the constructs in the proposed model and provide insights for practitioners to enhance DTA in SMEs.

Evaluation of Indirect Effect

The study utilized a bootstrapping technique recommended by Zhao et al. (2010) to examine the mediating effects between constructs in the model. This involved checking if the 97.5% confidence intervals excluded zero, indicating a significant mediator effect between two observed variables (Preacher & Hayes, 2004). Table 8 indicates that TEC acted as a mediator in the connections between ENV and ORG with DTA (.157, p < .01 and .173, p < .001 respectively).

Table 8.

Evaluation of Indirect Relationships.

Hypothesis	Indirect relationships	Coefficient	t Values	p Values	Lower (2.5%)	Upper (97.5%)
H6b	ENV → TEC → DTA	.157	3.092	.002	0.066	0.266
H6c	ORG → TEC → DTA	.173	3.398	.001	0.074	0.275

Discussions

Analysis of Main Findings and Theoretical Contribution

The post-epidemic era has posed significant challenges for SMEs in their efforts towards DT. These challenges span a range of predicaments and obstacles that impede the progress of these businesses in their DT journey. The digitalization gap between SMEs and large corporations is widening, adversely affecting the performance of the former (Li et al., 2023). People have a limited understanding of the fundamental resources and processing mechanisms that are imperative for accomplishing successful DT in SMEs (Matarazzo et al., 2021), despite the fact that SMEs are important to the economy. When dealing with the difficulties link with the DT of SMEs, many managers of SMEs realize that they become confused by the variety of potential considerations to take into account what the most important ones are, how to establish the digital transformation road map before launching on a DT. This is especially true in regards to effectively managing the intricacies linked with the DT of businesses. Hence, it is vital to ascertain and define the major determinants and influence processes affecting the adoption of DT. This holds significant pertinence for allocation of enterprise resources, supporting the execution with achievement of DT, and attaining long-lasting growth. Identifying and clarifying these factors is necessary because they exert a substantial influence on the efficacy of DT in SMEs (Zhang et al., 2022). Based on the theory of resource dependence, capabilities-based theory and TOE framework, in three different dimensions, we identified the primary components as follows: technology (technology integration, cyber-security), organization (governance, top management support), and environment (customers, suppliers).

The findings of the study highlight the crucial role played by technological factors in the DTA process among SMEs. Verhoef et al. (2021) posit that technological factors are the most significant drivers of DT adoption by SMEs, underscoring their criticality for enhancing the value of both the company and its customers by optimizing business procedures. Specifically, technological advancements such as cloud computing, the Internet of Things (IoT), artificial intelligence (AI), and big data analytics have significantly impacted the pace and extent of DT in SMEs. These technologies have enabled SMEs to streamline their operations, enhance their efficiency, and improve their decision-making capabilities (Li et al., 2023). This finding is in line with the supplementary perspective of the Theory of resource dependence (RDT) that emphasizes the necessity for digital technology to be incorporated seamlessly into the business and managerial processes of an enterprise, along with its strategic goals and the digital proficiencies of its personnel. This integration is critical for establishing a robust dynamic capacity that can unlock the potential of technological advances (Vial, 2019).

Furthermore, technological factors play an intermediary role, as evidenced in Table 8. While organizational innovation as a mediator has been discussed in many studies on DT (Tsou & Chen, 2023), there has been little research exploring the intermediary role of technological factors. Verhoef et al. (2021) argue that relying solely on technological factors is not enough to achieve a competitive advantage since they can be easily replicated by other organizations. Instead, it is the strategic implementation of these various aspects within a particular context, which enables businesses to discover innovative ways of creating value. This view is consistent with Vardarlier and Ozsahin’s (2021) argument that technology cannot by itself create a competitive advantage; rather, it should serve just like a tool to attain strategically significant aims. The introduction of digital technologies within an organization entails their interaction with other pre-existing organizational antecedents, most notably the organization’s strategy and legacy, in addition to managerial traits, such as the management team’s ability to launch DT (Kane, 2019). In line with this viewpoint, Qalati et al. (2021) assert that digital technologies interact with organizational antecedents, such as managerial capacities and top management support, to initiate DT.

The study findings confirm the significant impact of environmental factors on both organizational and technological factors, as previously established in prior research (Hoque, 2004; Malik et al., 2021; Tornatzky & Fleischer, 1990). Environmental factors are known to affect an organization’s structure, culture, and strategy. Additionally, environmental factors can influence the development and adoption of new technologies, such as changes in government regulations affecting the availability of resources and funding for research and development. Knowledge sharing can be facilitated by working together and collaborating with stakeholders, including consumers, suppliers, and providers of information technology. This can help managers improve their ability to make decisions. Behaviors between partnerships that contribute to the co-creation of value, including mutually establishing plans and adapting those plans in a flexible manner, can help reduce mistakes when making decisions and improve top management (Zhang et al., 2022). The efficacy of the partnership in relation to the digital strategy remains inadequately supported, suggesting that inter-business collaboration is limited to operational considerations and does not extend to the foundational level of strategic planning. It is absolutely necessary for businesses to collaborate in order to develop more robust value chains in preparation for DT (Fischer et al., 2020). Therefore, it can be concluded that the impact of environmental factors on organizational and technical factors is multifaceted and complex, involving various interrelated factors. Organizations that can effectively manage and adapt to these factors are more likely to succeed and thrive in the continuously evolving business landscape.

The study results reveal the impact of environmental factors on the adoption of DT in SMEs. Specifically, external pressures from stakeholders, including customers and suppliers, are found to influence SMEs to adopt digital technologies to meet their changing needs and expectations. In addition, the agility of an organization in developing partnerships with customers and other businesses across industries is an essential factor in determining the level of success that may be achieved when adopting DT. This finding is consistent with prior research of Hillman et al. (2009) indicating that external pressures and partnerships are crucial determinants of DTA in SMEs. However, the study’s contribution lies in the empirical evidence that supports the theoretical arguments.

The outcomes of this investigation indicate that organizational factors, specifically top management support and governance, have a notable effect on the adoption of DT. This aligns with what has been previously reported in earlier studies (Chong et al., 2016; Fletcher & Griffiths, 2020). Enterprises that exhibit strong governance, along with top management support, are better positioned to succeed in DT initiatives. This discovery is in line with what previous research has indicated, which is that governance is critical for firms to integrate all essential resources and enhance their competitiveness (Johnson, 2010). Empirical evidence further highlights the importance of governance for guiding decision-making and preventing resource waste during the DT process, especially for SMEs. Furthermore, top management plays a vital role in the success of DT initiatives, as confirmed by previous research (Fischer et al., 2020). Effective managers have the ability to identify and exploit technological and market opportunities to create business prospects, encourage both employees and partners to take an active role in the decision-making process, and ensure that effective governance is in place and execution to drive the transformation forward. Therefore, these results provide additional evidence supporting the conclusions drawn by Ko et al. (2022) in their study.

The proposed model in this research holds significant promise for advancing the field of predictive modeling and contributing to a more comprehensive understanding of complex phenomena. Therefore, it represents a crucial contribution to the field and offers significant opportunities for further research and implementation. In addition to its contributions to predictive modeling for DTA of SMEs, the study has provided quantitative evidence to the field of DT in SMEs, which has traditionally been dominated by qualitative studies (Verhoef et al., 2021; Vial, 2019). This contribution is noteworthy, as it underscores the necessity for further empirical research to supplement and augment existing qualitative investigations, ultimately leading to a more comprehensive understanding of the phenomenon of DT in SMEs (Kraus et al., 2021).

Furthermore, the present study has contributed significantly to the literature by examining the mediating effects of technological factors on the relationship between TOE variables and the DTA in SMEs. These results have also expanded the understanding of the role of mediating variables in DT, as studied by P. Chen and Kim (2023). This represents a novel perspective, as there exists few studies that have explored this aspect of the TOE model. The utilization of second-order constructs in the examination of the three primary components of the TOE model represents a significant progression, as it offers a more comprehensive comprehension of the elements that contribute to the DTA in SMEs.

Finally, the results of the present study highlight the critical role played by both technology and organizations in the successful adoption of DT in SMEs. This finding is consistent with prior research and emphasizes the importance of considering both technical and organizational factors when implementing DT initiatives. It highlights the need for a comprehensive approach that aligns technical and organizational factors to achieve optimal results in the DTA in SMEs (Kane et al., 2015; Solberg et al., 2020; Verhoef et al., 2021).

Practical Implications

The findings of this study hold significant implications for SMEs. Such organizations typically depend on their top-level or senior management to make crucial decisions, including those related to investing in the integration of information systems. Previous research has shown that the adoption of new technology in SMEs is strongly influenced by top management or owners/managers (Ali et al., 2022; Ghobakhloo & Ching, 2019; Sutanonpaiboon & Pearson, 2006). Commitment and involvement from management are essential components in order to guarantee the availability of sufficient resources for the efficient implementation and utilization of information technology (Premkumar, 2003). Hence, it is crucial for senior management to proactively and unambiguously communicate and exhibit their unwavering support for digital transformation, through both verbal and non-verbal cues. Such visionary leadership is paramount in circumventing the myriad challenges and barriers that can hinder the successful adoption and implementation of DT initiatives, including but not limited to organizational resistance, bureaucratic inertia, and resource constraints.

The importance of customers in the acceptance and implementation of DT by SMEs is growing as businesses progressively use modern technology. It refers specifically to the extent to which customers are competent and comfortable using digital technologies to communicate with businesses and conduct transactions. Customers today expect businesses to provide digital channels for browsing, shopping, and making purchases, given the widespread usage of internet-enabled devices, such as smartphones and tablets (Bacile, 2020). Thus, businesses must consider the digital preparedness of their clients and adjust their strategy accordingly. This may involve providing a variety of digital channels, such as mobile apps, social media platforms, and online chat services, to facilitate seamless client interaction. It may also entail the delivery of relevant, customized material that is tailored to the specific needs and interests of each customer. Customers’ digital preparedness is a crucial factor for organizations aiming to survive in the present digital environment (Mittal et al., 2018). By understanding their consumers’ needs and preferences, businesses may modify their tactics and offers to produce a unified and appealing digital experience that exceeds their expectations.

In addition, the mediating effect that technological elements have brings up some interesting considerations. The adoption of DT is impacted in two ways by technological factors: first, technological factors have a direct impact on the adoption of DT, and second, technological factors have a mediating impact on the impact that environmental and organizational factors have on the adoption of DT. This implies that SMEs must allocate appropriate and adequate resources to invest in technology, specifically in cybersecurity and the integrated utilization of technology across the organization (Armenia et al., 2021; Ghobakhloo & Ching, 2019). Implementation of these measures will serve as a critical foundation for successful adoption of their digital transformation strategy.

Conclusion

Given the crucial role of SMEs in the global economy, it is essential to comprehend the factors that influence their decision to adopt DT and the relationships between these factors. The study findings suggest that the adoption of DT is the outcome of a combination of environmental, organizational, and technological factors. Specifically, DT is positively influenced by various technological factors such as cybersecurity and IT integration capabilities, environmental factors such as clients and partnerships, and organizational capabilities such as top management support and oversight. These findings are consistent with previous research that has shown the influence of TOE factors on the acceptance of technology in general and the adoption of DT in SMEs in particular (Ngo et al., 2022; Zhang et al., 2022). Furthermore, the study also indicates that the relationship between environmental and organizational factors and the adoption of DT by SMEs is strengthened by technological considerations. The findings of this study hold great importance for the Vietnamese government and non-governmental organizations, as they seek to support SMEs in their efforts to undergo DT. This support can play a crucial role in bolstering the country’s economy, particularly as it seeks to establish a stronger foothold in the global marketplace.

Although the research objectives were achieved, it is crucial to recognize the constraints of this study. A key constraint of the research is its exclusive focus on SMEs located in Danang, a prominent urban center in Vietnam renowned for its thriving commercial landscape. Consequently, the generalizability of the findings to SMEs in other areas of Vietnam may be limited. Furthermore, as a result of limitations in resources and time, the investigation employed a convenience sampling technique, which could have potentially led to sample biases. In order to overcome these constraints, there exist various avenues for prospective investigation. Firstly, future studies could be conducted in multiple cities across Vietnam to obtain more representative results for SMEs in the country. Secondly, the proposed model could be tested on large enterprises to identify any differences in the factors affecting DTA. Thirdly, expanding the research sample size would provide greater statistical power and support for the current findings. Moreover, examining demographic factors and organizational positions could yield important implications.

Footnotes

Appendix A

Construct		Measurement items	Sources
Customer centricity	CUS1	Digital technologies have enhanced the customer experience.	Maduku et al. (2016) and Qalati et al. (2021)
	CUS2	The organization’s application of digital technology makes it easier for customers to find information about the organization and its products.
	CUS3	The organization is leveraging digital technology to approach and attract customers.
	CUS4	The organization leverages various digital technologies to enhance customers’ experience with its products and services.
Supplier centricity	SUP1	Suppliers are connected and exchanged with through the use of digital technology.	Maduku et al. (2016) and Yadegaridehkordi et al. (2020)
	SUP2	Digital technology is used to collect and analyze data related to suppliers.
	SUP3	Several supplier management processes are automated through digital technology.
Top management support	TMS1	Top managers are well aware of the benefits and trends of digital transformation	Chan and Chong (2013) and Qalati et al. (2021)
	TMS2	Top managers interested and invested in technology
	TMS3	Top managers will make more efforts in digital transformation
Governance	MAN1	The organization manage human resources with HRM software systems	Ghobakhloo and Iranmanesh (2021) and Qalati et al. (2021)
	MAN2	Online meetings are used frequently at the organization
	MAN3	The current workforce is qualified to carry out digital transformation tasks
Cybersecurity	CBS1	The organization utilizes proprietary anti-virus software.	Ghobakhloo and Iranmanesh (2021) and Yadegaridehkordi et al. (2020)
	CBS2	The organization utilizes a firewall system as a preventive measure against data leakage.
	CBS3	The organization utilizes digital technologies to defend against cyber attacks.
IT integration	INT1	The company possesses software required for carrying out daily activities with stakeholders.	Chan and Chong (2013) and Yadegaridehkordi et al. (2020)
	INT2	The organization leverages digital technology to support its business activities.
	INT3	All parts of the organization are integrated with technology systems.
Digital transformation adoption	DTA1	The organization has implemented new technologies, including cloud computing and artificial intelligence.	Garzoni et al. (2020) and Malodia et al. (2023)
	DTA2	The organization has constructed a large data warehouse to enable comprehensive data analysis at the enterprise level.
	DTA3	The organization’s internal connection network has been built and deployed

Acknowledgements

We would like to express our deepest gratitude to all individuals and organizations who have contributed to the success of this research. We would like to express our sincere appreciation to the editors and four anonymous reviewers for their valuable feedback and comments. Their diligent efforts and scholarly expertise have significantly assisted us in refining and enhancing the completeness of this article, resulting in an improved quality of our work.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

Ethical Approval

We asked consent from all survey respondents, which was gained after a thorough description of the goal and scope of the study. In addition, we strictly maintained the anonymity of the responders to prevent any personally identifying information from being linked to a specific individual.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Science and Technology Development Department—The University of Danang, Vietnam (Approval code 14/HD-KHCN-2021, Approval date: December 1st, 2021).

ORCID iD

Ha Hoang

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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