Enhancing Information Technology Adoption Potential in MSMEs: a Conceptual Model Based on TOE Framework

- The adoption of Information Technology (IT) by Micro, Small, and Medium Enterprises (MSMEs) has become essential in the digital era. Nevertheless, challenges persist, such as enhancing IT adoption in the MSMEs sector and optimizing its benefits. This research aims to create a comprehensive model based on the Technology-Organization-Environment (TOE) framework by analyzing technological, organizational, and environmental factors influencing IT adoption among MSMEs in Pangandaran, Indonesia. Employing a quantitative approach, an online questionnaire was distributed to MSMEs, and data were analyzed using Partial Least Square-Structural Equation Modeling (PLS-SEM) through SmartPLS. The study significantly contributes to understanding IT adoption, emphasizing organizational context as the primary predictor, followed by technological and environmental contexts. Positive relationships were found between four contextual constructs: complexity, top management support, organizational readiness, and competitive pressure towards IT adoption in MSMEs. Conversely, compatibility and government support exhibited negative impacts. These findings have practical implications for Indonesian MSMEs by enhancing understanding of factors influencing IT adoption to support business operations. Furthermore, these findings hold the potential to assist MSMEs and the Indonesian government in optimizing IT adoption success. The generated data can be employed by MSMEs management authorities to devise strategies for enhancing IT adoption among MSMEs.


I. INTRODUCTION
In the current era of digital transformation, the utilization of Information Technology (IT) has become a key factor in successfully addressing the increasingly complex challenges of business [1].The Micro, Small, and Medium Enterprises (MSMEs) sector plays a crucial role in economic growth [2].This sector serves as a key driver for national [3] and international economic development [4].In many countries, including Indonesia, MSMEs are considered the economic foundation, making substantial contributions to economic recovery and social inequality reduction.Therefore, the sustainability of MSMEs must be maintained to avoid negative impacts on the economy and the nation's social standing [5].
Several key issues pose challenges for MSMEs in adopting IT.Firstly, MSMEs often struggle to comprehend and select new technologies or systems that align with their needs and integrate them into their business operations [6].Secondly, organizational factors, such as a lack of skilled human resources or organizational competence, contribute to these challenges [7].Thirdly, the inability of MSMEs to adopt and leverage IT can also be influenced by environmental conditions, such as the lack of IT infrastructure availability and policy and regulatory support [8].Therefore, the extent to which MSMEs can adopt and utilize IT effectively and efficiently is crucial for success in overcoming these challenges.
Previous research aimed to analyze factors influencing technology implementation in MSMEs [9]- [11].These studies encompass various aspects, such as those utilized to enhance management effectiveness [12], performance improvement [10], [13], and company market expansion [14]- [15].Previous research has implemented various theories of IT adoption, such as the Technology Acceptance Model (TAM), Diffusion of Innovation (DoI), and Unified Theory of Acceptance and Use Of Technology (UTAUT).The TOE (Technology, Organization, and Environment) framework was chosen because it can examine how organizational characteristics influence technology adoption decisions, divided or expanded into three categories: (a) technology; (b) organization; and (c) environmental context [16]- [17]."Moreover, the Technology-Organization-Environment (TOE) theory stands as the most validated framework for investigating the adoption of new technologies at the organizational level [18].After reviewing existing literature on TOE-based innovation adoption and considering the characteristics of the organizational and environmental environments, particularly in Indonesia, this research model presents relevant constructs.
The problem statement in this study is as follows: 1).What are the factors influencing the adoption of IT among MSMEs?, 2).How can the development of an IT adoption model based on the TOE framework contribute to improving the level of IT adoption among MSMEs?, 3).How can the proposed model enhance the effectiveness of IT adoption in MSMEs?.Thus, this research aims to develop a comprehensive model based on the TOE framework.This framework can be utilized to understand, analyze, and enhance the success rate of IT adoption among MSMEs.
The following discussion describes the three factors, constructs, and the research hypotheses.

A. Technology Factors
The qualities and capacities of the technology itself are referred to as technological factors [19].Complexity is the difficulty of understanding and implementing innovation to achieve business goals, requiring more effort to find ways to use innovation [20].Then, one of the main components influencing IT adoption by MSMEs is compatibility [21]- [23].The extent to which creativity blends with a company's procedures, technical systems and processes can be calculated in terms of compatibility [24].Thus, the following hypothesis is formulated: Hypothesis 1 (H1): Complexity has a significant and positive impact on IT adoption in MSMEs.
Hypothesis 2 (H2): Compatibility has a significant and negative impact on IT adoption in MSMEs.

B. Organizational Factors
Organizational factors explain organizational elements that impact the implementation of the latest and innovative technology by the organization [25].Top Management Support (TMS) is an essential factor in influencing an organization's choice to implement new technology or systems [26].Therefore, support from top management is needed to foster creativity in the organization [27].Then, A company is considered ready for business when it has the necessary services to implement technology [28].The capacity of MSMEs to use IT will increase with improving supportive conditions.Thus, the following hypothesis is formulated: Hypothesis 3 (H3): Top Management Support has a significant and positive impact on IT adoption in MSMEs.
Hypothesis 4 (H4): Organizational Readiness has a significant and positive impact on IT adoption in MSMEs.

C. Environmental Factors
The environmental TOE model construction explains how external pressure impacts how quickly businesses adopt new technology [29].Competitive pressure is crucial for innovation adoption [30].Rapid technological industry developments force companies to compete and be aware of the latest advancements by their competitors.Then, Government support refers to the opportunities and government initiatives to encourage innovation [31] [32].Favorable business and corporate tax laws can be enacted by the government to increase IT usage in organizations or companies.Thus, the following hypothesis is formulated: Hypothesis 5 (H5): Competitive Pressure has a significant and positive impact on IT adoption in MSMEs.
Hypothesis 6 (H6): Government Support has a significant and positive impact on IT adoption in MSMEs.
The hypotheses regarding interactions among factors are illustrated in the conceptual model in Fig. 1.

II. METHOD
This section outlines the procedures undertaken to complete the study, consisting of nine steps.The research flow is depicted in Fig. 2.

A. Research Type
The researcher employs a quantitative approach.To formulate hypotheses, the researcher reviews literature published on SMEs and technology adoption.Subsequently, a questionnaire is developed and evaluated based on expert feedback.

B. Population and Research Sample
The study's population consists of several MSMEs that are part of the WhatsApp group for MSMEs in Pangandaran.The research sample is non-probability sampling with a purposive sampling technique.The criteria used in this study are SMEs located in Pangandaran, and the business has been running for at least 1 year.

C. Questionnaire Design
The research questionnaire comprises three sections.Section A contains demographic questions about the respondents, section B contains information about the organization, and section C includes questions about various constructs in the research model.

D. Data Collection Technique
An online questionnaire was developed using Google Forms and distributed to respondents.Respondent responses were quantified using a five-point Likert scale, with response options ranging from 1 ("strongly disagree") to 5 ("strongly agree").This scale was utilized to evaluate question items constructing exogenous variables (technology factors, organizational factors, environmental factors), as well as the intervening variable (IT usage)."

E. Data Analysis Technique
Data were categorized by gender, position, age, industry classification, and firm size.Statistical analysis and correlation were conducted using SmartPLS as renowned software program for Partial Least Squares Structural Equation Model (PLS-SEM).

F. Validity and Reliability
Composite Reliability (CR) and Cronbach's alpha assessed the research instrument's reliability.This study ensured convergent and discriminant validity.Latent variables were deemed reliable with CR and Cronbach's alpha values surpassing 0.70.Convergent validity used Average Variance Extracted (AVE), exceeding 0.50.Discriminant validity was verified by the square root of AVE, surpassing construct correlations.

III. RESULT AND DISCUSSION
This section comprises respondent characteristics and demographic profiles, measurement models, followed by the structural model.

A. Characteristics and Demographic Profiles of Respondents
The demographic characteristics of respondents and their companies are presented in Table I.Based on the research findings, the majority are women (60.3%), holding the position of CEO or business owner actively involved in day-to-day operations (79.4%).Most respondents are aged between 20-30 years (38.1%).Concerning industries, the majority of respondents have businesses in accommodation, food, and beverage sectors (46%).Regarding the company size category, most respondents have 1-4 employees (66.7%).

B. Measurement Model
Reliability and validity must be assessed before testing hypotheses [33].The study's average variance extracted (AVE), reliability, and convergent validity are shown in Table II.The idea behind convergent validity is that there should be a strong correlation between the measures of a construct; this is usually indicated by factor loading values of at least 0.   This study examines discriminant validity by comparing the square root of AVE and the correlation coefficients between constructs using the criteria of Heterotrait-Monotrait (HTMT) suggested by Henseler et al. [34], Fornell & Larcker criteria [35], and Cross Loadings.The degree to which a construct differs from other constructs according to empirical standards is known as discriminant validity.Based on the requirements that each indicator's loading must be greater than all cross-loadings, Table III displays crossloadings.
Table IV displays satisfactory values as the square root of the AVE for each construct is greater than the correlation coefficients between construct coefficients.It is clear from this that discriminant validity is appropriate.
The HTMT correlation ratio was used to further investigate discriminant validity.The HTMT correlation ratio data are shown in Table V, where all values are ≤ 0.90.Therefore, it can be concluded that respondents understand that the seven constructs are distinct.Thus, overall, this validity test indicates that the measurement items are valid and reliable.

C. Structural Model
Fig. 4 illustrates the structural model.Concerning the predictive capacity of the model, the R2 values were evaluated for six prediction constructs.The obtained results indicate that the R2 for technology adoption is 0.828.This implies that 82.8% of the variance in technology adoption can be elucidated by factors such as complexity, compatibility, top management support, organizational readiness, competitive pressure, and government support.Threshold values used to assess the R2 are 0.25 (depicted as weak), 0.50 (depicted as moderate), and 0.75 (depicted as substantial).According to the PLS algorithm, the R2 for technology adoption is 0.828.This signifies that the six contextual readiness constructs collectively explain 82% of the variance in technology adoption.Therefore, the conceptual model demonstrates a highly adequate predictive capacity in elucidating technology adoption.VI.The study evaluated the hypothesized relationships and path coefficients among the constructs.Path coefficients have values ranging from -1 to 1.If the value falls within 0 to 1, it is considered positive, whereas if the value is within -1 to 0, it is considered negative.The results, as depicted in Fig. 5, indicate a positive relationship for four contextual constructs: complexity, top management support, organizational readiness, and competitive pressure on IT adoption in SMEs, while compatibility and government support have a negative impact on IT adoption in SMEs.To further test the model hypotheses, t-statistics were evaluated.If T Statistics > 1.96, the impact is considered significant.Subsequently, a hypothesis is accepted to have a significant relationship if the p values < 0.05.Table 8 presents the results of hypothesis testing in this study.
Hypothesis testing on the technological factor indicates that complexity does not have a significant influence on the adoption of IT in SMEs, which contradicts the research conducted by [36].Conversely, compatibility has a significant influence on the adoption of IT in SMEs, as revealed by [37].Furthermore, the results of hypothesis testing on the organizational factor state that top management support significantly affects the adoption of IT in SMEs, contrary to the research by [23].Similarly, organizational readiness significantly affects the adoption of IT in SMEs, as indicated by [38].Then, in the environmental factor, competitive pressure does not have a significant influence on the adoption of IT in SMEs, as revealed by [23].Likewise, government support does not have a significant influence on the adoption of IT in SMEs, as indicated by [39] and [23].This study also found a positive relationship between complexity, top management support, organizational readiness, and competitive pressure on the adoption of IT in SMEs.Whereas compatibility and government support have a negative relationship with the adoption of IT in SMEs.Thus, it can be said that some key factors underlying SMEs adopting IT are technological compatibility, top management support, and organizational readiness.If SMEs have suitable technology, receive support from top management, and the organization has good readiness, they will adopt IT and explore online platforms related to their business until they find the platform most beneficial for their business endeavors.
Resistance to complexity may stem from the lack of specificity in the applications being assessed, leading to respondent bias in evaluating the ease of use of IT.Respondents' perceptions of ease of use may vary depending on the range of apps they have mastered and utilized.Because the applications that the participants in this study utilized were not under the researchers' control, some participants may have found it difficult to use one application while finding it easy to use another.These variations may have an impact on respondents' ultimate perceptions.
The lack of significant influence of competitive pressure on IT adoption in this study may be due to the limited market competition in small towns in Indonesia, thus there is not much pressure from competitors affecting SMEs in adopting IT.Government support also does not affect the use of IT due to the government's lack of knowledge about the conditions and readiness of SMEs, especially in developing countries like Indonesia.The readiness of SMEs includes the experience and ability they possess in utilizing IT in their business activities.Each SME faces different challenges and needs.
Therefore, this research provides a new conceptual model different from previous studies, which can be observed in Fig. 5.

Fig. 5 The developed conceptual model
This research contributes to the existing literature on Information Technology (IT) adoption by companies, aiming to enhance the utilization of IT to derive benefits among Micro, Small, and Medium Enterprises (MSMEs).The study theoretically addresses the limitations in the literature concerning IT adoption for MSMEs in Indonesia.This research fills the research gap in understanding the key factors influencing IT adoption.The researchers successfully explore the factors influencing IT adoption and ensure alignment with suggestions from prior literature.The findings of this research can be employed to formulate strategies and policies to enhance the level of IT adoption among MSMEs in Indonesia.Moreover, these findings offer insights to MSMEs regarding the factors influencing IT adoption.Ultimately, the study's findings are crucial for policymakers and managers, potentially aiding in the improvement of IT implementation.

IV. CONCLUSION
The PLS algorithm reveals a robust R2 of 0.828 for IT adoption, denoting that the six contextual readiness constructs collectively contribute significantly, accounting for 82.8% of the variance in IT adoption.Organizational factors prove most influential, followed by technological and environmental aspects.Positive relationships are apparent between key contextual constructs: complexity, top management support, organizational readiness, and competitive pressure towards IT adoption in MSMEs.Conversely, compatibility and government support negatively impact MSMEs' IT adoption.This research significantly enhances understanding of the relationship between technological, organizational, and environmental factors and their impact on IT adoption in MSMEs.The study provides practical guidance for formulating effective IT adoption strategies for entrepreneurs and policymakers.It establishes a foundational framework for practitioners applying the TOE framework in the context of Indonesian MSMEs.Acknowledging methodological limitations, the research offers valuable insights into addressing the challenge of low IT usage among MSMEs, stimulating further discussion and theoretical development for enhanced understanding.Recognizing limited generalizability to the Pangandaran population, future research should encompass diverse contexts.Adopting a mixed-methods approach, combining quantitative and qualitative research, holds potential for conceptual advancements beyond existing literature.

Fig. 1
Fig. 1 Conceptual model 70.The term "reliability" describes the degree of trustworthiness or dependability of a measurement tool; this is often determined by Composite Reliability (CR) and Cronbach's Alpha values of ≥ 0.70.The coefficient known as AVE, which is usually expressed in terms of values > 0.50, is responsible for explaining the variance in indicators that can be explained by the common component.The factor loading values, CR, Cronbach's alpha, and AVE, satisfy the suggested standards.The results of the measurement model are illustrated in Fig. 3.

Fig. 4
Fig. 4 Structural modelThis study used bootstrapping technique for additional statistical evaluation, and the outcomes are shown in TableVI.The study evaluated the hypothesized relationships and path coefficients among the constructs.Path coefficients have values ranging from -1 to 1.If the value falls within 0 to 1, it is considered positive, whereas if the value is within -1 to 0, it is considered negative.The results, as depicted in Fig.5, indicate a positive relationship for four contextual constructs: complexity, top management support, organizational readiness, and competitive pressure on IT adoption in SMEs, while compatibility and government support have