Navigating the Digital Landscape: Empowering Women Entrepreneurs in Kedah through Digital Platforms

Small and Medium Enterprises (SMEs) are significant contributors to the economic development of a nation. The utilization of digital platforms by small and medium-sized enterprise (SME) entrepreneurs has the potential to enhance business competitiveness, foster sustainability, and augment profitability. Nevertheless, there exists a cohort of small and medium-sized enterprise (SME) entrepreneurs who have yet to fully exploit the potential of digital platforms. Furthermore, there are even individuals within this group who lack the incentive to incorporate digital platforms into their business operations. The objective of this study is to utilize the Unified Theory of Acceptance and Use of Technology (UTAUT) to forecast the determinants that impact the adoption of digital platforms among female entrepreneurs in small and medium-sized enterprises (SMEs) in the state of Kedah. In order to achieve these objectives, a quantitative research approach was employed. A survey instrument was employed to collect data from a sample of 440 participants, specifically individuals who identified as owners or managers of small and medium-sized enterprises (SMEs). The employed sample methodology was purposive sampling. Subsequently, the data underwent analysis utilizing the Partial Least Squares (PLS) method. The results of this study indicate that several variables, including performance expectancy, social influence, effort expectancy, and facilitating conditions, exert a substantial influence on the behavioral intention of female entrepreneurs in small and medium enterprises (SMEs) in the state of Kedah to use digital platforms. The findings of this study hold significant implications for governments, business support organizations, and creators of digital platforms. It is crucial to incorporate these research findings into the development of interventions and strategies that aim to enhance the effective utilization of digital platforms among women entrepreneurs.


Introduction
The phenomena of digital use in Industrial Revolution 4.0 (Industry 4.0) are rapidly expanding.This movement has altered the terrain of human life, including the economy, the work environment, and even the human way of life.These developments also have a significant effect on the industrial sector, particularly on small and medium-sized businesses (SMEs) (Mittal et al., 2018;Yahaya et al., 2018).According to the Ministry of Entrepreneurship and Cooperative Development (MEDAC) (2019), the Malaysian economy registered RM341.7 billion in gross domestic product (GDP) in the first quarter of 2019 compared to the previous quarter.This economic output is also fueled by the entrepreneurial activity of small and medium-sized enterprises, especially online businesses.According to the findings of Cenamor et al (2019) study, in order to compete with larger businesses, small and medium-sized enterprises (SME) entrepreneurs must improve their strategy in order to better utilize the resources and capabilities of digital platforms.In point of fact, the application of technology that is reliable and appropriate for Industry 4.0 can be beneficial to the company by assisting in the mapping of the company's path to the subsequent stage of its expansion.According to Muller (2019), a "multi-sided platform" can bring together customers, suppliers, and partners on a single platform to build a new ecosystem.This digital ecosystem can give organizations with cost advantages, boost productivity, and enhance their competitiveness (Ramdan et al., 2020).Even the Policy Guideline on Digitization of ASEAN Micro Enterprises recommends the use of digital platforms by micro and small enterprises throughout ASEAN in order to deliver more commercial benefits (SME Corp. Malaysia, 2019).There are now more digital or online business platforms due to the evolution of the entrepreneurial landscape.Digital technology, or digitization, has the potential to transform business models and create new value-creation opportunities for SME entrepreneurs (Giotopoulos et al., 2017).Digital technology is the foundation of information and communication technology (ICT) systems that enable organizations to store, process, and disseminate information in order to make decisions and internal control more organized and manageable (Markus et al., 2006;Williams et al., 2009).According to research conducted by Brynjolfsson and McAfee (2014); Melville et al (2004), ICT and digital technology can enhance the overall performance of an organization by increasing operational efficiency and customer focus.However, SME entrepreneurs still lack the capabilities and resources necessary to implement new business models (Gupta & Bose, 2018;Karimi & Walter, 2016).A 2018 survey of small and medium-sized enterprises in Malaysia revealed that infrastructure obstacles, lack of access to digital skills, and regulatory and administrative burdens are among the obstacles faced by SMEs (SME Corp. Malaysia, 2018).According to the Department of Statistics Malaysia (2021), there are a number of approved business continuity plans for SME entrepreneurs.Among these are the Tekun Business Recovery Scheme (TBRS) and the Penjana SME Financing (PSF), which encourage SME entrepreneurs to participate in the transition to the digital economy.This RM11.1 billion plan aids 23,637 small and medium-sized enterprises supervised by Bank Negara Malaysia (BNM).In general, digitization among SME entrepreneurs can assist businesses, particularly SMEs, in becoming more competitive, sustainable, and profitable during the global pandemic (Malaysia Digital Economy Corporation (MDEC), 2021).According to the findings of Cenamor et al (2019) study, in order to compete with larger businesses, small and medium-sized enterprises (SME) entrepreneurs must improve their planning in order to better utilize the resources and capabilities of digital platforms.In fact, the use of technology that is accurate and suitable for Industry 4.0 will benefit the organization by aiding in the mapping of its next growth phase.The digital platform emphasizes not only e-commerce platforms but also the digitization of business processes and the most recent innovations.This study focused on the digital platform applications used by entrepreneurs, such as Facebook, Instagram, and other social media platforms.Women-owned small and medium-sized enterprises are among the entrepreneurs impacted by the transition to the digital economy.According to Department of Statistics Malaysia (2017), 187,265 women-owned businesses contributed 20.3 percent of the national economy in 2015.In actuality, the gross output and value-added created by women-owned enterprises totaled RM85 billion (3.4%) and RM39.6 billion (4%) respectively.As key stakeholders in the national economic sector, women-owned SMEs must utilize digital platforms to align with the government's digital goals detailed in the National Economic Recovery Plan (PENJANA) (Prime Minister's Office, 2020).In fact, according to the findings of a study by Hamzah and Othman (2020), the mastery of digital skills is crucial for women-owned SMEs to gain a competitive advantage.This indicates that the use of digital platforms by SME entrepreneurs can assist their businesses in becoming more competitive, sustainable, and profitable (Malaysia Digital Economy Corporation (MDEC), 2021).However, the use of digital platforms in businesses remains low, particularly in developing nations (Cenamor et al., 2019).In addition, the majority of micro and small businesses are still unable to maximize the use of digital platforms, and some are unmotivated to use digital platforms for business purposes (Atnafu et al., 2018).Hence, this research aims to conduct a comprehensive literature analysis on digital platforms, focusing specifically on the Unified Theory of Acceptance and Use of Technology (UTAUT) model.The objective is to identify the key determinants that have a major influence on the intention of small and medium-sized enterprise (SME) entrepreneurs to adopt and utilize digital platforms.

Literature Review Digital Platform
A digital platform is a socio-technical group within an organization that comprises technical elements such as software, hardware, processes, and standards (Tilson et al., 2012).A digital platform, as defined by Ghazawneh and Henfridsson (2015), is a software-based platform that provides basic functions shared by modules and interfaces that interact with one another.This digitization will completely transform business processes, including advertising, customer relations, transactions, payments, order taking, services, and feedback, into a digital environment.From another perspective, reliance on digital platforms alone to improve the performance of an organization is still not sufficient, because according to Salleh et al (2017), companies need more detailed changes.In particular, the digital platform approach as a complex transformation is able to influence the backbone of an organization.With that, the capabilities of digital platforms need to focus on technology networks that involve drastic changes in the use of internal and external organizational resources (Mikalef & Pateli, 2017).In addition, the literature review found that digital platform capabilities do not necessarily boost company performance directly but with dynamic capabilities (Kroh et al., 2018) due to its nature that changes in parallel with current technological changes.This relationship is very appropriate in the context of SME entrepreneurs because the digital platform approach is in line with the digital initiatives outlined by the government in the National Economic Revitalization Plan (PENJANA) (Prime Minister's Office, 2020) as well as the tendency of SME entrepreneurs towards technology networks (Shu et al., 2018).Ajzen (1991) states that behavioral intention is the most reliable indicator of future actions and serves as a preliminary step leading to real-world usage.In alignment with the UTAUT model, the inclination to embrace and employ digital platforms among SME women entrepreneurs is influenced by various factors.These encompass performance expectations, effort expectations, the impact of social influence, and the presence of facilitating conditions (Venkatesh et al., 2003).The study conducted by Effendi et al (2020) found that the intention to adopt social media by SMEs in Indonesia is notably affected by several key factors, including the technological context, organizational context, environmental context, and social media awareness.The stronger the behavioral intention, the more likely an individual is to engage in the desired technology-related behavior.Venkatesh et al. (2003) posit that a stronger behavioral intention is a significant predictor of actual technology usage, highlighting the central role of intention in the adoption process.

Performance Expectancy and Behavioral Intention
Performance expectancy is the perceived benefit of using a new technology to complete a task (Venkatesh et al., 2003).This refers to the degree to which technology can improve user performance.Performance expectancy is one of the most reliable predictors of technology usage (Khazaei, 2016).This variable has a significant impact on the intention to use technology, according to (Moghavvemi et al., 2016).Due to digital business opportunities, small and medium-sized business (SME) owners can operate their companies more efficiently.Adam et al (2016) found that SME entrepreneurs' intentions to use e-commerce are influenced by their expectations of performance.On the other hand, they found that performance expectations did not have a significant correlation with the intention to use social media to improve business.Surachim et al (2018) validated the hypothesis among college students who aspire to enter the entrepreneurial field.Handoko (2020) demonstrates that the behavioral intentions of students studying entrepreneurship are influenced by performance expectancy.Contrary to the findings of Rozmi et al (2019), performance expectancy has little influence on SME entrepreneurs' intentions to adopt the latest technology in their businesses, despite their awareness of the benefits of this technology.It could be due to a lack of enthusiasm for using technology in their daily lives and a preference for employing traditional methods.Based on these considerations, this research proposes the following hypothesis H1: Performance expectancy has a positive effect on behavioral intention.

Effort Expectancy and Behavioral Intention
Effort expectancy is the user's perception of the amount of effort required to perform a behavior.The more user-friendly a technology is, the greater the intent to employ it.Effort expectancy is related to technological efficiency (Venkatesh et al., 2003).Several factors influence the efficiency of a small-to medium-sized business owner's use of digital business applications.Moreover, entrepreneurs of small and medium-sized enterprises can utilize digital platforms to enhance their business strategies.According to Yaseen and Qirem (2018), this variable was adapted and developed from the ease-of-use variable created by (Davis, 1989;Moore and Benbasat, 1991;Thompson et al., 1995).Al Mursalin (2012) confirmed the positive impact of the four direct constructs proposed by the UTAUT model on the adoption behavior of information technology among Bangladeshi entrepreneurs.In contrast, Mugambe's (2017) research revealed that SME entrepreneurs in Uganda who use mobile money services place the least importance on effort expectancy.Based on these considerations, this research proposes the following hypothesis H2: Effort expectancy has a positive effect on behavioral intention.

Social Influence and Behavioral Intention
Social influence has been defined as a person's perception of the significance of other people's opinions regarding the use of a particular product or technology (Venkatesh et al., 2003).It is demonstrated that social influence is a significant factor in the adoption of specific innovations (Khazaei and Khazaei, 2016).Then, according to Yaseen and El Qirem (2018), this construct is adapted from Ajzen's subjective norm construct 1991.A digital business is a business that utilizes technology for sales and daily operations.One can gain social influence from family, peers, and coworkers.There are numerous observations made by colleagues and the community regarding digital business.In addition, it can be stated that the positive attitude of innovators and early adopters toward digital business will increase the community's adoption of this technology.According to Abu (2016), in the SME food industry, in addition to social influence, expectations of effort and performance have a significant impact on behavioral intentions to use technology.Moreover, according to Rozmi et al. (2019), social influence significantly affects the intention to use ICT due to the encouragement of business partners, investors, stakeholders, and other entrepreneurs they interact with.Based on these considerations, this research proposes the following hypothesis H3: Social influence has a positive effect on behavioral intention.

Facilitating Conditions and Behavioural Intention
Facilitating conditions and behavioral intention are crucial constructs within technology adoption and acceptance literature.Facilitating conditions, as proposed in models represent the perceived availability of necessary resources and support for technology use.Research consistently shows that individuals are more inclined to exhibit a positive behavioral intention, such as adopting and using new technology, when they perceive that the facilitating conditions are favorable and that they have access to essential tools, training, and assistance (Venkatesh et a., 2012).This relationship underscores the significance of a supportive technological infrastructure and organizational environment in shaping individuals' intentions to embrace and employ technology.Understanding the interplay between facilitating conditions and behavioral intention is essential in explaining the dynamics of technology adoption and can inform strategies to enhance technology acceptance in various contexts.According to Dutta and Shivani (2020), facilitating conditions have a significant positive influence on behavioral intention.This means that when women entrepreneurs have access to the necessary resources and infrastructure, it increases their intention to adopt and use ecommerce technology.Similarly, a study conducted by Abushakra and Nikbin (2019) found that facilitating conditions have a significant positive influence on behavioral intention in the context of e-commerce adoption by women entrepreneurs.H4: Facilitating conditions have a positive effect on behavioral intention.Figure 1 depicts the framework of the study, which will be used to examine the factors that influence women-owned SME entrepreneurs with regard to their behavioral intention to use digital platforms in their business operations.

Research Methodology Sample and Data Collection
An online survey was used to collect data.The survey was distributed to women entrepreneurs in Kedah using social media such as WhatsApp, Facebook, and Instagram Direct.The total population of women entrepreneurs in Kedah is 11,812 out of 187,265 women SME in Malaysia (Department of Statistics Malaysia, 2017).Purposive sampling was used to draw out the samples from the targeted population.The sample selection criteria decided included, (a) SME businesses registered with the Companies Commission of Malaysia (Suruhanjaya Syarikat Malaysia, SSM) (b) business owners are women, and (c) business locations in the state of Kedah.Following Hair et al (2022) recommendation, this study used G-power developed by Faul et al (2009) to determine the minimum required sample size.As demonstrated in Memon et al (2020) work, the number of predictors was counted based on the rule of the maximum arrows pointing to one variable in the model.In this study, there were three arrows pointed at behavioral intention.G*Power demonstrates that the minimum sample size required for this study is 77 (effect size = 0.15, α = 0.05, power = 0.80).

Measures
The questionnaire consisted of Sections A and B. Section A comprised a demographic profile of respondents.Part B consists of 21 items used to assess all variables.Five items are used to measure performance expectancy, while twenty items are used to measure the variables of effort expectancy, social influence, facilitating conditions, and behavioral intention, with four items measuring each variable.The items in Section B were adapted from Venkatesh et al (2003).All items for all dependent variables and independent variables in this research were measured using a five-level Likert scale (1 = strongly disagree to 5 = strongly agree).The demographic section of the response is measured using nominal and ordinal scales.In total, 440 usable responses were received by February 2023.The PLS-SEM method using SmartPLS software (Ringle et al., 2022) was used to analyze the research data.This technique provides simultaneous evaluations of the relationships between variables, while minimizing measurement errors and maximizing prediction accuracy (Šiška, 2018).

Data Analysis and Results
The study used descriptive analysis to characterize the demographics of the respondents, with a specific emphasis on percentage analysis.The findings of this study indicate that a significant proportion of participants fall into the age brackets of 27-33 and 34-39, collectively accounting for about 60% of the total sample.The predominant educational attainment among the respondents is STPM/ASASI/Diploma, accounting for 34.5% of the sample.This is followed by SPM at 28.6% and Bachelor's Degree at 30%.The proportion of individuals with a primary level of education, as measured by the PMR indicator, is the lowest at 1.6%.Additionally, there exists a relatively modest percentage of individuals with varying degrees of education, amounting to 5.2%.In general, a significant proportion of participants were found to be in a marital relationship (79.1%), whereas a smaller percentage identified as single (18.4%) or single mothers (2.5%).In terms of prior employment information, it is noteworthy that the government sector exhibits the highest proportion, accounting for 46.4% of the total.The food industry is the largest business category, accounting for 45.7% of the market share.It is followed by service-related businesses, which make up 15% of the market.Beauty and health items occupy 13% of the market, while apparel firms hold a 10% share.The remaining 16.4% is comprised of various other company categories.The category of fewer than three years was identified as the most prominent business time among the respondents, comprising 41.4% of the total.Regarding the provision of financial resources, the empirical evidence indicates that 28% of the participants in the study express a willingness to acquire loans for entrepreneurial endeavors.However, it is deemed more advantageous to utilize one's personal financial means in the operation of their business.Ultimately, the data reveals that Facebook is the prevailing platform utilized by the participants, with a majority of 45%.The platforms that follow in popularity are WhatsApp, accounting for 27.5% of users, Instagram with a 5% share, and a combination of other platforms such as Twitter, LinkedIn, TikTok, and other social networking and messaging applications, which collectively make up 22.5% of users.

Assessment of the Measurement Model: Construct Validity
According to Hair et al (2014), the assessment of construct validity concerns the extent to which a measurement effectively captures the desired construct.The absence of an evaluation of construct validity might significantly affect the overall validity of the study's results.Convergent and discriminant validity are two fundamental indicators that should be utilized while evaluating the construct validity.Based on the research conducted by Byrne (2016), the results presented in Table 1 demonstrate that all constructs examined in the study satisfied the requirements for convergent validity.The validity of this assertion was substantiated by the factor loadings of all indicators surpassing the threshold of 0.5.Furthermore, it was observed that the constructs' average variance extracted (AVE) values above 0.5, as documented by Ramayah et al. (2018).The composite reliability values for all constructs examined in the study satisfied the minimum threshold of 0.7, as stipulated by (Hair et al., 2019).Subsequently, in order to establish that the measurement variables are not interrelated or exhibit only a restricted correlation, a discriminant validity test was conducted employing a heterotrait-monotrait (HTMT) ratio.The HTMT is computed using the multitrait-multimethod matrix (Henseler et al., 2015).Based on the results reported in the study conducted by Henseler et al (2015), it is recommended that the HTMT should not exceed a threshold of 0.9 in order to demonstrate favorable discriminant validity.Based on the findings shown in Table 2, it can be observed that the HTMT values consistently fall below the threshold of 0.9 across all instances.The highest recorded value is 0.749, while the lowest value stands at 0.666.The findings indicate that the test demonstrates satisfactory discriminant validity.In general, the measuring model assessments in this study were deemed satisfactory.

Assessment of the Structural Model: Hypothesis Testing
After completing the measurement model evaluation and confirming that the values fit all of the requirements, the study progressed to test the hypothesized relationships in the structural model assessments by executing the bootstrapping procedure (see Figure 2).Recommended settings to run the procedure were applied.Whereby, 5,000 sub-samples, 0.05 significance level, and confidence interval method of bias-corrected and accelerated (Hair et al., 2022) were set.The complete reporting of structural model results includes collinearity assessment, path coefficients (β-value), significance testing, effect size (f 2 ), model's explanatory power (R 2 ), and model's predictive power (Q 2 predict).3 showed that VIFs were less than 2.552, which indicated that collinearity was not a threat to the structural model results (Diamantopoulos & Siguaw, 2006).Table 4 shows that all hypotheses were supported with t-values more than 1.65 in the one-tailed test.Besides, the lower bound and upper bound of the bias-corrected confidence interval did not contain a zero value.The strongest and most significant relationship was performance expectancy and behavioral intention (β = 0.327), followed by facilitating conditions and behavioral intention (β = 0.200), effort expectancy and behavioral intention (β = 0.188), and the least significant relationship was social influence and behavioral intention (β = 0.166).According to Sarstedt and Mooi (2019), R 2 values of 0.67, 0.33, and 0.19 suggest strong, moderate, and weak explanatory power, respectively.The research model demonstrated moderate explanatory power for behavioral intention (R 2 = 0.596).Meanwhile, Cohen (1988) suggested effect size, f 2 , of 0.02, 0.1,5, and 0.35 as small, medium, and large, respectively.The effect size of this research was categorized as small (i.e.ranging from 0.029 to 0.104).Finally, the structural model assessment was concluded with the reporting of PLSpredict results.PLSpredict is a procedure used to produce out-of-sample prediction or better known as the model's predictive power (Hair et al., 2019).It is important to note that PLSpredict results should only be interpreted for a key endogenous construct which is behavioural intention in this study.As a guideline, Q 2 values should be larger than zero for a specific endogenous construct to indicate the predictive accuracy of the structural model for that construct.Table 5 revealed that the structural model in this study had a moderate predictive power with all indicators of the key endogenous construct (i.e.behavioral intention) exhibiting the same number of PLS-SEM RMSE values compared to LM RMSE values (Hair et al., 2019;Shmueli et al., 2019).In addition, Q 2 predict values at both levels, indicator and latent were above 0, suggesting that the interpretation of RMSE comparisons between PLS-SEM and LM was accurate.

Discussion
The findings of this study offer strong empirical support for the propositions that performance expectancy, effort expectancy, social influence, and facilitating conditions exert a considerable impact on the behavioral intention of female entrepreneurs in small and medium-sized enterprises (SMEs) in the state of Kedah with regards to their use of digital platforms.The findings suggest that there is a noteworthy impact of performance expectancy on behavioral intention.This finding implies that female entrepreneurs who have a greater perception of positive performance consequences, such as heightened productivity, better efficiency, and improved company outcomes, are more inclined to demonstrate a stronger propensity to utilize digital platforms.These findings are consistent with the hypothesis that individuals are more inclined to embrace technology when they see distinct advantages and favorable outcomes linked to their utilization.Additionally, the data demonstrates that effort expectancy exerts a substantial influence on behavioral intention.Female entrepreneurs who regard digital platforms as user-friendly and requiring low work are more likely to demonstrate a higher inclination towards utilizing them.The aforementioned statement highlights the significance of user-friendliness and operational simplicity in facilitating the acceptance and utilization of digital platforms among female entrepreneurs.When platforms are viewed as being easy to use and convenient, there is a higher likelihood that they will be accepted and incorporated into business operations.Furthermore, the findings indicate that social influence has a substantial impact on individuals' behavioral intentions.The perspectives, suggestions, and firsthand encounters of peers, coworkers, and social networks exert a substantial influence on the inclination of women entrepreneurs to adopt digital platforms.This finding indicates that the presence of social networks and interpersonal ties significantly impacts the extent to which women entrepreneurs in Kedah adopt and embrace digital platforms.The establishment of a conducive entrepreneurial ecosystem that promotes collaboration, the exchange of knowledge, and the adoption of desirable social norms have the potential to augment the perceived worth and legitimacy of digital platforms.Consequently, this can have a beneficial impact on individuals' behavioral intentions towards these platforms.Lastly, the findings indicate that facilitating conditions exert a noteworthy influence on behavioral intention.This implies that the desire of women-owned small and medium-sized enterprise (SME) entrepreneurs to adopt and utilize digital platforms would be enhanced provided they have access to the required resources and infrastructure.
In general, the findings of this study offer robust evidence in favor of the hypotheses and underscore the significance of performance expectancy, effort expectancy, social influence, and facilitating conditions in influencing the behavioral intention of female entrepreneurs in small and medium-sized enterprises (SMEs) in the state of Kedah to adopt digital platforms.The aforementioned discoveries enhance comprehension of the variables that impact the adoption of platforms and can provide insights for developing strategies and interventions to encourage the proficient utilization of digital platforms by female entrepreneurs in Kedah.
Ultimately, this will enhance their business achievements and economic empowerment.

Conclusion
In summary, this research study presents substantial evidence that supports the hypotheses positing that performance expectancy, effort expectancy, social influence, and facilitating conditions exert a significant influence on the behavioral intention of female entrepreneurs in small and medium-sized enterprises (SMEs) in the region of Kedah with regards to their adoption of digital platforms.The implications of these findings extend to policymakers, business support organizations, and digital platform developers.They should consider these findings when designing interventions and strategies aimed at promoting the adoption and effective utilization of digital platforms by women entrepreneurs.Ultimately, these efforts can contribute to the growth and success of women-owned businesses.