Digital Franchising in the Age of Transformation: Insights from the Motivation-Opportunity-Ability Framework

Abstract

Digital franchising is increasingly recognized as a technological advancement and a specialized subset of e-commerce, yet its unique entrepreneurial dynamics remain insufficiently explored in the existing literature. Previous studies have primarily focused on platform usability or general e-commerce adoption, often overlooking the motivational, contextual, and capability-based factors that influence individuals’ willingness to engage in digital franchising as either entrepreneurs or consumers. To address this research gap, the present study applies the Motivation-Opportunity-Ability (MOA) framework to examine how personal motivations (e.g., self-expression, financial rewards), perceived platform opportunities (e.g., support, attractiveness), and individual capabilities (e.g., digital literacy, self-efficacy) shape entrepreneurial intention and, in turn, influence consumption adoption intention in digital franchising environments. An online survey was conducted using a non-probability purposive sampling method. The final sample consisted of 491 respondents from Taiwan, all of whom were either entrepreneurs operating digital franchises in the fashion industry or consumers who had purchased fashion products through digital franchising platforms, thereby ensuring contextual relevance to the study’s focus. Data were analyzed using structural equation modeling (SEM). The results indicate that expected external rewards (β = 0.456, p < 0.001) and platform support (β = 0.315, p < 0.001) are the most influential factors in shaping entrepreneurial intention. Furthermore, entrepreneurial intention significantly mediates the relationship between MOA antecedents and consumption adoption intention (β = 0.176, p < 0.001), highlighting its role as a key behavioral mechanism. These findings extend the MOA framework to a new empirical setting and offer practical implications for platform developers, franchisors, and policymakers seeking to promote participation in digital franchising. Future research is encouraged to explore cross-industry comparisons, generational differences, and longitudinal approaches to further enrich the understanding of digital franchising adoption dynamics.

1. Introduction

Micro-entrepreneurship in Taiwan has grown significantly over the past decade, a trend largely driven by the rapid development of digital technology, which has effectively lowered barriers to market entry. As of September 2024, Taiwan’s Ministry of Economic Affairs reported a record 78,000 franchise businesses nationwide, with over 80% operated by small teams of fewer than ten people [1]. Furthermore, according to the latest Taiwan Chain Stores Census conducted by the Taiwan Chain Stores and Franchise Association (TCFA), the total number of franchise headquarters in Taiwan reached 2984 in 2023, reflecting a 2.1% increase from the previous year, while the total number of chain stores increased to 122,610, representing a 1.2% growth with 1448 new stores added. This data underscores the ongoing expansion of franchise businesses in Taiwan, with digital franchising playing an increasingly critical role in this growth. The proliferation of franchise businesses aligns with broader trends observed globally, where the adoption of digital tools such as big data analytics, AI-driven operational systems, and cloud-based management solutions has empowered entrepreneurs to optimize resource allocation, improve customer engagement, and scale their businesses efficiently. For instance, automation and IoT technologies in the food tech sector have transformed traditional franchise operations, enabling entrepreneurs to streamline processes, reduce costs, and enhance service quality [2]. The integration of information and communications technology (ICT) into franchising has transformed traditional models reliant on physical storefronts, standardized operations, and location-based management into digitally driven ecosystems [3]. Digital franchising leverages group purchasing and e-commerce, reducing the financial burden of brick-and-mortar operations while enabling franchisees to capitalize on brand equity through structured and scalable online sales systems. Notably, Fora Travel exemplifies this model, allowing independent travel consultants to operate as franchisees without the overhead costs of a traditional agency while benefiting from brand credibility and operational support. This digital transformation has also reshaped logistics-oriented franchises, where data-driven technologies and centralized data systems enhance supply chain management and support operational continuity across diverse business environments. These systems enable organizations to derive actionable insights, improve responsiveness, and optimize decision-making efficiency. As such, digital transformation constitutes a dynamic capability that reinforces both resilience in times of disruption and competitiveness in stable operational contexts [4]. The evolution of digital franchising models has enabled businesses to integrate customer data for precision marketing, real-time decision-making, and cost reduction, ultimately expanding market reach [5,6]. Research further highlights the strategic advantages of digital marketing platforms and virtual storefronts in enhancing brand visibility, driving customer engagement, and fostering long-term franchise growth [7]. Research underscores the impact of digital franchising applications in expanding market penetration, enhancing brand recognition, and equipping franchisees with comprehensive digital infrastructure [4]. In Taiwan, the digitalization of franchising offers strategic advantages, including localized marketing automation, AI-powered virtual storefronts, and predictive analytics, enabling businesses to adapt to dynamic and competitive market environments [8].

The Motivation-Opportunity-Ability (MOA) framework has emerged as a comprehensive and robust theoretical tool for analyzing adoption behaviors across various domains, including entrepreneurship, technology integration, and franchise systems. By delineating the key elements of motivation, opportunity, and ability, the MOA framework facilitates an in-depth understanding of how internal and external factors converge to influence decision-making processes. For example, studies have demonstrated how higher levels of perceived utility and resource accessibility positively correlate with increased entrepreneurial engagement and willingness to innovate [9]. In smart retail environments, MOA-based research revealed that opportunity factors, such as retailer support and social influence, play a moderating role, amplifying the impact of motivation on adoption behaviors [10]. Similarly, studies on virtual communities highlight that the interplay of motivation and ability directly enhances consumption adoption intention, offering a model for understanding entrepreneurial ecosystems in digital settings [11].

In the context of franchise adoption, the MOA framework has proven equally valuable. Studies on franchise systems have utilized this model to understand how potential franchisees evaluate and decide to join specific networks [12,13,14]. Motivation, such as the pursuit of financial stability or leveraging an established brand, is often cited as a primary driver of franchise adoption [4]. Opportunity factors, including franchisor support systems, market access, and digital tools, enhance the attractiveness of franchise models by mitigating risks and reducing operational complexities [4,15]. Meanwhile, the ability to effectively manage a franchise encompassing business acumen, training, and familiarity with franchising operations is crucial for ensuring long-term success [10].

Nonetheless, despite the growing importance of digital franchising as a hybrid between traditional franchising and online commerce, current research remains disproportionately focused on technological infrastructure and platform usability [16,17]. The existing literature offers limited insights into the entrepreneurial intentions that underpin digital franchise adoption—particularly how motivation, opportunity, and ability interrelate to drive engagement in such platforms. While prior studies have examined user interface design or consumer-side adoption intentions, the entrepreneurial decision-making processes involved in digital franchising adoption have not been adequately addressed [18]. Furthermore, how entrepreneurial intention acts as a bridge between these antecedents and actual consumption behaviors within digital franchising ecosystems remains poorly understood. To address these gaps, this study employs the MOA framework to explore two central research questions: (1) How do motivation, opportunity, and ability influence entrepreneurial intention toward digital franchising platforms? (2) To what extent does entrepreneurial intention mediate the relationship between these antecedents and individuals’ consumption adoption intentions? By applying this framework within Taiwan’s fashion industry—an ideal empirical context given its digital fluency, brand focus, and entrepreneurial dynamism—this research aims to contribute both theoretical and practical insights into the behavioral mechanisms driving digital franchise adoption.

The remainder of this paper is structured as follows: Section 2 reviews the literature on digital franchising, entrepreneurial intentions, and MOA-based theoretical applications. Section 3 outlines the research design, data collection, and analytical methods. Section 4 presents findings from confirmatory factor analysis (CFA) and structural equation modeling (SEM). Section 5 discusses theoretical contributions, managerial implications, and future research directions.

2. Literature Review and Hypothesis Development

2.1. The Rise and Advantages of Digital Franchising

Traditional franchising, characterized by its dependence on physical infrastructure and standardized operations, is undergoing a paradigm shift driven by digital transformation. Digital franchising leverages advanced technologies such as e-commerce, artificial intelligence (AI), big data analytics, and the Internet of Things (IoT) to transcend geographical limitations, streamline operational efficiencies, and enable scalable business models. For instance, logistics-oriented franchises that adopt data-driven digital transformation approaches such as centralized data systems have enhanced their supply chain management and operational continuity across varying business conditions. These technologies enable organizations to derive actionable insights and continuously adapt, positioning digital transformation as a dynamic capability that strengthens responsiveness and operational efficiency in both stable and turbulent environments [4]. These technologies integrate customer data for precision marketing and real-time decision-making, reducing operational costs and increasing market reach [6,19]. Furthermore, digital franchising lowers entry barriers for potential franchisees by reducing reliance on physical assets and enabling remote operations, making franchising accessible to a broader demographic of entrepreneurs [20]. It enhances agility and responsiveness, allowing franchises to adapt quickly to market changes through data-driven insights and automation [3]. Digital tools also improve franchisor–franchisee communication and collaboration, enabling real-time support, training, and performance tracking critical for long-term success [21]. Additionally, digital franchising supports personalized marketing strategies through advanced analytics, improving customer retention and satisfaction [17], while fostering sustainability by minimizing the environmental footprint of traditional operations, such as reducing the need for physical stores and streamlining inventory management [16]. By facilitating dynamic, data-driven interactions with consumers, digital franchising exemplifies the synergy between technological innovation and entrepreneurship, providing cost efficiency, scalability, and adaptability that position it as a transformative model for modern business growth [22].

2.2. Motivation-Opportunity-Ability (MOA) Framework

The Motivation-Opportunity-Ability (MOA) framework has emerged as a comprehensive and robust theoretical tool for analyzing adoption behaviors across various domains, including entrepreneurship, technology integration, and franchise systems [23]. By delineating the key elements of motivation, opportunity, and ability, the MOA framework facilitates an in-depth understanding of how internal and external factors converge to influence decision-making processes. Motivation, opportunity, and ability core constructs of the MOA framework jointly explain how individuals engage with digital franchising. Motivation encompasses both intrinsic and extrinsic drivers, such as profit-seeking and self-fulfillment [24]; opportunity reflects external enablers like technological infrastructure and policy support that lower entry barriers [25]; and ability refers to the skills, knowledge, and resources required to effectively operate within a digital franchise system [26]. Integrated within a unified framework, these factors help explain not only why individuals intend to pursue digital franchising but also how they translate that intention into action. For managers and franchisors, applying this framework offers practical value: it identifies which psychological, contextual, or capability-related levers should be enhanced—such as platform design, reward structures, or digital training—to increase franchisee engagement and operational success. Although the MOA framework has been extensively employed to examine user adoption behaviors in technology-intensive and retail-oriented contexts [27], its application within the specific domain of digital franchising, particularly as an entrepreneurial pathway, remains underexplored. The existing literature tends to investigate the three MOA components in isolation, often emphasizing general adoption mechanisms rather than capturing the complex, interrelated factors that influence entrepreneurial engagement with digital franchise platforms. This study extends the MOA framework by positioning it as a comprehensive theoretical foundation to bridge the conceptual gap between the formation of entrepreneurial intention and actual adoption behavior in digital franchising environments. Specifically, it conceptualizes how individual motivations (e.g., the desire for self-expression and expected external rewards), perceived platform attributes (e.g., operational support and brand attractiveness), and individual capabilities (e.g., digital literacy and entrepreneurial self-efficacy) interact to shape entrepreneurial intention, which in turn influences subsequent consumption adoption behavior. This theoretical approach directly addresses the research gap outlined in Section 1, offering a holistic understanding of the behavioral antecedents that drive participation in digitally mediated franchise ecosystems. In smart retail environments, MOA-based research revealed that opportunity factors, such as retailer support and social influence, play a moderating role, amplifying the impact of motivation on adoption behaviors [10]. Similarly, studies on virtual communities highlight that the interplay of motivation and ability directly enhances Consumption Adoption Intention for Digital Franchising, offering a model for understanding entrepreneurial ecosystems in digital settings [11].

Building on the MOA framework, this study advances a set of conceptual assumptions that are empirically examined. With respect to motivation, it is proposed that individuals who exhibit a strong desire for self-expression and anticipate meaningful external rewards such as financial gain or recognition are more likely to develop entrepreneurial intentions toward digital franchising platforms. This proposition aligns with the existing literature emphasizing the role of both intrinsic (e.g., self-fulfillment) and extrinsic (e.g., material incentives) motivational drivers in shaping entrepreneurial engagement in digitally mediated environments [28,29]. In the context of opportunity, the study conceptualizes platform support and attractiveness as perceived environmental enablers that lower perceived barriers and enhance entrepreneurial confidence. Prior research indicates that platforms characterized by high usability, comprehensive support, and value-added features can foster entrepreneurial participation by reducing operational uncertainties and enhancing perceived feasibility [30,31]. Accordingly, it is hypothesized that greater perceived opportunity positively influences entrepreneurial intention. The ability dimension is operationalized through two core constructs: self-efficacy and digital literacy. Self-efficacy refers to an individual’s confidence in their capacity to perform entrepreneurial tasks effectively and has been consistently identified as a critical determinant of success in technology-driven ventures [32,33]. Digital literacy, by contrast, reflects the entrepreneur’s capability to harness digital tools and platforms for innovation, communication, and operational efficiency [34]. Both elements are posited to exert direct and positive effects on engagement with digital franchise platforms. Lastly, the model posits that entrepreneurial intention functions as a mediating mechanism that channels the effects of motivational, opportunity, and ability-related antecedents into actual behavioral outcomes. This mediation perspective is grounded in well-established theories of behavioral intention and innovation adoption, which recognize intention as a pivotal construct in the transition from attitudinal readiness to action [35,36]. By integrating these dimensions, the research provides a comprehensive understanding of the factors influencing entrepreneurial engagement with digital franchising platforms.

2.3. Motivation: Desire for Self-Expression and Expected External Rewards

The concept of self-expression was first introduced by [37] in The Presentation of Self in Everyday Life. It refers to the process by which individuals manage and control the impressions others form of them, often with the expectation of receiving rewards such as recognition, respect, or financial benefits. In this study, the desire for self-expression is defined as the degree to which individuals aim to project a desired image through entrepreneurial activities on digital franchising platforms. Entrepreneurs with a high desire for self-expression are likely to view digital franchising as a means of showcasing their abilities and desired image to others. When this desire is strong, it can significantly stimulate entrepreneurial intentions, reflecting their motivation to engage in digital franchising to fulfill their self-presentation goals. Similarly, expected external rewards refer to the tangible and intangible benefits that individuals anticipate receiving through their entrepreneurial engagement, such as financial returns, social recognition, and career advancement opportunities [28,38]. In the context of digital franchising, these rewards are particularly salient as platform-based business models often promise scalable income potential, broader brand exposure, and access to entrepreneurial networks. Entrepreneurs who perceive digital franchising as a viable pathway to attaining such outcomes are more likely to exhibit stronger entrepreneurial intentions. The anticipation of external rewards thus not only reinforces performance expectations but also enhances the perceived value of participation in digitally mediated franchise ecosystems. Based on this reasoning, the following hypotheses are proposed:

H1: 

Desire for self-expression positively influences entrepreneurial intentions toward digital franchising platforms.

H2: 

Expected external rewards positively influence entrepreneurial intentions toward digital franchising platforms.

2.4. Opportunity: Platform Support and Platform Attractiveness

In the opportunity dimension, external environmental or contextual factors play a significant role in shaping entrepreneurial intentions. Even if entrepreneurs possess the necessary skills and motivation, a lack of support or unfavorable conditions on a digital franchising platform can deter their entrepreneurial aspirations. Platform support refers to the extent to which a digital franchising platform provides the necessary technical, informational, and operational resources to facilitate entrepreneurial activity. This includes features such as a user-friendly interface, ongoing technical assistance, business training modules, interactive communities, and reliable transaction management systems. In the context of digital franchising, platform support plays a critical role in reducing operational uncertainty and enhancing entrepreneurial confidence by ensuring that franchisees have access to the tools and guidance needed to manage their businesses effectively. When entrepreneurs perceive the platform as responsive, informative, and well-equipped to support their needs, they are more likely to develop stronger entrepreneurial intentions and sustained engagement with the platform [39,40]. Additionally, platform attractiveness defined as the degree to which the platform’s unique features and offerings spark entrepreneurial interest also plays a pivotal role. To effectively distinguish between the two opportunity-related constructs, this study conceptualizes platform support as the extent to which a digital franchising platform provides functional assistance—such as technical tools, user guidance, and operational resources that reduce complexity and enhance entrepreneurial confidence. In contrast, platform attractiveness refers to the perceived value and appeal of the platform based on its brand reputation, visual interface, and unique offerings that spark entrepreneurial interest. For example, trademark protections and distinctive platform features such as exclusive branding, customer base size, or curated marketplace benefits contribute to the perceived attractiveness of the platform by signaling trustworthiness and strategic advantage [41,42]. While platform support facilitates operational feasibility, platform attractiveness enhances the perceived desirability of participation. These two constructs, though interrelated, exert distinct influences on entrepreneurial intention and are therefore treated as separate but complementary antecedents within the MOA framework. When entrepreneurs perceive a high level of platform attractiveness, their entrepreneurial intentions are further strengthened. In addition, strong platform support such as accessible technical assistance, clear operational tools, and ongoing guidance helps reduce uncertainty and builds confidence. This supportive environment makes it easier for entrepreneurs to engage with the platform, thereby increasing their intention to adopt digital franchising. Accordingly, the following hypotheses are proposed:

H3: 

Platform support positively influences entrepreneurial intentions toward digital franchising platforms.

H4: 

Platform attractiveness positively influences entrepreneurial intentions toward digital franchising platforms.

2.5. Ability: Self-Efficacy and Digital Literacy

Self-efficacy, first introduced by Bandura [43], refers to an individual’s belief in their capability to execute specific actions required to attain desired outcomes using their existing skills and knowledge. Schwarzer and Warner [44] later expanded this concept, highlighting its applicability across diverse domains, including entrepreneurship. In the context of this study, self-efficacy is defined as the entrepreneur’s confidence in their ability to utilize digital franchising platforms effectively such as managing operations, resolving challenges, and achieving business goals like generating income or building brand presence. This construct captures not only general confidence but also the perceived capacity to accomplish tangible entrepreneurial objectives within a digital platform environment. Entrepreneurs with high self-efficacy are more likely to engage in entrepreneurial activities on digital franchising platforms, as they feel equipped to overcome challenges and achieve their goals [42]. Similarly, digital literacy refers to an individual’s ability to effectively access, evaluate, and utilize digital technologies to perform tasks, solve problems, and communicate within online environments [45,46]. In this study, digital literacy is operationalized as the entrepreneur’s capacity to use digital tools including social media platforms, data analytics, and e-commerce technologies for promoting products, managing operations, and making informed business decisions on digital franchising platforms. Entrepreneurs who demonstrate higher levels of digital literacy are more capable of navigating platform interfaces, optimizing digital marketing strategies, and responding to customer feedback, all of which enhance their entrepreneurial engagement. Prior studies have shown that digital literacy is positively associated with innovation adoption, digital entrepreneurship success, and operational efficiency in technology-enabled business contexts [34,47]. Accordingly, entrepreneurs with greater digital proficiency are more likely to develop stronger entrepreneurial intentions toward digital franchising platforms. Based on this, the following hypothesis is proposed:

H5: 

Self-efficacy positively influences entrepreneurial intentions toward digital franchising platforms.

H6: 

Digital literacy positively influences entrepreneurial intentions toward digital franchising platforms.

2.6. Entrepreneurial Intentions and Consumption Adoption Intention for Digital Franchising

This study posits that entrepreneurial intentions reflect the combined effects of motivation, opportunity, and ability factors in the context of digital franchising. Entrepreneurs are likely to channel their creativity and entrepreneurial aspirations into digital franchising platforms to realize their goals. Entrepreneurial intentions toward digital franchising platforms encompass the desire to express intrinsic and extrinsic motivations, leverage opportunities provided by the platform, and demonstrate the capabilities needed to succeed. Moreover, when entrepreneurial intentions toward a specific digital franchising platform are high, they positively influence the entrepreneur’s usage intentions for that platform [42,48]. Consumption Adoption Intention for Digital Franchising (CAIDF) refers to an individual’s likelihood, confidence, and behavioral intention to engage with and continue using digital franchising platforms for product or service consumption. This construct is conceptually grounded in the digital consumer behavior literature, where sustained engagement with digital platforms is influenced by users’ perceived utility, trust, and interactive experience [49,50]. In the context of franchising, consumer adoption is not only shaped by functional ease-of-use but also by ongoing digital engagement and platform familiarity, which contribute to repeat usage and brand loyalty. CAIDF captures this multifaceted behavioral intention by accounting for both initial intention to engage and long-term adoption orientation within digital franchise environments. In other words, entrepreneurial intentions act as a precursor to the actual adoption and sustained use of digital franchising platforms. Thus, the following hypothesis is proposed:

H7: 

Entrepreneurial intention toward digital franchising positively influences individuals’ intention to use digital franchising platforms for purchasing products and services.

3. Research Methodology

3.1. Research Model

This study examines the relationships between motivational factors (desire for self-expression, expected external rewards), opportunity factors (platform support, platform attractiveness), and ability factors (self-efficacy, digital literacy) in shaping Entrepreneurial Intention in Digital Franchising (EIDF). Furthermore, EIDF is proposed as a mediating variable influencing Consumption Adoption Intention for Digital Franchising (CAIDF). The conceptual framework is designed to evaluate how intrinsic and extrinsic motivations, external platform-related opportunities, and entrepreneurial capabilities collectively drive engagement with digital franchising ecosystems. Figure 1 Research Model.

3.2. Questionnaire Design

A structured questionnaire was developed to quantitatively measure the key constructs of this study, including Desire for Self-Expression, Expected External Rewards, Platform Support, Platform Attractiveness, Self-Efficacy, Digital Literacy, Entrepreneurial Intention in Digital Franchising (EIDF), and Consumption Adoption Intention for Digital Franchising (CAIDF).The scale for Desire for Self-Expression, consisting of four items, was adapted from [37,43], capturing the extent to which individuals aim to showcase their abilities and present a desired image through digital franchising platforms. Expected External Rewards, comprising four items, was adapted from [26,36], assessing the anticipated financial and career development benefits, along with external recognition gained from engaging in digital franchising. Platform Support, measured using five items adapted from [44,48], focused on the adequacy of technical assistance, resources, and user guidance provided by digital franchising platforms. Platform Attractiveness, derived from [51,52], included four items capturing perceptions of platform design, reputation, and unique features that enhance its appeal. Self-Efficacy, assessed through four items based on [39,53], examined individuals’ confidence in their ability to operate successfully within the digital franchising context. Digital Literacy, consisting of three items adapted from [45,46], measured proficiency in utilizing digital tools, social media, data analytics, and technology for business operations. Entrepreneurial Intention in Digital Franchising, measured through three items derived from [54,55], focused on individuals’ determination and plans to engage in digital franchising. Finally, Consumption Adoption Intention for Digital Franchising, adapted from [47,56], consisted of four items assessing the likelihood of consumption adoption intentions with digital franchising platforms and exploring their services. All items were measured using a 7-point Likert scale ranging from “strongly disagree” (1) to “strongly agree” (7). The 7-point scale was selected over the more traditional 5-point alternative to increase response sensitivity and allow for greater variability in participants’ attitudes and perceptions. Prior research suggests that 7-point scales offer improved discriminant validity and psychometric precision, particularly in behavioral and psychological studies where nuanced responses are important for structural equation modeling. This approach enhances measurement reliability and facilitates more accurate modeling of latent constructs.

The second section of the questionnaire collected demographic information including gender, age, education level, and monthly personal income. Monthly income was included to evaluate the respondents’ financial capacity, which may influence their ability and willingness to participate in digital franchising activities. To ensure the validity and clarity of the questionnaire items, a two-stage pre-testing process was conducted. First, an expert validity review was carried out involving a panel of five professionals, each with over ten years of experience in retail or e-commerce platform operations. These experts evaluated the questionnaire for content accuracy, semantic clarity, and contextual relevance. Based on their feedback, minor modifications were made to improve item phrasing and alignment with the digital franchising context. Following the expert review, a pilot survey was administered to a sample of 30 respondents who met the target profile—young adults actively engaged or interested in digital entrepreneurship. The pilot participants completed the refined questionnaire under real testing conditions. Data from the pilot were analyzed using Cronbach’s alpha to assess internal consistency for each construct. The results showed that all constructs met or exceeded the accepted threshold of α = 0.70, indicating strong reliability and supporting the readiness of the instrument for full-scale deployment.

3.3. Sample and Data Collection

This study focuses on the fashion industry as the primary context for investigating digital franchising behavior. The fashion sector is particularly well-suited for digital franchise adoption due to its strong emphasis on brand identity, visual presentation, and fast-moving consumer engagement patterns. Moreover, fashion brands increasingly employ digital tools such as virtual storefronts, influencer-based selling models, and mobile commerce platforms to support decentralized franchise operations. Recent studies have shown that the fashion industry leads in digital transformation through the integration of AI, social commerce, and omnichannel strategies to build franchise scalability and consumer intimacy [57,58]. By selecting this industry, the study ensures relevance to sectors undergoing rapid digitalization and provides a meaningful environment for analyzing how motivational, contextual, and capability-based factors shape entrepreneurial and consumer behavior in digital franchising ecosystems.

Acknowledging the advancements in research methodologies and digital data collection, this study adopted an online questionnaire distribution approach through social media platforms. This transition from traditional survey methods aligns with recent scholarly recommendations highlighting the efficiency and broad reach of digital data collection techniques [59,60]. The survey was disseminated through multiple online channels, including Facebook, Instagram, Line groups, online business communities, and word-of-mouth referrals, to ensure broad accessibility among the target population. A non-probability purposive sampling method was employed, targeting young digital entrepreneurs and potential franchisees. To minimize sampling errors associated with online distribution such as ineligible respondents and duplicate submissions, several quality control measures were implemented. The survey platform restricted IP address duplication, and submission timestamps were reviewed to identify potential irregularities. Furthermore, screening questions were included to ensure that only qualified individuals participated. Specifically, respondents were required to confirm that they had at least six months of experience using digital franchising platforms, either as entrepreneurs or consumers. This inclusion criterion ensured that participants had sufficient familiarity with the digital franchising environment to provide informed responses. To maintain ethical research standards, the first page of the questionnaire provided detailed information about the study’s purpose, data confidentiality, and anonymity assurance, reinforcing participant consent and trust.

To address the research objectives and test the proposed hypotheses, structural equation modeling (SEM) was employed for statistical analysis. The appropriate sample size for SEM is guided by a participant-to-item ratio of 10:1 to 15:1 [61]. Given the 25 items included in the questionnaire, the target sample size was set between 250 and 375 respondents. This study focused on Generation Z as the primary demographic due to their high digital fluency, entrepreneurial mindset, and preference for flexible, technology-driven work models. Prior studies have noted that Gen Z exhibits strong intentions toward online business engagement and is more receptive to digital entrepreneurship models due to their familiarity with social media, digital platforms, and e-commerce ecosystems [62,63]. Digital franchising requiring low upfront capital, offering remote operational models, and providing technological infrastructure aligns closely with the values and behavioral patterns of this generation, making them an ideal focus for exploring entrepreneurial intentions in digital contexts. Data collection took place from May to July 2024, yielding 563 responses, of which 491 valid responses were retained after verification and data cleaning. The demographic composition of the sample, as summarized in

Table 1. Demographic analysis.

Variable	Description	Frequency	Percentage (%)
Gender	Male	273	55.6
	Female	218	44.4
Age	18–22	197	40.1
	23–30	172	35.0
	31–40	74	15.1
	41–50 or older	48	9.8
Level of Education	High school/vocational or below	23	4.7
	Bachelor’s degree or currently studying	392	79.8
	Master’s or above	76	15.5
Monthly Personal Income	Less than TWD 20,000	187	38.1
	TWD 20,001~TWD 40,000	234	47.7
	TWD 40,001~TWD 60,000	39	7.9
	TWD 60,001~TWD 80,000	26	5.3
	Above TWD 80,001	5	1

Note 1: TWD: Taiwan Dollar.

, included 55.6% male and 44.4% female respondents, with 75.2% aged 30 or younger, aligning with the study’s focus on young entrepreneurs. In terms of education, 79.8% held a college or university degree, reflecting a well-educated participant pool. Regarding financial status, 47.7% reported a monthly income between TWD 20,001 and TWD 40,000, indicating moderate financial stability. To assess non-response bias, a wave analysis was conducted by comparing early and late respondents across key demographic and substantive variables. Following the approach suggested by [64], the responses were divided into two waves based on their submission timestamps: the first 25% (early wave) and the last 25% (late wave). Independent-sample t-tests were conducted to examine significant differences between the two groups across variables such as age, gender, education level, income, entrepreneurial intention, and consumption adoption intention. Results revealed no significant differences (p > 0.05), indicating minimal non-response bias and supporting the representativeness of the sample.

3.4. Methods for Data Analysis

This study adopts a quantitative research paradigm, utilizing survey data analyzed with IBM SPSS Statistics 26 and AMOS 24. The analytical approach includes descriptive statistics, reliability analysis, validity assessments, and confirmatory factor analysis (CFA) performed using maximum likelihood estimation within the structural equation modeling (SEM) framework. These techniques are employed to test the proposed hypotheses and evaluate the overall model fit, ensuring robust validation of the research constructs and theoretical framework.

4. Results

4.1. Reliability and Validity

A two-stage analytical approach was implemented to ensure the robustness and validity of the measurement model. In the first stage, confirmatory factor analysis (CFA) was conducted to assess construct validity, followed by the second stage, which tested the structural model within the structural equation modeling (SEM) framework. CFA was employed to examine the relationships between observed variables and their corresponding latent constructs, confirming that indicators appropriately reflected the theoretical dimensions. The analysis included eight sub-constructs: Self-Expression, Expected External Rewards, Platform Support, Platform Attractiveness, Self-Efficacy, Digital Literacy, Entrepreneurial intention in Digital Franchising, and Consumption Adoption Intention for Digital Franchising (CAIDF). Items with standardized factor loadings below 0.50 were removed to improve measurement precision, in accordance with established guidelines for indicator reliability and convergent validity [65]. The CFA process was iterative and utilized modification indices (MIs) to enhance model fit. Model fit was assessed using the Root Mean Square Error of Approximation (RMSEA), with a threshold of ≤0.08 considered acceptable, consistent with recommendations by [66]. Constructs failing to meet this criterion underwent refinement or were modeled as saturated constructs. As a result, Digital Literacy was reduced from four to three items, and CAIDF from five to four items, ensuring psychometric compliance.

To establish internal consistency, convergent validity, and reliability, the study assessed factor loadings, composite reliability (CR), average variance extracted (AVE), and Cronbach’s alpha (α). The results, presented in

Table 2. Results related to factor loading, reliability, and validity.

Variable	Items	Standardized Factor Loading	CR	AVE	Cronbach’s α
Desire for Self-Expression	I aim to showcase my abilities and talents through digital franchising.	0.915 ***	0.921	0.771	0.873
	Achieving my goals through digital franchising provides me with a greater sense of self-worth.	0.872 ***
	Digital franchising offers me an opportunity to demonstrate my leadership skills.	0.783 ***
	I aspire to gain recognition from others by successfully managing a digital franchise.	0.814 ***
Expected External Rewards	I believe participating in digital franchising will provide me with greater financial rewards.	0.923 ***	0.945	0.792	0.813
	Digital franchising offers me a stable financial income.	0.831 ***
	Successfully operating a digital franchise enhances my social status.	0.865 ***
	The success of digital franchising helps me expand my network and increase my social influence.	0.661 ***
Platform Support	I feel that the digital franchising platform provides sufficient technical support.	0.889 ***	0.923	0.812	0.880
	I can obtain the necessary resources (e.g., tools or data) from the digital franchising platform.	0.922 ***
	The digital franchising platform offers continuous business guidance and training.	0.814 ***
	I find the digital franchising platform helpful in solving my entrepreneurial challenges.	0.795 ***
	The platform provides clear processes and guidelines to help me succeed in franchising.	0.684 ***
Platform Attractiveness	I find the design and functionality of the digital franchising platform user-friendly.	0.874 ***	0.822	0.702	0.894
	The platform’s reputation and credibility enhance its attractiveness to me as an entrepreneur.	0.741 ***
	The platform offers unique advantages that attract more customers.	0.698 ***
	I prefer a more attractive digital franchising platform over starting a business independently.	0.789 ***
Self-Efficacy	I am confident in achieving my digital franchising goals, even with limited resources.	0.941 ***	0.917	0.802	0.903
	I can effectively solve challenges encountered in operating a digital franchise.	0.864 ***
	I can maintain the operation of my digital franchise in challenging market conditions.	0.867 ***
	I feel confident in achieving the expected outcomes of my digital franchising efforts.	0.872 ***
Digital Literacy	I know how to use social media to promote digital franchising products or services.	0.733 ***	0.816	0.694	0.914
	I am familiar with data analysis tools and their application in business decisions for digital franchising.	0.784 ***
	I can utilize digital technologies to improve the efficiency of my digital franchising operations.	0.759 ***
Entrepreneurial Intention in Digital Franchising	I am motivated to explore entrepreneurial opportunities through digital franchising.	0.694 ***	0.875	0.713	0.814
	I plan to take specific actions toward starting a digital franchise.	0.713 ***
	I feel determined to launch a business using digital franchising platforms.	0.721 ***
Consumption adoption intention of digital franchises	I am likely to use a digital franchising platform for purchasing goods or services.	0.889 ***	0.942	0.795	0.849
	I would recommend digital franchising platforms to others.	0.911 ***
	I feel confident engaging with businesses hosted on digital franchising platforms.	0.874 ***
	I intend to continue using digital franchising platforms for future transactions.	0.924 ***

Note 1: CR: Composite Reliability; AVE: Average Variance Extracted. Note 2: *** p < 0.001.

, confirm that all constructs satisfied the recommended thresholds (CR > 0.60, AVE > 0.50, Cronbach’s α > 0.70), affirming the reliability and validity of the measurement model [67].

To address potential issues of common method bias (CMB), both procedural and statistical remedies were implemented following the guidelines of [68]. Procedurally, anonymity and confidentiality were assured, and the questionnaire design minimized item ambiguity and varied response formats across sections. Analytically, Harman’s single-factor test was performed, with results showing that the first unrotated factor accounted for less than 50% of the variance, indicating that CMB was not a major concern. Additionally, a common latent factor model was incorporated in the SEM to test for bias more rigorously; this model showed no substantial changes in structural paths, further confirming that common method variance did not significantly affect the results.

Following the validation of the questionnaire’s sub-constructs, Composite Reliability (CR) and Average Variance Extracted (AVE) were assessed to determine the reliability and convergent validity of the measurement model. CR, ranging from 0 to 1, reflects the internal consistency of a construct, with values above 0.60 considered acceptable for establishing construct reliability [65]. AVE represents the proportion of variance explained by a construct’s indicators, with a threshold of 0.50 recommended for adequate convergent validity [65,67].

The findings presented in Table 2 demonstrate that all constructs satisfy the recommended thresholds for Composite Reliability (CR > 0.6), Average Variance Extracted (AVE > 0.5), and Cronbach’s α (>0.7), confirming strong internal consistency and convergent validity across the measurement model. Additionally, all standardized factor loadings exceeded the acceptable minimum of 0.5, verifying that the observed variables effectively represent their corresponding latent constructs. These results establish the adequacy of the measurement scales in capturing the underlying dimensions of entrepreneurial and Consumption Adoption Intention for Digital Franchising context. The robust reliability and validity metrics enhance confidence in the constructs’ ability to accurately measure the hypothesized relationships, thereby ensuring the methodological rigor and credibility of the structural analyses conducted in this study.

To evaluate the overall adequacy of the measurement model, multiple model fit indices were examined—namely, absolute, incremental, and parsimonious fit indices—as presented in

Table 3. The Fitness Indexes Assessment.

Name of Category	Name of Index	Index Value	Comments
Absolute Fit	RMSEA	0.064	The required level is achieved.
Incremental Fit	CFI	0.913	The required level is achieved.
Parsimonious Fit	Chisq/df	2.734	The required level is achieved.

. The root mean square error of approximation (RMSEA) was 0.064, which falls below the recommended upper limit of 0.08, indicating acceptable model fit [66]. The comparative fit index (CFI) was 0.913, exceeding the widely accepted benchmark of 0.90, suggesting satisfactory incremental fit [69]. Additionally, the chi-square to degrees of freedom ratio (χ²/df) was 2.734, which falls within the acceptable range of less than 3, indicating an adequate parsimonious fit [70].

Table 4. Discriminant validity test.

Variable	Mean	SD	1	2	3	4	5	6	7	8
Desire for Self-Expression	6.1	0.74	0.960
Expected External Rewards	5.9	0.80	0.810 **	0.972
Platform Support	6.3	0.75	0.737 **	0.703 **	0.961
Platform Attractiveness	6.1	0.93	0.847 **	0.726 **	0.773 **	0.907
Self-Efficacy	6.2	1.11	0.723 **	0.844 **	0.671 **	0.637 **	0.958
Digital Literacy	6.0	0.80	0.669 **	0.644 **	0.736 **	0.704 **	0.611 **	0.903
EIDF	5.9	0.94	0.894 **	0.788 **	0.714 **	0.818 **	0.702 **	0.646 **	0.935
CAIDF	6.3	0.98	0.708 **	0.876 **	0.613 **	0.626 **	0.835 **	0.554 **	0.742 **	0.971

Note 1: The values in bold font are the square roots of the AVE; the non-diagonal numbers represent the correlation coefficients of each dimension. Note 2: ** p < 0.01.

presents a comparison of the correlation coefficients among all constructs with the square root of their respective Average Variance Extracted (AVE). The square root of the AVE for each construct exceeds the correlation coefficients between constructs, adhering to the standards proposed by [71]. This indicates that the constructs exhibit adequate discriminant validity. Overall, the evaluation results of the measurement model confirm that the model demonstrates strong internal consistency and external validity, ensuring its robustness for further structural analysis.

The structural model was evaluated through path analysis, with the results presented in Figure 2 and detailed in

Table 5. Results of the path analysis and confirmation of hypotheses.

Hypothesized Paths	Path Coefficient	SE	t-Value	Results
H1: Desire for Self-Expression → EIDF	0.187 ***	0.43	3.412 ***	Supported
H2: Expected External Rewards → EIDF	0.456 ***	0.41	9.539 ***	Supported
H3: Platform Support → EIDF	0.315 ***	0.52	7.898 ***	Supported
H4: Platform Attractiveness → EIDF	0.201 ***	0.31	5.317 ***	Supported
H5: Self-Efficacy → EIDF	0.157 ***	0.35	2.574 ***	Supported
H6: Digital Literacy → EIDF	0.283 ***	0.47	6.153 ***	Supported
H7: EIDF → CAIDF	0.176 ***	0.42	2.797 ***	Supported

Note: *** p < 0.001.

. All t-values exceed 1.96, confirming the statistical significance of each hypothesized relationship. The coefficient of determination (R²) = 0.67 indicates that the model accounts for 67% of the variance in entrepreneurial intentions, demonstrating strong explanatory power. Within the motivation dimension, both Desire for Self-Expression (H1: β = 0.187, p < 0.001) and Expected External Rewards (H2: β = 0.456, p < 0.001) exhibit significant positive effects on Entrepreneurial Intention in Digital Franchising (EIDF), underscoring the influence of intrinsic and extrinsic motivational factors. Notably, H2 (Expected External Rewards → EIDF, t = 9.539) represents the strongest relationship, indicating that anticipated financial benefits are the most critical driver of entrepreneurial engagement in digital franchising. In the opportunity dimension, Platform Support (H3: β = 0.315, p < 0.001) and Platform Attractiveness (H4: β = 0.201, p < 0.001) significantly enhance EIDF, highlighting the role of platform resources, usability, and perceived value in fostering entrepreneurial participation. Regarding ability factors, Self-Efficacy (H5: β = 0.157, p < 0.01) and Digital Literacy (H6: β = 0.283, p < 0.001) positively influence EIDF, emphasizing the importance of entrepreneurial confidence and digital competence. Among these, H5 (Self-Efficacy → EIDF, t = 2.574) exhibits the weakest but still significant effect, suggesting that while self-efficacy contributes to entrepreneurial intentions, its impact is comparatively lower than platform support or financial incentives. Furthermore, Entrepreneurial Intention in Digital Franchising (EIDF) significantly predicts Consumption Adoption Intention for Digital Franchising (CAIDF) (H7: β = 0.176, p < 0.001, t = 2.797), confirming its mediating role in translating motivation-, opportunity-, and ability-related factors into consumption adoption intention for digital franchise platforms. These findings validate the applicability of the Motivation-Opportunity-Ability (MOA) framework in understanding entrepreneurial behavior and decision-making processes within the digital franchising ecosystem, providing empirical evidence on the factors influencing digital franchise adoption and consumption adoption intention.

In addition to the reported coefficient of determination (R² = 0.67) for Entrepreneurial Intention in Digital Franchising (EIDF), the structural model also explains 42% of the variance (R² = 0.42) in Consumption Adoption Intention for Digital Franchising (CAIDF). These results demonstrate that the integrated MOA framework has strong explanatory power not only in predicting entrepreneurial engagement but also in accounting for subsequent consumer behavior intentions within digital franchising contexts. The robust R² values across both key constructs reinforce the model’s utility in capturing the dual dynamics of entrepreneurial intention and consumption adoption orientation in digital franchise ecosystems.

4.2. Mediation Analysis: Direct and Indirect Effects

The mediation analysis, as summarized in

Table 6. Summary of direct and indirect relationships based on the results of mediation analysis.

Path	Effect Type	β	p-Value
Motivation → EIDF → CAIDF	Indirect	0.081	<0.01
Opportunity → EIDF → CAIDF	Indirect	0.074	<0.01
Ability → EIDF → CAIDF	Indirect	0.069	<0.01
EIDF → CAIDF	Direct	0.176	<0.001

, was conducted to determine whether the direct effects of the MOA antecedents—motivation, opportunity, and ability—on Consumption Adoption Intention for Digital Franchising (CAIDF) would be reduced when accounting for the mediating role of Entrepreneurial Intention in Digital Franchising (EIDF). The results confirmed that EIDF significantly mediates these relationships. Indirect effects from each antecedent to CAIDF through EIDF were statistically significant: motivation (β = 0.081, p < 0.01), opportunity (β = 0.074, p < 0.01), and ability (β = 0.069, p < 0.01). Furthermore, the direct path from EIDF to CAIDF (β = 0.176, p < 0.001) remained significant, suggesting a partial mediation structure.

These findings indicate that the influence of motivational drives, perceived opportunities, and digital capabilities on consumption adoption is not only direct but is also significantly channeled through entrepreneurial intention. This mediating role is theoretically grounded in the behavioral intention literature [72], which identifies intention as a key mechanism that translates individual and contextual factors into actual adoption behaviors. The inclusion of mediation testing therefore serves to clarify the extent to which entrepreneurial intention explains the pathway from MOA constructs to digital franchising adoption behavior.

5. Discussion

The findings of this study validate the proposed MOA-based model, demonstrating the integrated influence of motivation, opportunity, and ability on entrepreneurial and consumption adoption intentions within the digital franchising ecosystem. Among these dimensions, Expected External Rewards (H2: β = 0.456, p < 0.001) emerged as the strongest determinant, highlighting the significant role of extrinsic motivations such as financial gain and career development in shaping entrepreneurial intentions. This aligns with self-determination theory, which posits that extrinsic rewards significantly influence goal-directed behaviors [28,73]. Additionally, Platform Support (H3: β = 0.315, p < 0.001) played a critical role, consistent with prior research highlighting the importance of technical resources, usability, and operational guidance in reducing uncertainties and fostering entrepreneurial engagement [44,48]. Within the ability dimension, both Self-Efficacy (H5: β = 0.157, p < 0.01) and Digital Literacy (H6: β = 0.283, p < 0.001) were significant predictors of Entrepreneurial Intention in Digital Franchising (EIDF). While self-efficacy has been widely recognized as a driver of entrepreneurial behavior [37], its relatively weak effect compared to other constructs suggests that personal confidence alone may not translate into entrepreneurial action without sufficient support and tangible incentives. Notably, constructs such as Platform Attractiveness (H4) showed statistically significant but comparatively moderate effects, indicating that branding or reputational appeal may not be as influential as practical and reward-based considerations. Moreover, EIDF significantly influenced Consumption Adoption Intention for Digital Franchising (CAIDF) (H7: β = 0.176, p < 0.001), demonstrating its mediating role in translating motivation-, opportunity-, and ability-related factors into consumption adoption behaviors [53]. To assess whether these antecedents maintained their impact when entrepreneurial intention was accounted for, a mediation analysis was conducted. The results revealed partial mediation, confirming that EIDF plays a central role in transmitting the effects of MOA constructs to CAIDF. This outcome is consistent with prior research emphasizing the mediating role of intention in shaping behavior [72]. These results not only validate the structural logic of the proposed model but also extend the theoretical application of the MOA framework by integrating both entrepreneurial and consumer perspectives. The findings emphasize that successful digital franchising requires a combination of individual motivation, platform-driven opportunities, and the necessary entrepreneurial capabilities, reinforcing previous studies on digital business model transformation [16,21]. However, some hypotheses received only partial empirical support. For instance, although Platform Attractiveness was statistically significant, its effect was relatively weaker than other variables, suggesting the need to further investigate how perceptions of brand value or platform image interact with structural and motivational factors. These limitations may also reflect characteristics of the sample, such as the age group (predominantly Gen Z), which could influence how constructs are prioritized.

6. Conclusions

This study provides a comprehensive analysis of the factors influencing entrepreneurial intentions and consumption adoption intentions within the digital franchising ecosystem, leveraging the Motivation-Opportunity-Ability (MOA) framework. The results confirm the significant roles of motivation, opportunity, and ability in shaping entrepreneurial intentions and subsequent Consumption Adoption Intention for Digital Franchising. Among the key findings, expected financial rewards and platform support emerged as the strongest drivers of digital franchising adoption, while self-efficacy and digital literacy also played crucial roles in enabling entrepreneurial success. The study further validates the mediating role of entrepreneurial intention, linking motivation-, opportunity-, and ability-related factors to Consumption Adoption Intention for Digital Franchising. These findings offer actionable insights for franchisors, platform developers, and policymakers, emphasizing the importance of robust support systems, clear reward structures, and targeted digital competency training programs. By addressing these factors, digital franchising platforms can enhance entrepreneurial participation and consumption adoption intentions, fostering a more sustainable and competitive digital franchising ecosystem. Future research should expand on these findings by examining industry-specific adoption patterns, incorporating generational perspectives, and exploring additional mediating and moderating variables to further refine the understanding of digital franchising dynamics.

6.1. Theoretical Implications

This study contributes to the theoretical advancement of the digital entrepreneurship literature by extending the Motivation–Opportunity–Ability (MOA) framework to the underexplored context of digital franchising, specifically within the fashion industry. As a sector characterized by rapid digitization, fast-changing consumer trends, and high reliance on brand identity, fashion provides an ideal empirical setting for examining digital entrepreneurial behaviors. This study demonstrates the framework’s explanatory power in understanding both entrepreneurial intention and consumption adoption intention in fashion-related digital franchise models. More critically, this study addresses a theoretical gap in the application of the MOA framework to digital franchising by focusing on how motivational, environmental, and individual capacity factors jointly stimulate entrepreneurial intention. While the MOA model has been widely applied in marketing and organizational behavior, its use in explaining entrepreneurial intention in digitally enabled, platform-based business models remains limited. This study responds to that gap by empirically showing how expected external rewards, platform support and attractiveness, and individual traits such as self-efficacy and digital literacy influence the formation of entrepreneurial intentions among digital franchisees. The findings offer evidence that entrepreneurial intention plays a supporting but not exclusive role in linking MOA antecedents to downstream behavioral outcomes. Although a partial mediation effect was observed, the emphasis of the study lies in demonstrating that MOA constructs themselves—particularly expected external rewards and platform support—are strong and direct drivers of entrepreneurial motivation in the context of digital franchising. This underscores the need to view MOA not merely as a predictor of adoption behavior but as a framework that captures the precursors to entrepreneurial decision-making in platform-based ecosystems. By integrating constructs from both entrepreneurial motivation and digital consumer behavior, the study contributes to expanding the theoretical applicability of MOA in platform entrepreneurship research and sheds light on how digital infrastructure and user capabilities intersect with motivational processes to drive entrepreneurial engagement.

6.2. Practical Implications

This study provides a comprehensive summary of its key findings, confirming that motivational, opportunity-related, and ability-based factors significantly influence both entrepreneurial intention and consumption adoption intention within the context of digital franchising. Notably, expected external rewards and platform support emerged as the most influential drivers, underscoring the importance of financial incentives and operational resources. These results affirm the theoretical applicability of the MOA framework and demonstrate that the study’s research objectives have been achieved. From a practical standpoint, the findings offer actionable strategies for platform operators, educators, policymakers, and franchisors—particularly within the fashion industry. Given the fast-paced nature of fashion trends and the need for agile market responses, digital franchising serves as an effective channel for fashion brands to expand their market penetration quickly and with reduced operational barriers. By adopting flexible, low-cost digital franchise models such as affiliate-based selling or micro-franchise platforms, fashion brands can better reach niche and emerging segments, especially among younger digital-native entrepreneurs. Furthermore, targeted digital literacy and e-commerce training programs remain essential for equipping these emerging entrepreneurs with the skills necessary to operate in dynamic, trend-sensitive environments. Collectively, these insights offer a practical roadmap for leveraging digital franchising to strengthen entrepreneurial engagement and brand scalability in the fashion sector.

6.3. Limitations and Future Research Directions

While this study provides important insights into the determinants of entrepreneurial intention and consumption adoption intention in digital franchising, it is not without limitations. First, the study employed a cross-sectional survey design, which limits the ability to establish causal inferences or capture temporal changes in entrepreneurial behavior. Longitudinal studies are needed to observe how digital franchising engagement evolves over time. Second, the sampling method relied on non-probability purposive sampling via online platforms, which may introduce self-selection bias and limit representativeness. Although appropriate for targeting digitally active Gen Z populations, this method may underrepresent individuals who are less engaged in digital entrepreneurship. Third, while the sample size of 491 exceeds the recommended thresholds for SEM, it remains geographically constrained to Taiwan, limiting the generalizability of findings across different cultural and economic contexts. Future studies should aim for cross-national comparisons to validate the model across diverse digital ecosystems. Fourth, although structural equation modeling (SEM) is well-suited for analyzing complex relationships and mediating effects, it is inherently sensitive to model specifications and assumptions. Alternative modeling techniques, such as multi-group SEM or partial least squares (PLS), could provide complementary insights. Finally, this research focuses specifically on the fashion industry, which—although highly suitable for digital franchising due to its brand-centric and trend-driven nature—may not fully represent other sectors. Future research should explore how industry-specific characteristics, such as operational complexity or regulatory constraints, influence digital franchising adoption. In addition, future studies should also incorporate moderating variables (e.g., platform trust, cultural factors) to examine potential boundary conditions of the proposed model.