Modeling Digital Transition in Regional Agro-Food Districts in Ialy : A PLS-SEM Analysis

Modeling Digital Transition in Regional Agro-Food Districts in Ialy : A PLS-SEM Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Modeling Digital Transition in Regional Agro-Food Districts in Ialy : A PLS-SEM Analysis Senour Ahmadi, Vito Amendolagine, Piermichele LaSala This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7602553/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract This research explores the multi-faceted factors influencing the digital transformation of Italy's Agro-Food Districts (IAFDs) concerning the governance systems, the environment, adoption behavioral elements, and contextual structure. It tests a conceptual model based on ecological modernization, governance, and technology adoption with PLS-SEM (Partial Least Squares Structural Equation Modeling). The findings indicate that social-contextual structures, financial resources, and infrastructure enhance governance effectiveness, while legal frameworks have minimal impact. Furthermore, governance, in conjunction with attitudes toward digital adoption and ecological sustainability, becomes a vital driver of digital transition. Most importantly, stakeholders’ digital adoption strongly drives the transition, emphasizing the importance of perception, attitudes, and digital adoption perceptions. These outcomes emphasize that digital shifts within agro-food systems are not only driven by technology; instead, they are profoundly social and ecological shifts requiring integrated governance and active stakeholder participation. This study makes important contributions to the literature and offers guidance toward resilient and sustainable policy, managerial, and practical frameworks for fostering digital transition in rural food systems. The findings also support the wider European policies concerning green and digital transitions, illustrating the need for tailored rural innovation strategies. Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The agri-food sector is increasingly confronted with unprecedented global and regional challenges, including rising demand driven by demographic pressures, climate change impacts, and the imperative to ensure food security while maintaining environmental sustainability (FAO, 2019; European Commission, 2020). Within this context, digitalization emerges as a strategic driver of territorial transformation, offering tools to optimize resource use, reduce environmental footprints, and enhance resilience in food systems (Vahdanjoo et al., 2025; Bhardwaj et al., 2025). Yet, the benefits of digital technologies are not equally distributed across regions: rural and peripheral areas often face infrastructural gaps, lower digital literacy, and institutional weaknesses that risk widening existing socio-economic and territorial inequalities (Tewathia et al., 2020; de Clercq et al., 2025). Unlocking the transformative potential of digitalization in food districts therefore requires more than technological innovation alone; it demands regionally grounded governance frameworks, inclusive policies, and an understanding of the sociological and ecological dynamics shaping the digital transition across territories (Geels, 2005; Klerkx & Rose, 2020). The digitization of agri-food systems has become as both key soloution and urgent need in facing of multi-faceted challenges affecting food systems, including climate change,territorial disruption , and incoherent practices (Vahdanjoo et al., 2025). Even though substantial investments are made towards the advancement of digital transitions in the agri-food systems , many of them do not sustain their growth due to fragmented governance, lack of multi-stakeholder engagement, and absence of enabling institutional frameworks (Vahdanjoo et al., 2025). Italian food districts are composed of rural, agro-food and supply-chain bio districts. They greatly contribute to the regional economy by integrating multiple stakeholders, which consist of farmers, processors, cooperatives, public institutions, as well as civil society organizations. These districts act as a belt governance (Ostrom, 2017) , promote social capital within the district and social multi-functionality, which does not limit the district to economic growth only, but also encourages environmental conservation and cultural heritage preservation (Ostrom, 2017; Poponi et al., 2021). Their meso-structure allows them to connect the micro level of farm activities and the macro level of national and EU policy structures to foster integrated, rural development (Ostrom, 2017). Countries across Europe, including Italy, utilize other district_forms, including clusters, LEADER local action groups, and community-led local development models. These approaches have been recognized as instrumental in achieving the EU's goals of territorial cohesion and sustainability, especially under the European Green Deal and the Farm to Fork Strategy (Ostrom, 2017; European Commission, 2020). However, the effectiveness of these frameworks is reliant on the governance approach, networked relationships, and underlying policy frameworks, which are often overlooked in the analysis of digital transformation. Most studies examining the transition and adoption of technological tools within agriculture disassociate the digital device from the broader socio-institutional context. This narrow view ignores the intricate relationships among governance systems, stakeholder networks built on ecosystem limits (Ritzema et al., 2019; Ostrom, 2017; Folke et al., 2005; Rhodes, 1997). These interdependencies are crucial in averting the risks of reinforcing existing inequalities, or completely failing to achieve the objectives of digital transformation. This study uses Partial Least Square Strutural EquationModel (PLS- SEM) approach (Sohail, 2023; Tama et al., 2023) to develop and empirically validate the conceptual model that integrates gaps of concern by digitally transitioning to contextual, social, and governance factors. The approach captures the multifactorial nature of digital transition in agro-food districts and how governance structures, participation, sustainability frameworks, and socio-territorial specificities impact digitalization (Martínez-Filgueira et al., 2022; Kunal et al., 2025) . In the current policy window for digital and green transitions, the study provides both theoretical and practical contributions (Yuan et al., 2025). It aims to provide policy and district level decision makers, along with local actors, strategies based on scientific evidence designed for participatory, context-sensitive, and ecological innovation pathways. The conclusions are not limited to Italy but extend to other European regions seeking digitally facilitated rural development embedded within territorial agri-food systems (Yuan et al., 2025; Brounori, 2022; Mol & Sonnenfeld, 2014; Barca et al., 2012). The main objective of the research is to assess the impacts of impacting factors on the digital transformation of Italy’s food districts, focusing on governance, digital adoption, contextual factors. Therefore the main question is which factors have the most significant impact on the digital transition in IAFDs? More specifically, the objectives lead to the following set of research questions: 1. What is the impact of governance structure on digital transition overall the IAFDs? 2. What impacts do stakeholders’ behavioral components like attitudes, social norms, and perceived control ( digital adoption pillars) have on the digital transition? 3. What is the impact of contextual factors , including financial, legal and structural status on digital transition overall the IAFDs? 2. Theoretical Framework This study attempts to understand the complex factors that catalyze the digital transition of Italian Agro-Food Districts (IAFDs) and goes beyond the factors of technology adoption to include the governance factor, the social and legal structure , and other contextual elements. In the process of constructing the theoretical framework of this literature review, it becomes clear that effective digital transition in IAFDs is not a function of only having the requisite resources, technology, but rather the outcome of the synthesis of all these interrelated factors. Each section below discusses relevant literature and subsequently formulates a hypothesis that is designed to answer the research questions posed in the introduction. The Governance in IAFDs is not accomplished in a vacuum, but rather is conditioned by the context under which these districts function. Context encompasses factors such as the economic environment (funding and investment opportunities), legal and regulatory environment (policies that aid or impede digital innovation), and the socio-structural environment (social capital, trust, and knowledge networks) of the territory (Brunori, 2022; Muluneh, 2021; Forney & Epiney, 2022; Mendes & Viola, 2023). Thus, these contextual factors for enabling and constraining forces need to be understood and addressed in order to successfully digital transform IAFDs. Drawing on the literature on institutional economics and regional development, we propose the following: H1: Positive contextual elements such as legal and financial frameworks, infrastructure, contextual structure within Italian Agro-Food Districts strongly enhance the overall governance in IAFDs. More recent research gives focus to understanding how multi-level governance aids in addressing digital transformation challenges as multifaceted agro-food districts (Gao, 2025; Alabrese & Saba, 2023; Atik, 2022; Ehlers et al., 2021). Concerning IAFDs, governance systems include the national, legal and local policies as well as systems through which different actors within the district ’ s development framework interact, negotiate and implement actions as defined by the development strategies (Muluneh, 2021; Martens, K., & Zscheischler, 2022; Vahdanjoo et al., 2025; Sun et al., 2022). The literature on regional innovation systems has noted the importance of governance for technological innovation barriers to adopt technology and promote collective learning (Cooke et al., 1997). In the case of IAFDs, this means that governance frameworks that enhance collaboration between farmers, processors, supportive research institutions, and public bodies are essential for effective digital transition (Forney & Epiney, 2022). Drawing from the above-mentioned literature, we hypothesize the following: H2: The governance structures in Italian Agro-Food Districts, as to their nature and effectiveness, positively impact the digital transition within those districts. Furthermore, within IAFDs, the adoption of digital technologies is essentially a step in a workflow that is controlled by numerous choices made by farmers and other value chain participants. The Theory of Planned Behavior (Ajzen, 1991) explains best these intentions and actions for adoption. It states that intention to perform a certain behavior in the future, which is the immediate precursor of a certain action is a product of three factors: attitude towards the behavior (evaluation of the behavior as either positive or negative), subjective norms (perception of social pressure to engage or not to engage in the behavior), and perceived behavioral control (individual ’ s belief about his.her ability to perform the behavior). In the case of farmers, support from relevant social groups and their confidence toward the use of digital agriculture tools together with positive perceptions about the advantages of these tools drive digital technology adoption (Gabriel & Gandorfer, 2023). Aditionally, the aforementioned behavioral components can be enhanced by stakeholder engagement and participation towards the digital transition that enhances ownership and collective efficacy (sun et al., 2022). Building upon the Framework of Planned Behavior and the relevant works regarding stakeholders participation, we propose that: H3: The Agri-food stakeholders ’ perceptions of digital adoption barriers and enabling factors, positive attitudes towards new technologies and social perceptions held by peers and leaders within the region strongly predict the level of digital adoption which positively-alters the digital transition phenomenon in the district. In addition, in the context of Italian agro-food districts, the digital transformation entails more than just the technological or organizational dimensions. It is also deeply linked to green transition practices. This hypothesis (H4) draws from ecological modernization theory (Mol & Sonnenfeld, 2000) alongside socio-technical transitions theory (Dayıoğlu & Turker, 2021), which both focus on the interplay between environmental practices and technological innovation. According to ecological modernization theory, fostering a sustainable environment can act as a powerful incentive, encouraging various stakeholders to develop new form-oriented technologies and governance systems. Within agro-food districts, the adoption of green practices such as circular economy principles, biodiversity, and water sustainability creates a necessity and opportunity for digital systems that monitor, optimize, and verify ecological outcomes (Poponi et al., 2021; Eastwood et al., 2019). When viewed through the lens of socio-technical transitions, green practices can be considered as niche innovations that confront established production and consumption systems (Eastwood et al., 2019). In a more applied form, this hypothesis suggests that the more advanced the food districts’ ecological practices, the more advanced the district’s digital capabilities are developed, not only to improve efficiency, but also to monitor and enhance environmental performance. These districts are more willing to adopt measures that integrate the environmental and digital transitions powered by data technologies aimed at achieving sustainability. Consequently, the model claims that green practices within agro-foods districts are fundamental enablers not only from an ecological perspective but also for the acceleration of the digital transition—because it stimulates the demand for digital tools, alters governance logics, and sets digital actions within ecological sociocultural frameworks. Hypothesis H4: The integration of green practices in Italian food districts alongside the ecological transition positively influences the level of digital transition in the districts. This theoretical foundation, seeks to understand the integrated social and governance drivers of digital transition in Italy ’ s Agro-Food Districts using a PLS-SEM methodology to test the digital transition hypotheses. The The outcomes will inform policy and practice on how to promote responsible digitally enabled innovations in the regional food systems. 3. Descriptive analysis of data The descriptive analysis emerging from responses of the 29 regional representative’s primary data confirms the diversity, inequality and changing dynamics of the food districts in Italy as summarized in Table 1 in the annex. Structurally, more than a third of the districts concentrate on the production of locally grown agricultural and high-value food commodities. The rest are in the value-adding organic production agro-industrial supply chains. The greatest concentrations of food districts are within the agro-ecological regions of Italy and are in Sicily, Campania, and Lombardy. These regions almost all contain food districts. Most districts (55%) having less than 4 years of existence are a positive indicator of growth since expansion sustains more than 15 years of operation within a small number of districts, which means a steady expansion pattern. Licensing is diverse. Governance is usually structured and informal, with 70% of the districts with legal entities having multiple licenses. This results in more than 15 years of operation within the districts being informally owned. The low level of public ownership in the districts relates to the mixed and private ownership derived from the hybrid governance of the Italian agri-food clusters. The paradox is that everyone agrees to work on ecologically sustainable practices and to some extent ecologically friendly practices, but only about a third of the participants totally disengaged from self-driven strategies having formal road maps. Districts primarily depend on self-financing and public funding, neglecting traditional bank loans and venture capital entirely. Focus areas include quality and regional branding, as well as productive investment, with recently rising attention to diversification and environmental management. Environmental sustainability continues to be paramount, along with strong emphasis on adopting eco-friendly processes and obtaining green certifications. Regarding the level of technology adoption, the respondents are more appreciative of practical digital tools like compliance applications and management, marketing and trade platforms, ignoring more advanced tools like AI, block chains, and 3D printing. Barriers involve scarce digital skills, management costs, and inadequate infrastructure and support like technical and research transfer services. 4. Methodology Italy's agro-food districts were studied through the lens of governance, stakeholder behavior, contextual factors, green practices, and digital transition using PLS-SEM. The empirical model was validated using Partial Least Squares Structural Equation Modeling (PLS–SEM). PLS-SEM is a multivariate technique useful in estimating complicated causal relationships in models with several latent variables and indicators. It is ideal for exploratory research or predictive models in their developmental stages. Hair et al. (2022) emphasizes the versatility of PLS-SEM, noting its effectiveness in predictive and exploratory research scenarios. After PLS-SEM estimates the outer models, it tests reliability and validity alongside testing the inner model for hypothesized relations, which strengthens insights into direct, indirect, and total effects among constructs. The use of PLS-SEM was motivated by multiple aspects within the scope of this study. First, in regard to the overarching theme of digital transition within agro-food districts—with its governance, societal, ecological, and contextual intricacies—its novel and multifaceted character alongside the interdependency relations PLS-SEM accommodates makes it well-suited, as it does not have to rely on strict distribution assumptions like normality. Furthermore, the limited sample size (n = 29 districts) poses a restriction for utilizing covariance-based SEM, since PLS-SEM focuses on variance enables its use in small sample settings and predictive models (Hair et al., 2019). Lastly, we chose SmartPLS 4 due to its intuitive features and more advanced algorithms that allow for easy iterative model sharpening, calculation of path coefficients, and evaluation of critical hypothesis mediating effects essential to our model. The methodological process comprised several key steps. First, we constructed a measurement model by defining reflective constructs governance structures, stakeholder behavioral dimensions, contextual factors, green practices, and digital transition. Evaluation of indicator reliability (outer loadings), construct reliability (composite reliability and Cronbach’s alpha), convergent validity (average variance extracted), and discriminant validity (Fornell-Larcker criterion and HTMT ratios) was performed. These steps confirmed the reliability of the constructs and their theoretical dimensions within our conceptual framework. Next, we evaluated the structural model for the relationships between constructs to verify the hypotheses. To obtain stable estimates of significance, we calculated bootstrapped path coefficients (β) with 5000 resamples. Model fit was evaluated through the R² values for the endogenous constructs, predictive relevance (Q²) from blindfolding, and effect sizes (f²). Stakeholder behavioral components were treated as mediators for the indirect effects with governance structures as the predictor and digital transition as the outcome for mediation analysis. This thorough PLS-SEM approach provided a full picture of the interaction of governance, social factors, and ecological determinants on the digital evolution of the agro-food districts of Italy. 5. Conceptual Model Design the SEM-PLS The conceptual model provided in the Fig. 1 demonstrates the interplay of governance, contextual variables, technology adoption, and ecologic engagements in facilitating the digital transformation of Italian Agro-Food Districts (IAFDs). This specific model was created in order to validate the four hypotheses described in the theoretical framework. Then the conceptual model was operationalized using the PLS-SEM method in the SmartPLS 4 software. The SmartPLS graphics of conceptual model (Fig. 2) depict the itemized loadings of the reflective measurement models along with the significant structural paths, denoted by the red lines showing relationships of importance, between constructs. The model is evaluated with bootstrapping using subsamples to test the significance of path coefficients and mediation effects while also evaluating R² values to assess the model's predictive capabilities. At the center of the model stands the construct “Digital Transition” which indicates the assimilation and progress of (IAFDs) agro food districts in adopting modern practices and technologies. Its impacts are directed through governance captured by the effectiveness, inclusiveness, and the overall capacity of the governance systems. The “Governance” construct is in turn influenced by contextual variables Legal Structure, Infrastructure, Contextual Structure, Financing & Funding Vehicles which shower the governance (H1) with legal and sociocommunal support. All these contextual variables were measured reflectively (e.g., q2 – q14, q9, q10, q11, q12, q13, q14, q16, q45). Hypothesis H2 is assessed through the pathway Governance to Digital Transition which assumes that well-structured governance leads to more effective digital transformations in food districts. This interaction is assessed through governance and digital transition indicators (e.g., q2, q3, q4, q9, q11, q12, q12a, q13, q14, q16, q19, q20, q22–q27, q45). The stakeholder behavioral elements of barriers and enabling factors and social norms fit under the label "Digital Adoption." This label predicts the Digital Transition variable, thus corroborating H3, which posits that stakeholders’ perceptions and readiness to engage digitally impacts adoption, and, in turn, drives digital transformation. Indicators reflectively measuring digital adoption attitudes are found in (q23–q27). Ecological Practice, measured by indicators (q19, q20, q22), is included for testing H4—Investigating the role of green and ecological transition practices in IAFDs as dual catalysts of digital transformation. This variable has a direct impact on the Digital Transition construct, demonstrating the synergy that exists between ecological innovation and the adoption of digital technologies in sustainable food districts. This comprehensive PLS-SEM strategy substantiates all four hypotheses with empirical evidence within the context of Italy's agro-food districts, emphasizing the complexity and integrative aspects of digital transformation. 5.2. Assessing Relevance and Magnitude Of The Factor Loadings: In order to assess the reliability of the items in capturing their respective constructs, the factor loadings of the measurement model were analyzed. In structural equation modeling, a factor loading greater than 0.70 is deemed ideal (Klein), albeit a minimum of 0.40, as Holland posited, is accepted for exploratory frameworks. In this research, the accepted minimum limit of factor loading was retained at 0.40. The initial conceptual model and factor loadings of all items are shown in Fig. 3 and Table 3 . Table 3 Factor loadings in the relationship between constructs and items in the conceptual model under study . Structure Question Factorial load Structure Question Factorial load Digital Adoption 23 0.09 Infrastructure 45 1 25 0.85 Contextual Structure 2 0.09- 26 0.95 3 0.41 27 0.73 4 0.99 Legal structure 9 0.27 Digital Transition 2 0.06 10 0.53 3 0.29 11 -0.37 4 0.54 12 -0.29 9 0.21 13 -0.46 10 0.03 14 0.73 11 0.21 Governance 2 0.03 12 -0.14 3 0.09 12A 0.41 4 0.74 13 0.03 9 0.08 14 0.25 10 0.19 16 0.38 11 -0.22 19 0.77 12 -0.16 20 0.34 12A 0.66 22 0.41 13 -0.11 23 0.03- 14 0.35 25 0.79 16 0.57 26 0.89 45 0.60 27 0.64 Ecological Practice 19 0.92 45 0.57 20 0.40 Finance & Funding Structure 12A 0.80 22 0.50 16 0.72 From the evaluation of Fig. 3 and Table 3 , a number of items are noted to have factor loadings below the 0.40 cut-off point, signifying weakness in representational power for their latent constructs. To enhance model reliability and validity, these low loading items were removed from the measurement model. In particular, items related to legal structure, governance, and some contextual constructs showed factor loadings below the acceptable level. All changes have been incorporated into the conceptual model, which is illustrated in Fig. 3 . Based on this improved version, all remaining items have factor loadings greater than 0.40, which fulfills the previously set threshold for reliability and confirms that the constructs are well captured in the final model. As a result of this refinement process, the holistic credibility of the measurement model is improved, which serves as a solid basis for assessing the structural pathways within the SEM-PLS framework. 5.3. Examining the validity and reliability of the questionnaire The verification of the reliability and validity of the constructs within the structural model was performed after completing the validation preliminary tests. This was done in succession of the confirmatory factor analysis that ensures model identification and validity accuracy takes place within the model instrument with sufficient factor loadings. Validity was evaluated through both convergent and discriminant (diverging) validity measures, whereas reliability was assessed through these measures as well. Convergent validity can be assessed using AVE which tells the ratio of variance a construct captures realatively to the variance captured through measurement error. Validity can be established based on AVE values. A value of 0.50 according to Fornell and Larcker (1981) indicates sufficient convergent validity. These values are presented in Table 4 showcasing AVE values for every construct. This suggests that most of the constructs did meet the limit but the ones failing were Digital Transition (0.43), Ecological Practice (0.41), Governance (0.45) and especially Legal Structure (0.36) which was considerably lower than expected. While it can be accepted these constructs encapsulate their intended variance, the results do indicate there is a degree of measurement problem or conceptual overlap needing less refinement in future studies. Table 4 Average ​ Variance Extraction Done Agent I see In Model Conceptual Case Study Factor Average Variance Extracted (AVE) Contextual Structure 1.00 Digital Adoption 0.72 Digital Transition 0.43 Ecological Practice 0.41 Finance & Funding Sources 0.58 Governance 0.45 Infrastructure 1.00 Legal Structure 0.36 Discriminant validity was checked by comparing the cross loadings of the constructs with the square root of their AVE (Fornell-Larcker criterio), as well as performing a cross loading test. The square root of the AVE is supposed to be greater than the correlations of the constructs to any other latent variable which allows to confirm that the boundaries of the constructs are different. For cross loading tests, if an item with it’s own construct gets a higher correlation than with other constructs, discriminant validity is supported. 5.3.1 Cross Loading Test In relation to these results, the model meets the requirements of the cross loading test since the correlation of each indicator with their factor was greater than the correlation with the other constructs. The evidence made by the gray cells of the cross loading matrix verify the cross validity of the model and corroborate the measurement model of the study. All validity methods, collectively strengthen the SEM-PLS analysis by enabling thorough testing of the structural relationships in the system. Table 5 corellation measurement Item Contextual Structure Digital Adoption Digital Transition Ecological Practice Finance & Funding Sources Governance Infrastructure Legal Structure q4 1 0.438 0.59 0.215 0.414 0.696 0.197 -0.173 q11 -0.151 0.292 0.122 0.267 -0.336 -0.227 0.02 0.803 q12a 0.51 0.277 0.45 0.19 0.752 0.636 0.091 -0.181 q13 0.307 0.097 0.039 -0.024 -0.302 -0.069 -0.084 0.42 q14 -0.252 -0.202 -0.165 -0.056 0.012 -0.141 -0.115 0.493 q16 0.129 0.165 0.432 0.38 0.773 0.662 0.52 -0.335 q19 0.234 0.604 0.757 0.929 0.323 0.473 0.527 0.198 q20 0.029 0.23 0.314 0.409 0.334 0.319 0.329 0.061 q22 0.076 0.387 0.338 0.458 0.065 0.149 0.216 0.029 q25 0.491 0.857 0.786 0.563 0.266 0.449 0.281 0.132 q26 0.437 0.952 0.866 0.649 0.268 0.455 0.348 0.135 q27 0.144 0.727 0.616 0.484 0.194 0.275 0.289 0.144 q45 0.197 0.36 0.61 0.583 0.406 0.692 1 -0.062 5.3.2 Fornell-Larcker Test Results In the discriminant validity assessment, the Fornell-Larcker criteria was employed. This method involves assessing if the square root of a construct’s AVE value is greater than the correlation it has with other constructs. The square root of the AVE values which form the principal diagonal of the Fornell-Larcker matrix should be greater than the correlation of that particular construct with all other latent variables. As is clear from the matrix, this requirement is satisfied by all first-order constructs of the study: the square root diagonals (square roots of AVEs) are greater than all off-diagonal correlations. This demonstrates that every construct captures a greater portion of variance with its respective indicators than with other consturcts, thereby supporting divergent validity and achieving a satisfactory overall model fit. Therefore, the measurement model confirms that the model sufficiently captures distinct conceptual dimensions in the analysis of digital transformation of Italian agri-food districts. Table 6 Fornell-Larcker test results Factor Contextual Structure Digital Adoption Digital Transition Ecological Practice Finance & Funding Sources Governance Infrastructure Legal Structure Contextual Structure 1.00 Digital Adoption 0.44 0.85 Digital Transition 0.59 0.90 0.66 Ecological Practice 0.22 0.67 0.80 0.64 Finance & Funding Sources 0.41 0.29 0.58 0.38 0.76 Governance 0.70 0.47 0.78 0.52 0.85 0.67 Infrastructure 0.20 0.36 0.61 0.58 0.41 0.69 1.00 Legal Structure -0.173 0.158 0.025 0.181 -0.34 -0.271 -0.062 0.596 5.3.3. Checking the Reliability of the Questionnaire Using Cronbach’s Alpha and Composite Reliability After establishing validity, reliability was calculated with Cronbach’s alpha and composite reliability, using composite reliability as the main criterion within the context of structural equation modeling. Both indices for the constructs are provided in the Table 7 . Consistent with suggestions that composite reliability should exceed 0.70 to ensure reasonable reliability, most constructs in this study have met this benchmark. The Ecological Practice and Legal Structure constructs, however, do have composite reliability figures slightly less than 0.70, raising possible concerns regarding the measurement of these two factors and indicating that further work is needed in these areas. In any case, the analysis of reliability indicates that most constructs show high internal consistency, which adds confidence in the quality of the measurement model. Table 7 Reliability​ Alpha Cronbach and Reliability Cronbach's Alpha Composite Reliability Contextual Structure 1 1 Digital Adoption 0.803 0.886 Digital Transition 0.797 0.851 Ecological Practice 0.203 0.647 Finance & Funding Sources 0.281 0.735 Governance 0.595 0.766 Infrastructure 1 1 Legal Structure 0.188 0.603 3.2 PredictivFigure 2: SEM-PLS analysis of study e Capability The ability to make predictions about future occurrences with a conceptual framework stands as a significant aspect of its suitability and utility. In this research, the associated Governance and Digital Transition constructs were able to explain 98% and 99%, respectively, of the variance in the outcomes associated with these constructs, indicating very high predictive ability (stronger than what is required). These values are bound to the ensure structural strength of the parameters of the model. 5.3.4. Validation of Overall Fit of The Model The overall model fit, including measurement and structural ones, was assessed with the GOF metric as defined by Tenenhaus et al. (2004). The GOF index is defined as the square root of the average communality multiplied by the mean R² of the endogenous constructs. In this research, the R² values for the endogenous variables are 0.36 (strong), 0.25 (moderate), and 0.01 (weak), indicating multi-level predictive capacity (and some, even low levels of, prediction inaccuracy) across the model. All in all, these findings are sufficient for confirming the adequacy and consistency of the model for understanding the digital transition processes in the agro-food districts in Italy. $$\:GOF=\sqrt{\overline{Communality}\times\:\overline{RSquare}}$$ $$\:GOF=\sqrt{0.99*0.44}=0.66$$ The overall fit of the conceptual model is confirmed because the size of the fit index is strong . 5.4. Coefficient Path (P-Value) Relationships Between the Factors of the Model From Table 8 , we can observe that the structural model analysis using the PLS-SEM technique has been carried out, with the primary focus of the study being the path coefficients (value) and their associated t-value, p-value, and significance level within the relationships of the core constructs of the conceptual model of digital transition in Italy’s agro food districts. The table illustrates the effect of a number of contextual factors such as Infrastructure, Contextual Structure, Legal Structure, and Finance & Funding Sources on Governance. Both Infrastructure and Contextual Structure have positive and significant impacts on Governance (β = 0.40, p < 0.05), which means that the available infrastructure as well as the socio-contextual factors have a value-add toward governance. The Contextual Structure also shows positive impact on Governance (β = 0.40, p < 0.05) alongside Legal Structure which does not pose relevant impact (β = 0.001, p = 0.947), indicating that the current legal structures being utilized tend not to add substantial value toward enhancing governance within this context. In regard to Finance & Funding Sources, these have proven to be the dominating determinants of Governance (β = 0.52, p < 0.001), affirming that financial resources significantly contribute toward the effectiveness of governance systems. The table provides the detail of the various causes of Digital Transition. It shows that the effect of Digital Adoption on Digital Transition is positive and substantial (β = 0.57, p < 0.001), supporting the hypothesis that the stakeholders’ attitude towards adoption and technology usage is pivotal for wider digital transformation. Governance positively affects Digital Transition as well (β = 0.41, p = 0.002), supporting the claim that sound and participatory governance promotes the use and expansion of digital technologies. Finally, Ecological Practice, representing green practices and measures of sustainability, has a positive impact on Digital Transition (β = 0.21, p < 0.001) which proves the relationship between environmental sustainability and digital innovation. All together, these data strengthen the hypotheses of the study, illustrating the complex character of the digital transformation within the Italian agro-food districts. The emphasized significant paths indicate that although regulatory policies may have little relevance in improving governance, the most important drivers of digital transition are found in infrastructure and financial resources, alongside the adoption of digital technologies and ecological practices. Table 8 Coefficient​ Path ( P - Value ) relationships among factors within the model Factor Standardized Path Coefficient t(P-value) Significance From To Infrastructure​​ Governance 0.40 2.51(0.012) Yes Contextual Structure​ Governance 0.40 2.64(0.009) Yes Legal Structure​ Governance 0.001 0.07(0.947) No Finance & Funding Source Governance 0.52 6.76(< 0.001) Yes Digital Adoption​ Digital Transition​ 0.57 6.09(< 0.001) Yes Governance​​ Digital Transition 0.41 3.15(0.002) Yes Ecological Practice​ Digital Transition 0.21 4.60(< 0.001) Yes 5.5. Understanding the Final Model and Structural Relationships The last PLS-SEM conceptual model captures the interaction of governance, digital adoption, ecological practices, and the digital transition in the context of the Italian Agro-Food Districts (IAFDs), depicting also the interrelations of infrastructure, contextual factors, legal frameworks, and financial resources as their foundation. The standardized path coefficients along with their respective p-values are provided on the arrows of the model’s figures (Fig. 5) . Infrastructure has a strong, positive, and direct impact on Governance (β = 0.40, p = 0.01). Thus, it can be confirmed that enhancements in infrastructure like digital and logistic connections translates to increased governance effectiveness, meaning that an increase in governance score is observed when there is an increase in infrastructure score. To put this differently, if the infrastructure score increases by one unit, governance undergoes an average increase of 0.40. Contextual Structure also has a strong, positive and direct impact on Governance (β = 0.40, p = 0.01). This means that it is equally important to have supportive contextual drivers of social cohesion, networks of partnership, and local culture to promote governance, adding another 0.40 to governance for a total of 0.80 unit increase for each unit increase in these social variables. Legal Structure does not have any significant impact on Governance (β = 0.001, p = 0.95) and this confirms that the legal instruments shaped in these forms do not directly influence governance in these districts meaningfully. Financial Resources (Finance & Funding Sources) have the strongest direct positive effect on Governance (β = 0.52, p < 0.001). This finding reinforces the importance of financial resources and investments in effective governance since every unit increase in financial resources comes with average 0.52 unit increase in governance. Concerning digital transformation, Digital Adoption has a noteworthy positive impact on the Digital Transition (β = 0.57, p < 0.001). This shows that positive behavior and willingness from stakeholders toward technology is instrumental in driving the advanced capabilities; boosting digital adoption scores contributes 0.57 units to digital transition for every one unit increase. Governance has positive direct impact on Digital Transition as well (β = 0.41, p = 0.00). This affirms strong governance as a requisite pillar for the successful scaling and implementational digitization within the districts. Lastly, Ecological Practice—which embodies efforts towards green practices and sustainability—displays a notable encouraging and direct impact in the digital transition (β = 0.21, p = 0.00). This finding underpins the hypothesis that ecological and digital transitions are synergistically beneficial: every unit increase in Ecological Practice score allows a 0.21 average increase in Digital Transition. All these validated pathways together advance the digitally transformative equilibrium model, to offer new insights into the dynamics of change in Italy's agro food districts with special focus on the central role of governance, social and physical infrastructure, finances, stakeholder commitment and ecological engagement. 6. Discussion This study aimed to explore the intricate relationships among the social mechanisms, governance structure, context, finances and laws, adoption behavior of technology, and ecological actions taken in regard to digital transition within Italy’s Agro-Food Districts (IAFDs). The outcome strongly supports the majority of the hypotheses and provides additional more subtle understanding which corresponds to the existing literature on rural digitalization and governance. H1 suggested that legal and financial frameworks, social and contextual structures, as well as infrastructure, create a context which strongly explains governance effectiveness in IAFDs. Our findings confirm this hypothesis because infrastructure (β = 0.40, p = 0.01), contextual structure (β = 0.40, p = 0.01), and financial resources (β = 0.52, p < 0.001) all had significant positive effects on governance. These results were consistent with the multi-level governance approach (Hooghe & Marks, 2001) as well as network governance theory (Rhodes, 1997), which identifies social, financial, and infrastructural resources as critical enablers of fragmented governance collaboration. These have also been shared by Vahdanjoo et al. ( 2025 ), who describe local social capital and supportive infrastructures as salient features of governance effectiveness in Italian food districts. The absence of a significant effect from legal structure (β = 0.001, p = 0.95) provides supporting arguments that legal policies, although essential, are not synergistic enough to enhance governance in fractured or history-dependent systems lacking institutional and financial aid (Gao, 2025 ; Alabrese & Saba, 2023 ; Atik, 2022 ; Ehlers et al., 2021 ). H2 posited that governance systems have a favorable effect on the rate of digital transition in IAFDs. This is strongly confirmed by our data (β = 0.41, p = 0.00), reinforcing theories of polycentric governance (Forney& Epiney, 2022 ) and adaptive governance (Mulneh, 2021), which argue that innovation, whether technological or social, in complex systems is enabled by participatory and flexible governance frameworks. These results are consistent with finding that governance networks in rural settings afford the trust and legitimacy needed for digital transitions (Forney& Epiney, 2022 ; Mol & Sonnenfeld; 2014 ). The findings also support Geels' (2005) multi-level perspective to the extent that it shows governance structures provide a ‘regime’ layer that facilitates or constrains the use of digital tools as emerging innovations within the system. H3 described how the perceptions of enabling and disabling factors, mobile attitudes, and social norms of agri-food stakeholders predict the degree of digital adoption and, subsequently, the progression towards a digital transition. This hypothesis was confirmed because the effect of digital adoption on digital transition is strong and significant (β = 0.57, p < 0.001), supporting the Technology Acceptance Model (Davis, 1989 ) and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003 ). Both models argue that a person’s beliefs, subjective perceptions surrounding those beliefs, and control over the situation influence the adoption of new technology. In addition, our results also support Sunet al. ( 2022 ), who underscored the adoption sociological perceptions and trust networks, particularly the rural and agro-food systems, in regard to their adoption processes. H4 argued that the combination of green policies and environmental shifts has a positive effect on the digital transition of IAFDs. The relationship was indeed robust and positive (β = 0.21, p = 0.00), which corroborated the hypothesis and makes sense with ecological modernization theory (Mol & Sonnenfeld, 2014 ), which claims that technological development and sustainability can support each other. This synergy is further supported by Yuan et al. ( 2024 ) who claim that digital technologies in remote rural food systems tend to develop as instruments for monitoring green practices, thus reinforcing the nexus between green and digital transitions. This finding exemplifies how greater ecological concern increases the likelihood of adopting digital technologies integrating broader sustainability frameworks (Bhardwaj et al., 2025 ; Mendes & Viola, 2023 ; Martens & Zscheischler, 2022 ) It certainly notes that efficient digital transitions in rural agro-food systems are responsive to change from social-ecological systems. It might be worth stating as a hypothesis that the social-ecological system is the governance system with all the constituents around it, a legal approach does not seem sufficient by itself, but creates conditions for the governance system to catalyze a profound systemic change when coupled with financial support, social context, infrastructural funding, and green policy frameworks; (Dayıoğlu & Turker, 2021 ; Eastwood, 2019; Barca et al., 2012 ) including the specific socio-territorial and ecological aspects of rural areas. Equally, the predominant impact of stakeholder adoption behavior indicates that the readiness and learning campaigns, as well as social capital nurturance, intervention strategies, should be prioritized for policy actions aimed at transforming digital infrastructure in rural regions. Overall, the outcomes of the research deepen the understanding of the digitally-enabled transformation processes in the agro-food districts by validating the need for a comprehensive, multi-dimensional and systems approach—consisting of the governing frameworks, stakeholder actions, funding and social support structures, and environmental engagement—to shape the digitally transformative future of Italy’s rural regions. 7. Conclusion This research was undertaken to thoroughly analyze the drivers of the digital shift in Italy’s Agro-Food Districts (IAFDs) focusing on the governance system, social and contextual system, actor paradigms, and ecological practices to understand their interplay towards digital transformation of rural agrifood systems. Using the PLS-SEM approach, the evaluation has effectively articulated answers to the research problems within the scope of rural innovation and sustainability. The study illustrates that the perceptions and behaviors of stakeholders concerning the adoption of digital technologies is the main determining factor of the digital transition, depicting strong reliance on social constructs and human actions. Governance frameworks remains crucial as well, reinforcing the concept that socio-technical systems approach is more appropriate since the digital transformation process is mainly guided to, through vigorous hands-on governance, decision making, and cooperation. This also approaches the increasing streams of research that regards the restructuring of rural areas with advanced technologies and perspectives, as the outcome of sound governance enables coordination among diverse rural actors. Financial resources, infrastructure, and sociocultural context make the most significant contributions to the capacity of governance systems. In contrast, legal frameworks have little impact on the governance results in this case. As such, the governance of regions undergoing digital transitions must be socially capitalized on and financially invested in to be more effective, thus requiring targeted approach methodologies. In conjunction with other findings, the impact of sustainability practices on digital transition in the context of agro-food systems suggests the growing importance of the intersection between the adoption of modern technologies and ecological practices, which strengthens the understanding of the combined green and digital transitions phenomena. This research highlighted the systemic and multi-dimensional nature of the digital transition in Integrated Agri-Food Districts (IAFDs). It simultaneously emphasizes the advanced sustenance requirement of strategic technological resources and ecologically responsible governance, social unity, and resilient financial frameworks. Sustainable digital strategies in Italy’s agro-food landscapes focus on the need to actively involve citizens and stakeholders in decision-making and consider integrated, participatory governance frameworks alongside rural infrastructure development. The study contributes towards the discourse around rural innovation, particularly the digital transformation of rural agri-food systems, highlighting the importance of adapting to regional inequality threats and drawing attention towards ecological impact and collaborative innovation approaches. Declarations Author Contribution S.A. conceived the research design, carried out the data collection and analysis, and wrote the initial draft of the manuscript. V.A. contributed to the development of the conceptual framework, supervised the methodological approach, and provided critical revisions to strengthen the analysis. P.L.S. offered overall supervision, contributed to the theoretical framing, and provided critical feedback to refine the final version of the manuscript. All authors reviewed, edited, and approved the final manuscript. Acknowledgement The authors gratefully acknowledge the use of DeepL Write for language editing support, which assisted in improving the clarity and readability of the English in this manuscript. Data Availability “Data is provided within the manuscript or supplementary information files” References Alabrese, M., & Saba, A. (2023). Digitalising Agricultural and Food Systems: policy challenges and actions for the sustainable transition in the EU. Perspectives on Federalism , 14 (3), 34-47. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. Atik, C. (2022). Towards comprehensive European agricultural data governance: Moving beyond the “data ownership” debate. IIC-International Review of Intellectual Property and Competition Law , 53 (5), 701-742. Ahmadi, S., Amendolagine, V. & LaSala, P. Unpacking the impacts of digitalization of knowledge transfer in agri-food sector, through sociotechnical systems theory: a systematic literature review. Agric Econ 13, 51 (2025). https://doi.org/10.1186/s40100-025-00393-3. Barca, F., McCann, P., & Rodríguez‐Pose, A. (2012). The case for regional development intervention: place‐based versus place‐neutral approaches. Journal of regional science , 52 (1), 134-152. Brunori, G. (2022). Agriculture and rural areas facing the “twin transition”: principles for a sustainable rural digitalisation. Italian Review of Agricultural Economics (REA) , 77 (3), 3-14. Bhardwaj, M., Kumar, P., & Singh, A. (2025). Bibliometric review of digital transformation in agriculture: innovations, trends and sustainable futures. Journal of Agribusiness in Developing and Emerging Economies. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly , 319-340. Dayıoğlu, M. A., & Turker, U. (2021). Digital transformation for sustainable future-agriculture 4.0: A review. Journal of Agricultural Sciences , 27 (4), 373-399. de Clercq, M., D'Haese, M., & Buysse, J. (2023). Economic growth and broadband access: The European urban-rural digital divide. Telecommunications Policy , 47 (6), 102579. Eastwood, C., Klerkx, L., Ayre, M., & Dela Rue, B. (2019). Managing socio-ethical challenges in the development of smart farming: from a fragmented to a comprehensive approach for responsible research and innovation. Journal of agricultural and environmental ethics , 32 (5), 741-768. European Commission. (2020). Farm to fork strategy: for a fair, healthy and environmentally-friendly food system. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions, 381, 1-9. Ehlers, M. H., Huber, R., & Finger, R. (2021). Agricultural policy in the era of digitalisation. Food policy , 100 , 102019 Forney, J., & Epiney, L. (2022). Governing Farmers through data? Digitization and the Question of Autonomy in Agri-environmental governance. Journal of Rural Studies , 95 , 173-182 Fao. (2019). The state of food and agriculture 2019: Moving forward on food loss and waste reduction . UN. Folke, C., Hahn, T., Olsson, P., & Norberg, J. (2005). Adaptive governance of social-ecological systems. Annu. Rev. Environ. Resour. , 30 (1), 441-473. Gao, X. (2025). The EU’s twin transitions towards sustainability and digital leadership: a coherent or fragmented policy field?. Regional Studies , 59 (1), 2360053. Klerkx, L., Jakku, E., & Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS-Wageningen journal of life sciences , 90 , 100315. Gabriel, A., & Gandorfer, M. (2023). Adoption of digital technologies in agriculture—an inventory in a european small-scale farming region. Precision Agriculture , 24 (1), 68-91. Geels, F. W. (2005). Processes and patterns in transitions and system innovations: Refining the co-evolutionary multi-level perspective. Technological forecasting and social change , 72 (6), 681-696. Klerkx, L., & Rose, D. (2020). Dealing with the game-changing technologies of Agriculture 4.0: How do we manage diversity and responsibility in food system transition pathways?. Global Food Security , 24 , 100347. Kunal, K., Ramprakash, K. R., & Prasad, A. (2025). Role of Productive Sectors in Driving Socio-Economic Advancement: Identifying Critical Factors Using PLS-SEM. Qubahan Academic Journal , 5 (2), 224-236. Mol, A. P., & Sonnenfeld, D. A. (2014). Ecological modernisation around the world: Perspectives and critical debates . Routledge. Martínez-Filgueira , X. M., Peón, D., & López-Iglesias, E. (2022). Determinants of innovation by agri-food firms in rural Spain: an MCA PLS-SEM analysis. International Food and Agribusiness Management Review , 25 (1), 103-120. Mendes, V., & Viola, E. (2023). Green digitalization? Agriculture 4.0 and the challenges of environmental governance in Brazil. In Sustainability challenges of Brazilian agriculture: Governance, inclusion, and innovation (pp. 207-226). Cham: Springer International Publishing. Martens, K., & Zscheischler, J. (2022). The digital transformation of the agricultural value chain: discourses on opportunities, challenges and controversial perspectives on governance approaches. Sustainability , 14 (7), 3905. Muluneh, T. (2021). Conceptualizing Digital Agricultural Governance. International Journal of Engineering Research & Technology , 10 (1). Ostrom, E. (2017). Polycentric systems for coping with collective action and global environmental change. In Global justice (pp. 423-430). Routledge. Poponi, S., Arcese, G., Mosconi, E. M., Pacchera, F., Martucci, O., & Elmo, G. C. (2021). Multi-actor governance for a circular economy in the agri-food sector: bio-districts. Sustainability , 13 (9), 4718. Ritzema, R. S., Douxchamps, S., Fraval, S., Bolliger, A., Hok, L., Phengsavanh, P., ... & van Wijk, M. T. (2019). Household-level drivers of dietary diversity in transitioning agricultural systems: evidence from the Greater Mekong Subregion. Agricultural Systems , 176 , 102657. Sun, S., Wen, X., Jie, S., Gao, Q., Zhu, Y., & Wen, S. (2022). Drivers of Farmers’ Intention to Use the Digital Agricultural Management System: Integrating Theory of Planned Behavior and Behavioral Economics. Frontiers in Psychology , 13 , 901169. Strambi, G. (2024). Food districts as a form of local food system governance Regulated by italian law. Revista Catalana de Dret Ambiental , 15 (1). Sohail, M. T. (2023). A PLS-SEM approach to determine farmers’ awareness about climate change mitigation and adaptation strategies: pathway toward sustainable environment and agricultural productivity. Environmental Science and Pollution Research , 30 (7), 18199-18212. Tama, R. A. Z., Hoque, M. M., Liu, Y., Alam, M. J., & Yu, M. (2023). An application of partial least squares structural equation modeling (PLS-SEM) to examining farmers’ behavioral attitude and intention towards conservation Agriculture in Bangladesh. Agriculture , 13 (2), 503. Tewathia, N., Kamath, A., & Ilavarasan, P. V. (2020). Social inequalities, fundamental inequities, and recurring of the digital divide: Insights from India. Technology in society , 61 , 101251. Vahdanjoo, M., Sørensen, C. G., & Nørremark, M. (2025). Digital transformation of the agri-food system. Current Opinion in Food Science , 101287. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478 Yuan, Y., Guo, X., & Shen, Y. (2024). Digitalization drives the green transformation of agriculture-related enterprises: A case study of a-share agriculture-related listed companies. Agriculture , 14 (8), 1308. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 03 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers invited by journal 25 Sep, 2025 Editor assigned by journal 20 Sep, 2025 Submission checks completed at journal 16 Sep, 2025 First submitted to journal 12 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7602553","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524886709,"identity":"c8f0ef77-62a7-45aa-925e-d20c5efc04ba","order_by":0,"name":"Senour Ahmadi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBCDBCA2OPCBgYGHvYGB4QDRWg7OAGrhAao/QIwesBZmHiAJ0oLXGvnZzcekKxjs8nTbD288bNt2R4aHfwHj4Q94tBjcOZYmeYYhudjsTFrB4dy2Zzw8Eg/wO8xAIsdMsoGBOXHbgRwDoJbDPPYSB/BrkZ8B1lKfuO38G4PDlkAtPIS0MNwAazmcuO0G0BZGkBb+BgIOu5GWbNlgcByo5VnBwZ5zIFsYGw6cweuw5IM3GyqqgQ5L3vzhR9lhex7+w4c/VOBzGMQuZI5EYgNBDWiA/wCpOkbBKBgFo2CYAwDCtVeFtgklTwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Foggia","correspondingAuthor":true,"prefix":"","firstName":"Senour","middleName":"","lastName":"Ahmadi","suffix":""},{"id":524886710,"identity":"399a612b-6f57-495b-9325-0997cb7d8cf5","order_by":1,"name":"Vito Amendolagine","email":"","orcid":"","institution":"University of Foggia","correspondingAuthor":false,"prefix":"","firstName":"Vito","middleName":"","lastName":"Amendolagine","suffix":""},{"id":524886713,"identity":"5d4431c7-0318-4cb9-8d33-0cf1ba7dfd84","order_by":2,"name":"Piermichele LaSala","email":"","orcid":"","institution":"University of Foggia","correspondingAuthor":false,"prefix":"","firstName":"Piermichele","middleName":"","lastName":"LaSala","suffix":""}],"badges":[],"createdAt":"2025-09-12 17:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7602553/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7602553/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93371813,"identity":"09ec884a-2d76-45e3-a666-15005f3f2d9b","added_by":"auto","created_at":"2025-10-13 06:51:27","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1203769,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptSEMModelofDigitaltransitionFoodDistricts2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7602553/v1/0ead00befb6fec47603f8179.docx"},{"id":93371810,"identity":"c0e15924-d8a2-459a-8a06-03416fc2008d","added_by":"auto","created_at":"2025-10-13 06:51:27","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5473,"visible":true,"origin":"","legend":"","description":"","filename":"bead64807f4649fbb5850311c219fc88.json","url":"https://assets-eu.researchsquare.com/files/rs-7602553/v1/553000e4e4b641f243ec23dc.json"},{"id":93371814,"identity":"31bc8bdd-b43c-4503-b17b-c69749d954b9","added_by":"auto","created_at":"2025-10-13 06:51:27","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":134057,"visible":true,"origin":"","legend":"","description":"","filename":"bead64807f4649fbb5850311c219fc881enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7602553/v1/e52e2e3259f64d871a0f5e54.xml"},{"id":93371809,"identity":"114e4bbc-46e6-44bc-86a1-1779ac7e0496","added_by":"auto","created_at":"2025-10-13 06:51:27","extension":"jpeg","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":47255,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7602553/v1/695db3acb44fe8718516883f.jpeg"},{"id":93371812,"identity":"23fe7334-c5e1-4fa5-b2f5-525ca96297dc","added_by":"auto","created_at":"2025-10-13 06:51:27","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13336,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7602553/v1/2bda1adf4983634f52f7cc66.png"},{"id":93371817,"identity":"0c938290-2ea1-4cf9-8cff-6cab910eaf0b","added_by":"auto","created_at":"2025-10-13 06:51:27","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":130861,"visible":true,"origin":"","legend":"","description":"","filename":"bead64807f4649fbb5850311c219fc881structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7602553/v1/f233fbd40e253d2ca398eade.xml"},{"id":93371816,"identity":"eb2dbb9e-f9a6-4725-a5da-bd2267110c83","added_by":"auto","created_at":"2025-10-13 06:51:27","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":137080,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7602553/v1/cd5fa493abbe71f873b5d9f6.html"},{"id":93372473,"identity":"19065c7b-86eb-46f2-ad05-aa728ade6c05","added_by":"auto","created_at":"2025-10-13 06:59:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":169378,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual model based on theoretcal framework of the study\u003c/p\u003e\n\u003cp\u003eSource: Authors’elaboraton\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7602553/v1/59c5ddc52ab8be8a6b581116.png"},{"id":93371807,"identity":"8a640b27-9e5b-4d5d-aed6-18e7c4252900","added_by":"auto","created_at":"2025-10-13 06:51:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":82750,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3. Examining the significance and size of factor loadings of model\u003c/p\u003e\n\u003cp\u003eSource: Smart PLS software’s output\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7602553/v1/710189e2af40657bdb907a0c.png"},{"id":93371811,"identity":"b4cc2f9e-c588-4797-9b17-9777dadbc129","added_by":"auto","created_at":"2025-10-13 06:51:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":447438,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 2: SEM-PLS analysis of study\u003c/p\u003e\n\u003cp\u003eSource: Smart PLS software output\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7602553/v1/de7a7c02b89f48347f3c0d44.png"},{"id":93372474,"identity":"08649ed2-cce8-426d-91a7-b493d1f3354b","added_by":"auto","created_at":"2025-10-13 06:59:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":116388,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 5. Final model\u003c/p\u003e\n\u003cp\u003eSource: output of Smart Pls software\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7602553/v1/9daa3dd6b826faeb3e7265fa.png"},{"id":93372646,"identity":"10968774-040a-4f48-8a89-0ecb3a812593","added_by":"auto","created_at":"2025-10-13 07:07:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1820878,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7602553/v1/a8a6bde4-0f81-4755-bcf1-3fbd1b35f233.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eModeling Digital Transition in Regional Agro-Food Districts in Ialy : A PLS-SEM Analysis\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe agri-food sector is increasingly confronted with unprecedented global and regional challenges, including rising demand driven by demographic pressures, climate change impacts, and the imperative to ensure food security while maintaining environmental sustainability (FAO, 2019; European Commission, 2020). Within this context, digitalization emerges as a strategic driver of territorial transformation, offering tools to optimize resource use, reduce environmental footprints, and enhance resilience in food systems (Vahdanjoo et al., 2025; Bhardwaj et al., 2025). Yet, the benefits of digital technologies are not equally distributed across regions: rural and peripheral areas often face infrastructural gaps, lower digital literacy, and institutional weaknesses that risk widening existing socio-economic and territorial inequalities (Tewathia et al., 2020; de Clercq et al., 2025). Unlocking the transformative potential of digitalization in food districts therefore requires more than technological innovation alone;\u0026nbsp;it demands regionally grounded governance frameworks, inclusive policies, and an understanding of the sociological and ecological dynamics shaping the digital transition across territories (Geels, 2005; Klerkx \u0026amp; Rose, 2020).\u003c/p\u003e\n\u003cp\u003eThe digitization of agri-food systems has become as both key soloution and \u0026nbsp;urgent need in facing of multi-faceted challenges affecting food systems, including climate change,territorial disruption\u003cu\u003e,\u003c/u\u003e and incoherent practices (Vahdanjoo et al., 2025). Even though substantial investments are made towards the advancement of digital transitions in the agri-food systems , many of them do not sustain their growth due to fragmented governance, lack of multi-stakeholder engagement, and absence of enabling institutional frameworks (Vahdanjoo et al., 2025). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eItalian food districts are composed of rural, agro-food and supply-chain bio districts. They greatly contribute to the regional economy by integrating multiple stakeholders, which consist of farmers, processors, cooperatives, public institutions, as well as civil society organizations. These districts act as a belt governance (Ostrom, 2017) , promote social capital within the district and social multi-functionality, which does not limit the district to economic growth only, but also encourages environmental conservation and cultural heritage preservation (Ostrom, 2017; Poponi\u0026nbsp;et al.,\u0026nbsp;2021). Their meso-structure allows them to connect the micro level of farm activities and the macro level of national and EU policy structures to foster integrated, rural development (Ostrom, 2017).\u003c/p\u003e\n\u003cp\u003eCountries across Europe, including Italy, utilize other district_forms, including clusters, LEADER local action groups, and community-led local development models. These approaches have been recognized as instrumental in achieving the EU's goals of territorial cohesion and sustainability, especially under the European Green Deal and the Farm to Fork Strategy (Ostrom, 2017; European Commission, 2020). However, the effectiveness of these frameworks is reliant on the governance approach, networked relationships, and underlying policy frameworks, which are often overlooked in the analysis of digital transformation. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMost studies examining the transition and adoption of technological tools within agriculture disassociate the digital device from the broader socio-institutional context. This narrow view ignores the intricate relationships among governance systems, stakeholder networks built on ecosystem limits (Ritzema et al., 2019; Ostrom, 2017; Folke et al., 2005; Rhodes, 1997). These interdependencies are crucial in averting the risks of reinforcing existing inequalities, or completely failing to achieve the objectives of digital transformation.\u003c/p\u003e\n\u003cp\u003eThis study uses Partial Least Square Strutural EquationModel (PLS- SEM) approach (Sohail, 2023; Tama et al., 2023) to \u0026nbsp;develop and empirically validate the conceptual model that integrates gaps of concern by digitally transitioning to contextual, social, and governance factors. The approach captures the multifactorial nature of digital transition in agro-food districts and how governance structures, participation, sustainability frameworks, and socio-territorial specificities impact digitalization (Martínez-Filgueira et al., 2022; Kunal et al., 2025) . \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the current policy window for digital and green transitions, the study provides both theoretical and practical contributions (Yuan et al., 2025). It aims to provide policy and district level decision makers, along with local actors, strategies based on scientific evidence designed for participatory, context-sensitive, and ecological innovation pathways. The conclusions are not limited to Italy but extend to other European regions seeking digitally facilitated rural development embedded within territorial agri-food systems (Yuan et al., 2025; Brounori, 2022; Mol \u0026amp; Sonnenfeld, 2014; Barca et al., 2012).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The main objective \u0026nbsp;of the research is to assess the impacts of impacting factors on the digital transformation of Italy’s food districts, focusing on governance, digital adoption, contextual factors. Therefore the main question is which factors have the most significant impact on the digital transition in IAFDs? More specifically, the objectives lead to the following set of research questions:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;1. What is the impact of governance structure \u0026nbsp;on digital transition \u0026nbsp;overall the IAFDs?\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;2. What impacts do stakeholders’ behavioral components like attitudes, social norms, and perceived control ( digital adoption pillars) \u0026nbsp;have on the digital transition?\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 3. What is the impact of contextual \u0026nbsp;factors , including financial, legal and structural status on digital transition \u0026nbsp; overall the IAFDs?\u003c/p\u003e"},{"header":"2. Theoretical Framework ","content":"\u003cp\u003eThis study attempts to understand the complex factors that catalyze the digital transition of Italian Agro-Food Districts (IAFDs) and goes beyond the factors of technology adoption to include the governance factor, the social and legal structure , and other contextual elements. In the process of constructing the theoretical framework of this literature review, it becomes clear that effective digital transition in IAFDs is not a function of only having the requisite resources, technology, but rather the outcome of the synthesis of all these interrelated factors. Each section below discusses relevant literature and subsequently formulates a hypothesis that is designed to answer the research questions posed in the introduction.\u003c/p\u003e\n\u003cp\u003eThe Governance in IAFDs is not accomplished in a vacuum, but rather is conditioned by the context under which these districts function. Context encompasses factors such as the economic environment (funding and investment opportunities), legal and regulatory environment (policies that aid or impede digital innovation), and the socio-structural environment (social capital, trust, and knowledge networks) of the territory (Brunori, 2022; Muluneh, 2021; Forney \u0026amp; Epiney, 2022; Mendes \u0026amp; Viola, 2023). Thus, these contextual factors for enabling and constraining forces need to be understood and addressed in order to successfully digital transform IAFDs. Drawing on the literature on institutional economics and regional development, we propose the following: \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eH1: Positive contextual elements such as legal and financial frameworks, infrastructure, contextual structure within Italian Agro-Food Districts strongly enhance the overall governance in IAFDs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMore recent research gives focus to understanding how multi-level governance aids in addressing digital transformation challenges as multifaceted agro-food districts (Gao, 2025; Alabrese \u0026amp; Saba, 2023; Atik, 2022; Ehlers et al., 2021). Concerning IAFDs, governance systems include the national, legal and local policies as well as systems through which different actors within the district\u003cspan dir=\"RTL\"\u003e\u0026rsquo;\u003c/span\u003es development framework interact, negotiate and implement actions as defined by the development strategies (Muluneh, 2021; Martens, K., \u0026amp; Zscheischler, 2022; Vahdanjoo et al., 2025; Sun et al., 2022). The literature on regional innovation systems has noted the importance of governance for technological innovation barriers to adopt technology and promote collective learning (Cooke et al., 1997). In the case of IAFDs, this means that governance frameworks that enhance collaboration between farmers, processors, supportive research institutions, and public bodies are essential for effective digital transition (Forney \u0026amp; Epiney, 2022). Drawing from the above-mentioned literature, we hypothesize the following: \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eH2: The governance structures in Italian Agro-Food Districts, as to their nature and effectiveness, positively impact the digital transition within those districts.\u003c/p\u003e\n\u003cp\u003eFurthermore, within IAFDs, the adoption of digital technologies is essentially a step in a workflow that is controlled by numerous choices made by farmers and other value chain participants. The Theory of Planned Behavior (Ajzen, 1991) explains best these intentions and actions for adoption. It states that intention to perform a certain behavior in the future, which is the immediate precursor of a certain action is a product of three factors: attitude towards the behavior (evaluation of the behavior as either positive or negative), subjective norms (perception of social pressure to engage or not to engage in the behavior), and perceived behavioral control (individual\u003cspan dir=\"RTL\"\u003e\u0026rsquo;\u003c/span\u003es belief about his.her ability to perform the behavior).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the case of farmers, support from relevant social groups and their confidence toward the use of digital agriculture tools together with positive perceptions about the advantages of these tools drive digital technology adoption (Gabriel \u0026amp; Gandorfer, 2023). Aditionally, the aforementioned behavioral components can be enhanced by stakeholder engagement and participation towards the digital transition that enhances ownership and collective efficacy (sun et al., 2022). Building upon the Framework of Planned Behavior and the relevant works regarding stakeholders participation, we propose that:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eH3: The Agri-food stakeholders\u003cspan dir=\"RTL\"\u003e\u0026rsquo;\u0026nbsp;\u003c/span\u003eperceptions of digital adoption barriers and enabling factors, positive attitudes towards new technologies and social perceptions held by peers and leaders within the region strongly predict the level of digital adoption which positively-alters the digital transition phenomenon in the district.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, in the context of Italian agro-food districts, the digital transformation entails more than just the technological or organizational dimensions. It is also deeply linked to green transition practices. This hypothesis (H4) draws from ecological modernization theory (Mol \u0026amp; Sonnenfeld, 2000) alongside socio-technical transitions theory (Dayıoğlu \u0026amp; Turker, 2021), which both focus on the interplay between environmental practices and technological innovation.\u003c/p\u003e\n\u003cp\u003eAccording to ecological modernization theory, fostering a sustainable environment can act as a powerful incentive, encouraging various stakeholders to develop new form-oriented technologies and governance systems. Within agro-food districts, the adoption of green practices such as circular economy principles, biodiversity, and water sustainability creates a necessity and opportunity for digital systems that monitor, optimize, and verify ecological outcomes (Poponi et al., 2021; Eastwood et al., 2019).\u003c/p\u003e\n\u003cp\u003eWhen viewed through the lens of socio-technical transitions, green practices can be considered as niche innovations that confront established production and consumption systems (Eastwood et al., 2019). In a more applied form, this hypothesis suggests that the more advanced the food districts\u0026rsquo; ecological practices, the more advanced the district\u0026rsquo;s digital capabilities are developed, not only to improve efficiency, but also to monitor and enhance environmental performance. These districts are more willing to adopt measures that integrate the environmental and digital transitions powered by data technologies aimed at achieving sustainability. Consequently, the model claims that green practices within agro-foods districts are fundamental enablers not only from an ecological perspective but also for the acceleration of the digital transition\u0026mdash;because it stimulates the demand for digital tools, alters governance logics, and sets digital actions within ecological sociocultural frameworks.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHypothesis H4: The integration of green practices in Italian food districts alongside the ecological transition positively influences the level of digital transition in the districts.\u003c/p\u003e\n\u003cp\u003eThis theoretical foundation, seeks to understand the integrated social and governance drivers of digital transition in Italy\u003cspan dir=\"RTL\"\u003e\u0026rsquo;\u003c/span\u003es Agro-Food Districts using a PLS-SEM methodology to test the digital transition hypotheses. The The outcomes will inform policy and practice on how to promote responsible digitally enabled innovations in the regional food systems.\u003c/p\u003e"},{"header":"3. Descriptive analysis of data","content":"\u003cp\u003eThe descriptive analysis emerging from responses of the 29 regional representative\u0026rsquo;s primary data confirms the diversity, inequality and changing dynamics of the food districts in Italy as summarized in Table\u0026nbsp;1 in the annex. Structurally, more than a third of the districts concentrate on the production of locally grown agricultural and high-value food commodities. The rest are in the value-adding organic production agro-industrial supply chains. The greatest concentrations of food districts are within the agro-ecological regions of Italy and are in Sicily, Campania, and Lombardy. These regions almost all contain food districts.\u003c/p\u003e\u003cp\u003eMost districts (55%) having less than 4 years of existence are a positive indicator of growth since expansion sustains more than 15 years of operation within a small number of districts, which means a steady expansion pattern. Licensing is diverse. Governance is usually structured and informal, with 70% of the districts with legal entities having multiple licenses. This results in more than 15 years of operation within the districts being informally owned. The low level of public ownership in the districts relates to the mixed and private ownership derived from the hybrid governance of the Italian agri-food clusters. The paradox is that everyone agrees to work on ecologically sustainable practices and to some extent ecologically friendly practices, but only about a third of the participants totally disengaged from self-driven strategies having formal road maps.\u003c/p\u003e\u003cp\u003eDistricts primarily depend on self-financing and public funding, neglecting traditional bank loans and venture capital entirely. Focus areas include quality and regional branding, as well as productive investment, with recently rising attention to diversification and environmental management. Environmental sustainability continues to be paramount, along with strong emphasis on adopting eco-friendly processes and obtaining green certifications.\u003c/p\u003e\u003cp\u003eRegarding the level of technology adoption, the respondents are more appreciative of practical digital tools like compliance applications and management, marketing and trade platforms, ignoring more advanced tools like AI, block chains, and 3D printing. Barriers involve scarce digital skills, management costs, and inadequate infrastructure and support like technical and research transfer services.\u003c/p\u003e"},{"header":"4. Methodology","content":"\u003cp\u003eItaly's agro-food districts were studied through the lens of governance, stakeholder behavior, contextual factors, green practices, and digital transition using PLS-SEM. The empirical model was validated using Partial Least Squares Structural Equation Modeling (PLS\u0026ndash;SEM). PLS-SEM is a multivariate technique useful in estimating complicated causal relationships in models with several latent variables and indicators. It is ideal for exploratory research or predictive models in their developmental stages. Hair et al. (2022) emphasizes the versatility of PLS-SEM, noting its effectiveness in predictive and exploratory research scenarios. After PLS-SEM estimates the outer models, it tests reliability and validity alongside testing the inner model for hypothesized relations, which strengthens insights into direct, indirect, and total effects among constructs.\u003c/p\u003e\u003cp\u003eThe use of PLS-SEM was motivated by multiple aspects within the scope of this study. First, in regard to the overarching theme of digital transition within agro-food districts\u0026mdash;with its governance, societal, ecological, and contextual intricacies\u0026mdash;its novel and multifaceted character alongside the interdependency relations PLS-SEM accommodates makes it well-suited, as it does not have to rely on strict distribution assumptions like normality. Furthermore, the limited sample size (n\u0026thinsp;=\u0026thinsp;29 districts) poses a restriction for utilizing covariance-based SEM, since PLS-SEM focuses on variance enables its use in small sample settings and predictive models (Hair et al., 2019). Lastly, we chose SmartPLS 4 due to its intuitive features and more advanced algorithms that allow for easy iterative model sharpening, calculation of path coefficients, and evaluation of critical hypothesis mediating effects essential to our model.\u003c/p\u003e\u003cp\u003eThe methodological process comprised several key steps. First, we constructed a measurement model by defining reflective constructs governance structures, stakeholder behavioral dimensions, contextual factors, green practices, and digital transition. Evaluation of indicator reliability (outer loadings), construct reliability (composite reliability and Cronbach\u0026rsquo;s alpha), convergent validity (average variance extracted), and discriminant validity (Fornell-Larcker criterion and HTMT ratios) was performed. These steps confirmed the reliability of the constructs and their theoretical dimensions within our conceptual framework.\u003c/p\u003e\u003cp\u003eNext, we evaluated the structural model for the relationships between constructs to verify the hypotheses. To obtain stable estimates of significance, we calculated bootstrapped path coefficients (β) with 5000 resamples. Model fit was evaluated through the R\u0026sup2; values for the endogenous constructs, predictive relevance (Q\u0026sup2;) from blindfolding, and effect sizes (f\u0026sup2;). Stakeholder behavioral components were treated as mediators for the indirect effects with governance structures as the predictor and digital transition as the outcome for mediation analysis. This thorough PLS-SEM approach provided a full picture of the interaction of governance, social factors, and ecological determinants on the digital evolution of the agro-food districts of Italy.\u003c/p\u003e"},{"header":"5. Conceptual Model Design the SEM-PLS","content":"\u003cp\u003eThe conceptual model provided in the Fig.\u0026nbsp;1 demonstrates the interplay of governance, contextual variables, technology adoption, and ecologic engagements in facilitating the digital transformation of Italian Agro-Food Districts (IAFDs). This specific model was created in order to validate the four hypotheses described in the theoretical framework.\u003c/p\u003e\u003cp\u003eThen the conceptual model was operationalized using the PLS-SEM method in the SmartPLS 4 software. The SmartPLS graphics of conceptual model (Fig.\u0026nbsp;2) depict the itemized loadings of the reflective measurement models along with the significant structural paths, denoted by the red lines showing relationships of importance, between constructs. The model is evaluated with bootstrapping using subsamples to test the significance of path coefficients and mediation effects while also evaluating R\u0026sup2; values to assess the model's predictive capabilities.\u003c/p\u003e\u003cp\u003eAt the center of the model stands the construct \u0026ldquo;Digital Transition\u0026rdquo; which indicates the assimilation and progress of (IAFDs) agro food districts in adopting modern practices and technologies. Its impacts are directed through governance captured by the effectiveness, inclusiveness, and the overall capacity of the governance systems. The \u0026ldquo;Governance\u0026rdquo; construct is in turn influenced by contextual variables Legal Structure, Infrastructure, Contextual Structure, Financing \u0026amp; Funding Vehicles which shower the governance (H1) with legal and sociocommunal support. All these contextual variables were measured reflectively (e.g., q2 \u0026ndash; q14, q9, q10, q11, q12, q13, q14, q16, q45).\u003c/p\u003e\u003cp\u003eHypothesis H2 is assessed through the pathway Governance to Digital Transition which assumes that well-structured governance leads to more effective digital transformations in food districts. This interaction is assessed through governance and digital transition indicators (e.g., q2, q3, q4, q9, q11, q12, q12a, q13, q14, q16, q19, q20, q22\u0026ndash;q27, q45).\u003c/p\u003e\u003cp\u003eThe stakeholder behavioral elements of barriers and enabling factors and social norms fit under the label \"Digital Adoption.\" This label predicts the Digital Transition variable, thus corroborating H3, which posits that stakeholders\u0026rsquo; perceptions and readiness to engage digitally impacts adoption, and, in turn, drives digital transformation. Indicators reflectively measuring digital adoption attitudes are found in (q23\u0026ndash;q27).\u003c/p\u003e\u003cp\u003eEcological Practice, measured by indicators (q19, q20, q22), is included for testing H4\u0026mdash;Investigating the role of green and ecological transition practices in IAFDs as dual catalysts of digital transformation. This variable has a direct impact on the Digital Transition construct, demonstrating the synergy that exists between ecological innovation and the adoption of digital technologies in sustainable food districts.\u003c/p\u003e\u003cp\u003eThis comprehensive PLS-SEM strategy substantiates all four hypotheses with empirical evidence within the context of Italy's agro-food districts, emphasizing the complexity and integrative aspects of digital transformation.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e5.2. Assessing Relevance and Magnitude Of The Factor Loadings:\u003c/h2\u003e\u003cp\u003eIn order to assess the reliability of the items in capturing their respective constructs, the factor loadings of the measurement model were analyzed. In structural equation modeling, a factor loading greater than 0.70 is deemed ideal (Klein), albeit a minimum of 0.40, as Holland posited, is accepted for exploratory frameworks. In this research, the accepted minimum limit of factor loading was retained at 0.40. The initial conceptual model and factor loadings of all items are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFactor loadings in the relationship between constructs and items in the conceptual model under study .\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStructure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuestion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFactorial load\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStructure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQuestion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFactorial load\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eDigital Adoption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInfrastructure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eContextual Structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.09-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eLegal structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"18\" rowspan=\"19\"\u003e\u003cp\u003eDigital Transition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e\u003cp\u003eGovernance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.03-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eEcological Practice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFinance \u0026amp; Funding Structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFrom the evaluation of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e3\u003c/span\u003e, a number of items are noted to have factor loadings below the 0.40 cut-off point, signifying weakness in representational power for their latent constructs. To enhance model reliability and validity, these low loading items were removed from the measurement model. In particular, items related to legal structure, governance, and some contextual constructs showed factor loadings below the acceptable level.\u003c/p\u003e\u003cp\u003eAll changes have been incorporated into the conceptual model, which is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Based on this improved version, all remaining items have factor loadings greater than 0.40, which fulfills the previously set threshold for reliability and confirms that the constructs are well captured in the final model. As a result of this refinement process, the holistic credibility of the measurement model is improved, which serves as a solid basis for assessing the structural pathways within the SEM-PLS framework.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e5.3. Examining the validity and reliability of the questionnaire\u003c/h2\u003e\u003cp\u003eThe verification of the reliability and validity of the constructs within the structural model was performed after completing the validation preliminary tests. This was done in succession of the confirmatory factor analysis that ensures model identification and validity accuracy takes place within the model instrument with sufficient factor loadings. Validity was evaluated through both convergent and discriminant (diverging) validity measures, whereas reliability was assessed through these measures as well.\u003c/p\u003e\u003cp\u003eConvergent validity can be assessed using AVE which tells the ratio of variance a construct captures realatively to the variance captured through measurement error. Validity can be established based on AVE values. A value of 0.50 according to Fornell and Larcker (1981) indicates sufficient convergent validity. These values are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e4\u003c/span\u003e showcasing AVE values for every construct. This suggests that most of the constructs did meet the limit but the ones failing were Digital Transition (0.43), Ecological Practice (0.41), Governance (0.45) and especially Legal Structure (0.36) which was considerably lower than expected. While it can be accepted these constructs encapsulate their intended variance, the results do indicate there is a degree of measurement problem or conceptual overlap needing less refinement in future studies.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAverage\u003cem\u003e​\u003c/em\u003e Variance Extraction Done Agent I see In Model Conceptual Case Study\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage Variance Extracted (AVE)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eContextual Structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigital Adoption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigital Transition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEcological Practice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinance \u0026amp; Funding Sources\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfrastructure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLegal Structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eDiscriminant validity was checked by comparing the cross loadings of the constructs with the square root of their AVE (Fornell-Larcker criterio), as well as performing a cross loading test. The square root of the AVE is supposed to be greater than the correlations of the constructs to any other latent variable which allows to confirm that the boundaries of the constructs are different. For cross loading tests, if an item with it\u0026rsquo;s own construct gets a higher correlation than with other constructs, discriminant validity is supported.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e5.3.1 Cross Loading Test\u003c/h2\u003e\u003cp\u003eIn relation to these results, the model meets the requirements of the cross loading test since the correlation of each indicator with their factor was greater than the correlation with the other constructs. The evidence made by the gray cells of the cross loading matrix verify the cross validity of the model and corroborate the measurement model of the study. All validity methods, collectively strengthen the SEM-PLS analysis by enabling thorough testing of the structural relationships in the system.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ecorellation measurement\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContextual Structure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDigital Adoption\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDigital Transition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEcological Practice\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFinance \u0026amp; Funding Sources\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGovernance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eInfrastructure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLegal Structure\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eq4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.696\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.173\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eq11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.803\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eq12a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.636\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.181\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eq13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eq14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.493\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eq16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.335\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eq19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.198\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eq20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eq22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.387\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eq25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.132\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eq26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eq27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.484\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.144\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eq45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.062\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e5.3.2 Fornell-Larcker Test Results\u003c/h2\u003e\u003cp\u003eIn the discriminant validity assessment, the Fornell-Larcker criteria was employed. This method involves assessing if the square root of a construct\u0026rsquo;s AVE value is greater than the correlation it has with other constructs. The square root of the AVE values which form the principal diagonal of the Fornell-Larcker matrix should be greater than the correlation of that particular construct with all other latent variables.\u003c/p\u003e\u003cp\u003eAs is clear from the matrix, this requirement is satisfied by all first-order constructs of the study: the square root diagonals (square roots of AVEs) are greater than all off-diagonal correlations. This demonstrates that every construct captures a greater portion of variance with its respective indicators than with other consturcts, thereby supporting divergent validity and achieving a satisfactory overall model fit. Therefore, the measurement model confirms that the model sufficiently captures distinct conceptual dimensions in the analysis of digital transformation of Italian agri-food districts.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFornell-Larcker test results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContextual Structure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDigital Adoption\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDigital Transition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEcological Practice\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFinance \u0026amp; Funding Sources\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGovernance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eInfrastructure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLegal Structure\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eContextual Structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigital Adoption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigital Transition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEcological Practice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinance \u0026amp; Funding Sources\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfrastructure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLegal Structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.596\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e5.3.3. Checking the Reliability of the Questionnaire Using Cronbach\u0026rsquo;s Alpha and Composite Reliability\u003c/h2\u003e\u003cp\u003eAfter establishing validity, reliability was calculated with Cronbach\u0026rsquo;s alpha and composite reliability, using composite reliability as the main criterion within the context of structural equation modeling. Both indices for the constructs are provided in the Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Consistent with suggestions that composite reliability should exceed 0.70 to ensure reasonable reliability, most constructs in this study have met this benchmark. The Ecological Practice and Legal Structure constructs, however, do have composite reliability figures slightly less than 0.70, raising possible concerns regarding the measurement of these two factors and indicating that further work is needed in these areas. In any case, the analysis of reliability indicates that most constructs show high internal consistency, which adds confidence in the quality of the measurement model.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eReliability​ Alpha Cronbach and Reliability\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCronbach's Alpha\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eComposite Reliability\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eContextual Structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigital Adoption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.886\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigital Transition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.797\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.851\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEcological Practice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.647\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinance \u0026amp; Funding Sources\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.735\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.766\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfrastructure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLegal Structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.603\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 PredictivFigure 2: SEM-PLS analysis of study\u003c/h2\u003e\u003cp\u003ee Capability\u003c/p\u003e\u003cp\u003eThe ability to make predictions about future occurrences with a conceptual framework stands as a significant aspect of its suitability and utility. In this research, the associated Governance and Digital Transition constructs were able to explain 98% and 99%, respectively, of the variance in the outcomes associated with these constructs, indicating very high predictive ability (stronger than what is required). These values are bound to the ensure structural strength of the parameters of the model.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e5.3.4. Validation of Overall Fit of The Model\u003c/h2\u003e\u003cp\u003eThe overall model fit, including measurement and structural ones, was assessed with the GOF metric as defined by Tenenhaus et al. (2004). The GOF index is defined as the square root of the average communality multiplied by the mean R\u0026sup2; of the endogenous constructs. In this research, the R\u0026sup2; values for the endogenous variables are 0.36 (strong), 0.25 (moderate), and 0.01 (weak), indicating multi-level predictive capacity (and some, even low levels of, prediction inaccuracy) across the model. All in all, these findings are sufficient for confirming the adequacy and consistency of the model for understanding the digital transition processes in the agro-food districts in Italy.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:GOF=\\sqrt{\\overline{Communality}\\times\\:\\overline{RSquare}}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:GOF=\\sqrt{0.99*0.44}=0.66$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe overall fit of the conceptual model is confirmed because the size of the fit index is strong .\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e5.4. Coefficient Path (P-Value) Relationships Between the Factors of the Model\u003c/h2\u003e\u003cp\u003eFrom Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e8\u003c/span\u003e, we can observe that the structural model analysis using the PLS-SEM technique has been carried out, with the primary focus of the study being the path coefficients (value) and their associated t-value, p-value, and significance level within the relationships of the core constructs of the conceptual model of digital transition in Italy\u0026rsquo;s agro food districts.\u003c/p\u003e\u003cp\u003eThe table illustrates the effect of a number of contextual factors such as Infrastructure, Contextual Structure, Legal Structure, and Finance \u0026amp; Funding Sources on Governance. Both Infrastructure and Contextual Structure have positive and significant impacts on Governance (β\u0026thinsp;=\u0026thinsp;0.40, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which means that the available infrastructure as well as the socio-contextual factors have a value-add toward governance. The Contextual Structure also shows positive impact on Governance (β\u0026thinsp;=\u0026thinsp;0.40, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) alongside Legal Structure which does not pose relevant impact (β\u0026thinsp;=\u0026thinsp;0.001, p\u0026thinsp;=\u0026thinsp;0.947), indicating that the current legal structures being utilized tend not to add substantial value toward enhancing governance within this context. In regard to Finance \u0026amp; Funding Sources, these have proven to be the dominating determinants of Governance (β\u0026thinsp;=\u0026thinsp;0.52, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), affirming that financial resources significantly contribute toward the effectiveness of governance systems.\u003c/p\u003e\u003cp\u003eThe table provides the detail of the various causes of Digital Transition. It shows that the effect of Digital Adoption on Digital Transition is positive and substantial (β\u0026thinsp;=\u0026thinsp;0.57, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting the hypothesis that the stakeholders\u0026rsquo; attitude towards adoption and technology usage is pivotal for wider digital transformation. Governance positively affects Digital Transition as well (β\u0026thinsp;=\u0026thinsp;0.41, p\u0026thinsp;=\u0026thinsp;0.002), supporting the claim that sound and participatory governance promotes the use and expansion of digital technologies. Finally, Ecological Practice, representing green practices and measures of sustainability, has a positive impact on Digital Transition (β\u0026thinsp;=\u0026thinsp;0.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) which proves the relationship between environmental sustainability and digital innovation.\u003c/p\u003e\u003cp\u003eAll together, these data strengthen the hypotheses of the study, illustrating the complex character of the digital transformation within the Italian agro-food districts. The emphasized significant paths indicate that although regulatory policies may have little relevance in improving governance, the most important drivers of digital transition are found in infrastructure and financial resources, alongside the adoption of digital technologies and ecological practices.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCoefficient​ Path ( P - Value ) relationships among factors within the model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eStandardized Path Coefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003et(P-value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSignificance\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrom\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTo\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfrastructure​​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGovernance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.51(0.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eContextual Structure​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGovernance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.64(0.009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLegal Structure​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGovernance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.07(0.947)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinance \u0026amp; Funding Source\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGovernance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.76(\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigital Adoption​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDigital Transition​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.09(\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernance​​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDigital Transition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.15(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEcological Practice​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDigital Transition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.60(\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e5.5. Understanding the Final Model and Structural Relationships\u003c/h2\u003e\u003cp\u003eThe last PLS-SEM conceptual model captures the interaction of governance, digital adoption, ecological practices, and the digital transition in the context of the Italian Agro-Food Districts (IAFDs), depicting also the interrelations of infrastructure, contextual factors, legal frameworks, and financial resources as their foundation. The standardized path coefficients along with their respective p-values are provided on the arrows of the model\u0026rsquo;s figures (Fig.\u0026nbsp;5) .\u003c/p\u003e\u003cp\u003eInfrastructure has a strong, positive, and direct impact on Governance (β\u0026thinsp;=\u0026thinsp;0.40, p\u0026thinsp;=\u0026thinsp;0.01). Thus, it can be confirmed that enhancements in infrastructure like digital and logistic connections translates to increased governance effectiveness, meaning that an increase in governance score is observed when there is an increase in infrastructure score. To put this differently, if the infrastructure score increases by one unit, governance undergoes an average increase of 0.40.\u003c/p\u003e\u003cp\u003eContextual Structure also has a strong, positive and direct impact on Governance (β\u0026thinsp;=\u0026thinsp;0.40, p\u0026thinsp;=\u0026thinsp;0.01). This means that it is equally important to have supportive contextual drivers of social cohesion, networks of partnership, and local culture to promote governance, adding another 0.40 to governance for a total of 0.80 unit increase for each unit increase in these social variables.\u003c/p\u003e\u003cp\u003eLegal Structure does not have any significant impact on Governance (β\u0026thinsp;=\u0026thinsp;0.001, p\u0026thinsp;=\u0026thinsp;0.95) and this confirms that the legal instruments shaped in these forms do not directly influence governance in these districts meaningfully.\u003c/p\u003e\u003cp\u003eFinancial Resources (Finance \u0026amp; Funding Sources) have the strongest direct positive effect on Governance (β\u0026thinsp;=\u0026thinsp;0.52, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This finding reinforces the importance of financial resources and investments in effective governance since every unit increase in financial resources comes with average 0.52 unit increase in governance.\u003c/p\u003e\u003cp\u003eConcerning digital transformation, Digital Adoption has a noteworthy positive impact on the Digital Transition (β\u0026thinsp;=\u0026thinsp;0.57, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This shows that positive behavior and willingness from stakeholders toward technology is instrumental in driving the advanced capabilities; boosting digital adoption scores contributes 0.57 units to digital transition for every one unit increase.\u003c/p\u003e\u003cp\u003eGovernance has positive direct impact on Digital Transition as well (β\u0026thinsp;=\u0026thinsp;0.41, p\u0026thinsp;=\u0026thinsp;0.00). This affirms strong governance as a requisite pillar for the successful scaling and implementational digitization within the districts.\u003c/p\u003e\u003cp\u003eLastly, Ecological Practice\u0026mdash;which embodies efforts towards green practices and sustainability\u0026mdash;displays a notable encouraging and direct impact in the digital transition (β\u0026thinsp;=\u0026thinsp;0.21, p\u0026thinsp;=\u0026thinsp;0.00). This finding underpins the hypothesis that ecological and digital transitions are synergistically beneficial: every unit increase in Ecological Practice score allows a 0.21 average increase in Digital Transition.\u003c/p\u003e\u003cp\u003eAll these validated pathways together advance the digitally transformative equilibrium model, to offer new insights into the dynamics of change in Italy's agro food districts with special focus on the central role of governance, social and physical infrastructure, finances, stakeholder commitment and ecological engagement.\u003c/p\u003e\u003c/div\u003e"},{"header":"6. Discussion","content":"\u003cp\u003eThis study aimed to explore the intricate relationships among the social mechanisms, governance structure, context, finances and laws, adoption behavior of technology, and ecological actions taken in regard to digital transition within Italy\u0026rsquo;s Agro-Food Districts (IAFDs). The outcome strongly supports the majority of the hypotheses and provides additional more subtle understanding which corresponds to the existing literature on rural digitalization and governance.\u003c/p\u003e\u003cp\u003eH1 suggested that legal and financial frameworks, social and contextual structures, as well as infrastructure, create a context which strongly explains governance effectiveness in IAFDs. Our findings confirm this hypothesis because infrastructure (β\u0026thinsp;=\u0026thinsp;0.40, p\u0026thinsp;=\u0026thinsp;0.01), contextual structure (β\u0026thinsp;=\u0026thinsp;0.40, p\u0026thinsp;=\u0026thinsp;0.01), and financial resources (β\u0026thinsp;=\u0026thinsp;0.52, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) all had significant positive effects on governance. These results were consistent with the multi-level governance approach (Hooghe \u0026amp; Marks, 2001) as well as network governance theory (Rhodes, 1997), which identifies social, financial, and infrastructural resources as critical enablers of fragmented governance collaboration. These have also been shared by Vahdanjoo et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), who describe local social capital and supportive infrastructures as salient features of governance effectiveness in Italian food districts. The absence of a significant effect from legal structure (β\u0026thinsp;=\u0026thinsp;0.001, p\u0026thinsp;=\u0026thinsp;0.95) provides supporting arguments that legal policies, although essential, are not synergistic enough to enhance governance in fractured or history-dependent systems lacking institutional and financial aid (Gao, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Alabrese \u0026amp; Saba, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Atik, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ehlers et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eH2 posited that governance systems have a favorable effect on the rate of digital transition in IAFDs. This is strongly confirmed by our data (β\u0026thinsp;=\u0026thinsp;0.41, p\u0026thinsp;=\u0026thinsp;0.00), reinforcing theories of polycentric governance (Forney\u0026amp; Epiney, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and adaptive governance (Mulneh, 2021), which argue that innovation, whether technological or social, in complex systems is enabled by participatory and flexible governance frameworks. These results are consistent with finding that governance networks in rural settings afford the trust and legitimacy needed for digital transitions (Forney\u0026amp; Epiney, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mol \u0026amp; Sonnenfeld; \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The findings also support Geels' (2005) multi-level perspective to the extent that it shows governance structures provide a \u0026lsquo;regime\u0026rsquo; layer that facilitates or constrains the use of digital tools as emerging innovations within the system.\u003c/p\u003e\u003cp\u003eH3 described how the perceptions of enabling and disabling factors, mobile attitudes, and social norms of agri-food stakeholders predict the degree of digital adoption and, subsequently, the progression towards a digital transition. This hypothesis was confirmed because the effect of digital adoption on digital transition is strong and significant (β\u0026thinsp;=\u0026thinsp;0.57, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting the Technology Acceptance Model (Davis, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Both models argue that a person\u0026rsquo;s beliefs, subjective perceptions surrounding those beliefs, and control over the situation influence the adoption of new technology. In addition, our results also support Sunet al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who underscored the adoption sociological perceptions and trust networks, particularly the rural and agro-food systems, in regard to their adoption processes.\u003c/p\u003e\u003cp\u003eH4 argued that the combination of green policies and environmental shifts has a positive effect on the digital transition of IAFDs. The relationship was indeed robust and positive (β\u0026thinsp;=\u0026thinsp;0.21, p\u0026thinsp;=\u0026thinsp;0.00), which corroborated the hypothesis and makes sense with ecological modernization theory (Mol \u0026amp; Sonnenfeld, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), which claims that technological development and sustainability can support each other. This synergy is further supported by Yuan et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) who claim that digital technologies in remote rural food systems tend to develop as instruments for monitoring green practices, thus reinforcing the nexus between green and digital transitions. This finding exemplifies how greater ecological concern increases the likelihood of adopting digital technologies integrating broader sustainability frameworks (Bhardwaj et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mendes \u0026amp; Viola, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Martens \u0026amp; Zscheischler, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eIt certainly notes that efficient digital transitions in rural agro-food systems are responsive to change from social-ecological systems. It might be worth stating as a hypothesis that the social-ecological system is the governance system with all the constituents around it, a legal approach does not seem sufficient by itself, but creates conditions for the governance system to catalyze a profound systemic change when coupled with financial support, social context, infrastructural funding, and green policy frameworks; (Dayıoğlu \u0026amp; Turker, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Eastwood, 2019; Barca et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) including the specific socio-territorial and ecological aspects of rural areas. Equally, the predominant impact of stakeholder adoption behavior indicates that the readiness and learning campaigns, as well as social capital nurturance, intervention strategies, should be prioritized for policy actions aimed at transforming digital infrastructure in rural regions.\u003c/p\u003e\u003cp\u003eOverall, the outcomes of the research deepen the understanding of the digitally-enabled transformation processes in the agro-food districts by validating the need for a comprehensive, multi-dimensional and systems approach\u0026mdash;consisting of the governing frameworks, stakeholder actions, funding and social support structures, and environmental engagement\u0026mdash;to shape the digitally transformative future of Italy\u0026rsquo;s rural regions.\u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis research was undertaken to thoroughly analyze the drivers of the digital shift in Italy\u0026rsquo;s Agro-Food Districts (IAFDs) focusing on the governance system, social and contextual system, actor paradigms, and ecological practices to understand their interplay towards digital transformation of rural agrifood systems. Using the PLS-SEM approach, the evaluation has effectively articulated answers to the research problems within the scope of rural innovation and sustainability.\u003c/p\u003e\u003cp\u003eThe study illustrates that the perceptions and behaviors of stakeholders concerning the adoption of digital technologies is the main determining factor of the digital transition, depicting strong reliance on social constructs and human actions. Governance frameworks remains crucial as well, reinforcing the concept that socio-technical systems approach is more appropriate since the digital transformation process is mainly guided to, through vigorous hands-on governance, decision making, and cooperation. This also approaches the increasing streams of research that regards the restructuring of rural areas with advanced technologies and perspectives, as the outcome of sound governance enables coordination among diverse rural actors.\u003c/p\u003e\u003cp\u003eFinancial resources, infrastructure, and sociocultural context make the most significant contributions to the capacity of governance systems. In contrast, legal frameworks have little impact on the governance results in this case. As such, the governance of regions undergoing digital transitions must be socially capitalized on and financially invested in to be more effective, thus requiring targeted approach methodologies. In conjunction with other findings, the impact of sustainability practices on digital transition in the context of agro-food systems suggests the growing importance of the intersection between the adoption of modern technologies and ecological practices, which strengthens the understanding of the combined green and digital transitions phenomena.\u003c/p\u003e\u003cp\u003eThis research highlighted the systemic and multi-dimensional nature of the digital transition in Integrated Agri-Food Districts (IAFDs). It simultaneously emphasizes the advanced sustenance requirement of strategic technological resources and ecologically responsible governance, social unity, and resilient financial frameworks. Sustainable digital strategies in Italy\u0026rsquo;s agro-food landscapes focus on the need to actively involve citizens and stakeholders in decision-making and consider integrated, participatory governance frameworks alongside rural infrastructure development. The study contributes towards the discourse around rural innovation, particularly the digital transformation of rural agri-food systems, highlighting the importance of adapting to regional inequality threats and drawing attention towards ecological impact and collaborative innovation approaches.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.A. conceived the research design, carried out the data collection and analysis, and wrote the initial draft of the manuscript. V.A. contributed to the development of the conceptual framework, supervised the methodological approach, and provided critical revisions to strengthen the analysis. P.L.S. offered overall supervision, contributed to the theoretical framing, and provided critical feedback to refine the final version of the manuscript. All authors reviewed, edited, and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors gratefully acknowledge the use of DeepL Write for language editing support, which assisted in improving the clarity and readability of the English in this manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003e\u0026ldquo;Data is provided within the manuscript or supplementary information files\u0026rdquo;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlabrese, M., \u0026amp; Saba, A. (2023). Digitalising Agricultural and Food Systems: policy challenges and actions for the sustainable transition in the EU. \u003cem\u003ePerspectives on Federalism\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(3), 34-47.\u003c/li\u003e\n\u003cli\u003eAjzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179\u0026ndash;211.\u003c/li\u003e\n\u003cli\u003eAtik, C. (2022). Towards comprehensive European agricultural data governance: Moving beyond the \u0026ldquo;data ownership\u0026rdquo; debate. \u003cem\u003eIIC-International Review of Intellectual Property and Competition Law\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(5), 701-742.\u003c/li\u003e\n\u003cli\u003eAhmadi, S., Amendolagine, V. \u0026amp; LaSala, P. Unpacking the impacts of digitalization of knowledge transfer in agri-food sector, through sociotechnical systems theory: a systematic literature review. Agric Econ 13, 51 (2025). https://doi.org/10.1186/s40100-025-00393-3.\u003c/li\u003e\n\u003cli\u003eBarca, F., McCann, P., \u0026amp; Rodr\u0026iacute;guez‐Pose, A. (2012). The case for regional development intervention: place‐based versus place‐neutral approaches. \u003cem\u003eJournal of regional science\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e(1), 134-152.\u003c/li\u003e\n\u003cli\u003eBrunori, G. (2022). Agriculture and rural areas facing the \u0026ldquo;twin transition\u0026rdquo;: principles for a sustainable rural digitalisation. \u003cem\u003eItalian Review of Agricultural Economics (REA)\u003c/em\u003e, \u003cem\u003e77\u003c/em\u003e(3), 3-14.\u003c/li\u003e\n\u003cli\u003eBhardwaj, M., Kumar, P., \u0026amp; Singh, A. (2025). Bibliometric review of digital transformation in agriculture: innovations, trends and sustainable futures. \u003cem\u003eJournal of Agribusiness in Developing and Emerging Economies.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eDavis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. \u003cem\u003eMIS quarterly\u003c/em\u003e, 319-340.\u003c/li\u003e\n\u003cli\u003eDayıoğlu, M. A., \u0026amp; Turker, U. (2021). Digital transformation for sustainable future-agriculture 4.0: A review. \u003cem\u003eJournal of Agricultural Sciences\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(4), 373-399.\u003c/li\u003e\n\u003cli\u003ede Clercq, M., D\u0026apos;Haese, M., \u0026amp; Buysse, J. (2023). Economic growth and broadband access: The European urban-rural digital divide. \u003cem\u003eTelecommunications Policy\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e(6), 102579.\u003c/li\u003e\n\u003cli\u003eEastwood, C., Klerkx, L., Ayre, M., \u0026amp; Dela Rue, B. (2019). Managing socio-ethical challenges in the development of smart farming: from a fragmented to a comprehensive approach for responsible research and innovation. \u003cem\u003eJournal of agricultural and environmental ethics\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(5), 741-768.\u003c/li\u003e\n\u003cli\u003eEuropean Commission. (2020). Farm to fork strategy: for a fair, healthy and environmentally-friendly food system. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions, 381, 1-9.\u003c/li\u003e\n\u003cli\u003eEhlers, M. H., Huber, R., \u0026amp; Finger, R. (2021). Agricultural policy in the era of digitalisation. \u003cem\u003eFood policy\u003c/em\u003e, \u003cem\u003e100\u003c/em\u003e, 102019\u003c/li\u003e\n\u003cli\u003eForney, J., \u0026amp; Epiney, L. (2022). Governing Farmers through data? Digitization and the Question of Autonomy in Agri-environmental governance. \u003cem\u003eJournal of Rural Studies\u003c/em\u003e, \u003cem\u003e95\u003c/em\u003e, 173-182\u003c/li\u003e\n\u003cli\u003eFao. (2019). \u003cem\u003eThe state of food and agriculture 2019: Moving forward on food loss and waste reduction\u003c/em\u003e. UN.\u003c/li\u003e\n\u003cli\u003eFolke, C., Hahn, T., Olsson, P., \u0026amp; Norberg, J. (2005). Adaptive governance of social-ecological systems. \u003cem\u003eAnnu. Rev. Environ. Resour.\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(1), 441-473.\u003c/li\u003e\n\u003cli\u003eGao, X. (2025). The EU\u0026rsquo;s twin transitions towards sustainability and digital leadership: a coherent or fragmented policy field?. \u003cem\u003eRegional Studies\u003c/em\u003e, \u003cem\u003e59\u003c/em\u003e(1), 2360053.\u003c/li\u003e\n\u003cli\u003eKlerkx, L., Jakku, E., \u0026amp; Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. \u003cem\u003eNJAS-Wageningen journal of life sciences\u003c/em\u003e, \u003cem\u003e90\u003c/em\u003e, 100315.\u003c/li\u003e\n\u003cli\u003eGabriel, A., \u0026amp; Gandorfer, M. (2023). Adoption of digital technologies in agriculture\u0026mdash;an inventory in a european small-scale farming region. \u003cem\u003ePrecision Agriculture\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(1), 68-91.\u003c/li\u003e\n\u003cli\u003eGeels, F. W. (2005). Processes and patterns in transitions and system innovations: Refining the co-evolutionary multi-level perspective. \u003cem\u003eTechnological forecasting and social change\u003c/em\u003e, \u003cem\u003e72\u003c/em\u003e(6), 681-696.\u003c/li\u003e\n\u003cli\u003eKlerkx, L., \u0026amp; Rose, D. (2020). Dealing with the game-changing technologies of Agriculture 4.0: How do we manage diversity and responsibility in food system transition pathways?. \u003cem\u003eGlobal Food Security\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e, 100347.\u003c/li\u003e\n\u003cli\u003eKunal, K., Ramprakash, K. R., \u0026amp; Prasad, A. (2025). Role of Productive Sectors in Driving Socio-Economic Advancement: Identifying Critical Factors Using PLS-SEM. \u003cem\u003eQubahan Academic Journal\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(2), 224-236.\u003c/li\u003e\n\u003cli\u003eMol, A. P., \u0026amp; Sonnenfeld, D. A. (2014). \u003cem\u003eEcological modernisation around the world: Perspectives and critical debates\u003c/em\u003e. Routledge.\u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;nez-Filgueira , X. M., Pe\u0026oacute;n, D., \u0026amp; L\u0026oacute;pez-Iglesias, E. (2022). Determinants of innovation by agri-food firms in rural Spain: an MCA PLS-SEM analysis. \u003cem\u003eInternational Food and Agribusiness Management Review\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(1), 103-120.\u003c/li\u003e\n\u003cli\u003eMendes, V., \u0026amp; Viola, E. (2023). Green digitalization? Agriculture 4.0 and the challenges of environmental governance in Brazil. In \u003cem\u003eSustainability challenges of Brazilian agriculture: Governance, inclusion, and innovation\u003c/em\u003e (pp. 207-226). Cham: Springer International Publishing.\u003c/li\u003e\n\u003cli\u003eMartens, K., \u0026amp; Zscheischler, J. (2022). The digital transformation of the agricultural value chain: discourses on opportunities, challenges and controversial perspectives on governance approaches. \u003cem\u003eSustainability\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(7), 3905.\u003c/li\u003e\n\u003cli\u003eMuluneh, T. (2021). Conceptualizing Digital Agricultural Governance. \u003cem\u003eInternational Journal of Engineering Research \u0026amp; Technology\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1).\u003c/li\u003e\n\u003cli\u003eOstrom, E. (2017). Polycentric systems for coping with collective action and global environmental change. In \u003cem\u003eGlobal justice\u003c/em\u003e (pp. 423-430). Routledge.\u003c/li\u003e\n\u003cli\u003ePoponi, S., Arcese, G., Mosconi, E. M., Pacchera, F., Martucci, O., \u0026amp; Elmo, G. C. (2021). Multi-actor governance for a circular economy in the agri-food sector: bio-districts. \u003cem\u003eSustainability\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(9), 4718.\u003c/li\u003e\n\u003cli\u003eRitzema, R. S., Douxchamps, S., Fraval, S., Bolliger, A., Hok, L., Phengsavanh, P., ... \u0026amp; van Wijk, M. T. (2019). Household-level drivers of dietary diversity in transitioning agricultural systems: evidence from the Greater Mekong Subregion. \u003cem\u003eAgricultural Systems\u003c/em\u003e, \u003cem\u003e176\u003c/em\u003e, 102657.\u003c/li\u003e\n\u003cli\u003eSun, S., Wen, X., Jie, S., Gao, Q., Zhu, Y., \u0026amp; Wen, S. (2022). Drivers of Farmers\u0026rsquo; Intention to Use the Digital Agricultural Management System: Integrating Theory of Planned Behavior and Behavioral Economics. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e, 901169.\u003c/li\u003e\n\u003cli\u003eStrambi, G. (2024). Food districts as a form of local food system governance Regulated by italian law. \u003cem\u003eRevista Catalana de Dret Ambiental\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1).\u003c/li\u003e\n\u003cli\u003eSohail, M. T. (2023). A PLS-SEM approach to determine farmers\u0026rsquo; awareness about climate change mitigation and adaptation strategies: pathway toward sustainable environment and agricultural productivity. \u003cem\u003eEnvironmental Science and Pollution Research\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(7), 18199-18212.\u003c/li\u003e\n\u003cli\u003eTama, R. A. Z., Hoque, M. M., Liu, Y., Alam, M. J., \u0026amp; Yu, M. (2023). An application of partial least squares structural equation modeling (PLS-SEM) to examining farmers\u0026rsquo; behavioral attitude and intention towards conservation Agriculture in Bangladesh. \u003cem\u003eAgriculture\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(2), 503.\u003c/li\u003e\n\u003cli\u003eTewathia, N., Kamath, A., \u0026amp; Ilavarasan, P. V. (2020). Social inequalities, fundamental inequities, and recurring of the digital divide: Insights from India. \u003cem\u003eTechnology in society\u003c/em\u003e, \u003cem\u003e61\u003c/em\u003e, 101251. \u003c/li\u003e\n\u003cli\u003eVahdanjoo, M., S\u0026oslash;rensen, C. G., \u0026amp; N\u0026oslash;rremark, M. (2025). Digital transformation of the agri-food system. \u003cem\u003eCurrent Opinion in Food Science\u003c/em\u003e, 101287.\u003c/li\u003e\n\u003cli\u003eVenkatesh, V., Morris, M. G., Davis, G. B., \u0026amp; Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478\u003c/li\u003e\n\u003cli\u003eYuan, Y., Guo, X., \u0026amp; Shen, Y. (2024). Digitalization drives the green transformation of agriculture-related enterprises: A case study of a-share agriculture-related listed companies. \u003cem\u003eAgriculture\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(8), 1308.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-annals-of-regional-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arsc","sideBox":"Learn more about [The Annals of Regional Science](https://link.springer.com/journal/168)","snPcode":"168","submissionUrl":"https://submission.springernature.com/new-submission/168/3","title":"The Annals of Regional Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7602553/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7602553/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This research explores the multi-faceted factors influencing the digital transformation of Italy's Agro-Food Districts (IAFDs) concerning the governance systems, the environment, adoption behavioral elements, and contextual structure. It tests a conceptual model based on ecological modernization, governance, and technology adoption with PLS-SEM (Partial Least Squares Structural Equation Modeling). The findings indicate that social-contextual structures, financial resources, and infrastructure enhance governance effectiveness, while legal frameworks have minimal impact. Furthermore, governance, in conjunction with attitudes toward digital adoption and ecological sustainability, becomes a vital driver of digital transition. Most importantly, stakeholders’ digital adoption strongly drives the transition, emphasizing the importance of perception, attitudes, and digital adoption perceptions. These outcomes emphasize that digital shifts within agro-food systems are not only driven by technology; instead, they are profoundly social and ecological shifts requiring integrated governance and active stakeholder participation. This study makes important contributions to the literature and offers guidance toward resilient and sustainable policy, managerial, and practical frameworks for fostering digital transition in rural food systems. The findings also support the wider European policies concerning green and digital transitions, illustrating the need for tailored rural innovation strategies.","manuscriptTitle":"Modeling Digital Transition in Regional Agro-Food Districts in Ialy : A PLS-SEM Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-13 06:51:22","doi":"10.21203/rs.3.rs-7602553/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-11-03T12:25:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"133252036799392257387970425575828496802","date":"2025-11-03T11:46:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-25T11:49:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-21T01:46:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-16T14:10:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Annals of Regional Science","date":"2025-09-12T17:00:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-annals-of-regional-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arsc","sideBox":"Learn more about [The Annals of Regional Science](https://link.springer.com/journal/168)","snPcode":"168","submissionUrl":"https://submission.springernature.com/new-submission/168/3","title":"The Annals of Regional Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8be9e12e-0433-4d4f-a15a-f9ebfe372d6b","owner":[],"postedDate":"October 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T06:51:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-13 06:51:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7602553","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7602553","identity":"rs-7602553","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}