Seeing Green: How Visual Digital Supply Chains Enable Sustainable Business Model Innovation in Manufacturing

Seeing Green: How Visual Digital Supply Chains Enable Sustainable Business Model Innovation in Manufacturing | 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 Article Seeing Green: How Visual Digital Supply Chains Enable Sustainable Business Model Innovation in Manufacturing Shaofeng Wang, Hao Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9245607/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Manufacturing firms face mounting pressure to reconcile digital transformation with ecological responsibility, yet the mechanisms linking supply chain digitalization to sustainable innovation remain poorly understood. This study investigates how visual digital supply chain adoption influences green business model innovation among Chinese manufacturers, drawing on absorptive capacity theory and the Technology-Organization-Environment (TOE) framework. Three research questions are addressed: the pathways through which visual digital supply chains shape green business model innovation, the technological, organizational, and environmental antecedents of adoption, and the moderating function of digital strategy. Survey data from 254 manufacturing firms listed in China's Green Manufacturing Initiative database are analyzed using partial least squares structural equation modeling and fuzzy-set qualitative comparative analysis. Results reveal that all three TOE dimensions significantly drive visual digital supply chain adoption, which in turn enhances both potential and realized absorptive capacities. Digital strategy amplifies the translation of absorptive capacities into green business model innovation. Configurational analysis further demonstrates that no single factor is individually necessary; rather, distinct multi-factor combinations produce high levels of ecological innovation. These findings advance understanding of how digitally enabled supply chain visibility and strategic digital orientation jointly foster sustainable business practices. Business and commerce/Business and management Social science/Business and management Earth and environmental sciences/Environmental social sciences Social science/Environmental studies Business and commerce/Information systems and information technology Green business model innovation Visual digital supply chain Absorptive capacity Digital strategy Technology-Organization-Environment framework Sustainable manufacturing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The acceleration of digital transformation across manufacturing industries has intensified scholarly and practitioner interest in the determinants of sustainable business conduct (Mamirkulova et al., 2026 ). Business model innovation in the manufacturing sector has attracted growing attention (Xiao et al., 2025 ; Mei et al., 2026 ), yet rigorous theoretical and empirical inquiry into how firms effectively realize green business model innovation remains underdeveloped (Zhang et al., 2024 ; Wang & Shen, 2025 ). Much of the existing literature is confined to firm- or industry-specific case studies (Cucculelli et al., 2024 ), leaving the underlying mechanisms and contextual antecedents insufficiently explored. Notably, despite increasing interest in digitalized supply chain architectures, the pathways through which visual digital supply chains catalyze green business model innovation have received scant systematic investigation (Wang & Zhang, 2025 ; Wang et al., 2026 ). Competitive advantage has progressively shifted from intra-firm operational efficiency to supply chain-level coordination and responsiveness (Xiao et al., 2025 ; Yin et al., 2024 ). As conventional supply chain structures prove inadequate for escalating market demands, digitally orchestrated supply chains have emerged as a strategic response (Esfahbodi et al., 2023 ). These configurations deploy advanced information technologies and analytical tools to enhance supply chain agility and throughput (Dong et al., 2025 ), while visualization capabilities translate complex data into accessible and actionable formats (Sadeghi et al., 2023 ). Despite their recognized importance, empirical research on how visual digital supply chains shape green business model innovation remains limited (Wang & Zhang, 2025 ). Green business model innovation refers to the strategic reorientation of products, services, operational processes, and value architectures toward ecological stewardship, resource conservation, and decarbonization, thereby pursuing environmental and economic objectives simultaneously (Huang et al., 2025 ; Xiao et al., 2025 ). Advancing scholarly understanding in this domain is essential for equipping firms with viable pathways to sustainable development. Digital strategy denotes the deliberate deployment of digital technologies to reconfigure business processes, reinvent value propositions, and restructure supply-side and demand-side relationships (Ancillai et al., 2023 ), spanning the firm's technology roadmap, resource allocation, process design, and stakeholder interfaces. Although selected studies have recognized the salience of digital strategy for business model reconfiguration (Ndri & Su, 2024 ), theoretically grounded and empirically validated research on its role in green business model innovation remains insufficient, a gap amplified by the accelerating role of digital technologies in sustainability transitions (Kajtazi et al., 2023 ). Parallel efforts have examined the potential of digitalized supply chains to advance green business model innovation (Yin et al., 2024 ), yet the literature remains anchored in idiosyncratic case analyses with limited examination of generalizable mechanisms and their contingencies (Cucculelli et al., 2024 ). Although absorptive capacity theory recognizes the centrality of potential and realized absorptive capacity for organizational innovation, its application to the green business model innovation process warrants deeper exploration (Yildiz et al., 2024 ). Green business model innovation is conceptually adjacent to related constructs such as green ventures, circular business models, and sustainable entrepreneurship (Zhang et al., 2024 ). While these constructs share the objective of embedding sustainability within commercial activity, our focus pertains specifically to how incumbent firms reconfigure established business models toward greater environmental sustainability (Xiao et al., 2025 ). Absorptive capacity theory (Algarni et al., 2023 ) provides a conceptual apparatus for understanding how organizations detect, assimilate, and exploit novel knowledge. In the green business model innovation context, absorptive capacity is instrumental for incorporating emergent environmental knowledge and technologies into value creation logic. By examining the mediating role of absorptive capacity in linking visual digital supply chains to green business model innovation, we generate fresh theoretical insights into how digital technologies stimulate sustainable innovation (Yin et al., 2024 ). Recent scholarship on green ventures highlights the enabling function of digital technologies for sustainable business conduct (Wang & Zhang, 2025 ), yet the distinctive contribution of visual digital supply chains, particularly among established manufacturers, remains insufficiently examined. Against this backdrop, our study pursues three interrelated research questions. First, through what mechanisms do visual digital supply chains shape green business model innovation (RQ1)? Addressing this question may reveal novel pathways for promoting ecological innovation, a priority for economic, societal, and ecosystem resilience (Fang et al., 2026 ). While absorptive capacity theory offers relevant conceptual grounding, its application to visual digital supply chain contexts has received limited empirical attention (Yin et al., 2024 ). The precise influence of visual digital supply chains on green business model innovation remains theoretically underdetermined, and absorptive capacity theory, despite emphasizing an organization's competence in assimilating external knowledge (Sadeghi et al., 2023 ), offers limited explanatory traction regarding the dual channels of potential and realized absorptive capacities in this specific transmission process. Second, we examine the antecedent conditions governing visual digital supply chain adoption (RQ2). The Technology-Organization-Environment (TOE) framework (Zou et al., 2025 ) provides the theoretical scaffolding, yet limited research has identified the specific drivers of visual digital supply chain adoption despite the framework's tripartite decomposition. Integrating the TOE framework with absorptive capacity theory, our investigation offers a granular analysis of these adoption determinants. Third, we probe the contingent function of digital strategy in advancing green business model innovation (RQ3). Understanding how digital strategy catalyzes ecological innovation holds substantial value, equipping practitioners with actionable guidance for pursuing sustainability and competitive positioning simultaneously. Prevailing research, however, has largely neglected the moderating influence of digital strategy on green business model innovation outcomes. Although digitalized supply chains attract growing scholarly interest, systematic evidence on the pathways through which visual digital supply chains foster green business model innovation remains thin (Dong et al., 2025 ). Theoretical and empirical research linking visual digital supply chains to green business model innovation through dual absorptive capacity mechanisms, while delineating the boundary conditions imposed by digital strategy, is particularly scarce. Our study contributes by synthesizing absorptive capacity theory and the TOE framework into an integrated research model, extending the explanatory reach of both theoretical traditions (Algarni et al., 2023 ). Beyond identifying antecedent drivers of visual digital supply chain adoption, we elucidate how digital strategy moderates the innovation outcomes of absorptive capacity (Esfahbodi et al., 2023 ). Methodologically, our concurrent deployment of PLS-SEM and fsQCA constitutes a distinctive analytical approach seldom employed in prior work (Wang & Zhang, 2025 ), enabling multi-perspectival examination of the research questions and strengthening inferential robustness. Collectively, our findings offer substantive theoretical support for manufacturing firms navigating digital transformation, particularly in accelerating green business model innovation (Chen et al., 2025 ; Gyamerah et al., 2025 ). The remainder of this paper proceeds as follows. The literature review synthesizes pertinent scholarship and articulates the theoretical underpinnings. The theoretical model and hypotheses are then formalized. The methods section describes data sources, sample attributes, construct operationalization, and analytical procedures. Empirical results are subsequently reported, followed by a discussion of theoretical and practical implications alongside directions for future inquiry. 2. Literature Review and Theoretical Background 2.1. Green Business Model Innovation Scholarship has established the centrality of business model innovation in advancing organizational sustainability and broader societal welfare through ecological protection and social equity (Ancillai et al., 2023 ; Wang & Zhang, 2025 ). Emerging research has also examined the capacity of digitalized supply chains to stimulate green business model innovation (Yin et al., 2024 ). Nevertheless, the literature remains predominantly oriented toward idiosyncratic case analyses of particular firms or sectors, with comparatively sparse attention to generalizable mechanisms and their contextual moderators (Cucculelli et al., 2024 ). This orientation constrains the explanatory and predictive power of prevailing frameworks. Although existing work examines technological, organizational, and environmental antecedents of green business model innovation, these influences are empirically entangled and methodologically difficult to disentangle (Cucculelli et al., 2024 ). Critically, the specific transmission mechanisms through which these antecedents shape green business model innovation remain undertheorized. Our study addresses this gap by positioning potential and realized absorptive capacities as mediating constructs linking visual digital supply chains to green business model innovation, while treating digital strategy as a boundary condition that modulates these indirect pathways. Through rigorous theoretical integration, we aim to advance scholarly understanding, practitioner guidance, and policymaker insights for fostering sustainable industrial transformation. 2.2. Visual Digital Supply Chain Adoption Amid accelerating globalization and digitization, supply chain management has ascended to a strategic priority (Dong et al., 2025 ). Digitalized supply chains represent a transformative development with substantial implications for organizational competitiveness (Wang & Zhang, 2024 ). Visual digital supply chains encompass the entirety of transactional processes, information exchanges, and physical flows within supply networks. Their principal merit resides in delivering real-time, precise, and graphically rendered information that elevates decisional efficiency and quality (Kumar & Shankar, 2024 ). Visualization technologies convert intricate supply chain datasets into cognitively accessible graphical representations, affording firms transparent and accurate oversight of supply chain conditions (Wang & Zhang, 2024 ). For instance, process flow diagrams can delineate material, informational, and financial movements, enabling rapid detection of bottlenecks or latent disruption risks. These technologies also underpin continuous supply chain refinement; by rendering dynamic operational shifts in intuitive visual formats, firms can conduct real-time monitoring and diagnostic analyses, surfacing improvement opportunities (Sadowski et al., 2024 ). Despite broad acknowledgment of digital supply chain significance (Belhadi et al., 2022 ), a conspicuous void persists regarding the influence of visual digital supply chains on green business model innovation. This study addresses that void by offering both conceptual and empirical contributions: examining the transmission mechanisms linking visual digital supply chains to green business model innovation, the adoption antecedents, and the moderating function of digital strategy. Existing literature has concentrated primarily on definitional aspects of digital supply chains (Song et al., 2024 ) and their efficacy-enhancing properties (Kumar & Shankar, 2024 ), with inadequate consideration of how visual digital supply chains affect organizational knowledge processing capabilities, encompassing both potential and realized absorptive capacity, to foster green business model innovation. The contingent role of digital strategy in this process likewise remains empirically uncharted. 2.3. Absorptive Capacity Theory Absorptive capacity theory characterizes an organization's proficiency in detecting, assimilating, and leveraging external knowledge to generate innovation or elevate performance (Yildiz et al., 2024 ). The construct bifurcates into potential absorptive capacity, pertaining to knowledge recognition and assimilation, and realized absorptive capacity, concerning knowledge conversion and exploitation (Algarni et al., 2023 ). This theoretical lens provides an explanatory apparatus for tracing how visual digital supply chains stimulate green business model innovation by modulating both absorptive capacity dimensions. As a foundational construct in knowledge management and innovation scholarship, absorptive capacity carries substantial applied relevance: it informs how organizations strengthen learning and innovation routines (Dabić et al., 2023 ), navigate environmental shifts (Algarni et al., 2023 ), and cultivate competitive differentiation (Wang & Zhang, 2025 ). Within our research context, absorptive capacity theory yields critical insights into how visual digital supply chain deployment can catalyze green business model innovation. 2.4. Technology-Organization-Environment (TOE) framework The TOE framework constitutes a foundational architecture for understanding technology innovation, adoption, and diffusion (Zou et al., 2025 ). It posits that technology adoption decisions are jointly conditioned by technological, organizational, and environmental dimensions, each exerting direct influence on strategic technology choices (Wang & Zhang, 2024 ). Technological factors capture attributes intrinsic to the innovation, including technical competence, system compatibility, and infrastructural readiness (Dong et al., 2025 ). Organizational factors span cultural orientation, structural configuration, and resource endowments, collectively shaping adoption propensity (Yin et al., 2024 ). Environmental factors comprise the competitive milieu, regulatory landscape, and institutional pressures that constitute the firm's external operating context (Wang & Zhang, 2024 ). We invoke the TOE framework to explain visual digital supply chain adoption patterns (Zou et al., 2025 ). By synthesizing technological, organizational, and environmental antecedents, we identify the forces propelling adoption and trace their downstream consequences for green business model innovation. Our study targets three salient gaps in the literature. First, we delineate the mechanisms through which visual digital supply chains affect green business model innovation, a domain that has attracted limited empirical attention despite the rising strategic importance of both digital technologies and sustainability (Yin et al., 2024 ). Second, we systematically examine the adoption determinants through the TOE lens, offering an integrated account of technological, organizational, and environmental influences (Belhadi et al., 2022 ). Third, we assess the boundary-spanning role of digital strategy in promoting green business model innovation, a dimension largely absent from prior investigations yet pivotal for understanding how firms harness digital capabilities for sustainable innovation (Ndri & Su, 2024 ). 3. Theoretical Model and Hypotheses 3.1. Theoretical Model Absorptive capacity theory (Yildiz et al., 2024 ) has recognized the significance of both potential and realized dimensions for organizational innovation, yet its operationalization within the green business model innovation domain warrants further development. In our conceptualization, absorptive capacity functions as a capability augmented through visual digital supply chain adoption that subsequently facilitates green business model innovation; it is not positioned as a precursor to adoption itself. Figure 1 depicts the proposed theoretical model, which synthesizes absorptive capacity theory with the TOE framework. To bolster model stability, we incorporated firm-level control variables (firm age, size, type, and ownership) to account for potential confounding effects on green business model innovation (Wang & Zhang, 2024 ). 3.2. Impact of Technological, Organizational, and Environmental Factors on the Visual Digital Supply Chain Adoption The TOE framework specifies that technological maturity, system compatibility, implementation complexity, observability, and relative advantage collectively condition a firm's technology adoption decisions (Wang & Zhang, 2024 ). Visual digital supply chains, characterized by intuitive interface architectures and graphical rendering of complex datasets, exhibit enhanced relative advantage and compatibility, thereby elevating adoption likelihood (Sadowski et al., 2024 ). While visualization implementation may initially present technical hurdles, ongoing technological progress progressively reduces complexity and broadens organizational accessibility (Song et al., 2024 ). When firms perceive that adoption yields operational efficiency gains, lower error rates, and improved decision-making quality, their adoption propensity increases (Wang & Zhang, 2025 ). Similarly, employee perceptions that visualization augments task-level productivity reinforce technology acceptance (Kumar & Shankar, 2024 ). Organizational scale and innovativeness typically exhibit positive associations with technology adoption, such that larger and more innovation-oriented firms demonstrate greater readiness (Ndri & Su, 2024 ). Environmental contingencies, including competitive intensity, industry norms, and customer expectations, further shape adoption decisions (Yin et al., 2024 ). Firms confronting acute competitive rivalry or whose supply chain partners have already adopted visual digital supply chains face heightened isomorphic pressure to follow suit (Song et al., 2024 ). Where customer expectations for supply chain transparency are elevated, organizations may adopt visual digital supply chains to satisfy these demands (Lyu et al., 2023 ). Accordingly, we hypothesize: H1: Technology positively influences visual digital supply chain adoption. H2: Organization positively influences visual digital supply chain adoption. H3: Environment positively influences visual digital supply chain adoption. 3.3. Impact of Visual Digital Supply Chain Adoption on Potential and Realized Absorptive Capacity Absorptive capacity captures an organization's competence in recognizing, acquiring, integrating, and commercializing novel knowledge, decomposed into potential and realized components (Yildiz et al., 2024 ). Potential absorptive capacity pertains to knowledge acquisition and assimilation, processes substantially conditioned by the information technologies a firm employs (Sadeghi et al., 2023 ). Digitalized supply chain solutions enable more systematic collection, governance, and analysis of supply chain data (Zhang et al., 2023 ; Wang & Zhang, 2024 ), sharpening comprehension of market dynamics (Dabić et al., 2023 ). Visualization capabilities further augment data interpretability and usability, thereby strengthening potential absorptive capacity (Sadowski et al., 2024 ). Visual digital supply chain adoption heightens organizational sensitivity to supply chain environmental signals, reinforcing the knowledge identification and assimilation functions of potential absorptive capacity (Song et al., 2024 ). Concurrently, visual digital supply chains provide enriched datasets and graphically mediated operational interfaces that facilitate more effective knowledge utilization, bolstering realized absorptive capacity (Sadowski et al., 2024 ). Realized absorptive capacity denotes a firm's proficiency in converting newly acquired knowledge into commercial value (Yildiz et al., 2024 ). Information technologies, including visual digital supply chains, promote inter-organizational information sharing and collaborative sense-making, enabling firms to exploit supply chain knowledge more effectively (Wang & Zhang, 2024 ). Digital instruments support the application and leveraging of extant knowledge reservoirs, thereby fortifying realized absorptive capacity (Song et al., 2024 ). Digitalized supply chain solutions also contribute to operational improvements in supply chain governance, yielding performance benefits (Kumar & Shankar, 2024 ). The richer data environments and visually mediated decision support afforded by visual digital supply chains further enhance a firm's capacity to transform knowledge into actionable innovation (Sadowski et al., 2024 ). Accordingly, we hypothesize: H4: Visual digital supply chain adoption positively influences potential absorptive capacity. H5: Visual digital supply chain adoption positively influences realized absorptive capacity. 3.4. Impact of Potential and Realized Absorptive Capacity on Green Business Model Innovation Within the absorptive capacity framework, potential absorptive capacity principally encompasses the competencies of acquiring and comprehending novel knowledge and synthesizing it with existing cognitive structures to generate original insights (Algarni et al., 2023 ). This perspective explains why organizations must internalize digital supply chain technologies to cultivate potential absorptive capacity. Elevated potential absorptive capacity enables organizations to apprehend and deploy emergent technologies, facilitating adaptive responses to market evolution (Yildiz et al., 2024 ). Importantly, potential absorptive capacity also catalyzes green business model innovation: by assimilating nascent environmental paradigms and technological solutions, organizations can develop ecologically superior products and services (Wang & Zhang, 2025 ). It constitutes a substantial wellspring of organizational innovation (Xiao et al., 2025 ), influencing the reconfiguration of entire business model architectures (Dabić et al., 2023 ), as firms endowed with strong potential absorptive capacity more effectively scan and interpret the external environment, uncovering novel green business model innovation opportunities (Kajtazi et al., 2023 ). Realized absorptive capacity reflects a firm's proficiency in translating assimilated knowledge into operational practice and is likewise recognized as a pivotal driver of business model reconfiguration (Yildiz et al., 2024 ). This dimension is particularly consequential for green business model innovation, since authentic ecological innovation materializes only when organizations effectively operationalize environmental knowledge and technologies in product development and service improvement (Wang & Zhang, 2024 ). Firms possessing superior realized absorptive capacity exploit extant knowledge more productively, thereby surfacing additional innovation opportunities. Empirical evidence documents a robust positive association between realized absorptive capacity and organizational innovation outcomes (Dabić et al., 2023 ). Realized absorptive capacity is a decisive determinant of corporate green innovation, as it enables the conversion of newly assimilated knowledge into innovative products, services, or business model configurations (Zhang et al., 2024 ). Accordingly, we hypothesize: H6: Potential absorptive capacity positively influences green business model innovation. H7: Realized absorptive capacity positively influences green business model innovation. 3.5. Moderation Effect of Digital Strategy A well-articulated digital strategy is fundamental to sustaining competitive advantage and fostering innovation (Yin et al., 2024 ). Potential absorptive capacity reflects a firm's proficiency in recognizing and assimilating external knowledge, a competence integral to green business model reconfiguration. Digital strategy implementation expands the knowledge acquisition architecture and streamlines information procurement methods available to the firm (Ndri & Su, 2024 ). By reshaping how information is sourced and processed, digital strategy amplifies potential absorptive capacity (Song et al., 2024 ). Realized absorptive capacity captures a firm's competence in transforming and exploiting accumulated knowledge stocks (Yildiz et al., 2024 ). Converting a substantive knowledge base into executable innovative initiatives typically demands robust realized absorptive capacity (Dabić et al., 2023 ). Under the impetus of digital strategy, realized absorptive capacity can be strengthened, thereby accelerating green business model innovation (Kajtazi et al., 2023 ). Digital strategy furnishes diversified knowledge integration platforms and analytical instruments that facilitate the transition from knowledge accumulation to innovation realization (Yin et al., 2024 ). Accordingly, we hypothesize: H8: Digital strategy positively moderates the relationship between potential absorptive capacity and green business model innovation. H9: Digital strategy positively moderates the relationship between realized absorptive capacity and green business model innovation. 4. Research Methods The research design comprises three sequential phases (Figure 2). Phase one entails construct operationalization and measurement instrument development. Phase two encompasses data collection and robustness diagnostics. Phase three involves analytical procedures to address the research questions. 4.1. Measurement A multi-stage protocol was employed to ensure measurement accuracy and reliability (Table 1). Initial measurement indicators were derived from systematic literature analysis, with each item adapted from established scales to align with our study's context and objectives. Following iterative deliberations among the research team, a preliminary scale was produced. A five-member expert panel, comprising graduate researchers, established scholars, and industry practitioners spanning strategic management, operations, and environmental sustainability, was convened for scale refinement. Through three successive rounds of structured review, a revised instrument was finalized. Pre-testing was conducted with 25 senior manufacturing managers possessing substantial domain expertise. The pre-test yielded satisfactory internal consistency (Cronbach's alpha > 0.7). Participant feedback was systematically documented and incorporated; for instance, ambiguously worded items were reformulated to enhance clarity and precision. A rigorous back-translation protocol was applied to the refined instrument to ensure cross-linguistic semantic equivalence and cultural appropriateness (Wang & Zhang, 2024). Two bilingual translators, each a native speaker of one of the relevant languages and possessing professional academic credentials, independently executed forward and reverse translations. Discrepancies arising from linguistic ambiguity or culturally contingent interpretation were resolved through iterative consultation, referencing both source literature and domain-specific usage conventions. All constructs, including technology, organization, environment, visual digital supply chain adoption, potential absorptive capacity, realized absorptive capacity, digital strategy, and green business model innovation, were assessed using seven-point Likert scales (1 = strongly disagree, 7 = strongly agree). This scale granularity captures nuanced variation in respondent perceptions, enhancing measurement sensitivity (Wang & Zhang, 2024). To align with its measurement items, green business model innovation was evaluated on a corresponding seven-point semantic differential scale, where 1 indicates the lowest and 7 the highest level of innovativeness. The complete survey questionnaire, including introductory instructions, the screening item, firm demographic questions, and all construct measurement items with their response scales and source citations, is provided in Appendix A. Table 1 . Measurement Constructs and Measurement items Loadings VIF Visual Digital Supply Chain Adoption (VDSCA) (Kalaitzi & Tsolakis, 2022) Alpha: 0.883 CRA: 0.885 CRC: 0.914 AVE: 0.681 Mean: 4.408 SD: 1.684 VDSCA_1: Our firm recognizes the benefits of adopting a visual digital supply chain. 0.859 2.477 VDSCA_2: We have a strategic plan in place for the adoption of visual digital supply chain. 0.812 2.033 VDSCA_3: We have started allocating resources towards the adoption of a visual digital supply chain. 0.801 1.979 VDSCA_4: Our firm has initiated the process of adopting a visual digital supply chain. 0.841 2.143 VDSCA_5: Our firm has made significant progress towards adopting a visual digital supply chain. 0.810 1.923 Green Business Model Innovation (GBMI) (Bashir et al., 2022) Alpha: 0.875 CRA: 0.878 CRC: 0.909 AVE: 0.667 Mean: 4.544 SD: 1.586 GBMI_1: On a scale of 1-7, how would you rate your firm's innovativeness in introducing green products, services, or information, or a new combination of the three? 0.840 2.137 GBMI_2: On a scale of 1-7, how would you rate your firm's level of introducing new participants to implement a green business model? 0.783 1.800 GBMI_3: On a scale of 1-7, how would you rate your firm's level of providing new transaction incentives to attract participants to the green business model? 0.826 2.098 GBMI_4: On a scale of 1-7, how would you rate your firm's level of adopting new transaction methods to connect participants in the green business model? 0.828 2.138 GBMI_5: On a scale of 1-7, how would you rate your firm's innovativeness in other aspects of green business model innovation? 0.807 1.987 Technology (TE) (Zou et al., 2025) Alpha: 0.865 CRA: 0.882 CRC: 0.907 AVE: 0.710 Mean: 4.114 SD: 1.737 TE_1: Our firm has the technical capabilities required to implement a visual digital supply chain. 0.855 2.109 TE_2: Our firm already has the infrastructure required to implement a visual digital supply chain. 0.847 2.187 TE_3: Our firm's technological systems are compatible with the systems of the visual digital supply chain. 0.848 1.861 TE_4: Our firm already has the technological resources needed to use a visual digital supply chain. 0.819 2.119 Organization (OR) (Wang & Zhang, 2024) Alpha: 0.869 CRA: 0.878 CRC: 0.910 AVE: 0.718 Mean: 4.089 SD: 1.640 OR_1: Our organizational structure adapts to the implementation of the visual digital supply chain. 0.854 1.986 OR_2: Our organization has sufficient resources to support the application of the visual digital supply chain. 0.863 2.266 OR_3: Our organizational culture supports the innovation and transformation of the visual digital supply chain. 0.847 2.127 OR_4: Our organization shows obvious leadership and support for the adoption of the visual digital supply chain. 0.824 2.034 Environment (EN) (Kalaitzi & Tsolakis, 2022) Alpha: 0.821 CRA: 0.828 CRC: 0.893 AVE: 0.736 Mean: 4.495 SD: 1.756 EN_1: Our business environment is conducive to the adoption of visual digital supply chain. 0.837 1.793 EN_2: In our industry, competitors adopting visual digital supply chain put pressure on us to do the same. 0.887 2.008 EN_3: The regulatory environment in our industry encourages the adoption of visual digital supply chain. 0.849 1.774 Potential Absorptive Capacity (PAC) (Algarni et al., 2023) Alpha: 0.842 CRA: 0.849 CRC: 0.894 AVE: 0.678 Mean: 4.297 SD: 1.562 PAC_1: Our firm frequently interacts with clients and competitors to acquire new knowledge. 0.839 2.016 PAC_2: We quickly recognize shifts in our markets such as competition, regulation, and demography. 0.833 1.793 PAC_3: Our firm is able to identify and understand external knowledge. 0.791 1.765 PAC_4: We are able to introduce and organize external knowledge quickly. 0.830 1.938 Realized Absorptive Capacity (RAC) (Algarni et al., 2023) Alpha: 0.828 CRA: 0.832 CRC: 0.885 AVE: 0.659 Mean: 4.316 SD: 1.566 RAC_1: We record and store newly acquired knowledge for future reference. 0.796 1.648 RAC_2: We quickly recognize the usefulness of new external knowledge to existing knowledge. 0.840 1.990 RAC_3: Our firm has the ability to transform external knowledge into our own knowledge. 0.829 1.776 RAC_4: Our firm is able to provide new knowledge quickly and efficiently, and apply new knowledge to value creation and related services. 0.781 1.703 Digital Strategy (DS) (Gyamerah et al., 2025) Alpha: 0.819 CRA: 0.819 CRC: 0.892 AVE: 0.734 Mean: 5.102 SD: 1.563 DS_1: In my organization, the integration of our business strategy with digital technology promotes strategic alignment with the government and other partners. 0.859 1.889 DS_2: In our organization, we jointly plan how to use digital technology to implement the business strategy. 0.850 1.734 DS_3: Before making strategic decisions, we discuss and take into consideration the impact of digital technology. 0.861 1.881 Note: Cronbach’s alpha = Alpha; Composite reliability (rho_a) = CRA; Composite reliability (rho_c) = CRC; Average variance extracted = AVE 4.2. Data Collection Manufacturing firms were selected as the empirical context owing to their substantial environmental impact and intensifying institutional pressures to transition toward sustainable operations. To ensure baseline familiarity with green business model concepts, we drew our sampling frame from the Green Manufacturing Initiative database administered by China's Ministry of Industry and Information Technology. Firms listed in this database have either adopted or are actively developing green business models, establishing requisite conceptual awareness. The sampled provinces, Zhejiang, Guangdong, and Sichuan, rank among China's ten largest manufacturing regions and represent eastern, southern, and western geographic coverage. This geographic dispersion enhances both the breadth and representativeness of the surveyed population (Wang & Zhang, 2025). Purposive sampling was adopted for data collection (Wang & Zhang, 2024). This strategy was selected for two complementary reasons: it enables targeted identification of information-rich respondents possessing substantive knowledge of the surveyed constructs, thereby strengthening data quality and theoretical yield; and it facilitates efficient recruitment by directing survey invitations to firms predisposed to participate. The focal constructs, including digital strategy and absorptive capacity, are inherently abstract and perceptual, rendering them unsuitable for direct observation or objective measurement. Questionnaire-based data collection is well suited to capturing such latent constructs. Given the wide geographic dispersion of our target firms across three provinces, survey methodology further overcomes spatial constraints while enabling cost-effective large-scale data acquisition (Wang & Zhang, 2024). An electronic questionnaire was created on Questionnaire Star (wjx.cn) and disseminated through WeChat, email, and affiliated channels. The instrument comprised two sections: the first solicited firm demographic information (Table 2), and the second measured the eight model constructs (Table 1). Respondents were managers, administrators, and innovation officers possessing comprehensive knowledge of their firm's strategic and operational profile. A screening item was positioned at the outset; respondents indicating unfamiliarity with the survey topic were automatically redirected to survey termination, safeguarding respondent relevance and data quality. Respondent privacy was protected by omitting personal identifiers from the questionnaire. An introductory statement confirmed that all responses would be used exclusively for academic purposes and handled with strict confidentiality. Voluntary withdrawal was permitted at any stage. Data collection followed a temporally separated two-wave design (Wang & Zhang, 2024). Wave one captured respondents' assessments of visual digital supply chain adoption, technology, organization, environment, potential absorptive capacity, realized absorptive capacity, and firm characteristics (age, size, type, ownership). Wave one yielded 385 responses (80.3% response rate). Participants provided contact emails for wave two, with a commitment to delete all contact information upon study completion. Wave two assessed digital strategy, green business model innovation, and a marker variable (perceived ease of use), generating 279 responses (72.5% wave-two response rate). A single email reminder was dispatched to non-completers to improve data completeness. Additionally, follow-up telephone interviews with a randomly drawn 10% subsample verified response consistency and elicited supplementary qualitative insights regarding firms' green practices and digital strategies. Rigorous data cleaning eliminated questionnaires exhibiting uniform response patterns or completion times below five minutes. Remaining responses underwent manual inspection. The final usable sample comprised 254 firms, yielding an effective rate of 66.0%. Prior to data collection, G*Power 3.1.9.7 was used for a priori sample size estimation. Under recommended parameters (F-test family, linear multiple regression fixed model, R² increase, effect size f² = 0.15, α = 0.05, power = 0.8) (Wang & Zhang, 2024), the minimum required sample was 123. The tenfold indicator rule similarly indicated a minimum of 120. Our final sample of 254 substantially exceeds both thresholds. Ethical Approval This study was reviewed by the Institutional Review Board (IRB) of the International Business School, Fuzhou University of International Studies and Trade. As the research involved a survey in which substantive responses were anonymized for analysis, while contact information for the second-wave follow-up was collected separately from the survey responses and deleted after data collection, and posed no more than minimal risk to participants, the IRB granted an exemption from full ethical review. All research procedures were performed in accordance with the ethical standards of the institutional review board and with the ethical review regulations and guidelines for research involving human participants in China, including the Declaration of Helsinki. Informed Consent Informed consent was obtained from all participants in this study. Prior to completing the questionnaire, each participant was presented with an introductory statement describing the purpose of the research, the voluntary nature of participation, the anonymity and confidentiality protections (including an explicit assurance that all responses would be used exclusively for academic purposes and would not be publicly disclosed), and the right to withdraw at any point without consequence. Consent was indicated by the participant's voluntary decision to proceed with and complete the survey. Table 2 . Firm characteristics Characteristics Percentage Firm Age ≤ 5 years 10.2 6-15 years 66.2 ≥16 years 23.6 Firm Size ≤ 50 employees 23.6 51-499 employees 56.7 ≥ 500 employees 19.7 Firm Type B2C 46.5 B2B 22.0 Hybrid 31.5 Ownership State-owned 17.3 Private 53.9 Foreign 16.5 Others 12.3 4.3. Data Analysis Approach Measurement and structural model estimation employed Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4. PLS-SEM is extensively utilized in management research for its capacity to estimate complex multivariate models and to yield robust results under conditions of distributional non-normality (Wang & Zhang, 2024). Given the model complexity, the presence of multiple latent constructs, and the study’s predictive orientation, PLS-SEM constituted an appropriate analytical technique. Hypothesis testing and moderation analysis were likewise conducted within SmartPLS 4, enabling precise assessment of inter-variable relationships, particularly the contingent role of digital strategy. Importance-Performance Map Analysis (IPMA) was performed using SmartPLS 4 to visualize the relative importance and performance of each construct, thereby generating actionable analytical insights (Wang & Zhang, 2024). IPMA facilitated nuanced understanding of individual variable contributions to green business model innovation. Fuzzy-set Qualitative Comparative Analysis (fsQCA) was executed using fsQCA 3 software for sufficient condition analysis. FsQCA is a configurational method that accommodates complexity by identifying combinations of conditions jointly sufficient for a given outcome (Wang & Zhang, 2025). This method proved instrumental in uncovering the configurational antecedents of green business model innovation. 4.4. Robustness Analysis Common method bias poses a potential threat to the reliability of self-report survey findings. Multiple procedural and statistical remedies were implemented. Ex ante, iterative expert consultations refined item definitions and inter-indicator logic, and a pilot study informed further questionnaire optimization. During administration, respondents received assurances of strict confidentiality and exclusive academic use. Ex post, Harman's single-factor test indicated that the first unrotated factor accounted for 31.791% of total variance, well below the 50% threshold, suggesting no dominant common factor. The marker variable technique was additionally applied using perceived ease of use (PEU), a construct theoretically unrelated to our focal variables (Cronbach's Alpha = 0.835, rho_a = 0.875, rho_c = 0.899, AVE = 0.748). PEU exhibited no statistically significant correlations with any model construct, corroborating that common method bias does not substantively distort our results. Multicollinearity was assessed through Variance Inflation Factors (VIF) (Wang & Zhang, 2024). VIF values ranged from 1.648 to 2.477, uniformly below both the conservative threshold of 5 and the conventional benchmark of 10, confirming the absence of problematic multicollinearity. Nonresponse bias was evaluated by partitioning the sample chronologically into early and late respondent cohorts and conducting independent-samples t-tests in SPSS 27 across firm age (P = 0.527), firm size (P = 0.843), firm type (P = 0.486), and ownership (P = 0.303) (Wang & Zhang, 2024). All comparisons were non-significant (P > 0.05), indicating negligible nonresponse bias. 5. Study 1: Structural Equation Modeling Results 5.1. Measurement Model The measurement model encompasses eight focal constructs. As reported in Table 1, all Cronbach's alpha coefficients exceed 0.8, confirming adequate internal consistency. Composite reliability (rho_a and rho_c) and Average Variance Extracted (AVE) values uniformly satisfy established thresholds, attesting to measurement reliability and convergent validity. Item-level factor loadings range from 0.781 to 0.887, each surpassing the 0.7 benchmark, indicating strong indicator-construct correspondence. Discriminant validity was assessed through both the Fornell-Larcker criterion and the Heterotrait-Monotrait (HTMT) ratio (Table 3). The square root of each construct's AVE (diagonal entries) exceeds all inter-construct correlations, satisfying the Fornell-Larcker criterion. Additionally, all HTMT ratios fall below the 0.85 threshold, providing further confirmation of discriminant validity. Table 3 . Discriminant Validity. Fornell-Larcker Criterion and Heterotrait-Monotrait Ratio Construct DS EN GBMI OR PAC RAC TE VDSCA DS 0.857 0.105 0.366 0.128 0.245 0.305 0.219 0.237 EN 0.043 0.858 0.156 0.118 0.288 0.315 0.050 0.328 GBMI 0.311 0.134 0.817 0.274 0.467 0.480 0.284 0.292 OR 0.108 0.097 0.239 0.847 0.319 0.336 0.149 0.260 PAC 0.203 0.237 0.407 0.272 0.824 0.707 0.322 0.438 RAC 0.252 0.261 0.412 0.283 0.589 0.812 0.288 0.438 TE 0.184 0.045 0.248 0.131 0.276 0.243 0.843 0.332 VDSCA 0.201 0.283 0.259 0.232 0.380 0.377 0.297 0.825 Cross Loadings Items DS EN GBMI OR PAC RAC TE VDSCA DS1 0.859 0.069 0.260 0.134 0.168 0.222 0.155 0.185 DS2 0.850 -0.043 0.274 0.045 0.148 0.182 0.154 0.145 DS3 0.861 0.088 0.266 0.100 0.207 0.244 0.163 0.188 EN1 0.103 0.837 0.140 0.131 0.209 0.232 0.025 0.220 EN2 -0.011 0.887 0.082 0.083 0.205 0.274 0.044 0.264 EN3 0.030 0.849 0.128 0.039 0.198 0.164 0.045 0.241 GBMI1 0.276 0.209 0.840 0.207 0.391 0.369 0.204 0.259 GBMI2 0.225 0.126 0.783 0.205 0.330 0.315 0.142 0.166 GBMI3 0.287 0.054 0.826 0.222 0.284 0.361 0.252 0.255 GBMI4 0.207 0.087 0.828 0.198 0.367 0.328 0.211 0.183 GBMI5 0.273 0.055 0.807 0.138 0.282 0.305 0.201 0.186 OR1 0.092 0.060 0.183 0.854 0.273 0.201 0.104 0.223 OR2 0.115 0.086 0.225 0.863 0.204 0.233 0.163 0.199 OR3 0.127 0.101 0.190 0.847 0.219 0.237 0.057 0.191 OR4 0.022 0.085 0.215 0.824 0.217 0.305 0.122 0.165 PAC1 0.176 0.167 0.286 0.258 0.839 0.473 0.275 0.344 PAC2 0.154 0.176 0.396 0.177 0.833 0.446 0.227 0.348 PAC3 0.145 0.216 0.297 0.239 0.791 0.500 0.232 0.289 PAC4 0.196 0.230 0.347 0.232 0.830 0.534 0.175 0.264 RAC1 0.215 0.218 0.374 0.225 0.509 0.796 0.254 0.285 RAC2 0.194 0.238 0.287 0.222 0.492 0.840 0.200 0.342 RAC3 0.212 0.206 0.361 0.276 0.491 0.829 0.179 0.339 RAC4 0.195 0.184 0.310 0.188 0.414 0.781 0.149 0.254 TE1 0.101 0.003 0.199 0.063 0.257 0.205 0.855 0.259 TE2 0.184 0.035 0.242 0.171 0.250 0.236 0.847 0.239 TE3 0.174 0.091 0.202 0.142 0.216 0.171 0.848 0.295 TE4 0.163 0.006 0.193 0.052 0.204 0.218 0.819 0.185 VDSCA1 0.125 0.246 0.219 0.196 0.307 0.306 0.267 0.859 VDSCA2 0.177 0.152 0.202 0.137 0.355 0.274 0.243 0.812 VDSCA3 0.197 0.238 0.209 0.161 0.265 0.297 0.251 0.801 VDSCA4 0.227 0.317 0.237 0.181 0.337 0.328 0.243 0.841 VDSCA5 0.103 0.203 0.201 0.276 0.303 0.347 0.223 0.810 Note: The bolded parts represent the square root of the Average Variance Extracted (AVE). Correlation coefficients between constructs are found in the lower left part of the diagonal. Conversely, the upper right section displays the Heterotrait-Monotrait ratios (HTMT) among the constructs. 5.2. Structural Model The structural model was estimated in SmartPLS 4 with 5,000 bootstrap resamples to evaluate path significance (Wang & Zhang, 2024). Figure 3 reports path coefficients and significance levels for all hypothesized relationships, alongside R² (explanatory power) and Q² (predictive relevance) values for endogenous constructs. All hypothesized paths attained statistical significance: environment → visual digital supply chain adoption (β = 0.254, p < 0.001), organization → visual digital supply chain adoption (β = 0.175, p < 0.01), technology → visual digital supply chain adoption (β = 0.262, p < 0.001), visual digital supply chain adoption → potential absorptive capacity (β = 0.379, p < 0.001), visual digital supply chain adoption → realized absorptive capacity (β = 0.378, p < 0.001), potential absorptive capacity → green business model innovation (β = 0.226, p < 0.01), realized absorptive capacity → green business model innovation (β = 0.203, p < 0.001), digital strategy × potential absorptive capacity → green business model innovation (β = 0.184, p < 0.01), and digital strategy × realized absorptive capacity → green business model innovation (β = 0.246, p < 0.001). The structural model assessment indicated that none of the firm-level control variables (firm age, firm size, firm type, and ownership) exerted a statistically significant influence on green business model innovation (all p > 0.05), ensuring that the core relationships observed are not driven by these demographic characteristics. These uniformly significant results provide a robust foundation for subsequent IPMA. The standardized root mean square residual (SRMR) is 0.047, below the 0.08 acceptability threshold, indicating satisfactory model fit. R² and Q² values exceed 0.1 and 0, respectively, confirming adequate explanatory power and out-of-sample predictive capacity. 5.3. Moderation Effects Structural model results (Figure 3) reveal that digital strategy exerts a statistically significant positive moderating effect on the potential absorptive capacity → green business model innovation pathway (β = 0.184, p < 0.01). This suggests that more robust digital strategy execution amplifies the efficacy of potential absorptive capacity in generating green business model innovation. The corresponding effect size (f² = 0.036 for DS × PAC) substantiates this moderation. Digital strategy likewise positively moderates the realized absorptive capacity → green business model innovation relationship (β = 0.246, p < 0.001). Simple slope analyses were performed and graphically depicted (Figures 4 and 5) to elucidate the nature of these interaction effects (Wang & Zhang, 2024). The plots confirm that at elevated levels of digital strategy implementation, both potential and realized absorptive capacities exert substantially stronger effects on green business model innovation. 5.4. Importance Performance Map Analysis IPMA was employed to jointly assess the importance and performance of each construct in relation to green business model innovation (Wang & Zhang, 2024). This analysis offers a dual-dimensional perspective, clarifying each variable's relative contribution and current attainment level. In Figure 6, the vertical axis denotes construct importance and the horizontal axis denotes performance. Constructs in the upper-right quadrant exert the strongest influence on green business model innovation and exhibit the highest sample-level attainment. Digital strategy registers an importance score of 0.24 and a performance score of 68.354, underscoring its pivotal function in catalyzing green business model innovation and its relatively high realization among sampled firms. Potential absorptive capacity (importance = 0.225) and realized absorptive capacity (importance = 0.205) yield somewhat lower performance scores yet remain substantively important for green business model innovation advancement. Technology (importance = 0.043, performance = 51.807) resides in the lower-left quadrant, suggesting a comparatively modest direct effect on green business model innovation in this sample. Visual digital supply chain adoption (importance = 0.163, performance = 56.836), though ranking below digital strategy and both absorptive capacity dimensions in importance, nonetheless demonstrates a meaningful contributory role warranting managerial attention. 6. Study 2: Configurational Analysis Results 6.1. Calibration FsQCA was deployed to identify combinatorial configurations of conditions associated with green business model innovation. The initial analytical step involves calibrating raw data into fuzzy-set membership scores corresponding to full membership, crossover, and full non-membership anchors (Wang & Zhang, 2025), thereby enabling systematic evaluation of each condition's role. Table 4 presents the calibration parameters. Table 4 . Calibration. Variables EN TE OR VDSCA RAC PAC DS GBMI Full non-membership 1.667 1.500 1.500 1.800 1.750 1.750 2.333 2.000 Crossover point 4.667 4.000 4.250 4.400 4.500 4.250 5.667 4.600 Full membership 7.000 6.750 6.750 6.680 6.600 6.500 7.000 6.800 6.2. Necessary Conditions Analysis Necessary Condition Analysis (NCA) constitutes the second fsQCA stage, ascertaining whether any individual condition is requisite for the outcome's occurrence (Wang & Zhang, 2025). A condition is classified as necessary when its consistency exceeds 0.90. Table 5 indicates that all consistency values fall below this threshold, confirming that no single condition is individually necessary for green business model innovation. This result highlights the inherent complexity of green business model innovation, demonstrating that no isolated strategic lever or resource endowment suffices; rather, the outcome hinges on the conjunctural interplay of multiple conditions. Table 5 . Necessary conditions analysis. Consistency Coverage EN 0.646 0.635 ~EN 0.584 0.566 TE 0.679 0.675 ~TE 0.548 0.526 OR 0.700 0.663 ~OR 0.553 0.557 VDSCA 0.654 0.669 ~VDSCA 0.583 0.544 RAC 0.730 0.523 ~RAC 0.529 0.717 PAC 0.707 0.704 ~PAC 0.546 0.514 DS 0.714 0.686 ~DS 0.525 0.521 6.3. Sufficient Conditions Analysis Sufficient condition analysis evaluates whether specific combinatorial configurations of the seven antecedent conditions are subsets of the green business model innovation outcome (Wang & Zhang, 2025). We applied a raw consistency cut-off of 0.80, a PRI consistency cut-off of 0.60, and a frequency threshold of 3. Table 6 reports seven distinct configurations. The overall solution consistency is 0.854 (> 0.8) and solution coverage is 0.513 (> 0.5). The seven configurations cluster into three theoretically meaningful pathways. First, Configurations 2, 4, 6, and 7 jointly exhibit visual digital supply chain adoption as a core or peripheral condition combined with potential and/or realized absorptive capacity, representing a VDSCA-enabled absorptive-capacity pathway consistent with H4–H9. Second, Configurations 1 and 3 represent absorptive-capacity-driven pathways in which both potential and realized absorptive capacities are present while VDSCA is not a binding condition, indicating that strong dual absorptive capacities can independently sustain green business model innovation when supported by complementary TOE conditions. Third, Configuration 5 reveals a contrasting substitution pathway: in the absence of visual digital supply chain adoption, potential absorptive capacity, environmental pressure, and organizational readiness, firms can still attain high green business model innovation by combining strong technological readiness, realized absorptive capacity, and digital strategy. This finding qualifies, rather than contradicts, the PLS-SEM results by showing that realized absorptive capacity and digital strategy can serve as compensatory mechanisms when upstream digital infrastructure and knowledge-acquisition capacity are lacking, thereby enriching the equifinality perspective of green business model innovation. Table 6 . Configurations leading to high-level green business model innovation. Configurations 1 2 3 4 5 6 7 EN ● ● ⨂ ● ● TE ● ● ○ ● ● ● OR ● ● ● ○ ● ● VDSCA ● ● ⨂ ● ● RAC ● ● ● ● ● ● PAC ● ● ● ● ⨂ ● ● DS ● ● ⨂ ● ● ⨂ Raw coverage 0.335 0.310 0.229 0.248 0.178 0.255 0.276 Unique coverage 0.048 0.030 0.022 0.015 0.025 0.003 0.000 Consistency 0.888 0.867 0.913 0.914 0.930 0.937 0.914 Solution coverage 0.513 Solution consistency 0.854 Note: "●" or "●" indicate the existence of core or peripheral conditions, respectively. "⨂" or "○" represent the non-existence of core or peripheral conditions. Any cells left vacant indicate a “do not care” situation. 7. Discussion and Conclusion This study examines the complex interdependencies among technological, organizational, and environmental antecedents, visual digital supply chain adoption, absorptive capacity dimensions, digital strategy, and green business model innovation. Our findings offer novel insights into the mechanisms through which visual digital supply chain adoption facilitates green business model innovation. By integrating the TOE framework with absorptive capacity theory, we foreground the pivotal function of visual digital supply chain adoption in the green business model innovation process. The results illuminate how these constructs operate both individually and in specific combinatorial configurations to foster green business model innovation. This contribution accentuates the importance of configurational perspectives in understanding the antecedents of green business model innovation, broadening scholarly understanding of both green business model innovation and digital supply chain adoption. The study yields substantive theoretical and practical insights regarding how digitally enabled supply chain visibility can advance green business model innovation, enriching the literatures on digital supply chains and business model reconfiguration while equipping practitioners with evidence-based guidance for achieving sustainable business models through digital transformation. 7.1. Discussion and Theoretical Implications This research explicates the determinants and transmission mechanisms of green business model innovation. Employing PLS-SEM, IPMA, and fsQCA in a complementary analytical framework, we obtained several substantive findings. All proposed hypotheses received empirical support, yielding actionable insights into the interrelationships among visual digital supply chain adoption, green business model innovation, absorptive capacity dimensions, and digital strategy. Technological, organizational, and environmental factors each exert significant positive effects on visual digital supply chain adoption, consistent with the TOE framework (Dong et al., 2025). At the technological level, the availability of advanced IT infrastructure and the perceived value of supply chain visualization substantially influence adoption decisions; firms with more mature technological capabilities demonstrate greater propensity to implement visual digital supply chains, reflecting superior capacity for seamless technology integration. At the organizational level, top management support and workforce digital competencies prove instrumental. Organizations whose leadership emphasizes evidence-based decision-making and invests in human capital development exhibit more successful adoption trajectories. At the environmental level, competitive intensity and regulatory stringency shape adoption patterns; firms confronting vigorous rivalry or exacting compliance mandates gravitate toward adoption to preserve competitive standing and fulfill regulatory obligations (Zou et al., 2025). Collectively, these findings indicate that the three TOE dimensions are jointly salient for visual digital supply chain adoption and merit systematic assessment when deploying novel supply chain solutions (Wang & Zhang, 2025). Visual digital supply chain adoption exerts a positive effect on both potential and realized absorptive capacity, aligning with absorptive capacity theory (Sadeghi et al., 2023). It strengthens firms' capacity to comprehend and operationalize new knowledge and technologies, thereby increasing the probability of green business model innovation (Esfahbodi et al., 2023). This finding offers one explanation for why certain firms achieve green business model innovation more rapidly: their more effective utilization of visual digital supply chains to enhance absorptive capacity. Digital strategy exerts a significant positive moderating effect on the absorptive capacity to green business model innovation relationships, consistent with dynamic capability logic (Gyamerah et al., 2025). Under the guidance of a coherent digital strategy, firms can more productively leverage visual digital supply chain adoption to fortify absorptive capacity, thereby accelerating green business model innovation (Yildiz et al., 2024). Our findings additionally underscore the strategic importance of digital strategy in the green business model innovation process, an aspect insufficiently addressed in prior research, thereby extending the theoretical repertoire surrounding green business model innovation. A central finding is that technological, organizational, and environmental factors significantly shape visual digital supply chain adoption, advancing both absorptive capacity theory (Yildiz et al., 2024) and the TOE framework (Wang & Zhang, 2024). Prior scholarship positioned these three dimensions as primary determinants of technology innovation and diffusion (Ancillai et al., 2023). Our evidence demonstrates their salience extends to visual digital supply chain adoption, broadening the empirical applicability of both theories (Lyu et al., 2023). The finding that visual digital supply chain adoption augments both potential and realized absorptive capacity advances absorptive capacity theory by identifying a novel technological antecedent. Whereas prior work predominantly examined how absorptive capacity facilitates knowledge transfer and innovation outcomes (Yildiz et al., 2024), our study reveals how absorptive capacity itself is shaped by visual digital supply chain adoption, establishing a new domain of application for the theory (Belhadi et al., 2022). The significant moderating role of digital strategy on the absorptive capacity to green business model innovation pathways (Xiao et al., 2025) reinforces and extends the dynamic capability perspective. Earlier scholarship documented the broad impact of dynamic capabilities on innovation and strategic performance (Yildiz et al., 2024). Our evidence specifies that digital strategy, conceptualized as a dynamic capability, materially conditions green business model innovation, thereby strengthening the theoretical salience of this lens (Song et al., 2024). The fsQCA results demonstrate that while no individual variable constitutes a necessary condition for green business model innovation, specific multi-factor configurations substantially stimulate its emergence (Kajtazi et al., 2023). This configurational finding contributes to configurational theory, which posits that organizational and environmental elements combine in discrete, patterned ways. Our study furnishes a novel empirical application of this perspective within the green business model innovation domain (Wang & Zhang, 2025). 7.2. Practical Implications Our findings illuminate the multifaceted relationship among visual digital supply chain adoption, absorptive capacity dimensions, digital strategy, and green business model innovation, underscoring the strategic imperative for firms to account for the influence of supply chain visualization on ecological innovation (Wang & Zhang, 2025). Practitioners should attend to the joint effects of technological, organizational, and environmental factors on green business model innovation and operational performance. Investment in artificial intelligence and machine learning to elevate supply chain digital visibility, coupled with cultivation of managerial digital literacy to strengthen knowledge assimilation and application, represents a high-priority action portfolio. Such initiatives bolster absorptive capacity and, in turn, propel green business model innovation (Tian & Cui, 2025). The empirically documented effects of TOE factors on visual digital supply chain adoption yield actionable guidance (Zou et al., 2025). Firms contemplating adoption should conduct readiness assessments across all three dimensions. Technologically, adequate infrastructural foundations and a favorable benefit-to-cost calculus are prerequisite. Organizationally, cultivating an innovation-supportive culture and investing in workforce digital competency development have demonstrated effectiveness. Environmentally, maintaining awareness of competitive dynamics and evolving regulatory mandates that may trigger adoption is essential. A holistic appraisal across these dimensions enables firms to craft more effective adoption strategies and maximize resultant benefits (Xiao et al., 2025). To advance green business model innovation, firms should formalize a visual digital supply chain adoption roadmap embedded within their strategic planning processes (Dong et al., 2025). Such roadmaps should encompass leveraging existing data assets, intensifying operational utilization of supply chain visualization, and systematically harnessing visual digital supply chain capabilities to strengthen organizational innovation capacity. A well-structured implementation plan enables firms to more fully exploit the innovation potential of visual digital supply chain adoption. The configurational findings indicate that no single variable is individually sufficient for generating green business model innovation; rather, specific multi-factor combinations produce high levels of such innovation. Practitioners should recognize that universal prescriptions are unlikely to succeed. Instead, firms must assemble strategy-resource bundles tailored to their particular circumstances. For instance, a resource-constrained smaller firm may lack capacity for advanced AI deployment but can still optimize supply chain visualization by upskilling its workforce digitally and leveraging cloud-based service infrastructures. 7.3. Future Research Despite its contributions, this study is subject to several limitations. Our analytical scope centers on technological, organizational, and environmental determinants of visual digital supply chain adoption, yet does not fully account for additional contingencies such as industry-specific characteristics, managerial leadership orientation, and employee-level technology acceptance. These factors may exert meaningful influence on green business model innovation under particular conditions. Methodologically, our reliance on quantitative analysis, while affording systematic rigor, may not fully capture the nuances of complex organizational phenomena and behavioral dynamics. Subsequent research would benefit from integrating qualitative inquiry to achieve richer interpretive depth. Furthermore, our analysis operates predominantly at the firm level, leaving individual- and team-level dynamics underexamined. Future studies adopting a micro-level lens could illuminate more granular influence mechanisms underlying green business model innovation. Finally, despite the temporally separated two-wave design, our cross-sectional data preclude definitive causal inference. Although the hypothesized directionality is supported by theory and prior literature, longitudinal or quasi-experimental designs are needed to firmly establish the causal ordering among visual digital supply chain adoption, absorptive capacity, digital strategy, and green business model innovation. Declarations Ethical Approval This study was reviewed by the Institutional Review Board (IRB) of the International Business School, Fuzhou University of International Studies and Trade. As the research involved a survey in which substantive responses were anonymized for analysis, while contact information for the second-wave follow-up was collected separately from the survey responses and deleted after data collection, and posed no more than minimal risk to participants, the IRB granted an exemption from full ethical review. All research procedures were performed in accordance with the ethical standards of the institutional review board and with the ethical review regulations and guidelines for research involving human participants in China, including the Declaration of Helsinki. Informed Consent Informed consent was obtained from all participants in this study. Prior to completing the questionnaire, each participant was presented with an introductory statement describing the purpose of the research, the voluntary nature of participation, the anonymity and confidentiality protections (including an explicit assurance that all responses would be used exclusively for academic purposes and would not be publicly disclosed), and the right to withdraw at any point without consequence. Consent was indicated by the participant's voluntary decision to proceed with and complete the survey. Author Contribution Conceptualization, S.W.; methodology, S.W.; writing —original draft preparation, S.W., and H.Z.; funding acquisition, S.W. Acknowledgement We thank all respondents for participating in this survey. We also thank our colleagues and the digital tools that supported language editing. Data Availability The datasets are not publicly available due to the risk of reverse de-anonymization and specific informed consent conditions. During data collection, all participating firms were explicitly assured that the data would be used exclusively for the purposes of this specific study and would not be publicly disclosed or shared with third parties. This commitment was a critical condition under which firms agreed to provide sensitive strategic and operational data. Additionally, the specific combination of firm-level demographic variables (e.g., precise firm age, exact firm size, specific geographic location, and ownership type), combined with the publicly accessible and limited population of China’s Green Manufacturing Initiative database, could inadvertently allow third parties to deduce the identities of participating firms, compromising the confidentiality agreements under which the data were collected. To protect proprietary corporate information, adhere to our ethical commitments, and honor the informed consent provisions, the raw datasets cannot be deposited in a public repository. 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Humanit Social Sci Commun 12(1):1738 Wang C, Qin S, Zhou X, Tao K (2026) Information technology and supply chain collaborative drives supply chain cooperative: resource-based view perspective. Humanit Social Sci Commun 13., Article 291. https://doi.org/10.1057/s41599-026-06646-w Wang S, Zhang H (2024) Inter-organizational cooperation in digital green supply chains: A catalyst for eco-innovations and sustainable business practices. J Clean Prod 472:143383 Wang S, Zhang H (2025) Leveraging generative artificial intelligence for sustainable business model innovation in production systems. Int J Prod Res 63(18):6732–6757 Wang T, Shen Y (2025) Unlocking the potential of supply chain digitalization for enhancing enterprise green transformation performance: evidence from China. Humanit Social Sci Commun 12(1):1–16 Xiao S, Roh T, Park BI (2025) Entrepreneurial orientation, environmental dynamism, and disruptive sustainability in emerging markets: Evidence from MNE subsidiaries in China. Bus Strategy Environ 34(8):10012–10032 Yildiz HE, Murtic A, Zander U (2024) Re-conceptualizing absorptive capacity: The importance of teams as a meso-level context. Technol Forecast Soc Chang 199:123039 Yin S, Yu Y, Zhang N (2024) The Effect of Digital Green Strategic Orientation On Digital Green Innovation Performance: From the Perspective of Digital Green Business Model Innovation. SAGE Open 14(2):21582440241261130 Zhang X, Antonialli F, Bonnardel SM, Bareille O (2024) Where business model innovation comes from and where it goes: a bibliometric review. Creativity Innov Manage 33(2):109–126 Zhang Z, Hua C, Jiang MS, Miao J (2024) The spatial spillover effect of financial growth on high-quality development: Evidence from Yellow River Basin in China. Humanit Social Sci Commun 11(1):1–17 Zhang Z, Zhang G, Hu Y, Jiang Y, Zhou C, Ma J (2023) The evolutionary mechanism of haze collaborative governance: novel evidence from a tripartite evolutionary game model and a case study in China. Humanit Social Sci Commun 10(1):69 Zou T, Xiong F, Li S, Zhang W (2025) Understanding the Determinants of Firms' Usage of A/B Testing: A Technology-Organization-Environment Framework. IEEE Trans Eng Manage 72:378–400 Additional Declarations No competing interests reported. Supplementary Files AppendixASurveyQuestionnaire.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 May, 2026 Reviews received at journal 14 May, 2026 Reviewers agreed at journal 13 May, 2026 Reviewers invited by journal 27 Apr, 2026 Editor assigned by journal 27 Apr, 2026 Editor invited by journal 17 Apr, 2026 Submission checks completed at journal 15 Apr, 2026 First submitted to journal 15 Apr, 2026 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. 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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-9245607","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":633008808,"identity":"cd001367-1ec8-491a-978c-53654cfe8208","order_by":0,"name":"Shaofeng Wang","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Shaofeng","middleName":"","lastName":"Wang","suffix":""},{"id":633008809,"identity":"66a5693e-a673-4b17-8ee6-7cf5fdee423c","order_by":1,"name":"Hao Zhang","email":"data:image/png;base64,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","orcid":"","institution":"University Institute of Lisbon","correspondingAuthor":true,"prefix":"","firstName":"Hao","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-03-27 14:08:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9245607/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9245607/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108619666,"identity":"19164a70-5cfa-4db7-bd6a-768f165a5695","added_by":"auto","created_at":"2026-05-06 14:35:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80270,"visible":true,"origin":"","legend":"\u003cp\u003eTheoretical Model and Hypotheses.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9245607/v1/ea65f7b0852329e100551884.png"},{"id":108619668,"identity":"bbe3b4f7-26aa-46dc-b024-7051d74e1ba9","added_by":"auto","created_at":"2026-05-06 14:35:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":65783,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of research methods.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9245607/v1/4c29558e4fd6c3cff021ab32.png"},{"id":108804964,"identity":"526304f2-59d5-4e3f-bbd1-67a2396e1932","added_by":"auto","created_at":"2026-05-08 15:24:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":82262,"visible":true,"origin":"","legend":"\u003cp\u003eStructural model results.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9245607/v1/58742e96138095cfefde87d0.png"},{"id":108619669,"identity":"c61fa488-e87b-4c5f-8240-435a7404df3c","added_by":"auto","created_at":"2026-05-06 14:35:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":47596,"visible":true,"origin":"","legend":"\u003cp\u003eModeration effect of digital strategy on the relationship between potential absorptive capacity and green business model innovation.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9245607/v1/3986141b94f7077a232f908b.png"},{"id":108804965,"identity":"961b43ca-85e9-45e4-9e38-7793d49e55e2","added_by":"auto","created_at":"2026-05-08 15:24:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":49365,"visible":true,"origin":"","legend":"\u003cp\u003eModeration effect of digital strategy on the relationship between realized absorptive capacity and green business model innovation.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9245607/v1/7b4c56e8fde782f00b92ad8f.png"},{"id":108619672,"identity":"e9a630f7-4e29-4290-ba4c-2dc6514ea126","added_by":"auto","created_at":"2026-05-06 14:35:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":57159,"visible":true,"origin":"","legend":"\u003cp\u003eImportance Performance Map Analysis of green business model innovation.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9245607/v1/982451a2c4e45c83aeb2c908.png"},{"id":108976885,"identity":"7be5bcdb-f3e7-47e5-90c0-d6200ea44cc1","added_by":"auto","created_at":"2026-05-11 11:29:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":981712,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9245607/v1/ec26fba9-0f39-47db-b94a-ca78e107f5e4.pdf"},{"id":108619667,"identity":"32132768-91bb-4613-8a9b-dd381a28f329","added_by":"auto","created_at":"2026-05-06 14:35:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15315,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixASurveyQuestionnaire.docx","url":"https://assets-eu.researchsquare.com/files/rs-9245607/v1/cf03ea589e092be268f99faa.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Seeing Green: How Visual Digital Supply Chains Enable Sustainable Business Model Innovation in Manufacturing","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe acceleration of digital transformation across manufacturing industries has intensified scholarly and practitioner interest in the determinants of sustainable business conduct (Mamirkulova et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Business model innovation in the manufacturing sector has attracted growing attention (Xiao et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mei et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2026\u003c/span\u003e), yet rigorous theoretical and empirical inquiry into how firms effectively realize green business model innovation remains underdeveloped (Zhang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang \u0026amp; Shen, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Much of the existing literature is confined to firm- or industry-specific case studies (Cucculelli et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), leaving the underlying mechanisms and contextual antecedents insufficiently explored. Notably, despite increasing interest in digitalized supply chain architectures, the pathways through which visual digital supply chains catalyze green business model innovation have received scant systematic investigation (Wang \u0026amp; Zhang, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCompetitive advantage has progressively shifted from intra-firm operational efficiency to supply chain-level coordination and responsiveness (Xiao et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Yin et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As conventional supply chain structures prove inadequate for escalating market demands, digitally orchestrated supply chains have emerged as a strategic response (Esfahbodi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These configurations deploy advanced information technologies and analytical tools to enhance supply chain agility and throughput (Dong et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), while visualization capabilities translate complex data into accessible and actionable formats (Sadeghi et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite their recognized importance, empirical research on how visual digital supply chains shape green business model innovation remains limited (Wang \u0026amp; Zhang, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGreen business model innovation refers to the strategic reorientation of products, services, operational processes, and value architectures toward ecological stewardship, resource conservation, and decarbonization, thereby pursuing environmental and economic objectives simultaneously (Huang et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xiao et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Advancing scholarly understanding in this domain is essential for equipping firms with viable pathways to sustainable development. Digital strategy denotes the deliberate deployment of digital technologies to reconfigure business processes, reinvent value propositions, and restructure supply-side and demand-side relationships (Ancillai et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), spanning the firm's technology roadmap, resource allocation, process design, and stakeholder interfaces. Although selected studies have recognized the salience of digital strategy for business model reconfiguration (Ndri \u0026amp; Su, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), theoretically grounded and empirically validated research on its role in green business model innovation remains insufficient, a gap amplified by the accelerating role of digital technologies in sustainability transitions (Kajtazi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eParallel efforts have examined the potential of digitalized supply chains to advance green business model innovation (Yin et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), yet the literature remains anchored in idiosyncratic case analyses with limited examination of generalizable mechanisms and their contingencies (Cucculelli et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although absorptive capacity theory recognizes the centrality of potential and realized absorptive capacity for organizational innovation, its application to the green business model innovation process warrants deeper exploration (Yildiz et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Green business model innovation is conceptually adjacent to related constructs such as green ventures, circular business models, and sustainable entrepreneurship (Zhang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While these constructs share the objective of embedding sustainability within commercial activity, our focus pertains specifically to how incumbent firms reconfigure established business models toward greater environmental sustainability (Xiao et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Absorptive capacity theory (Algarni et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) provides a conceptual apparatus for understanding how organizations detect, assimilate, and exploit novel knowledge. In the green business model innovation context, absorptive capacity is instrumental for incorporating emergent environmental knowledge and technologies into value creation logic. By examining the mediating role of absorptive capacity in linking visual digital supply chains to green business model innovation, we generate fresh theoretical insights into how digital technologies stimulate sustainable innovation (Yin et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Recent scholarship on green ventures highlights the enabling function of digital technologies for sustainable business conduct (Wang \u0026amp; Zhang, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), yet the distinctive contribution of visual digital supply chains, particularly among established manufacturers, remains insufficiently examined.\u003c/p\u003e \u003cp\u003eAgainst this backdrop, our study pursues three interrelated research questions. First, through what mechanisms do visual digital supply chains shape green business model innovation (RQ1)? Addressing this question may reveal novel pathways for promoting ecological innovation, a priority for economic, societal, and ecosystem resilience (Fang et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). While absorptive capacity theory offers relevant conceptual grounding, its application to visual digital supply chain contexts has received limited empirical attention (Yin et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The precise influence of visual digital supply chains on green business model innovation remains theoretically underdetermined, and absorptive capacity theory, despite emphasizing an organization's competence in assimilating external knowledge (Sadeghi et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), offers limited explanatory traction regarding the dual channels of potential and realized absorptive capacities in this specific transmission process.\u003c/p\u003e \u003cp\u003eSecond, we examine the antecedent conditions governing visual digital supply chain adoption (RQ2). The Technology-Organization-Environment (TOE) framework (Zou et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) provides the theoretical scaffolding, yet limited research has identified the specific drivers of visual digital supply chain adoption despite the framework's tripartite decomposition. Integrating the TOE framework with absorptive capacity theory, our investigation offers a granular analysis of these adoption determinants. Third, we probe the contingent function of digital strategy in advancing green business model innovation (RQ3). Understanding how digital strategy catalyzes ecological innovation holds substantial value, equipping practitioners with actionable guidance for pursuing sustainability and competitive positioning simultaneously. Prevailing research, however, has largely neglected the moderating influence of digital strategy on green business model innovation outcomes.\u003c/p\u003e \u003cp\u003eAlthough digitalized supply chains attract growing scholarly interest, systematic evidence on the pathways through which visual digital supply chains foster green business model innovation remains thin (Dong et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Theoretical and empirical research linking visual digital supply chains to green business model innovation through dual absorptive capacity mechanisms, while delineating the boundary conditions imposed by digital strategy, is particularly scarce. Our study contributes by synthesizing absorptive capacity theory and the TOE framework into an integrated research model, extending the explanatory reach of both theoretical traditions (Algarni et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Beyond identifying antecedent drivers of visual digital supply chain adoption, we elucidate how digital strategy moderates the innovation outcomes of absorptive capacity (Esfahbodi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Methodologically, our concurrent deployment of PLS-SEM and fsQCA constitutes a distinctive analytical approach seldom employed in prior work (Wang \u0026amp; Zhang, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), enabling multi-perspectival examination of the research questions and strengthening inferential robustness. Collectively, our findings offer substantive theoretical support for manufacturing firms navigating digital transformation, particularly in accelerating green business model innovation (Chen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Gyamerah et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe remainder of this paper proceeds as follows. The literature review synthesizes pertinent scholarship and articulates the theoretical underpinnings. The theoretical model and hypotheses are then formalized. The methods section describes data sources, sample attributes, construct operationalization, and analytical procedures. Empirical results are subsequently reported, followed by a discussion of theoretical and practical implications alongside directions for future inquiry.\u003c/p\u003e"},{"header":"2. Literature Review and Theoretical Background","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Green Business Model Innovation\u003c/h2\u003e \u003cp\u003eScholarship has established the centrality of business model innovation in advancing organizational sustainability and broader societal welfare through ecological protection and social equity (Ancillai et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang \u0026amp; Zhang, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Emerging research has also examined the capacity of digitalized supply chains to stimulate green business model innovation (Yin et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Nevertheless, the literature remains predominantly oriented toward idiosyncratic case analyses of particular firms or sectors, with comparatively sparse attention to generalizable mechanisms and their contextual moderators (Cucculelli et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This orientation constrains the explanatory and predictive power of prevailing frameworks. Although existing work examines technological, organizational, and environmental antecedents of green business model innovation, these influences are empirically entangled and methodologically difficult to disentangle (Cucculelli et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Critically, the specific transmission mechanisms through which these antecedents shape green business model innovation remain undertheorized. Our study addresses this gap by positioning potential and realized absorptive capacities as mediating constructs linking visual digital supply chains to green business model innovation, while treating digital strategy as a boundary condition that modulates these indirect pathways. Through rigorous theoretical integration, we aim to advance scholarly understanding, practitioner guidance, and policymaker insights for fostering sustainable industrial transformation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Visual Digital Supply Chain Adoption\u003c/h2\u003e \u003cp\u003eAmid accelerating globalization and digitization, supply chain management has ascended to a strategic priority (Dong et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Digitalized supply chains represent a transformative development with substantial implications for organizational competitiveness (Wang \u0026amp; Zhang, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Visual digital supply chains encompass the entirety of transactional processes, information exchanges, and physical flows within supply networks. Their principal merit resides in delivering real-time, precise, and graphically rendered information that elevates decisional efficiency and quality (Kumar \u0026amp; Shankar, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Visualization technologies convert intricate supply chain datasets into cognitively accessible graphical representations, affording firms transparent and accurate oversight of supply chain conditions (Wang \u0026amp; Zhang, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For instance, process flow diagrams can delineate material, informational, and financial movements, enabling rapid detection of bottlenecks or latent disruption risks. These technologies also underpin continuous supply chain refinement; by rendering dynamic operational shifts in intuitive visual formats, firms can conduct real-time monitoring and diagnostic analyses, surfacing improvement opportunities (Sadowski et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Despite broad acknowledgment of digital supply chain significance (Belhadi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), a conspicuous void persists regarding the influence of visual digital supply chains on green business model innovation. This study addresses that void by offering both conceptual and empirical contributions: examining the transmission mechanisms linking visual digital supply chains to green business model innovation, the adoption antecedents, and the moderating function of digital strategy. Existing literature has concentrated primarily on definitional aspects of digital supply chains (Song et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and their efficacy-enhancing properties (Kumar \u0026amp; Shankar, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), with inadequate consideration of how visual digital supply chains affect organizational knowledge processing capabilities, encompassing both potential and realized absorptive capacity, to foster green business model innovation. The contingent role of digital strategy in this process likewise remains empirically uncharted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Absorptive Capacity Theory\u003c/h2\u003e \u003cp\u003eAbsorptive capacity theory characterizes an organization's proficiency in detecting, assimilating, and leveraging external knowledge to generate innovation or elevate performance (Yildiz et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The construct bifurcates into potential absorptive capacity, pertaining to knowledge recognition and assimilation, and realized absorptive capacity, concerning knowledge conversion and exploitation (Algarni et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This theoretical lens provides an explanatory apparatus for tracing how visual digital supply chains stimulate green business model innovation by modulating both absorptive capacity dimensions. As a foundational construct in knowledge management and innovation scholarship, absorptive capacity carries substantial applied relevance: it informs how organizations strengthen learning and innovation routines (Dabić et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), navigate environmental shifts (Algarni et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and cultivate competitive differentiation (Wang \u0026amp; Zhang, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Within our research context, absorptive capacity theory yields critical insights into how visual digital supply chain deployment can catalyze green business model innovation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Technology-Organization-Environment (TOE) framework\u003c/h2\u003e \u003cp\u003eThe TOE framework constitutes a foundational architecture for understanding technology innovation, adoption, and diffusion (Zou et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). It posits that technology adoption decisions are jointly conditioned by technological, organizational, and environmental dimensions, each exerting direct influence on strategic technology choices (Wang \u0026amp; Zhang, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Technological factors capture attributes intrinsic to the innovation, including technical competence, system compatibility, and infrastructural readiness (Dong et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Organizational factors span cultural orientation, structural configuration, and resource endowments, collectively shaping adoption propensity (Yin et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Environmental factors comprise the competitive milieu, regulatory landscape, and institutional pressures that constitute the firm's external operating context (Wang \u0026amp; Zhang, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). We invoke the TOE framework to explain visual digital supply chain adoption patterns (Zou et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). By synthesizing technological, organizational, and environmental antecedents, we identify the forces propelling adoption and trace their downstream consequences for green business model innovation. Our study targets three salient gaps in the literature. First, we delineate the mechanisms through which visual digital supply chains affect green business model innovation, a domain that has attracted limited empirical attention despite the rising strategic importance of both digital technologies and sustainability (Yin et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Second, we systematically examine the adoption determinants through the TOE lens, offering an integrated account of technological, organizational, and environmental influences (Belhadi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Third, we assess the boundary-spanning role of digital strategy in promoting green business model innovation, a dimension largely absent from prior investigations yet pivotal for understanding how firms harness digital capabilities for sustainable innovation (Ndri \u0026amp; Su, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Theoretical Model and Hypotheses","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Theoretical Model\u003c/h2\u003e \u003cp\u003eAbsorptive capacity theory (Yildiz et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) has recognized the significance of both potential and realized dimensions for organizational innovation, yet its operationalization within the green business model innovation domain warrants further development. In our conceptualization, absorptive capacity functions as a capability augmented through visual digital supply chain adoption that subsequently facilitates green business model innovation; it is not positioned as a precursor to adoption itself. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the proposed theoretical model, which synthesizes absorptive capacity theory with the TOE framework. To bolster model stability, we incorporated firm-level control variables (firm age, size, type, and ownership) to account for potential confounding effects on green business model innovation (Wang \u0026amp; Zhang, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Impact of Technological, Organizational, and Environmental Factors on the Visual Digital Supply Chain Adoption\u003c/h2\u003e \u003cp\u003eThe TOE framework specifies that technological maturity, system compatibility, implementation complexity, observability, and relative advantage collectively condition a firm's technology adoption decisions (Wang \u0026amp; Zhang, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Visual digital supply chains, characterized by intuitive interface architectures and graphical rendering of complex datasets, exhibit enhanced relative advantage and compatibility, thereby elevating adoption likelihood (Sadowski et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While visualization implementation may initially present technical hurdles, ongoing technological progress progressively reduces complexity and broadens organizational accessibility (Song et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). When firms perceive that adoption yields operational efficiency gains, lower error rates, and improved decision-making quality, their adoption propensity increases (Wang \u0026amp; Zhang, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Similarly, employee perceptions that visualization augments task-level productivity reinforce technology acceptance (Kumar \u0026amp; Shankar, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Organizational scale and innovativeness typically exhibit positive associations with technology adoption, such that larger and more innovation-oriented firms demonstrate greater readiness (Ndri \u0026amp; Su, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Environmental contingencies, including competitive intensity, industry norms, and customer expectations, further shape adoption decisions (Yin et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Firms confronting acute competitive rivalry or whose supply chain partners have already adopted visual digital supply chains face heightened isomorphic pressure to follow suit (Song et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Where customer expectations for supply chain transparency are elevated, organizations may adopt visual digital supply chains to satisfy these demands (Lyu et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Accordingly, we hypothesize:\u003c/p\u003e \u003cp\u003eH1: Technology positively influences visual digital supply chain adoption.\u003c/p\u003e \u003cp\u003eH2: Organization positively influences visual digital supply chain adoption.\u003c/p\u003e \u003cp\u003eH3: Environment positively influences visual digital supply chain adoption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Impact of Visual Digital Supply Chain Adoption on Potential and Realized Absorptive Capacity\u003c/h2\u003e \u003cp\u003eAbsorptive capacity captures an organization's competence in recognizing, acquiring, integrating, and commercializing novel knowledge, decomposed into potential and realized components (Yildiz et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Potential absorptive capacity pertains to knowledge acquisition and assimilation, processes substantially conditioned by the information technologies a firm employs (Sadeghi et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Digitalized supply chain solutions enable more systematic collection, governance, and analysis of supply chain data (Zhang et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang \u0026amp; Zhang, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), sharpening comprehension of market dynamics (Dabić et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Visualization capabilities further augment data interpretability and usability, thereby strengthening potential absorptive capacity (Sadowski et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Visual digital supply chain adoption heightens organizational sensitivity to supply chain environmental signals, reinforcing the knowledge identification and assimilation functions of potential absorptive capacity (Song et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Concurrently, visual digital supply chains provide enriched datasets and graphically mediated operational interfaces that facilitate more effective knowledge utilization, bolstering realized absorptive capacity (Sadowski et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRealized absorptive capacity denotes a firm's proficiency in converting newly acquired knowledge into commercial value (Yildiz et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Information technologies, including visual digital supply chains, promote inter-organizational information sharing and collaborative sense-making, enabling firms to exploit supply chain knowledge more effectively (Wang \u0026amp; Zhang, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Digital instruments support the application and leveraging of extant knowledge reservoirs, thereby fortifying realized absorptive capacity (Song et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Digitalized supply chain solutions also contribute to operational improvements in supply chain governance, yielding performance benefits (Kumar \u0026amp; Shankar, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The richer data environments and visually mediated decision support afforded by visual digital supply chains further enhance a firm's capacity to transform knowledge into actionable innovation (Sadowski et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Accordingly, we hypothesize:\u003c/p\u003e \u003cp\u003eH4: Visual digital supply chain adoption positively influences potential absorptive capacity.\u003c/p\u003e \u003cp\u003eH5: Visual digital supply chain adoption positively influences realized absorptive capacity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Impact of Potential and Realized Absorptive Capacity on Green Business Model Innovation\u003c/h2\u003e \u003cp\u003eWithin the absorptive capacity framework, potential absorptive capacity principally encompasses the competencies of acquiring and comprehending novel knowledge and synthesizing it with existing cognitive structures to generate original insights (Algarni et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This perspective explains why organizations must internalize digital supply chain technologies to cultivate potential absorptive capacity. Elevated potential absorptive capacity enables organizations to apprehend and deploy emergent technologies, facilitating adaptive responses to market evolution (Yildiz et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Importantly, potential absorptive capacity also catalyzes green business model innovation: by assimilating nascent environmental paradigms and technological solutions, organizations can develop ecologically superior products and services (Wang \u0026amp; Zhang, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). It constitutes a substantial wellspring of organizational innovation (Xiao et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), influencing the reconfiguration of entire business model architectures (Dabić et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), as firms endowed with strong potential absorptive capacity more effectively scan and interpret the external environment, uncovering novel green business model innovation opportunities (Kajtazi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRealized absorptive capacity reflects a firm's proficiency in translating assimilated knowledge into operational practice and is likewise recognized as a pivotal driver of business model reconfiguration (Yildiz et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This dimension is particularly consequential for green business model innovation, since authentic ecological innovation materializes only when organizations effectively operationalize environmental knowledge and technologies in product development and service improvement (Wang \u0026amp; Zhang, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Firms possessing superior realized absorptive capacity exploit extant knowledge more productively, thereby surfacing additional innovation opportunities. Empirical evidence documents a robust positive association between realized absorptive capacity and organizational innovation outcomes (Dabić et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Realized absorptive capacity is a decisive determinant of corporate green innovation, as it enables the conversion of newly assimilated knowledge into innovative products, services, or business model configurations (Zhang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Accordingly, we hypothesize:\u003c/p\u003e \u003cp\u003eH6: Potential absorptive capacity positively influences green business model innovation.\u003c/p\u003e \u003cp\u003eH7: Realized absorptive capacity positively influences green business model innovation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Moderation Effect of Digital Strategy\u003c/h2\u003e \u003cp\u003eA well-articulated digital strategy is fundamental to sustaining competitive advantage and fostering innovation (Yin et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Potential absorptive capacity reflects a firm's proficiency in recognizing and assimilating external knowledge, a competence integral to green business model reconfiguration. Digital strategy implementation expands the knowledge acquisition architecture and streamlines information procurement methods available to the firm (Ndri \u0026amp; Su, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). By reshaping how information is sourced and processed, digital strategy amplifies potential absorptive capacity (Song et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRealized absorptive capacity captures a firm's competence in transforming and exploiting accumulated knowledge stocks (Yildiz et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Converting a substantive knowledge base into executable innovative initiatives typically demands robust realized absorptive capacity (Dabić et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Under the impetus of digital strategy, realized absorptive capacity can be strengthened, thereby accelerating green business model innovation (Kajtazi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Digital strategy furnishes diversified knowledge integration platforms and analytical instruments that facilitate the transition from knowledge accumulation to innovation realization (Yin et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Accordingly, we hypothesize:\u003c/p\u003e \u003cp\u003eH8: Digital strategy positively moderates the relationship between potential absorptive capacity and green business model innovation.\u003c/p\u003e \u003cp\u003eH9: Digital strategy positively moderates the relationship between realized absorptive capacity and green business model innovation.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Research Methods","content":"\u003cp\u003eThe research design comprises three sequential phases (Figure 2). Phase one entails construct operationalization and measurement instrument development. Phase two encompasses data collection and robustness diagnostics. Phase three involves analytical procedures to address the research questions.\u003c/p\u003e\n\u003ch2\u003e4.1. Measurement\u003c/h2\u003e\n\u003cp\u003eA multi-stage protocol was employed to ensure measurement accuracy and reliability (Table 1). Initial measurement indicators were derived from systematic literature analysis, with each item adapted from established scales to align with our study\u0026apos;s context and objectives. Following iterative deliberations among the research team, a preliminary scale was produced. A five-member expert panel, comprising graduate researchers, established scholars, and industry practitioners spanning strategic management, operations, and environmental sustainability, was convened for scale refinement. Through three successive rounds of structured review, a revised instrument was finalized. Pre-testing was conducted with 25 senior manufacturing managers possessing substantial domain expertise. The pre-test yielded satisfactory internal consistency (Cronbach\u0026apos;s alpha \u0026gt; 0.7). Participant feedback was systematically documented and incorporated; for instance, ambiguously worded items were reformulated to enhance clarity and precision.\u003c/p\u003e\n\u003cp\u003eA rigorous back-translation protocol was applied to the refined instrument to ensure cross-linguistic semantic equivalence and cultural appropriateness (Wang \u0026amp; Zhang, 2024). Two bilingual translators, each a native speaker of one of the relevant languages and possessing professional academic credentials, independently executed forward and reverse translations. Discrepancies arising from linguistic ambiguity or culturally contingent interpretation were resolved through iterative consultation, referencing both source literature and domain-specific usage conventions.\u003c/p\u003e\n\u003cp\u003eAll constructs, including technology, organization, environment, visual digital supply chain adoption, potential absorptive capacity, realized absorptive capacity, digital strategy, and green business model innovation, were assessed using seven-point Likert scales (1 = strongly disagree, 7 = strongly agree). This scale granularity captures nuanced variation in respondent perceptions, enhancing measurement sensitivity (Wang \u0026amp; Zhang, 2024). To align with its measurement items, green business model innovation was evaluated on a corresponding seven-point semantic differential scale, where 1 indicates the lowest and 7 the highest level of innovativeness. The complete survey questionnaire, including introductory instructions, the screening item, firm demographic questions, and all construct measurement items with their response scales and source citations, is provided in Appendix A.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Measurement\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"593\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstructs and Measurement items\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLoadings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVIF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 593px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVisual Digital Supply Chain Adoption\u003c/strong\u003e (VDSCA) (Kalaitzi \u0026amp; Tsolakis, 2022)\u003c/p\u003e\n \u003cp\u003eAlpha: 0.883 CRA: 0.885 CRC: 0.914 AVE: 0.681 Mean: 4.408 SD: 1.684\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eVDSCA_1: Our\u0026nbsp;firm\u0026nbsp;recognizes\u0026nbsp;the\u0026nbsp;benefits\u0026nbsp;of\u0026nbsp;adopting\u0026nbsp;a\u0026nbsp;visual digital\u0026nbsp;supply\u0026nbsp;chain.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.859\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.477\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eVDSCA_2: We\u0026nbsp;have\u0026nbsp;a\u0026nbsp;strategic\u0026nbsp;plan\u0026nbsp;in\u0026nbsp;place\u0026nbsp;for\u0026nbsp;the\u0026nbsp;adoption\u0026nbsp;of\u0026nbsp;visual digital\u0026nbsp;supply\u0026nbsp;chain.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.812\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eVDSCA_3: We\u0026nbsp;have\u0026nbsp;started\u0026nbsp;allocating\u0026nbsp;resources\u0026nbsp;towards\u0026nbsp;the\u0026nbsp;adoption\u0026nbsp;of\u0026nbsp;a\u0026nbsp;visual digital\u0026nbsp;supply\u0026nbsp;chain.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.801\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.979\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eVDSCA_4: Our\u0026nbsp;firm\u0026nbsp;has\u0026nbsp;initiated\u0026nbsp;the\u0026nbsp;process\u0026nbsp;of\u0026nbsp;adopting\u0026nbsp;a\u0026nbsp;visual digital\u0026nbsp;supply\u0026nbsp;chain.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.841\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eVDSCA_5: Our\u0026nbsp;firm\u0026nbsp;has\u0026nbsp;made\u0026nbsp;significant\u0026nbsp;progress\u0026nbsp;towards\u0026nbsp;adopting\u0026nbsp;a\u0026nbsp;visual digital\u0026nbsp;supply\u0026nbsp;chain.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.810\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.923\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 593px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGreen Business Model Innovation\u003c/strong\u003e (GBMI) (Bashir et al., 2022)\u003c/p\u003e\n \u003cp\u003eAlpha: 0.875 CRA: 0.878 CRC: 0.909 AVE: 0.667 Mean: 4.544 SD: 1.586\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eGBMI_1: On a scale of 1-7, how would you rate your firm\u0026apos;s innovativeness in introducing green products, services, or information, or a new combination of the three?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.840\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.137\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eGBMI_2: On a scale of 1-7, how would you rate your firm\u0026apos;s level of introducing new participants to implement a green business model?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.783\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.800\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eGBMI_3: On a scale of 1-7, how would you rate your firm\u0026apos;s level of providing new transaction incentives to attract participants to the green business model?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.826\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.098\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eGBMI_4: On a scale of 1-7, how would you rate your firm\u0026apos;s level of adopting new transaction methods to connect participants in the green business model?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.828\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.138\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eGBMI_5: On a scale of 1-7, how would you rate your firm\u0026apos;s innovativeness in other aspects of green business model innovation?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.807\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.987\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 593px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTechnology\u0026nbsp;\u003c/strong\u003e(TE) (Zou et al., 2025)\u003c/p\u003e\n \u003cp\u003eAlpha: 0.865 CRA: 0.882 CRC: 0.907 AVE: 0.710 Mean: 4.114 SD: 1.737\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eTE_1: Our firm has the technical capabilities required to implement a visual digital supply chain.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eTE_2: Our firm already has the infrastructure required to implement a visual digital supply chain.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eTE_3: Our firm\u0026apos;s technological systems are compatible with the systems of the visual digital supply chain.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.861\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eTE_4: Our firm already has the technological resources needed to use a visual digital supply chain.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 593px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrganization\u003c/strong\u003e (OR) (Wang \u0026amp; Zhang, 2024)\u003c/p\u003e\n \u003cp\u003eAlpha: 0.869 CRA: 0.878 CRC: 0.910 AVE: 0.718 Mean: 4.089 SD: 1.640\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eOR_1: Our organizational structure adapts to the implementation of the visual digital supply chain.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.986\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eOR_2: Our organization has sufficient resources to support the application of the visual digital supply chain.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.266\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eOR_3: Our organizational culture supports the innovation and transformation of the visual digital supply chain.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eOR_4: Our organization shows obvious leadership and support for the adoption of the visual digital supply chain.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 593px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnvironment\u0026nbsp;\u003c/strong\u003e(EN)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(Kalaitzi \u0026amp; Tsolakis, 2022)\u003c/p\u003e\n \u003cp\u003eAlpha: 0.821 CRA: 0.828 CRC: 0.893 AVE: 0.736 Mean: 4.495 SD: 1.756\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eEN_1: Our business environment is conducive to the adoption of visual digital supply chain.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.793\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eEN_2: In our industry, competitors adopting visual digital supply chain put pressure on us to do the same.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eEN_3: The regulatory environment in our industry encourages the adoption of visual digital supply chain.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 593px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePotential Absorptive Capacity\u003c/strong\u003e (PAC) (Algarni et al., 2023)\u003c/p\u003e\n \u003cp\u003eAlpha: 0.842 CRA: 0.849 CRC: 0.894 AVE: 0.678 Mean: 4.297 SD: 1.562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003ePAC_1: Our firm frequently interacts with clients and competitors to acquire new knowledge.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.839\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003ePAC_2: We quickly recognize shifts in our markets such as competition, regulation, and demography.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.833\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.793\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003ePAC_3: Our firm is able to identify and understand external knowledge.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.791\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.765\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003ePAC_4: We are able to introduce and organize external knowledge quickly.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.830\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.938\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 593px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRealized Absorptive Capacity\u0026nbsp;\u003c/strong\u003e(RAC) (Algarni et al., 2023)\u003c/p\u003e\n \u003cp\u003eAlpha: 0.828 CRA: 0.832 CRC: 0.885\u0026nbsp;AVE: 0.659 Mean: 4.316 SD: 1.566\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eRAC_1: We record and store newly acquired knowledge for future reference.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.796\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.648\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eRAC_2: We quickly recognize the usefulness of new external knowledge to existing knowledge.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.840\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.990\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eRAC_3: Our firm has the ability to transform external knowledge into our own knowledge.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.829\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.776\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eRAC_4: Our firm is able to provide new knowledge quickly and efficiently, and apply new knowledge to value creation and related services.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.781\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.703\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 593px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDigital Strategy\u003c/strong\u003e (DS) (Gyamerah et al., 2025)\u003c/p\u003e\n \u003cp\u003eAlpha: 0.819 CRA: 0.819 CRC: 0.892\u0026nbsp;AVE: 0.734 Mean: 5.102 SD: 1.563\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eDS_1: In my organization, the integration of our business strategy with digital technology promotes strategic alignment with the government and other partners.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.859\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.889\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eDS_2: In our organization, we jointly plan how to use digital technology to implement the business strategy.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.850\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.734\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003eDS_3: Before making strategic decisions, we discuss and take into consideration the impact of digital technology.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.861\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.881\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 593px;\"\u003e\n \u003cp\u003eNote: Cronbach\u0026rsquo;s alpha =\u0026nbsp;Alpha; Composite reliability (rho_a) = CRA; Composite reliability (rho_c) = CRC; Average variance extracted = AVE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e4.2. Data Collection\u003c/h2\u003e\n\u003cp\u003eManufacturing firms were selected as the empirical context owing to their substantial environmental impact and intensifying institutional pressures to transition toward sustainable operations. To ensure baseline familiarity with green business model concepts, we drew our sampling frame from the Green Manufacturing Initiative database administered by China\u0026apos;s Ministry of Industry and Information Technology. Firms listed in this database have either adopted or are actively developing green business models, establishing requisite conceptual awareness. The sampled provinces, Zhejiang, Guangdong, and Sichuan, rank among China\u0026apos;s ten largest manufacturing regions and represent eastern, southern, and western geographic coverage. This geographic dispersion enhances both the breadth and representativeness of the surveyed population (Wang \u0026amp; Zhang, 2025).\u003c/p\u003e\n\u003cp\u003ePurposive sampling was adopted for data collection (Wang \u0026amp; Zhang, 2024). This strategy was selected for two complementary reasons: it enables targeted identification of information-rich respondents possessing substantive knowledge of the surveyed constructs, thereby strengthening data quality and theoretical yield; and it facilitates efficient recruitment by directing survey invitations to firms predisposed to participate.\u003c/p\u003e\n\u003cp\u003eThe focal constructs, including digital strategy and absorptive capacity, are inherently abstract and perceptual, rendering them unsuitable for direct observation or objective measurement. Questionnaire-based data collection is well suited to capturing such latent constructs. Given the wide geographic dispersion of our target firms across three provinces, survey methodology further overcomes spatial constraints while enabling cost-effective large-scale data acquisition (Wang \u0026amp; Zhang, 2024).\u003c/p\u003e\n\u003cp\u003eAn electronic questionnaire was created on Questionnaire Star (wjx.cn) and disseminated through WeChat, email, and affiliated channels. The instrument comprised two sections: the first solicited firm demographic information (Table 2), and the second measured the eight model constructs (Table 1). Respondents were managers, administrators, and innovation officers possessing comprehensive knowledge of their firm\u0026apos;s strategic and operational profile. A screening item was positioned at the outset; respondents indicating unfamiliarity with the survey topic were automatically redirected to survey termination, safeguarding respondent relevance and data quality.\u003c/p\u003e\n\u003cp\u003eRespondent privacy was protected by omitting personal identifiers from the questionnaire. An introductory statement confirmed that all responses would be used exclusively for academic purposes and handled with strict confidentiality. Voluntary withdrawal was permitted at any stage.\u003c/p\u003e\n\u003cp\u003eData collection followed a temporally separated two-wave design (Wang \u0026amp; Zhang, 2024). Wave one captured respondents\u0026apos; assessments of visual digital supply chain adoption, technology, organization, environment, potential absorptive capacity, realized absorptive capacity, and firm characteristics (age, size, type, ownership). Wave one yielded 385 responses (80.3% response rate). Participants provided contact emails for wave two, with a commitment to delete all contact information upon study completion. Wave two assessed digital strategy, green business model innovation, and a marker variable (perceived ease of use), generating 279 responses (72.5% wave-two response rate). A single email reminder was dispatched to non-completers to improve data completeness. Additionally, follow-up telephone interviews with a randomly drawn 10% subsample verified response consistency and elicited supplementary qualitative insights regarding firms\u0026apos; green practices and digital strategies.\u003c/p\u003e\n\u003cp\u003eRigorous data cleaning eliminated questionnaires exhibiting uniform response patterns or completion times below five minutes. Remaining responses underwent manual inspection. The final usable sample comprised 254 firms, yielding an effective rate of 66.0%.\u003c/p\u003e\n\u003cp\u003ePrior to data collection, G*Power 3.1.9.7 was used for a priori sample size estimation. Under recommended parameters (F-test family, linear multiple regression fixed model, R\u0026sup2; increase, effect size f\u0026sup2; = 0.15, \u0026alpha; = 0.05, power = 0.8) (Wang \u0026amp; Zhang, 2024), the minimum required sample was 123. The tenfold indicator rule similarly indicated a minimum of 120. Our final sample of 254 substantially exceeds both thresholds.\u003c/p\u003e\n\u003cp\u003eEthical Approval\u003c/p\u003e\n\u003cp\u003eThis study was reviewed by the Institutional Review Board (IRB) of the International Business School, Fuzhou University of International Studies and Trade. \u0026nbsp;As the research involved a survey in which substantive responses were anonymized for analysis, while contact information for the second-wave follow-up was collected separately from the survey responses and deleted after data collection, and posed no more than minimal risk to participants, the IRB granted an exemption from full ethical review. All research procedures were performed in accordance with the ethical standards of the institutional review board and with the ethical review regulations and guidelines for research involving human participants in China, including the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eInformed Consent\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants in this study. Prior to completing the questionnaire, each participant was presented with an introductory statement describing the purpose of the research, the voluntary nature of participation, the anonymity and confidentiality protections (including an explicit assurance that all responses would be used exclusively for academic purposes and would not be publicly disclosed), and the right to withdraw at any point without consequence. Consent was indicated by the participant\u0026apos;s voluntary decision to proceed with and complete the survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Firm characteristics\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 77px;\"\u003e\n \u003cp\u003eFirm\u0026nbsp;Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e\u0026le;\u0026nbsp;5\u0026nbsp;years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e6-15\u0026nbsp;years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e66.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e\u0026ge;16\u0026nbsp;years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e23.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 77px;\"\u003e\n \u003cp\u003eFirm\u0026nbsp;Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e\u0026le; \u0026nbsp;50 employees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e23.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e51-499\u0026nbsp;employees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e56.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e\u0026ge;\u0026nbsp;500\u0026nbsp;employees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 77px;\"\u003e\n \u003cp\u003eFirm\u0026nbsp;Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eB2C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e46.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eB2B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e31.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 77px;\"\u003e\n \u003cp\u003eOwnership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eState-owned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e17.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e53.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eForeign\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e4.3. Data Analysis Approach\u003c/h2\u003e\n\u003cp\u003eMeasurement and structural model estimation employed Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4. PLS-SEM is extensively utilized in management research for its capacity to estimate complex multivariate models and to yield robust results under conditions of distributional non-normality (Wang \u0026amp; Zhang, 2024). Given the model complexity, the presence of multiple latent constructs, and the study\u0026rsquo;s predictive orientation, PLS-SEM constituted an appropriate analytical technique. Hypothesis testing and moderation analysis were likewise conducted within SmartPLS 4, enabling precise assessment of inter-variable relationships, particularly the contingent role of digital strategy.\u003c/p\u003e\n\u003cp\u003eImportance-Performance Map Analysis (IPMA) was performed using SmartPLS 4 to visualize the relative importance and performance of each construct, thereby generating actionable analytical insights (Wang \u0026amp; Zhang, 2024). IPMA facilitated nuanced understanding of individual variable contributions to green business model innovation. Fuzzy-set Qualitative Comparative Analysis (fsQCA) was executed using fsQCA 3 software for sufficient condition analysis. FsQCA is a configurational method that accommodates complexity by identifying combinations of conditions jointly sufficient for a given outcome (Wang \u0026amp; Zhang, 2025). This method proved instrumental in uncovering the configurational antecedents of green business model innovation.\u003c/p\u003e\n\u003ch2\u003e4.4. Robustness Analysis\u003c/h2\u003e\n\u003cp\u003eCommon method bias poses a potential threat to the reliability of self-report survey findings. Multiple procedural and statistical remedies were implemented. Ex ante, iterative expert consultations refined item definitions and inter-indicator logic, and a pilot study informed further questionnaire optimization. During administration, respondents received assurances of strict confidentiality and exclusive academic use. Ex post, Harman\u0026apos;s single-factor test indicated that the first unrotated factor accounted for 31.791% of total variance, well below the 50% threshold, suggesting no dominant common factor. The marker variable technique was additionally applied using perceived ease of use (PEU), a construct theoretically unrelated to our focal variables (Cronbach\u0026apos;s Alpha = 0.835, rho_a = 0.875, rho_c = 0.899, AVE = 0.748). PEU exhibited no statistically significant correlations with any model construct, corroborating that common method bias does not substantively distort our results. Multicollinearity was assessed through Variance Inflation Factors (VIF) (Wang \u0026amp; Zhang, 2024). VIF values ranged from 1.648 to 2.477, uniformly below both the conservative threshold of 5 and the conventional benchmark of 10, confirming the absence of problematic multicollinearity.\u003c/p\u003e\n\u003cp\u003eNonresponse bias was evaluated by partitioning the sample chronologically into early and late respondent cohorts and conducting independent-samples t-tests in SPSS 27 across firm age (P = 0.527), firm size (P = 0.843), firm type (P = 0.486), and ownership (P = 0.303) (Wang \u0026amp; Zhang, 2024). All comparisons were non-significant (P \u0026gt; 0.05), indicating negligible nonresponse bias.\u003c/p\u003e"},{"header":"5.\tStudy 1: Structural Equation Modeling Results","content":"\u003ch2\u003e5.1. Measurement Model\u003c/h2\u003e\n\u003cp\u003eThe measurement model encompasses eight focal constructs. As reported in Table 1, all Cronbach\u0026apos;s alpha coefficients exceed 0.8, confirming adequate internal consistency. Composite reliability (rho_a and rho_c) and Average Variance Extracted (AVE) values uniformly satisfy established thresholds, attesting to measurement reliability and convergent validity. Item-level factor loadings range from 0.781 to 0.887, each surpassing the 0.7 benchmark, indicating strong indicator-construct correspondence. Discriminant validity was assessed through both the Fornell-Larcker criterion and the Heterotrait-Monotrait (HTMT) ratio (Table 3). The square root of each construct\u0026apos;s AVE (diagonal entries) exceeds all inter-construct correlations, satisfying the Fornell-Larcker criterion. Additionally, all HTMT ratios fall below the 0.85 threshold, providing further confirmation of discriminant validity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e. Discriminant Validity.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"621\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 621px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFornell-Larcker Criterion and Heterotrait-Monotrait Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstruct\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eDS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eEN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eGBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eTE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eVDSCA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.857\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.105\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.366\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.128\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.245\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.305\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.219\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.237\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.043\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.858\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.156\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.118\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.288\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.315\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.050\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.328\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eGBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.311\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.134\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.817\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.274\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.467\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.480\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.284\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.292\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.108\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.097\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.239\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.847\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.319\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.336\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.149\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.260\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.203\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.237\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.407\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.272\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.824\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.707\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.322\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.438\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eRAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.252\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.261\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.412\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.283\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.589\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.812\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.288\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.438\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.184\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.045\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.248\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.131\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.276\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.243\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.843\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.332\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eVDSCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.201\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.283\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.259\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.232\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.380\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.377\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.297\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.825\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"bottom\" style=\"width: 621px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCross Loadings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n 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\u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eDS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.850\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e-0.043\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.274\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.045\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.148\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.182\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.154\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.145\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eDS3\u003c/p\u003e\n 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nowrap=\"\"\u003e\n \u003cp\u003e0.207\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.391\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.369\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.204\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.259\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eGBMI2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.225\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.126\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.783\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.205\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.330\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.315\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.142\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.166\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eGBMI3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.287\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.054\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.826\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.222\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.284\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.361\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.252\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.255\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eGBMI4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.207\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.087\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.828\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.198\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.367\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.328\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.211\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.183\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eGBMI5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.273\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.055\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.807\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.138\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.282\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.305\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.201\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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\u003cp\u003e0.184\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.035\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.242\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.171\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.250\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.236\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.847\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.239\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTE3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.174\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.091\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.202\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.142\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.216\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.171\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.848\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.295\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTE4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.163\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.006\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.193\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.052\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.204\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.218\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.819\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.185\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eVDSCA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.125\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.246\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.219\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.196\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.307\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.306\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.267\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.859\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eVDSCA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.177\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.152\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.202\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.137\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.355\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.274\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.243\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.812\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eVDSCA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.197\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.238\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.209\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.161\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.265\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.297\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.251\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.801\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eVDSCA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.227\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.317\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.237\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.181\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.337\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.328\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.243\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.841\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eVDSCA5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.103\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.203\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.201\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.276\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.303\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.347\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.223\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.810\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 621px;\"\u003e\n \u003cp\u003eNote: The bolded parts represent the square root of the Average Variance Extracted (AVE). Correlation coefficients between constructs are found in the lower left part of the diagonal. Conversely, the upper right section displays the Heterotrait-Monotrait ratios (HTMT) among the constructs.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e5.2. Structural Model\u003c/h2\u003e\n\u003cp\u003eThe structural model was estimated in SmartPLS 4 with 5,000 bootstrap resamples to evaluate path significance (Wang \u0026amp; Zhang, 2024). Figure 3 reports path coefficients and significance levels for all hypothesized relationships, alongside R\u0026sup2; (explanatory power) and Q\u0026sup2; (predictive relevance) values for endogenous constructs.\u003c/p\u003e\n\u003cp\u003eAll hypothesized paths attained statistical significance: environment \u0026rarr; visual digital supply chain adoption (\u0026beta; = 0.254, p \u0026lt; 0.001), organization \u0026rarr; visual digital supply chain adoption (\u0026beta; = 0.175, p \u0026lt; 0.01), technology \u0026rarr; visual digital supply chain adoption (\u0026beta; = 0.262, p \u0026lt; 0.001), visual digital supply chain adoption \u0026rarr; potential absorptive capacity (\u0026beta; = 0.379, p \u0026lt; 0.001), visual digital supply chain adoption \u0026rarr; realized absorptive capacity (\u0026beta; = 0.378, p \u0026lt; 0.001), potential absorptive capacity \u0026rarr; green business model innovation (\u0026beta; = 0.226, p \u0026lt; 0.01), realized absorptive capacity \u0026rarr; green business model innovation (\u0026beta; = 0.203, p \u0026lt; 0.001), digital strategy \u0026times; potential absorptive capacity \u0026rarr; green business model innovation (\u0026beta; = 0.184, p \u0026lt; 0.01), and digital strategy \u0026times; realized absorptive capacity \u0026rarr; green business model innovation (\u0026beta; = 0.246, p \u0026lt; 0.001). The structural model assessment indicated that none of the firm-level control variables (firm age, firm size, firm type, and ownership) exerted a statistically significant influence on green business model innovation (all p \u0026gt; 0.05), ensuring that the core relationships observed are not driven by these demographic characteristics. These uniformly significant results provide a robust foundation for subsequent IPMA.\u003c/p\u003e\n\u003cp\u003eThe standardized root mean square residual (SRMR) is 0.047, below the 0.08 acceptability threshold, indicating satisfactory model fit. R\u0026sup2; and Q\u0026sup2; values exceed 0.1 and 0, respectively, confirming adequate explanatory power and out-of-sample predictive capacity.\u003c/p\u003e\n\u003ch2\u003e5.3. Moderation Effects\u003c/h2\u003e\n\u003cp\u003eStructural model results (Figure 3) reveal that digital strategy exerts a statistically significant positive moderating effect on the potential absorptive capacity \u0026rarr; green business model innovation pathway (\u0026beta; = 0.184, p \u0026lt; 0.01). This suggests that more robust digital strategy execution amplifies the efficacy of potential absorptive capacity in generating green business model innovation. The corresponding effect size (f\u0026sup2; = 0.036 for DS \u0026times; PAC) substantiates this moderation. Digital strategy likewise positively moderates the realized absorptive capacity \u0026rarr; green business model innovation relationship (\u0026beta; = 0.246, p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eSimple slope analyses were performed and graphically depicted (Figures 4 and 5) to elucidate the nature of these interaction effects (Wang \u0026amp; Zhang, 2024). The plots confirm that at elevated levels of digital strategy implementation, both potential and realized absorptive capacities exert substantially stronger effects on green business model innovation.\u003c/p\u003e\n\u003ch2\u003e5.4. Importance Performance Map Analysis\u003c/h2\u003e\n\u003cp\u003eIPMA was employed to jointly assess the importance and performance of each construct in relation to green business model innovation (Wang \u0026amp; Zhang, 2024). This analysis offers a dual-dimensional perspective, clarifying each variable\u0026apos;s relative contribution and current attainment level.\u003c/p\u003e\n\u003cp\u003eIn Figure 6, the vertical axis denotes construct importance and the horizontal axis denotes performance. Constructs in the upper-right quadrant exert the strongest influence on green business model innovation and exhibit the highest sample-level attainment. Digital strategy registers an importance score of 0.24 and a performance score of 68.354, underscoring its pivotal function in catalyzing green business model innovation and its relatively high realization among sampled firms. Potential absorptive capacity (importance = 0.225) and realized absorptive capacity (importance = 0.205) yield somewhat lower performance scores yet remain substantively important for green business model innovation advancement. Technology (importance = 0.043, performance = 51.807) resides in the lower-left quadrant, suggesting a comparatively modest direct effect on green business model innovation in this sample. Visual digital supply chain adoption (importance = 0.163, performance = 56.836), though ranking below digital strategy and both absorptive capacity dimensions in importance, nonetheless demonstrates a meaningful contributory role warranting managerial attention.\u003c/p\u003e"},{"header":"6.\tStudy 2: Configurational Analysis Results","content":"\n\u003ch2\u003e6.1. Calibration\u003c/h2\u003e\n\u003cp\u003eFsQCA was deployed to identify combinatorial configurations of conditions associated with green business model innovation. The initial analytical step involves calibrating raw data into fuzzy-set membership scores corresponding to full membership, crossover, and full non-membership anchors (Wang \u0026amp; Zhang, 2025), thereby enabling systematic evaluation of each condition\u0026apos;s role. Table 4 presents the calibration parameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e. Calibration.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"457\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"8\" style=\"width: 385px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eTE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eVDSCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eRAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003ePAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eGBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eFull non-membership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1.667\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1.500\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1.500\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.800\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1.750\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1.750\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e2.333\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e2.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eCrossover point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e4.667\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e4.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e4.250\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e4.400\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e4.500\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e4.250\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e5.667\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e4.600\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eFull membership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e7.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e6.750\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e6.750\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e6.680\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e6.600\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e6.500\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e7.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e6.800\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e6.2. Necessary Conditions Analysis\u003c/h2\u003e\n\u003cp\u003eNecessary Condition Analysis (NCA) constitutes the second fsQCA stage, ascertaining whether any individual condition is requisite for the outcome\u0026apos;s occurrence (Wang \u0026amp; Zhang, 2025). A condition is classified as necessary when its consistency exceeds 0.90. Table 5 indicates that all consistency values fall below this threshold, confirming that no single condition is individually necessary for green business model innovation. This result highlights the inherent complexity of green business model innovation, demonstrating that no isolated strategic lever or resource endowment suffices; rather, the outcome hinges on the conjunctural interplay of multiple conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e. Necessary conditions analysis.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"287\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eConsistency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eCoverage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.646\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.635\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e~EN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.584\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.566\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eTE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.679\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.675\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e~TE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.548\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.526\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.700\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.663\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e~OR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.553\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.557\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eVDSCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.654\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.669\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e~VDSCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.583\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.544\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eRAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.730\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.523\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e~RAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.529\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.717\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003ePAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.707\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.704\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e~PAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.546\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.514\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.714\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.686\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e~DS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.525\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.521\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e6.3. Sufficient Conditions Analysis\u003c/h2\u003e\n\u003cp\u003eSufficient condition analysis evaluates whether specific combinatorial configurations of the seven antecedent conditions are subsets of the green business model innovation outcome (Wang \u0026amp; Zhang, 2025). We applied a raw consistency cut-off of 0.80, a PRI consistency cut-off of 0.60, and a frequency threshold of 3. Table 6 reports seven distinct configurations. The overall solution consistency is 0.854 (\u0026gt; 0.8) and solution coverage is 0.513 (\u0026gt; 0.5). The seven configurations cluster into three theoretically meaningful pathways. First, Configurations 2, 4, 6, and 7 jointly exhibit visual digital supply chain adoption as a core or peripheral condition combined with potential and/or realized absorptive capacity, representing a VDSCA-enabled absorptive-capacity pathway consistent with H4\u0026ndash;H9. Second, Configurations 1 and 3 represent absorptive-capacity-driven pathways in which both potential and realized absorptive capacities are present while VDSCA is not a binding condition, indicating that strong dual absorptive capacities can independently sustain green business model innovation when supported by complementary TOE conditions. Third, Configuration 5 reveals a contrasting substitution pathway: in the absence of visual digital supply chain adoption, potential absorptive capacity, environmental pressure, and organizational readiness, firms can still attain high green business model innovation by combining strong technological readiness, realized absorptive capacity, and digital strategy. This finding qualifies, rather than contradicts, the PLS-SEM results by showing that realized absorptive capacity and digital strategy can serve as compensatory mechanisms when upstream digital infrastructure and knowledge-acquisition capacity are lacking, thereby enriching the equifinality perspective of green business model innovation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e. Configurations leading to high-level green business model innovation.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"571\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" style=\"width: 485px;\"\u003e\n \u003cp\u003eConfigurations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e⨂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e○\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e○\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eVDSCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e⨂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eRAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003ePAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e⨂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e⨂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e⨂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;Raw coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.335\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.310\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.229\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eUnique coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.048\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.030\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.022\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eConsistency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.888\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.867\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.913\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eSolution coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" style=\"width: 485px;\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eSolution consistency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" style=\"width: 485px;\"\u003e\n \u003cp\u003e0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 571px;\"\u003e\n \u003cp\u003eNote: \u0026quot;●\u0026quot; or \u0026quot;●\u0026quot; indicate the existence of core or peripheral conditions, respectively. \u0026quot;⨂\u0026quot; or \u0026quot;○\u0026quot; represent the non-existence of core or peripheral conditions. Any cells left vacant indicate a \u0026ldquo;do not care\u0026rdquo; situation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"7.\tDiscussion and Conclusion ","content":"\u003cp\u003eThis study examines the complex interdependencies among technological, organizational, and environmental antecedents, visual digital supply chain adoption, absorptive capacity dimensions, digital strategy, and green business model innovation. Our findings offer novel insights into the mechanisms through which visual digital supply chain adoption facilitates green business model innovation. By integrating the TOE framework with absorptive capacity theory, we foreground the pivotal function of visual digital supply chain adoption in the green business model innovation process. The results illuminate how these constructs operate both individually and in specific combinatorial configurations to foster green business model innovation. This contribution accentuates the importance of configurational perspectives in understanding the antecedents of green business model innovation, broadening scholarly understanding of both green business model innovation and digital supply chain adoption. The study yields substantive theoretical and practical insights regarding how digitally enabled supply chain visibility can advance green business model innovation, enriching the literatures on digital supply chains and business model reconfiguration while equipping practitioners with evidence-based guidance for achieving sustainable business models through digital transformation.\u003c/p\u003e\n\u003ch2\u003e7.1. Discussion and Theoretical Implications\u003c/h2\u003e\n\u003cp\u003eThis research explicates the determinants and transmission mechanisms of green business model innovation. Employing PLS-SEM, IPMA, and fsQCA in a complementary analytical framework, we obtained several substantive findings. All proposed hypotheses received empirical support, yielding actionable insights into the interrelationships among visual digital supply chain adoption, green business model innovation, absorptive capacity dimensions, and digital strategy.\u003c/p\u003e\n\u003cp\u003eTechnological, organizational, and environmental factors each exert significant positive effects on visual digital supply chain adoption, consistent with the TOE framework (Dong et al., 2025). At the technological level, the availability of advanced IT infrastructure and the perceived value of supply chain visualization substantially influence adoption decisions; firms with more mature technological capabilities demonstrate greater propensity to implement visual digital supply chains, reflecting superior capacity for seamless technology integration. At the organizational level, top management support and workforce digital competencies prove instrumental. Organizations whose leadership emphasizes evidence-based decision-making and invests in human capital development exhibit more successful adoption trajectories. At the environmental level, competitive intensity and regulatory stringency shape adoption patterns; firms confronting vigorous rivalry or exacting compliance mandates gravitate toward adoption to preserve competitive standing and fulfill regulatory obligations (Zou et al., 2025). Collectively, these findings indicate that the three TOE dimensions are jointly salient for visual digital supply chain adoption and merit systematic assessment when deploying novel supply chain solutions (Wang \u0026amp; Zhang, 2025). Visual digital supply chain adoption exerts a positive effect on both potential and realized absorptive capacity, aligning with absorptive capacity theory (Sadeghi et al., 2023). It strengthens firms\u0026apos; capacity to comprehend and operationalize new knowledge and technologies, thereby increasing the probability of green business model innovation (Esfahbodi et al., 2023). This finding offers one explanation for why certain firms achieve green business model innovation more rapidly: their more effective utilization of visual digital supply chains to enhance absorptive capacity. Digital strategy exerts a significant positive moderating effect on the absorptive capacity to green business model innovation relationships, consistent with dynamic capability logic (Gyamerah et al., 2025). Under the guidance of a coherent digital strategy, firms can more productively leverage visual digital supply chain adoption to fortify absorptive capacity, thereby accelerating green business model innovation (Yildiz et al., 2024). Our findings additionally underscore the strategic importance of digital strategy in the green business model innovation process, an aspect insufficiently addressed in prior research, thereby extending the theoretical repertoire surrounding green business model innovation.\u003c/p\u003e\n\u003cp\u003eA central finding is that technological, organizational, and environmental factors significantly shape visual digital supply chain adoption, advancing both absorptive capacity theory (Yildiz et al., 2024) and the TOE framework (Wang \u0026amp; Zhang, 2024). Prior scholarship positioned these three dimensions as primary determinants of technology innovation and diffusion (Ancillai et al., 2023). Our evidence demonstrates their salience extends to visual digital supply chain adoption, broadening the empirical applicability of both theories (Lyu et al., 2023).\u003c/p\u003e\n\u003cp\u003eThe finding that visual digital supply chain adoption augments both potential and realized absorptive capacity advances absorptive capacity theory by identifying a novel technological antecedent. Whereas prior work predominantly examined how absorptive capacity facilitates knowledge transfer and innovation outcomes (Yildiz et al., 2024), our study reveals how absorptive capacity itself is shaped by visual digital supply chain adoption, establishing a new domain of application for the theory (Belhadi et al., 2022).\u003c/p\u003e\n\u003cp\u003eThe significant moderating role of digital strategy on the absorptive capacity to green business model innovation pathways (Xiao et al., 2025) reinforces and extends the dynamic capability perspective. Earlier scholarship documented the broad impact of dynamic capabilities on innovation and strategic performance (Yildiz et al., 2024). Our evidence specifies that digital strategy, conceptualized as a dynamic capability, materially conditions green business model innovation, thereby strengthening the theoretical salience of this lens (Song et al., 2024).\u003c/p\u003e\n\u003cp\u003eThe fsQCA results demonstrate that while no individual variable constitutes a necessary condition for green business model innovation, specific multi-factor configurations substantially stimulate its emergence (Kajtazi et al., 2023). This configurational finding contributes to configurational theory, which posits that organizational and environmental elements combine in discrete, patterned ways. Our study furnishes a novel empirical application of this perspective within the green business model innovation domain (Wang \u0026amp; Zhang, 2025).\u003c/p\u003e\n\u003ch2\u003e7.2. Practical Implications\u003c/h2\u003e\n\u003cp\u003eOur findings illuminate the multifaceted relationship among visual digital supply chain adoption, absorptive capacity dimensions, digital strategy, and green business model innovation, underscoring the strategic imperative for firms to account for the influence of supply chain visualization on ecological innovation (Wang \u0026amp; Zhang, 2025). Practitioners should attend to the joint effects of technological, organizational, and environmental factors on green business model innovation and operational performance. Investment in artificial intelligence and machine learning to elevate supply chain digital visibility, coupled with cultivation of managerial digital literacy to strengthen knowledge assimilation and application, represents a high-priority action portfolio. Such initiatives bolster absorptive capacity and, in turn, propel green business model innovation (Tian \u0026amp; Cui, 2025).\u003c/p\u003e\n\u003cp\u003eThe empirically documented effects of TOE factors on visual digital supply chain adoption yield actionable guidance (Zou et al., 2025). Firms contemplating adoption should conduct readiness assessments across all three dimensions. Technologically, adequate infrastructural foundations and a favorable benefit-to-cost calculus are prerequisite. Organizationally, cultivating an innovation-supportive culture and investing in workforce digital competency development have demonstrated effectiveness. Environmentally, maintaining awareness of competitive dynamics and evolving regulatory mandates that may trigger adoption is essential. A holistic appraisal across these dimensions enables firms to craft more effective adoption strategies and maximize resultant benefits (Xiao et al., 2025).\u003c/p\u003e\n\u003cp\u003eTo advance green business model innovation, firms should formalize a visual digital supply chain adoption roadmap embedded within their strategic planning processes (Dong et al., 2025). Such roadmaps should encompass leveraging existing data assets, intensifying operational utilization of supply chain visualization, and systematically harnessing visual digital supply chain capabilities to strengthen organizational innovation capacity. A well-structured implementation plan enables firms to more fully exploit the innovation potential of visual digital supply chain adoption.\u003c/p\u003e\n\u003cp\u003eThe configurational findings indicate that no single variable is individually sufficient for generating green business model innovation; rather, specific multi-factor combinations produce high levels of such innovation. Practitioners should recognize that universal prescriptions are unlikely to succeed. Instead, firms must assemble strategy-resource bundles tailored to their particular circumstances. For instance, a resource-constrained smaller firm may lack capacity for advanced AI deployment but can still optimize supply chain visualization by upskilling its workforce digitally and leveraging cloud-based service infrastructures.\u003c/p\u003e\n\u003ch2\u003e7.3. Future Research\u003c/h2\u003e\n\u003cp\u003eDespite its contributions, this study is subject to several limitations. Our analytical scope centers on technological, organizational, and environmental determinants of visual digital supply chain adoption, yet does not fully account for additional contingencies such as industry-specific characteristics, managerial leadership orientation, and employee-level technology acceptance. These factors may exert meaningful influence on green business model innovation under particular conditions. Methodologically, our reliance on quantitative analysis, while affording systematic rigor, may not fully capture the nuances of complex organizational phenomena and behavioral dynamics. Subsequent research would benefit from integrating qualitative inquiry to achieve richer interpretive depth. Furthermore, our analysis operates predominantly at the firm level, leaving individual- and team-level dynamics underexamined. Future studies adopting a micro-level lens could illuminate more granular influence mechanisms underlying green business model innovation. Finally, despite the temporally separated two-wave design, our cross-sectional data preclude definitive causal inference. Although the hypothesized directionality is supported by theory and prior literature, longitudinal or quasi-experimental designs are needed to firmly establish the causal ordering among visual digital supply chain adoption, absorptive capacity, digital strategy, and green business model innovation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical Approval\u003c/h2\u003e \u003cp\u003eThis study was reviewed by the Institutional Review Board (IRB) of the International Business School, Fuzhou University of International Studies and Trade. As the research involved a survey in which substantive responses were anonymized for analysis, while contact information for the second-wave follow-up was collected separately from the survey responses and deleted after data collection, and posed no more than minimal risk to participants, the IRB granted an exemption from full ethical review. All research procedures were performed in accordance with the ethical standards of the institutional review board and with the ethical review regulations and guidelines for research involving human participants in China, including the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed Consent\u003c/strong\u003e \u003cp\u003eInformed consent was obtained from all participants in this study. Prior to completing the questionnaire, each participant was presented with an introductory statement describing the purpose of the research, the voluntary nature of participation, the anonymity and confidentiality protections (including an explicit assurance that all responses would be used exclusively for academic purposes and would not be publicly disclosed), and the right to withdraw at any point without consequence. Consent was indicated by the participant's voluntary decision to proceed with and complete the survey.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, S.W.; methodology, S.W.; writing \u0026mdash;original draft preparation, S.W., and H.Z.; funding acquisition, S.W.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank all respondents for participating in this survey. We also thank our colleagues and the digital tools that supported language editing.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets are not publicly available due to the risk of reverse de-anonymization and specific informed consent conditions. During data collection, all participating firms were explicitly assured that the data would be used exclusively for the purposes of this specific study and would not be publicly disclosed or shared with third parties. This commitment was a critical condition under which firms agreed to provide sensitive strategic and operational data. Additionally, the specific combination of firm-level demographic variables (e.g., precise firm age, exact firm size, specific geographic location, and ownership type), combined with the publicly accessible and limited population of China\u0026rsquo;s Green Manufacturing Initiative database, could inadvertently allow third parties to deduce the identities of participating firms, compromising the confidentiality agreements under which the data were collected. To protect proprietary corporate information, adhere to our ethical commitments, and honor the informed consent provisions, the raw datasets cannot be deposited in a public repository. However, a fully de-identified and aggregated subset of the data that supports the findings is available to the editorial team for the purpose of analytical verification and result replication, subject to a confidential data use arrangement. The survey instruments, measurement items, construct operationalization details, analytical software specifications, and all calibration and model parameters are fully documented within the manuscript (Tables 1, 2, 4, and Sections 4.1\u0026ndash;4.3) to ensure methodological transparency and reproducibility. The complete survey questionnaire is provided in Appendix A.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlgarni MA, Ali M, Leal-Rodr\u0026iacute;guez AL, Albort-Morant G (2023) The differential effects of potential and realized absorptive capacity on imitation and innovation strategies, and its impact on sustained competitive advantage. J Bus Res 158:113674\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAncillai C, Sabatini A, Gatti M, Perna A (2023) Digital technology and business model innovation: A systematic literature review and future research agenda. 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IEEE Trans Eng Manage 72:378\u0026ndash;400\u003c/span\u003e\u003c/li\u003e\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":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Green business model innovation, Visual digital supply chain, Absorptive capacity, Digital strategy, Technology-Organization-Environment framework, Sustainable manufacturing","lastPublishedDoi":"10.21203/rs.3.rs-9245607/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9245607/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eManufacturing firms face mounting pressure to reconcile digital transformation with ecological responsibility, yet the mechanisms linking supply chain digitalization to sustainable innovation remain poorly understood. This study investigates how visual digital supply chain adoption influences green business model innovation among Chinese manufacturers, drawing on absorptive capacity theory and the Technology-Organization-Environment (TOE) framework. Three research questions are addressed: the pathways through which visual digital supply chains shape green business model innovation, the technological, organizational, and environmental antecedents of adoption, and the moderating function of digital strategy. Survey data from 254 manufacturing firms listed in China's Green Manufacturing Initiative database are analyzed using partial least squares structural equation modeling and fuzzy-set qualitative comparative analysis. Results reveal that all three TOE dimensions significantly drive visual digital supply chain adoption, which in turn enhances both potential and realized absorptive capacities. Digital strategy amplifies the translation of absorptive capacities into green business model innovation. Configurational analysis further demonstrates that no single factor is individually necessary; rather, distinct multi-factor combinations produce high levels of ecological innovation. These findings advance understanding of how digitally enabled supply chain visibility and strategic digital orientation jointly foster sustainable business practices.\u003c/p\u003e","manuscriptTitle":"Seeing Green: How Visual Digital Supply Chains Enable Sustainable Business Model Innovation in Manufacturing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-06 14:35:33","doi":"10.21203/rs.3.rs-9245607/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"28532708894788705854785348118671876907","date":"2026-05-19T01:16:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-14T09:54:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"307936684793603885732827258481620862740","date":"2026-05-14T01:13:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-27T15:06:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-27T15:04:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-18T02:04:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-15T14:51:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-04-15T14:21:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f6a29ba6-d1f8-4556-9774-c7d18dc570fd","owner":[],"postedDate":"May 6th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"28532708894788705854785348118671876907","date":"2026-05-19T01:16:35+00:00","index":57,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-14T09:54:40+00:00","index":55,"fulltext":""},{"type":"reviewerAgreed","content":"307936684793603885732827258481620862740","date":"2026-05-14T01:13:01+00:00","index":54,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67377997,"name":"Business and commerce/Business and management"},{"id":67377998,"name":"Social science/Business and management"},{"id":67377999,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":67378000,"name":"Social science/Environmental studies"},{"id":67378001,"name":"Business and commerce/Information systems and information technology"}],"tags":[],"updatedAt":"2026-05-06T14:35:33+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-06 14:35:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9245607","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9245607","identity":"rs-9245607","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}