PurposeKnowledge-intensive processes (KiPs) are typically viewed as business processes performed by knowledge workers making complex decisions. Increasing digitalization of KiPs through AI and other means reduces the adequacy of that view. This conceptual contribution proposes and illustrates an updated view of KiPs based on a work system perspective (WSP) that expands the scope of BPM. Design/methodology/approachThree WSP approaches are proposed to analyze KiPs. First, work system models are used with different degrees of specificity to support different stakeholders. Second, continuous design dimensions are proposed for visualizing important KiP issues and challenges. Third, the roles and responsibilities of digital agents in relation to different facets of work are analyzed. The three approaches are introduced using a running example and are evaluated using a real-world KiP involving diagnostic radiology. FindingsThe three WSP approaches help to analyze KiPs and attain a greater understanding by offering an integrated view of KiPs that spans management and technical viewpoints. Originality/valueThis conceptual contribution shows how ideas and models related to the WSP provide paths for integrated visualization and analysis of KiPs. The paper extends Business Process Management (BPM) by providing approaches that stakeholders with different concerns and interests can use for describing and analyzing KiPs that may be automated to varying degrees.
Purpose Global data generation continues to grow exponentially, yet Data Monetization remains fragmented as a field of study, lacking a comprehensive structure to guide both research and practice. This study addresses that gap by asking: (1) what are the key concepts in Data Monetization and how do they relate to each other? and (2) how can these concepts be integrated into a comprehensive framework? Design/methodology/approach We conducted a systematic review and synthesis of 35 academic articles and 31 patents, resulting in a novel conceptual framework. Findings The framework consolidates fragmented constructs and offers a holistic view of how data is transformed into value. It differentiates monetization pathways, specifies the mechanisms connecting actors, management and monetization strategies, internal capabilities and data products, and provides a process perspective that aligns with business process management. Research limitations/implications The framework enhances theoretical clarity by making explicit the mechanisms and contingencies that explain variation in monetization outcomes. It clarifies how monetization pathways, customer alignment and internal capabilities shape value capture and provides a foundation for cumulative theorizing on data monetization. Practical implications It offers a structured decision logic tool for crafting tailored monetization strategies aligned with value capture objectives. Originality/value The framework clarifies the conditions under which value created from data is appropriated. It explains how monetization pathways, customer alignment and internal capabilities jointly shape value capture outcomes.
Purpose This study examines variations in blockchain-enabled value chain governance across different contextual conditions and develops a typology to explain these variations in sustainability-oriented plantation value chains. Design/methodology/approach This study employed a multiple case study design focusing on buyer-driven plantation commodities in Indonesia, namely coffee, palm oil, cacao and rubber. The data were collected through document analyses, in-depth interviews and field observations to ensure the robustness and validity of the findings. Findings Blockchain-enabled governance varies across value chains depending on the interactions between system complexity and governance intensity, yielding four distinct governance configurations. The typology shows that blockchains function not only as a traceability tool but also as a context-dependent governance infrastructure that supports different combinations of coordination, control and verification. Research limitations/implications This study examined four Indonesian plantation commodity cases, which supports analytical rather than statistical generalizations. The findings reveal that governance outcomes depend on the alignment between the value chain structure and governance requirements, rather than on technology alone. Originality/value This study developed a configurational typology of blockchain-enabled value chain governance by integrating system complexity and governance intensity. It extends the global value chain and digital governance frameworks by explaining why blockchain technology performs different roles across contexts, shifting the understanding of blockchains from a universal traceability tool to a context-dependent governance infrastructure.
Purpose This paper examines how intelligent automation (IA) creates business value and how different value-creation pathways depend on the alignment between task characteristics and IA capabilities from a Task-Technology Fit perspective.Design/methodology/approach The study adopts a qualitative research design based on 25 IA implementation projects delivered by 2 global AI integrator firms across multiple industries. Guided by a realist and configurational perspective, the analysis combines Context-Mechanism-Outcome intra-case analysis with systematic cross-case comparison to identify theoretically informed and empirically grounded IA value-creation pathways.Findings The findings show five recurrent IA value-creation pathways: (1) efficiency improvement; (2) cost reduction and financial gains; (3) quality and compliance enhancement; (4) customer and employee experience improvement; and (5) integration and innovation. The study further shows that performance outcomes vary according to the fit between task characteristics and IA capabilities. Lower-level automation mainly generates efficiency and cost benefits in structured and rule-based contexts, whereas higher-level IA orchestration enables broader integration, innovation and strategic outcomes.Originality/value This study provides an integrated theoretical lens for understanding how IA creates business value across operational and strategic levels. The study also provides a practical framework that helps managers and AI integrators align IA solutions with task and process requirements, supporting effective implementation strategies and holistic performance metrics.
Purpose This study investigates the application of artificial intelligence (AI) within global procurement processes. It builds upon prior literature and offers novel methodological insights while pinpointing trends and outlining research gaps for future investigations. Design/methodology/approach The study combines a bibliometric literature review using VOSviewer with an AI-assisted review process. AI tools, including ChatGPT, are integrated into selected stages of the review and examined alongside researcher-driven approaches to assess their usefulness and limitations in supporting systematic literature analysis. Findings The results indicate a shift in AI use in procurement from isolated automation toward more strategic integration. The literature increasingly focuses on strategic areas such as supplier management, while comprehensive implementation frameworks remain limited. The analysis of AI-assisted reviewing suggests that such tools can support efficiency and consistency, but their application remains sensitive to conceptual ambiguity and requires human oversight, particularly in classification tasks. Originality/value This study offers two distinct contributions to the literature. First, it provides a focused analysis of how research on AI in procurement has evolved since previous systematic literature reviews (SLRs), emphasising studies that examine real-world implementation and effectiveness rather than merely discussing willingness or barriers to adoption. Second, it provides an exploratory assessment of AI-assisted reviewing as an emerging methodological approach, highlighting both its potential and its current limitations.
Purpose This study evaluates, from a business process management (BPM) perspective, four reverse logistics (RL) strategies (Lockers, Click & Collect, Microhubs and 3PL) once product returns are authorized. It addresses the critical first-stage decision – home collection vs designated drop-off – while explicitly analyzing the detrimental impact of failed collection attempts due to customer absence on urban logistics efficiency, aiming to enhance the business performance of e-commerce retailers and logistics providers. Design/methodology/approach A decision support framework is developed, applying the technique for order preference by similarity to ideal solution to assess each strategy and a simulation of 1,000 product return scenarios is conducted. The methodology incorporates a robust sensitivity analysis modeling varying rates of failed first-time collections, quantifying their effect on cost, cycle time, CO2 emissions and expected customer utility. Findings Baseline results identify Urban Microhubs as the most balanced strategy. However, sensitivity analysis reveals that as failed collections increase, the efficiency of attended strategies (3PL and Urban Microhubs) degrades significantly due to re-attempt costs. This shifts the strategic preference toward unattended strategies like lockers. Originality/value The study offers original value by integrating operational, environmental and customer-focused metrics into a unified framework for evaluating RL scenarios. Its multidimensional comparison of the four strategies provides novel insights that advance theory and guide the design of more efficient and sustainable return strategies, enabling e-commerce and logistics operators to adopt differentiated policy profiles (cost-driven or green-driven). Ultimately, the primary value of this research resides in providing a structured methodology to analyze complex operational scenarios. Additionally, the explicit modeling of failed collections provides novel insights that advance BPM theory, empowering context-specific managerial decision-making.
Purpose This study examines how artificial intelligence (AI)-powered knowledge networks support sustainable business model innovation, focusing on the mediating role of knowledge absorptive capacity and the moderating role of sustainable entrepreneurial orientation. Design/methodology/approach Drawing on the knowledge-based view and dynamic capability theory, the study tests a moderated mediation model using a final dataset of 400 valid responses affiliated with business schools in China. Partial least squares structural equation modelling was used to examine direct, mediated, moderated and moderated mediation effects. Findings The findings show that AI integration capability, innovation network strength and knowledge-sharing culture influence sustainable business model innovation both directly and indirectly through knowledge absorptive capacity. Sustainable entrepreneurial orientation strengthens selected relationships, particularly those involving AI capability, knowledge-sharing culture and absorptive capacity. Research limitations/implications The study is based on cross-sectional, self-reported data from business-school-affiliated respondents in China. Future research should test the model using practitioner-based samples, longitudinal designs and different industrial and national contexts. Practical implications Managers should not only invest in AI tools and collaborative networks but also develop employees' ability to interpret AI-generated knowledge and translate external knowledge into sustainability-oriented innovation. Social implications The findings highlight how AI-enabled knowledge networks can support sustainability-oriented innovation, resource efficiency, circular economy practices and broader social innovation goals. Originality/value The study integrates technological, relational, cultural, absorptive and entrepreneurial dimensions into a single framework explaining sustainable business model innovation.
PurposeThis study investigates how technological, organisational, and environmental factors combine to explain digital transformation success in the African pharmaceutical industry. The study identifies configurations leading to high and low levels of digital transformation.Design/methodology/approachGrounded in the Technology-Organisation-Environment (TOE) framework, this study takes an exploratory approach, using Morocco as a case study due to its position as the second-largest pharmaceutical industry in Africa. An online survey of professionals in the Moroccan pharmaceutical sector was conducted to assess the factors influencing digital transformation. After data quality assessments, Fuzzy-set Qualitative Comparative Analysis (fsQCA) was used to identify necessary and sufficient conditions for high and low levels of digital transformation.FindingsThe analysis found that IT and cybersecurity capabilities are the necessary conditions for attaining high digital transformation levels. The sufficiency analysis for high levels of digital transformation highlighted four configurations as alternative pathways, which combine various technological, organisational and environmental factors. Among the four, the most relevant pathway combines organisational readiness for digital transformation, technological readiness and investment, and IT and cybersecurity capability.Originality/valueThis study provides the first empirical examination of digital transformation in the African pharmaceutical industry through a Moroccan case study. Using a TOE-based configurational perspective, it presents several pathways to successful digital transformation, highlighting causal asymmetry, equifinality, the centrality of organisational readiness and the necessity of IT and cybersecurity. The findings provide practical insights for managers and policymakers working to advance digital transformation in emerging economies.
Purpose This study aims to develop a new hybrid algorithm that combines supervised and unsupervised ML techniques to improve the precision of predictions for project progress and cost performance measures. Design/methodology/approach This study utilizes a hybrid project cost forecasting methodology by combining supervised and unsupervised machine learning algorithms. Findings Computational results of this study demonstrate the superiority of the XGBoost and Random Forest algorithms, both ensemble methods, and confirm the accuracy of this forecasting methodology. Originality/value Unlike earlier studies that rely on artificially generated data, this study uses a dataset of 117 real-world projects to test the new forecasting technique. The clustering approach to the cost dataset, a novel contribution of the current study, demonstrates enhanced prediction accuracy. Cluster-aware estimate-at-completion forecasting is studied as a process-level decision-support tool.
Purpose Standard Business Process Model and Notation (BPMN) lacks native constructs to manage the physical tools and materials involved in manual work, creating a digital-physical divide. This research proposes the Passive Resource-integrated Modeling Extension (PRiME), a framework extending BPMN to formally model, execute, and monitor the lifecycles of passive resources in cyber-physical systems. Design/methodology/approach Following the Design Science Research Methodology we developed a formal ontology for passive resources, distinguishing between consumable materials and reusable tools. The resulting artifact of mapping the ontology to the BPMN 2.0 metamodel is a standard-compliant, executable extension validated through a prototype implementation and demonstrated in two scenarios. Findings PRiME provides a machine-readable mechanism to execute resource checks directly within the process engine. The framework enables (1) runtime support by generating real-time resource checklists and shortage alerts to prevent operational delays, and (2) design-time analysis via a Sankey-based visualization of material flows. Performance benchmarks confirm that the extension imposes negligible overhead, with sub-second processing of the analysis pipeline. Research limitations/implications This research establishes a foundation for Physical Business Process Management by elevating physical assets to first-class citizens within process orchestration. By enforcing algorithmic availability checks and visualizing material consumption, the framework addresses nonproductive time and supports sustainability (SDG) goals through precise waste tracking. Furthermore, it shifts the cognitive burden of resource management from the worker to the system, augmenting skilled labor and aligning operational efficiency with responsible consumption standards. Originality/value Unlike previous conceptual proposals or architectural frameworks, this research provides a fully executable BPMN extension for passive resources. It combines a formal ontological foundation with a practical, standard-compliant implementation that bridges the gap between theoretical process modeling and physical execution.
Purpose This study investigates the impact of blockchain technology (BCT) within supply chain management (SCM) to enhance organisational performance. It examines how BCT attributes influence supply chain collaboration (SCC), which affects environmental, economic and operational performance using the resource-based view (RBV) theory. Design/methodology/approach Data were collected from 288 supply chain professionals from China. The research framework was analysed using structural equation modelling through SPSS 23 and AMOS 24 software to validate the proposed relationships. Findings The analysis reveals that SCC significantly enhances operational, environmental and economic performance in SCM. Notably, environmental performance is found to have a robust and positive impact on operational performance, while economic performance similarly contributes to improving operational efficiency. Moreover, the study identifies IT alignment as a critical moderator, strengthening the influence of SCC on both environmental and economic performance. Ultimately, this research underscores the essential role of SCC and IT alignment in driving supply chain performance across these three dimensions, highlighting the strategic importance of these factors for organisations aiming to enhance their competitive advantage and operational success. Originality/value This study successfully presents SCC as a second-order construct and highlights its crucial role in driving performance outcomes in SCM. Interestingly, by introducing IT alignment as a moderator, the study provides new insights into how the integration of technology infrastructure enhances the effectiveness of SCC in improving performance, ultimately helping organisations enhance their competitive advantage and operational success.
Purpose Prescriptive implementation frameworks and inventories of factors that are required for successful continuous improvement (CI) implementation are widely available. However, the available guidance typically assumes linear implementation processes, where an organization is expected to go through a prescribed stepwise implementation framework or overcome clearly specified hurdles. Contemporary insights have confirmed the non-linear nature of CI implementation processes, but left their typical trajectories and characteristics unclear. The purpose of this study is to examine how actual organizational CI processes typically deviate from CI theory and prescribed management guidance.Design/methodology/approach Twenty-five key informants from multiple industries engaged in designing and implementing CI were interviewed and secondary CI implementation archival company data was reviewed. A systematic approach to data collection and analysis, combined with meticulous documentation and subsequent triangulation procedures were applied to mitigate validity and reliability concerns.Findings Our findings reveal four distinct CI implementation patterns, ranging from short-lived foundationless implementations to implementations reaching a consolidated state (plateauing). The emergence of these patterns is explained by several factors both internal and external to the CI implementations studied.Originality/value The relative priority of both internal and external factors for CI implementation processes, how these are interrelated and their association to the four patterns of CI implementation identified provides an understanding that transcend the fragmented nature of CI implementation theory and guidance to date. The study findings can be used by practitioners to better tailor CI implementation processes and pro-actively identify aversive internal factors and external events.
Purpose Integrating artificial intelligence (AI) technologies into knowledge management systems (KMSs) offers transformative potential for organizations by enhancing knowledge-intensive business processes. However, this integration may face barriers and challenges. This study investigates the organizational implications for effective change management and structured implementation processes required for a beneficial integration of AI into KMSs. Design/methodology/approach A quantitative research design was employed using an online survey developed for this study and completed by 378 professionals across diverse roles, organizational sizes, and sectors. The instrument assessed perceptions of four barrier categories – human, technological, financial, and ethical-regulatory – through validated multi-item scales. Statistical analyses, including repeated-measures ANOVA, MANOVA, and t-tests, were conducted to identify differences across demographic and organizational variables. Findings Technological and ethical-regulatory barriers are perceived as more significant than financial ones. Managers identified human-centered hurdles, such as resistance to change and skills gaps, as more immediate threats than financial constraints. Knowledge managers expressed significant concerns regarding technical integration with existing KMSs. Smaller organizations reported higher levels of human and technological complications compared to medium and large firms. Graduates demonstrated greater sensitivity to ethical-regulatory issues, whereas the high-tech sector perceived fewer obstacles overall. No significant differences emerged across gender, age, or seniority. Originality/value By connecting established models of technology adoption with empirical evidence from knowledge-intensive contexts, this study strengthens understanding of AI integration within KMSs as a socio-technical process shaped by organizational contingencies and governance imperatives. It contributes to the literature on business process management by framing AI integration as a process-oriented transformation rather than a purely technological upgrade.
Purpose This research illustrates how companies can leverage Artificial Intelligence (AI) within the context of industry convergence to foster stronger customer-machine interaction. We aim to unveil how convergence occurs within industries and what AI-enabled mechanisms help exploit these developments.Design/methodology/approach AI is increasingly adopted within convergent service industries. Such integrations are employed to shape customer-machine interactions across business processes. Yet existing research rarely examines AI use under conditions of industry convergence while also tracing effects across the full input-process-output (IPO) chain. To address this gap, we conduct a qualitative, multi-case study inductive design, set in the MedTech industry. This approach enables a holistic, end-to-end examination of AI outcomes across the IPO chain, while preserving the organizational and industry context in which convergence occurs.Findings Results reveal industries face different pathways amid industry convergence, leading to the following types of output: non-convergence, symmetric convergence, asymmetric convergence. Furthermore, we identified stage-specific AI mechanisms and classified them according to their impact on customer acceptance of machines.Originality/value This research introduces an industry convergence input-process-output (ICIPO) model, reframing the original framework around this phenomenon. Moreover, it provides mapping for AI mechanisms amid industry convergence, allowing managers to improve customer-machine interactions across business processes.
Purpose This study examines how organizational resilience (OR) and organizational agility (OA), as complementary capabilities in a dynamic capabilities (DC) logic, relate to business process management maturity (BPMM) in small and medium-sized enterprises (SMEs). It also examines whether Esprit de Corps (EdC) and managerial commitment (MC) are antecedents of OR. Design/methodology/approach Data were collected via an online survey (CAWI) from 500 Polish SMEs in knowledge-intensive sectors implementing transformation initiatives affecting strategy and processes. A single-informant survey design was applied (senior managers). The measurement and structural models were estimated in JASP using CB-SEM. Findings The results indicate that both EdC and MC strengthen OR, with a stronger association for MC. OR supports OA, consistent with OR providing a basis for rapid response and reconfiguration of process-related activities. OR is also directly associated with BPMM, suggesting that maintaining functioning and learning from disruptions supports process management maturity beyond OA. OA is positively related to BPMM and mediates the relationship between OR and BPMM, indicating that part of the association operates through OA. Originality/value The study contributes to BPM research by showing that BPMM reflects a configuration of organizational capabilities that includes OR and OA. It also shows that EdC and MC are antecedents of OR and that their associations with BPMM are primarily indirect through OR and OA.
Purpose Growing disruptions arising from pandemics, geopolitical conflicts, and technological changes have made supply chain resilience and long-term viability critical priorities for firms. While digital transformation (DT) and supply chain intelligence integration have been recognized as important capabilities for strengthening supply chains, limited research has examined how they jointly contribute to resilience and long-term viability, or which conditions are indispensable for achieving these outcomes. Drawing on the Knowledge-Based View (KBV) and the Stimulus-Organism-Response (SOR) framework, this study examined how digital transformation (DT) and supply chain intelligence integration (SCII) influence supply chain resilience and viability. It also examined the mediating role of supply chain intelligence integration and the moderating role of supply chain risk information analysis. Design/methodology/approach The proposed model was validated using survey data from 300 manufacturing firms in Ghana. Structural equation Modelling (SEM) using Partial Least Squares software and Necessary Condition Analysis (NCA) was employed to analyze the data. Findings The findings show that digital transformation and supply chain intelligence integration drive supply chain resilience (SCR), and subsequently translate into enhancing supply chain viability (SCV). Additionally, we found supply chain intelligence integration (customer and competitor intelligence) partially mediates the link between DT and SCR. We also found that supply chain resilience mediates the link between supply chain intelligence integration and viability and between digital transformation and supply chain viability. Although supply chain risk information analysis supports viability, it does not moderate the link between SCR and SCV. The NCA further differentiated between “must-have” (necessary) and “nice to have” capabilities needed for a higher level of resilience and viability of supply chains. Practical implications Managers in resource-constrained environments should prioritize customer and competitor intelligence over supplier intelligence when allocating limited resources. This does not imply that managers should neglect supplier relationships entirely, but rather that they should recognize that supplier intelligence is a sufficient but not necessary condition for achieving resilience or viability. Originality/value The study advances supply chain research by developing an integrated model that links digital transformation (stimulus), supply chain intelligence integration (organism), and supply chain viability (response). It extends prior research by showing how and when DT influences both resilience and viability, and by combining PLS-SEM with NCA for deeper analytical insights.
Purpose In the digital economy, digital transformation has become a strategic imperative for firms seeking to build core competitiveness. Yet how organizational structures adapt to digitalization remains insufficiently understood. In particular, the widespread claim that digital transformation inevitably leads to organizational flattening still lacks a systematic theoretical explanation and robust empirical evidence. This study therefore, examines whether and how digital transformation promotes organizational flattening and whether the resilience effects of flattening vary with firm size.Design/methodology/approach Drawing on transaction cost theory from a business process management perspective, this study uses panel data on Shanghai-Shenzhen A-share listed firms from 2008 to 2023. Two-way fixed-effects models and a dual-mediation framework are employed to test the effect of digital transformation on organizational flattening, the underlying mechanisms, and the resilience implications of flattening.Findings Digital transformation significantly promotes organizational flattening, and this result remains robust across multiple tests. The effect operates through two channels: reductions in internal managerial costs and external transaction costs, with the latter showing a stronger leverage effect. The flattening effect is more pronounced in large private firms. In addition, firm size positively moderates the relationship between organizational flattening and organizational resilience.Originality/value This study extends transaction cost analysis from firm boundaries to internal organizational design and reveals the dual-path mechanism through which digital transformation reshapes organizational structure. It also shows that the resilience value of flattening is contingent on firm size. Practically, the findings caution against indiscriminate flattening and support differentiated, scale-sensitive transformation strategies.
Purpose Drawing on adaptive structuration theory (AST), this study investigates how different customer relationship management system (CRMS) usage behaviors affect frontline employees' service performance, and examines how leader efficacy moderates these relationships, drawing on the notion that technology may substitute for leadership under certain conditions. Design/methodology/approach A matched dyadic survey was conducted with 150 bank branches in China. Data were collected from both frontline employees and their immediate supervisors. Structural equation modeling (SEM) was employed to test the hypothesized model. Findings Both consensus on appropriation and faithfulness of appropriation positively influence CRMS usage. Explorative usage enhances service performance, while exploitative usage unexpectedly has a negative effect. Leader efficacy directly improves performance and positively moderates the relationship between explorative usage and performance. Research limitations/implications The study's findings are based on data from the banking sector in China, which may limit generalizability to other industries or cultural contexts. The cross-sectional design also restricts causal inference. Practical implications Organizations may promote appropriate CRMS appropriation through training and communication, encourage explorative usage, and adopt situational leadership strategies—empowering employees during routine system use while providing active support during innovative system use. Originality/value This study extends AST to the individual level and offers a nuanced understanding of how technology appropriation and leadership interact to shape service performance. It challenges the universal positive view of IS usage by revealing the potential negative impacts and provides a contingent perspective on leadership in technology-mediated environments.
Purpose As business process management (BPM) evolves toward greater flexibility and agility, supply chain resilience (SCR) becomes a key objective of process redesign. Achieving SCR increasingly depends on technology that can reconfigure business processes in real time, rendering generative artificial intelligence (GAI) especially pertinent to BPM. Nevertheless, the influence of GAI deployment on SCR remains unclear. The model examines how the extent (in breadth and depth) of GAI deployment affects SCR across readiness, response and recovery. Furthermore, BPM's evolution extends beyond technology deployment and requires complementary organizational arrangements. Accordingly, the moderating role of relationship transparency is analyzed. Design/methodology/approach Building on organizational information processing theory (OIPT), this study uses multiple linear regression to analyze survey data from 287 Chinese manufacturing firms. Findings The results reveal that GAI deployment positively affects SCR, and that both the breadth and depth of GAI deployment contribute to higher SCR. A notable finding is that the breadth of GAI deployment does not show a significant main effect on recovery, but enhances recovery only when relationship transparency is high. In addition, relationship transparency strengthens the positive relationship between GAI deployment and SCR. Originality/value This study jointly examines three dimensions of SCR, which prior research has often treated separately. In addition, we explain that the breadth and depth of GAI deployment may have different effects on SCR. Finally, we examine the moderating role of relationship transparency and highlight information quality as a complement to OIPT.
Purpose Despite rising digital investments, manufacturers in emerging economies struggle to convert explorative IT capabilities into better performance, especially under environmental regulations. This research, grounded in Ambidexterity Theory, examines how explorative IT capabilities boost performance via green innovation in Pakistan's manufacturing sector. Design/methodology/approach The study employs multi-analytical methods, including partial least squares structural equation modeling, necessary condition analysis and importance-performance map analysis, to test the hypothesis among 620 respondents using survey data. Findings Findings show that explorative IT capability boosts organizational performance via green innovation, strengthened by environmental regulations. Analyses identify necessary and performance-enhancing capabilities. Originality/value This paper explains how explorative digital capabilities drive sustainability outcomes in manufacturing. Using sufficiency- and necessity-based methods, it offers a decision-focused view that guides managers to invest in the most impactful digital capabilities for maximum returns.
