Journal of Digital Economy最新文献

{"title":"AI adoption and corporate green behavior: Institutional isomorphism in the algorithmic age","authors":"Xi Mingming,&nbsp;Xia Ruoxuan,&nbsp;Pan Wulin","doi":"10.1016/j.jdec.2026.05.005","DOIUrl":"10.1016/j.jdec.2026.05.005","url":null,"abstract":"<div><div>Artificial intelligence (AI) has profoundly reshaped socio-economic landscapes and emerged as a pivotal force in advancing corporate sustainability. Although earlier studies have identified AI's capacity to curb corporate greenwashing, a systematic theoretical framework explaining the underlying mechanisms remains absent. We demonstrate that the new institutional perspective of isomorphism provides a compelling explanation for this phenomenon and develop an “organization-market-institution” synergy framework to explore the anti-greenwashing effects of AI adoption. Our results show that AI mitigates corporate greenwashing through three interrelated pathways: strengthening firms' organizational efficiency and substantive green transformation capacity, increasing external scrutiny and legitimacy expectations, and improving firms' alignment with formal institutional incentives and compliance requirements. Heterogeneity analyses confirm these mechanisms exist and clarify the contexts where their effects are stronger. Finally, we discuss the implications of AI-driven institutional isomorphism for corporate environmental behavior and propose actionable policy recommendations.</div></div>","PeriodicalId":100773,"journal":{"name":"Journal of Digital Economy","volume":"5 ","pages":"Pages 243-256"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148634851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"World Data Organization: Towards a sustainable global data ecosystem","authors":"Ke Rong ,&nbsp;Taoxiong Liu ,&nbsp;Changwen Zhao ,&nbsp;Hongwei Kou ,&nbsp;Tianxi Yang","doi":"10.1016/j.jdec.2026.04.001","DOIUrl":"10.1016/j.jdec.2026.04.001","url":null,"abstract":"<div><div>Data has become a key factor of production in the digital economy, yet its governance remains institutionally fragmented across borders. This paper asks why a resource with global quasi-public-good properties, whose value depends on cross-border reuse and recombination, has not generated a corresponding institutional supply for trusted international cooperation. Drawing on a public-good diagnosis and comparative institutional analysis, we argue that the central problem is not simply regulatory divergence, but the underprovision of the governance conditions required for credible, scalable, and sustainable cross-border data use. Existing arrangements make important but partial contributions, yet their mandates, scopes, and enforcement logics remain too fragmented to form a coherent governance architecture for a global data ecosystem. In response, we propose a World Data Organization (WDO) as a complementary focal arrangement designed to address this institutional gap. The paper contributes by reframing cross-border data governance as a digital economy problem of quasi-public-good governance, explaining why current arrangements remain structurally insufficient, and outlining a conceptual blueprint for a more integrated institutional response toward a sustainable global data ecosystem.</div></div>","PeriodicalId":100773,"journal":{"name":"Journal of Digital Economy","volume":"5 ","pages":"Pages 149-159"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147945068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chain-to-chain competition between electric and internal combustion vehicles supply chains considering pricing, advertisement, carbon emission reduction, and government intervention","authors":"Mohammad Mahdi Sheikhi-Nasrabadi ,&nbsp;Hossein Khosroshahi ,&nbsp;Ali Esfahbodi ,&nbsp;Yong Lin","doi":"10.1016/j.jdec.2025.09.001","DOIUrl":"10.1016/j.jdec.2025.09.001","url":null,"abstract":"<div><div>This paper investigates the competition between supply chains of New Electric Vehicles (NEV) and Internal Combustion engine Vehicles (ICV). These supply chains include a retailer and a manufacturer. We address the market environmental awareness as the missing link in an automobile supply chain. We formulated a differential game problem that allows us to investigate the decisions which an be dynamic in nature influencing the market in addition to static decisions. Furthermore, we focus on sustainable governmental policies and the impacts of enforcing these policies on demand for electric and gasoline vehicles, manufacturers, and the retailer. We find equilibrium decision variables. Then we analyze the result and propose several insights. For instance, we found thresholds for some parameters that can guarantee the NEV manufacturer gains a high profit, and manufacturers tend to produce NEVs if environmental criteria are imposed in governmental policies. In addition, regardless of any situation, the government is more supportive of both NEV consumers and ICV consumers if and only if environmental criteria are considered in governmental policies. However, if this policy is enforced, the retailer's prices will be higher for both types of vehicles. In addition, we found that regardless of the situation, ICV manufacturer has to invest more money in green production system than NEV manufacturer.</div></div>","PeriodicalId":100773,"journal":{"name":"Journal of Digital Economy","volume":"5 ","pages":"Pages 49-65"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147538710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring AI adoption and SME performance in resource-constrained environments: A TOE–RBV perspective with mediation and moderation effects","authors":"Faizan ul Haq ,&nbsp;Norazah Mohd Suki ,&nbsp;Hina Zaigham ,&nbsp;Asim Masood ,&nbsp;Amer Rajput","doi":"10.1016/j.jdec.2025.07.002","DOIUrl":"10.1016/j.jdec.2025.07.002","url":null,"abstract":"<div><div>This study examines the adoption of Artificial Intelligence (AI) and its impact on the performance of Small and Medium Enterprises (SMEs) in resource-constrained environments, with a focus on Pakistan's manufacturing sector. Grounded in the Technology-Organization-Environment (TOE) framework and Resource-Based View (RBV), the study investigates how technological readiness, leadership vision, and competitive pressure influence AI adoption, and how AI adoption mediates their effects on firm performance. While SMEs in developed economies benefit from advanced infrastructure and institutional support, firms in emerging markets face significant challenges such as skill shortages, financial constraints, and infrastructural gaps. Data were collected from 349 managerial-level respondents and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multiple Regression Analysis (MRA). The results show that AI adoption significantly mediates the relationship between key organizational and environmental factors and SME performance. Additionally, AI-driven marketing strategies moderate the link between AI adoption and firm performance, enhancing the benefits of digital transformation. The study contributes to the growing literature on digital innovation in emerging economies and offers practical implications for SME leaders and policymakers aiming to foster performance through strategic AI integration.</div></div>","PeriodicalId":100773,"journal":{"name":"Journal of Digital Economy","volume":"5 ","pages":"Pages 160-181"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Volatility spillover of cross-border fund flow and the geopolitical risk channels","authors":"Junlong Xu,&nbsp;Conghua Wen,&nbsp;Lu Zong","doi":"10.1016/j.jdec.2025.12.002","DOIUrl":"10.1016/j.jdec.2025.12.002","url":null,"abstract":"<div><div>This study investigates cross-border risk spillover led by fund flow volatility. With a focus on global and country-specific geopolitical risk (GPR), we explore vulnerability channels where volatility is transmitted and propagated based on the equity fund flow data of G20 countries. Following a data-driven methodology, we quantify long-term systemic risk spillovers for each country using the PageRank algorithm applied to a volatility network, capturing the intensity of directional, pairwise risk spillovers. Traditional measures of systemic risk often focus on direct pairwise relationships, leaving indirect and multi-step spillovers unquantified. To address this, we adopt a computational network approach, constructing a directed volatility spillover network from G20 fund flow data. PageRank, adapted from graph-based machine learning, captures both direct and indirect risk exposures by modeling risk transmission within the network as a Markov process, providing a data-driven measure of long-term systemic vulnerability for each country. We then analyze these network-derived risk measures using panel regressions with global and country-specific GPR indices, along with recognized push and pull factors, to explore how countries respond differently to geopolitical uncertainties. Our findings reveal cyclic patterns in fund flow dependence, with smaller emerging markets, such as Indonesia, Argentina, Saudi Arabia, and South Africa, showing heightened vulnerability during turmoil. Country-specific GPR emerges as a potent pull factor, especially for oil-exporting economies, while geographic location serves as a primary channel of spillover. In periods of global tension, Latin American emerging markets and the Asia-Pacific region display a “safe-haven” role, contrasting with higher exposure in North America, the Middle East, and Africa. This study demonstrates the potential of computational network methods to advance systemic risk modeling in finance, offering a scalable, data-driven framework for understanding how geopolitical risk interacts with cross-border capital flows.</div></div>","PeriodicalId":100773,"journal":{"name":"Journal of Digital Economy","volume":"5 ","pages":"Pages 286-313"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148769999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Platform-based supply chains: A dual-layered framework for digital transformation and ecosystem orchestration","authors":"Jing Luo ,&nbsp;Yong Lin ,&nbsp;Zhixue Liu ,&nbsp;Hanyan Jiang ,&nbsp;Haoyu Liu","doi":"10.1016/j.jdec.2026.02.002","DOIUrl":"10.1016/j.jdec.2026.02.002","url":null,"abstract":"<div><div>As global supply chains face increasing complexity, disruption, and sustainability pressures, platform-based supply chains have emerged as a new organizational form, digitally enabled, ecosystem-centric, and strategically adaptive. Unlike traditional linear and firm-centric models, these supply chains operate through the integration of advanced technologies, stakeholder collaboration, and dynamic governance. Drawing on platform ecosystem theory and socio-technical systems theory, this study develops a dual-layered framework based on an in-depth case study of a leading Chinese supply chain solutions provider. The framework comprises a <em>digital platform layer</em> that supports real-time data sharing, interoperability, and automation, and a <em>supply chain ecosystem layer</em> that facilitates stakeholder coordination, resource orchestration, and value co-creation. The findings reveal four mechanisms of strategic evolution, ecosystem orchestration, capability co-development, governance innovation, and continuous learning, driven by the alignment between digital infrastructure and social-organizational structure. By reconceptualizing platform-based supply chains as adaptive socio-technical ecosystems, the study advances theoretical understanding of their configuration, operational mechanisms, and strategic evolutions. It also provides practical guidance on platform design, stakeholder onboarding, governance structures, and phased implementation, offering a roadmap for managers and policymakers navigating digital transformation in contemporary supply chain environments.</div></div>","PeriodicalId":100773,"journal":{"name":"Journal of Digital Economy","volume":"5 ","pages":"Pages 66-86"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147538712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Supply chain intelligence: Integration of emerging digital innovations to promote sustainable supply chain practices","authors":"Kingsley Kofi Arthur ,&nbsp;Richard Kwasi Bannor ,&nbsp;Peter Darko ,&nbsp;Omega Hlortu ,&nbsp;Sampson Adom","doi":"10.1016/j.jdec.2026.01.002","DOIUrl":"10.1016/j.jdec.2026.01.002","url":null,"abstract":"<div><div>Promoting sustainable practices remains central to achieving the UN's sustainable development goals. In the business sector, reaching this objective requires organisations to incorporate emerging digital innovations into their supply chain activities to facilitate the effective realisation of this aim. As a result, the current study examines the impact of supply chain intelligence on organisational sustainable supply chain practices and performance. We systematically selected 109 relevant articles from the Scopus database to provide an overview, identify gaps, and offer new perspectives on sustainable practices in organisational supply chain activities. Through detailed content analysis, the study's findings showed that supply chain intelligence (SCI) significantly helps improve firms' sustainable practices and organisational performance, demonstrating its importance in maximising sustainable outcomes. This notable link is achieved through the integration of emerging digital technologies that enable firms to capture, process, store, and apply relevant data, while also developing strong information management and decision support systems. Furthermore, we found that the adoption of the SCI concept has been particularly impactful in the agri-food industry, underscoring its role in tackling food insecurity, poverty, and climate change issues. A conceptual framework based on information processing theory illustrates how organisations can embed supply chain intelligence into their operations to promote sustainable supply chain practices. As sustainable practices remain crucial in current debates, the results highlight the need for organisations to adopt and implement emerging digital innovations such as artificial intelligence (artificial neural network; machine learning; agent-based and expert systems; fuzzy logics; robust set theory; genetic algorithms), big data, IoT, and blockchain in their supply chain activities to reduce environmental impact. This can serve as a countermeasure for addressing climate change and lowering greenhouse gas emissions as the world seeks to prevent and mitigate its effects.</div></div>","PeriodicalId":100773,"journal":{"name":"Journal of Digital Economy","volume":"5 ","pages":"Pages 125-148"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147538718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting social media engagement in higher educational institutions: A machine learning approach","authors":"Biplab Bhattacharjee,&nbsp;Sheena Suresh,&nbsp;Moinak Maiti","doi":"10.1016/j.jdec.2026.04.002","DOIUrl":"10.1016/j.jdec.2026.04.002","url":null,"abstract":"<div><div>Social media engagement plays a vital part in the marketing activities of Higher Educational Institutes (HEIs). The extant studies clearly demonstrate that having a social media presence and posting content alone without a strategy in place would not lead to higher awareness, branding, or enrolments of HEIs. Accordingly, this study, attempts to build predictive models for social media engagement for HEIs in an emerging market context. The official Facebook insights data of a HEI have been utilized for this model-building purpose. Pre-processing and feature engineering steps are performed initially, followed by cluster-based-class-labelling. The labelled classes are further trained with several machine learning models, and the best-performing ones are selected based on K-fold cross-validation. Hyperparameter optimization has been further performed on the best-chosen classifiers. This study is a pioneering attempt to build a predictive model for social media engagements of HEIs in an emerging market context. The proposed predictive models would be a handy tool for devising social media content posting strategies.</div></div>","PeriodicalId":100773,"journal":{"name":"Journal of Digital Economy","volume":"5 ","pages":"Pages 182-197"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148523308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aggregate production functions in the digital economy: A survey of microfoundational approaches","authors":"Xiang Chen,&nbsp;Yaping Li","doi":"10.1016/j.jdec.2025.12.003","DOIUrl":"10.1016/j.jdec.2025.12.003","url":null,"abstract":"<div><div>This paper provides a comprehensive review of the microfoundations of production functions and suggests research directions for production theory in the digital economy. These three models, including the endogenous technology choice model, task-based model, and game theory framework, derive the Cobb-Douglas and CES production functions from different microfoundations. In the endogenous technology choice framework, we can derive the production functions from the technology feasible set by assuming different distributions of unit efficiency coefficients for capital and labor. Specifically, the Cobb-Douglas production function is associated with a Pareto distribution, whereas the CES production function is linked to a Weibull distribution. In the task-based models, the CES production function can be derived from aggregating different tasks by specifying the distribution of automation levels and skill-based tasks. In the game theory framework, bargaining games yield factor price curves and the Cobb-Douglas production function form can be derived from the duality between cost and production function, where factor shares are determined by bargaining power. In the digital economy era, the most important characteristic is the addition of data as a new factor in the production process. The non-rivalry and strong positive externalities of data, along with the production automation and intelligence driven by data-enabled technologies such as artificial intelligence and cloud computing, will reshape productivity and production relations. The three models proposed in this article can respectively characterize the feasible set of production technologies, the set of production tasks, and the bargaining power of factors driven by data, providing micro-level modeling approaches for research on production theory in the digital economy era.</div></div>","PeriodicalId":100773,"journal":{"name":"Journal of Digital Economy","volume":"5 ","pages":"Pages 87-98"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147538722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Government officials as social media influencers: Appeal and effects on destination eWOM","authors":"Panpan Sun,&nbsp;Xuefei Ji,&nbsp;Hui Jin,&nbsp;Xiaoying Li","doi":"10.1016/j.jdec.2026.05.003","DOIUrl":"10.1016/j.jdec.2026.05.003","url":null,"abstract":"<div><div>Government officials increasingly function as social media influencers in destination promotion, yet their effectiveness remains unmeasured. Drawing on source effectiveness research, this study develops and validates a framework for assessing government officials as institutional social media influencers in digital tourism marketing. Through thematic analysis of 39,481 online comments, we identified four appeal dimensions: personal, identity, professional, and interpersonal. Scale development and validation across 722 respondents confirmed the measurement structure. Structural equation modeling with 723 responses tested how these appeal dimensions influence electronic word-of-mouth (eWOM) through parasocial interaction and visualized consumption experience. Results show that each appeal dimension directly affects eWOM and operates indirectly through sequential mediation: appeal enhances parasocial interaction, which strengthens visualized consumption experience, ultimately driving eWOM. This research provides the first validated instrument for measuring institutional influencer effectiveness and distinguishes government officials from commercial influencers through their formal authority and dual competency requirements in governance and tourism promotion.</div></div>","PeriodicalId":100773,"journal":{"name":"Journal of Digital Economy","volume":"5 ","pages":"Pages 226-242"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148523306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}