With the rapid growth in mobile app usage, users face increasing privacy concerns when responding to app permission requests for sensitive resources such as location, camera, and contacts. This study investigates the impact of past privacy experience, technology anxiety, perceived privacy control, and app permission concerns on mobile users' information privacy concerns, and their subsequent effect on permission request acceptance. Grounded in the APCO macro model and extended with app permission concerns as a novel antecedent, the research analyzed survey data from 652 participants using PLS-SEM. Results confirm all four antecedents significantly influence privacy concerns, with app permission concerns emerging as the strongest predictor. Privacy concerns, in turn, negatively affect users' intention to accept permission requests. These findings highlight the critical role of permission request design in shaping user trust and offer actionable insights for developers and policymakers seeking to build more transparent and user-centered mobile experiences.
Cybersecurity risks affect an organization's data and intangible assets, while intellectual property (IP) laws promote innovation. This study examines how corporate governance, digital transformation, and IP regulations relate to cybersecurity risk. It investigates whether stronger IP protection increases enterprise cybersecurity risk and whether network governance mediates this relationship under varying institutional enforcement. Using a two-way fixed effects regression with instrumental variables on panel data from 950 publicly listed non-financial firms across 18 countries (2012–2022; 8,547 firm-year observations), the study draws on multiple global datasets. Results show a significant positive link between IP protection intensity and cybersecurity risk (β=0.127, p < 0.01). Network governance partially mediates this relationship, reducing its impact (indirect effect=0.031). Strong institutional enforcement weakens the IP–risk link (β=-0.112, p< 0.05). The study extends appropriability theory by highlighting cybersecurity externalities of IP protection.
The humanoid robotics market is projected to grow from USD 2.1 billion (2023) to USD 38 billion (2035). Against this backdrop, whether robotics adoption translates into efficient corporate investment rather than wasted capital has become a pressing concern. Existing literature has not examined the moderating role of corporate governance in Chinese listed firms. Using a panel of 412 A-share firms (2015–2022), this study constructs a humanoid robotics adoption (HRA) measure and a composite corporate governance index (CGI). Employing two-way fixed effects models, the authors find that HRA is significantly negatively correlated with investment inefficiency (β = -0.312); CGI positively moderates this relationship (interaction β = -0.148). Subsample analysis shows that non-state-owned firms with board independence gain the most. The findings extend agency theory to intelligent manufacturing, provide a validated text-based adoption proxy, and offer direct policy implications for China's 15th Five-Year Plan.
In the context of accelerating digital transformation and the imperative to expand domestic demand, this study examines how digital literacy influences household consumption inequality. Using nationally representative panel data from the China Family Panel Studies (CFPS) from 2014 to 2022, the authors construct a multidimensional digital literacy index. The empirical results show that digital literacy significantly reduces consumption inequality. Mechanism analysis reveals that this effect operates through reducing income inequality, improving employment quality, and lowering barriers to online consumption. Heterogeneity analysis indicates that the effect is more pronounced among households without computer use at work and those with longer internet usage duration. Further results show stronger equalizing effects for low- and middle-consumption groups and for developmental and hedonic consumption. The findings highlight digital literacy’s role in promoting inclusive consumption and reducing inequality in the digital economy.
The rapid global proliferation of AI has created an “AI paradox” where technical adoption fails to yield superior strategic outcomes. Grounded in Organizational Information Processing Theory (OIPT), this study investigates the human-centric “bridge” between capability and decision quality. The authors frame AI as an information-processing capacity and Algorithmic Trust as the essential cognitive processor. Using a sample of 365 managers in China—a global digital laboratory—they employed PLS-SEM to test a moderated-mediation model. Results show that AI Technical Competence (AITC) significantly predicts Algorithmic Trust, which fully mediates the link to Strategic Decision Quality. Crucially, Task Complexity exerts a “dampening effect,” weakening the impact of trust on quality in hyper-complex scenarios. This research contributes to JGIM by shifting focus from “what” AI can do to “how” managers trust it. Practitioners are urged to move toward hybrid-sequential workflows rather than full delegation to navigate the complexities of the global digital economy.
This study develops a digital information disclosure index that combines co-occurrence embeddings with contextual representations, trained on corporate reports and policy texts. Using a sample of over 31,000 firm-year observations from Chinese A-share firms (2013–2023), the study shows that this index significantly enhances the explanation of green patenting outcomes, outperforming traditional models. The analysis indicates that operational disclosures promote green innovation, while transactional disclosures have a negative impact. Additionally, the index helps foster innovation by alleviating financing constraints and increasing green human capital. Robustness checks, including firm fixed effects, Poisson models, and historical instrumental variables, confirm the causal relationship between digital information disclosure and green innovation outcomes. The study provides a replicable framework for measuring digital disclosures and offers insights for policymakers and managers on improving capital allocation and driving sustainable innovation.
Despite increasing interest, research on misinformation sharing in social networks (SN) remains fragmented. This study addresses three gaps: the dominance of technical detection approaches over behavioral analysis, the focus on attitudinal antecedents while neglecting outcomes such as boycott participation, and the limited attention to religiosity beyond Hofstede model. Integrating ELM, Social Influence Theory, and Psychological Reactance Theory, the study proposes a model linking religiosity, perceived injustice, perceived victimization, and argument quality to misinformation sharing and boycott behavior, with perceived influence on others (PIOO) as a mediator. Survey data from 613 heavy SN users reveal that PIOO plays a pivotal mediating role. Perceived injustice and perceived victimization strongly predict purposeful misinformation sharing and boycott intentions, outweighing central-route factors such as argument quality. The findings offer theoretical and policy-relevant insights into misinformation sharing.
Sustainable digital platforms rely on people’s willingness to share knowledge, make contributions over time, and take part in collective forms of value creation. Yet the literature still offers a fragmented account of why actors participate in these platforms and what keeps them engaged. This research reviewed 130 articles on sustainable, community-based, and agricultural knowledge platforms to examine how participation has been explained at the micro level. The review showed that existing studies tend to emphasize motivation, technology adoption, and value creation while paying less attention to recognition, cultural legitimacy, and the social conditions under which knowledge is contributed. Building on these findings, this study developed a multi-stage framework that links incentive architecture, recognition, cultural legitimacy, participation intention, and perceived value co-creation.
Against the backdrop of digitalization and green development, the sharing economy (SE) has become an important business model for manufacturing firms to long-term sustainable innovation. Based on resource orchestration theory and institutional theory, this study takes Chinese A-share-listed manufacturing firms from 2012 to 2021 as the research sample, empirically examines the impact of the sharing economy models (SEMs) on corporate sustainable green innovation (SGI), as well as the underlying mechanisms and boundary conditions. The results show that SEMs significantly promotes SGI, and resource allocation efficiency and servitization play partial mediating roles in this relationship. Furthermore, media attention and ESG ratings positively moderate the abovementioned relationship, whereas the perception of economic policy uncertainty has no significant moderating effect. This study expands the research scope of SEMs and green innovation and offers a practical guide for manufacturing firms to promote digital transformation and sustainable green development.
As generative AI becomes more integrated into organizational workflow, comprehending the mechanisms that promote its continued utilization by employees has become essential for management. This study investigates the factors influencing organizational members’ usage behaviors of generative AI in the workplace, incorporating perceived security and perceived risk into the expectation–confirmation model. Moreover, this study identifies the moderating effects of premium subscription on employees’ continuance decision-making. The theoretical framework was evaluated using data from 193 organizational members. The results reveal that perceived security exhibits no significant direct effects on satisfaction or continuance intention. Perceived risk negatively impacts generative AI use, user satisfaction, perceived usefulness, and perceived security, yet does not directly influence continuance intention. Premium subscription significantly enhances the relationship between continuance intention and generative AI use, while mitigating the adverse effect of perceived risk on generative AI use.
The legally binding international climate accord played a critical role in shaping global climate governance. This study evaluates its effectiveness in promoting clean energy transition using a panel difference-in-differences (DID) approach based on cross-national data. Results indicate that the Kyoto Protocol significantly increased the share of clean energy by approximately three percentage points in Annex B countries relative to non-Annex B nations. Robustness checks, including parallel trend tests, placebo tests, and propensity score matching DID, confirm the reliability of these findings. The Protocol influenced the adoption of clean energy through three key channels including adjustment of electricity generation structure, enhancement of energy efficiency, and substitution of fossil fuel. These findings highlight the critical role of international agreements in aligning national energy policies with climate goals, offering actionable insights for designing future frameworks like the Paris Agreement to ensure equitable and technology-inclusive climate action.
Remote work and digital labor have shifted from a niche phenomenon concentrated in a few technology sectors to a central feature of the global economy, yet knowledge about this evolution remains fragmented. Drawing on a corpus of 4,485 peer-reviewed articles, this study synthesizes about 50 years of academic research on remote work and digital labor. The authors first analyze the academic interest and then apply Structural Topic Modeling (STM) to identify 12 dominant topics that represent global HRM practices. The topics were mapped to create a framework that extends Job Demands-Resources theory, linking job demands, resources, and outcomes in remote work settings. Finally, they trace the progressive evolution of themes to highlight emerging research questions and actionable managerial implications.
This longitudinal study examines information technology management issues that senior IT executives have indicated to be the most critical to their organizations over a four-year period. Through association rule mining, it provides a comprehensive view of issues that persist and vary together over time. Findings provide insight into better leverage resources in managing these issues holistically. For example, there appears to be a stronger synergy among issues in three key areas of IT focus (transformation, strategy, value) than previously understood. The ability to leverage this synergy could lead to even greater ability for organizations to leverage and capitalize on IT for success.
Based on the quasi-natural experiment of the 2016 “Internet+Government Services” reform pilot, this paper examines the impact of government digital governance on corporate social responsibility (CSR). The authors find that government digital governance significantly improves both the quality of CSR information disclosure and the level of substantive CSR performance. The results remain robust after a series of tests. Mechanism analysis reveals that government digital governance operates through two main channels: enhancing information transparency and optimizing internal governance. Heterogeneity analysis further shows that the positive effect is more pronounced among firms in heavily polluting industries, firms with higher ownership concentration, and firms receiving greater media attention. This study provides micro-level evidence on the institutional effects of digital governance and offers implications for enhancing government regulation.
Academics and practitioners are becoming more interested in investigating emerging technology capabilities (ETC) to create economic value. This empirical study, based on resource-based enterprise theory, investigates the relationship between ETC, digital strategy (DS), and business model innovation (BMI) in order to improve organizational agility (OA). The study creates an instrument to quantify enterprises' ETC and discovers that a greater ETC is connected with increased DS and BMI, resulting in more substantial OA in both emerging and developed economies. Furthermore, post-hoc analysis reveals that Spanish enterprises with competitive strategies emphasizing differentiation, financial, services, and government sectors, as well as medium to high maturity age and large size, have more significant ETC effects on DS and BMI for enhancing OA than Brazilian enterprises with low-cost strategies, agribusiness, manufacturing, and commerce focus, as well as younger age and smaller size.
Digital retail management increasingly relies on business intelligence systems to support data-driven decision-making; however, existing approaches often treat predictive analytics and decision optimization as separate processes and struggle to effectively integrate heterogeneous retail data with large-model semantic reasoning. These limitations restrict the ability of current systems to generate accurate and actionable decisions in complex retail environments. To address these challenges, this paper proposes RLBIS, a large model–driven business intelligence decision system for digital retail management that integrates large-model reasoning, decision-aware knowledge fusion, hybrid predictive intelligence, and strategy optimization into a unified framework. The proposed method transforms heterogeneous retail data into structured business representations and combines semantic reasoning with predictive modeling to support end-to-end decision optimization.
This study develops a multi-level routine dynamics framework explaining how service organizations manage paradoxical tensions arising from artificial intelligence (AI) adoption. AI enhances efficiency and innovation but also creates conflicting demands—such as control versus flexibility and automation versus learning—that challenge conventional change management. Integrating paradox theory, routine dynamics, and dynamic capabilities, the framework identifies four AI-related paradoxes—performing, organizing, learning, and belonging—and links them to a hierarchical system of routines. It illustrates how paradox navigation recurs across individual, routine, and organizational levels through the interplay of ostensive and performative aspects. The study also introduces a paradox-responsive scorecard as a decision-support tool for assessing how effectively organizations handle competing AI outcomes. Overall, it offers a systematic understanding of how service organizations can navigate paradoxes and strengthen decision-making during AI implementation.
Generative AI (GAI) is driving the next wave of technological transformation in the business sector, and many companies have begun using it across various business functions. It is now mandatory for employees to use GAI to perform various tasks and meet job performance requirements. However, the understanding of how GAI is helping to improve their job performance remains unexplored. This empirical research examines the factors influencing employees' self-efficacy and its contribution to their job performance by validating a model grounded in social cognitive theory, including cognitive aspects of employees and GAI attributes. A survey of 405 employees and their line managers was conducted, and the data were analyzed using PLS-SEM. The results indicate that self-directed learning, GAI literacy, perceived intelligence, and perceived personalization positively influence employees' self-efficacy, while technological anxiety negatively influences the use of GAI. Further, it was found that self-efficacy has a positive association with employee’s job performance, and this relationship is negatively moderated by hallucination potential. This study enhances understanding of GAI use at the workplace and human-GAI dynamics in relation to employee job performance. This research provides insights for top management, organizational managers, and GAI marketers, developers, and designers.
This study examines how platform-based digitalization capability orchestrates customer knowledge to enhance value co-creation intensity in China’s digital retail sector. Drawing on Service-Dominant Logic and the Knowledge-Based View, a quantitative cross-sectional survey was conducted with 307 active platform users across five Chinese provinces. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the results demonstrate that platform capability exerts a strong direct effect on value co-creation and an even stronger total effect when mediated by customer knowledge processes. Both depth and diversity of customer knowledge significantly promote value co-creation; however, knowledge diversity emerges as the dominant driver. Platform trust plays a critical dual role by directly enabling knowledge sharing and by positively moderating the effects of platform capability on knowledge development, particularly knowledge diversity. The model explains 76.8% of the variance in value co-creation intensity, indicating strong explanatory and predictive power.
This study has constructed a dataset at the street-town level, utilizing geospatial information and comprehensive data from the restaurant industry. Employing double machine learning (DML), the authors empirically examine the impact of road transportation development on consumption vitality at the township level within the Yangtze River Delta region of China. The findings analysis reveals that road network density, connectivity, and structure significantly enhance consumption vitality, with heterogeneous effects across road types and geographic contexts. The analysis demonstrates that enhancing road transportation infrastructure not only facilitates new manufacturing firms’ entry but also promotes tourism development and reduces environmental pollution, thereby collectively boosting local consumption vitality. It is recommended that urban planners and policymakers focus on enhancing road connectivity and structure to stimulate economic activities and boost consumption at the township level.
