Journal of Innovation & Knowledge最新文献

{"title":"Innovation-driven sustainable development: Analysing the impact of digital technology on synergistic governance of pollution and carbon reduction","authors":"Yang Shen ,&nbsp;Xiuwu Zhang ,&nbsp;Xinzi Wang","doi":"10.1016/j.jik.2026.100978","DOIUrl":"10.1016/j.jik.2026.100978","url":null,"abstract":"<div><div>Air pollution and carbon emissions have a common source. Determining how to achieve coordinated emission reduction between the two is crucial for advancing sustainable development. Although digital technology (DT) is often regarded as an important tool for sustainable development, it remains uncertain whether it can simultaneously achieve dual benefits for the environment and climate. Based on a balanced panel data set of 269 cities in China from 2006 to 2023, this study empirically examined the impact of digital technology on the synergistic governance of pollution and carbon reduction (SGPCR) using dual machine learning. The results show that DT has a significant positive impact on SGPCR. This positive effect is nonlinear, starting slowly and then accelerating. From the perspectives of DT applications, big data analysis, intelligent manufacturing, the Internet of Things, artificial intelligence, and blockchain technology, these technologies contribute to enhancing co-benefits in the economy and society. The results of the mechanism analysis show that the positive impact of digital technology on collaborative emission reduction is achieved through channels such as promoting green technological innovation, enhancing energy efficiency, and driving the agglomeration of digital industries. The results of heterogeneity tests based on different standards show that the positive role of DT is more pronounced in non-resource-based cities, regions with a higher intensity of ecological protection and civilisation construction, and cities with more complete digital infrastructure.</div></div>","PeriodicalId":46792,"journal":{"name":"Journal of Innovation & Knowledge","volume":"14 ","pages":"Article 100978"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The knowledge of ethical AI decision-making: A behavioral economics perspective","authors":"Rita Bužinskienė ,&nbsp;Astrida Miceikienė ,&nbsp;Álvaro Hernández-Tamurejo ,&nbsp;José Ramón Saura","doi":"10.1016/j.jik.2026.100967","DOIUrl":"10.1016/j.jik.2026.100967","url":null,"abstract":"<div><div>Focusing on artificial intelligence (AI) ethics and behavioral economics, this paper demonstrates how integrating behavioral insights can inform ethical guidelines for AI-based systems. Recent developments conceptualize AI as a key to critical sectors such as finance, marketing, and healthcare, thereby prompting a widespread recognition of its potential to reinforce or mitigate societal biases. However, while AI ethics has emphasized technical concerns such as data fairness and algorithmic transparency, insights from behavioral economics remain largely untapped. To bridge this gap in the literature, the present study uses a three-stage methodology. First, a systematic literature review (SLR) is conducted to identify gaps in AI ethics frameworks and to establish how key behavioral economic principles can enhance transparent decision making. Second, in-depth interviews are performed to collect additional insights and to compare how the results compare with those obtained from the SLR. Third, a topic modeling-based model, latent Dirichlet allocation (LDA), supported by the Computer-Assisted Text Analysis (CATA) framework, is applied to the collected interview corpus in order to identify the main topics in the data. The results reveal that ethical AI is increasingly understood by professionals not only as a technical issue but also as a behavioral and organizational one. Specifically, we identify five main topics from the interview content, namely Data &amp; transparency, Behavioral insights &amp; design, Legal/ethical frameworks, Implementation challenges &amp; infrastructure, and Civic/stakeholder engagement. The first three of these topics dominate in the current discourse on AI decision making. The results also reveal that cognitive biases, such as anchoring and confirmation bias, affect the data that feed AI algorithms and user interaction with AI-driven outputs. If these biases remain unaddressed, they can amplify ethical risks. However, awareness of such patterns can inform practical design and governance strategies so as to improve transparency, fairness, and user engagement. Finally, based on the findings, four research proposals are advanced to clarify how behavioral economics can be systematically integrated into ethical AI decision making.</div></div>","PeriodicalId":46792,"journal":{"name":"Journal of Innovation & Knowledge","volume":"14 ","pages":"Article 100967"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consumer intentions to purchase wines with Blockchain and quick-response code tracking: Insights from an extended stimulus–organism–response model","authors":"Ángel Peiró-Signes ,&nbsp;Nino Adamashvili ,&nbsp;Magdaléna Tupá ,&nbsp;Antonino Galati","doi":"10.1016/j.jik.2026.100944","DOIUrl":"10.1016/j.jik.2026.100944","url":null,"abstract":"<div><div>Consumers’ demand for transparency, authenticity and safety in food and beverage products continues to rise. Blockchain technology (BCT) and quick-response (QR) codes have emerged as powerful tools for enhancing product traceability. Understanding the psychological and behavioural mechanisms underlying consumers’ acceptance of such technologies is crucial for producers and policymakers alike. Therefore, this study explores the factors influencing consumers’ intention to purchase wines traceable through BCT and QR codes through an extended stimulus–organism–response model enriched with constructs from the theory of perceived risk. A survey was conducted in February 2025 with a sample representative of the Italian population (<em>N</em> = 1001). The collected data were analysed using a validated partial least squares structural equation modelling approach to examine how external stimuli (transparency and traceability, diagnosticity, subjective norms and facilitating conditions), individual perceptions (perceived value, trust and usefulness), emotional responses (hedonic perception), risk perception, and BCT knowledge shape consumers’ purchase intention. The results show that diagnosticity and transparency significantly enhance perceived value, which in turn strongly influences purchase intention. While knowledge of BCT positively affects hedonic perception, it has a limited effect on perceived usefulness or trust, highlighting that technological familiarity is not a dominant driver of technology adoption. However, hedonic perception emerges as the strongest predictor of purchase intention, highlighting the importance of designing engaging and enjoyable traceability experiences. Social and contextual factors, particularly subjective norms and facilitating conditions, significantly affect perceived usefulness, which in turn contributes to purchase intention. Perceived risk has a significant but relatively small effect on purchase intention. The model explains 68.1% of the variance in purchase intention, offering novel insights for technology developers, marketers and policymakers seeking to foster consumers’ acceptance of digital traceability in the wine sector.</div></div>","PeriodicalId":46792,"journal":{"name":"Journal of Innovation & Knowledge","volume":"13 ","pages":"Article 100944"},"PeriodicalIF":15.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corporate alignment to innovation and knowledge: detecting patterns in multidimensional impact across global industries","authors":"Alexandra Horobet ,&nbsp;Arindam Banerjee ,&nbsp;Ioana-Alexandra Radu ,&nbsp;Cosmin-Alin Botoroga","doi":"10.1016/j.jik.2025.100917","DOIUrl":"10.1016/j.jik.2025.100917","url":null,"abstract":"<div><div>This study examines the impact of a commitment to Sustainable Development Goal 9 (SDG 9) objectives by exploring sectoral and regional disparities in how companies aligned with this goal leverage digital technologies and knowledge, foster innovation ecosystems, and deliver measurable contributions to sustainable infrastructure development. Using the SDG-aligned revenue share as the main metric for SDGs commitment across 5,323 global companies provided by the Upright platform, we employed a machine-learning-based k-means clustering algorithm to detect patterns of net impacts created across the Society, Knowledge, Health, and Environment (SKHE) dimensions. We also uncovered sectoral and geographical patterns of the companies investigated. Our findings show that alignment with SDG 9 (Industry, Knowledge, and Innovation) is associated with corporate commitment to SKHE dimensions, as well as sectoral and, to some extent, geographical scope. The results offer practical implications for investors, policymakers designing regulations and guidelines to improve sustainability disclosure, and company executives who are developing sustainability strategies.</div></div>","PeriodicalId":46792,"journal":{"name":"Journal of Innovation & Knowledge","volume":"13 ","pages":"Article 100917"},"PeriodicalIF":15.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Are net zero emissions a priority for able CEOs?","authors":"Isabel-María García-Sánchez","doi":"10.1016/j.jik.2026.100945","DOIUrl":"10.1016/j.jik.2026.100945","url":null,"abstract":"<div><div>It is vital for society to understand the climate ambitions of corporate leaders, as the commitments and actions they promote to address global warming can help create a more sustainable future for all. Furthermore, the endeavour to attain net zero will engender an irrevocable transformation of business as we know it. In this paper, we assess how companies respond to climate change, using a score that measures their strategic and operational efforts to achieve net zero emissions. For a balanced data panel of 5047 international companies in the period 2015–2022, our evidence shows that firms led by able CEOs present an upper level of implementation and progress with their climate action plan, better anticipating global challenges. In the case of less able CEOs, their interest arises as a response to solutions proposed for economic recovery or new needs in terms of energy management, derived from the disruptive events in the period 2020–2022, or appears in a company with stronger governance mechanisms that limit the CEO’s discretion.</div></div>","PeriodicalId":46792,"journal":{"name":"Journal of Innovation & Knowledge","volume":"13 ","pages":"Article 100945"},"PeriodicalIF":15.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Environmental, organizational, and individual determinants of AI adoption: A multilevel knowledge and analysis","authors":"Flávio Tiago ,&nbsp;António Almeida","doi":"10.1016/j.jik.2025.100934","DOIUrl":"10.1016/j.jik.2025.100934","url":null,"abstract":"<div><div>This study examined the determinants of artificial intelligence (AI) adoption in small- and medium-sized enterprises (SMEs) through a comprehensive dual-methodology approach. Drawing on the <strong>C</strong>ountry-<strong>H</strong>uman resources-<strong>A</strong>doption/technological-<strong>IN</strong>dividual <strong>AI</strong> adoption (CHAIN-AI) framework, we integrated different theoretical currents, namely, the Technology-Organization-Environment (TOE) framework, the Technology Acceptance Model (TAM), and Regret Theory (RT), to analyze adoption patterns across multiple levels. Our research methodology encompassed two distinct phases. First, we conducted a macro-level analysis using Eurobarometer (2023) survey data to examine AI adoption trends across European SMEs. Second, we administered an online survey to 186 digital marketing professionals to investigate micro-level adoption behaviors and attitudes. Study 1 revealed significant correlations between AI adoption rates and organizational characteristics, including firm size, sector, and geographic location. Larger and service-oriented firms demonstrated higher adoption propensities. Macroeconomic indicators such as gross domestic product (GDP) and innovation capacity were positively correlated with adoption rates, whereas cultural dimensions, particularly long-term orientation (LTO), exhibited negative correlations. Study 2 illustrated the psychological and organizational mechanisms underlying AI adoption, with perceived usefulness (PU) emerging as the primary predictor of behavioral intention. In contrast, perceived ease of use (PEOU) showed non-significant effects on adoption outcomes. Post-adoption regret (R) negatively influenced both attitudes and behavioral intentions toward AI implementation. This study contributes to the literature on technology adoption by providing an empirically grounded framework for understanding the dynamics of AI adoption in SMEs. This model offers evidence-based recommendations for policy development and organizational implementation strategies.</div></div>","PeriodicalId":46792,"journal":{"name":"Journal of Innovation & Knowledge","volume":"13 ","pages":"Article 100934"},"PeriodicalIF":15.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-driven HRM digitization in effect: Mechanisms, conditions, and employee outcomes","authors":"Ahsan Ali ,&nbsp;Dariusz Cichoń ,&nbsp;Asad Abbas","doi":"10.1016/j.jik.2025.100919","DOIUrl":"10.1016/j.jik.2025.100919","url":null,"abstract":"<div><div>Artificial intelligence (AI) is increasingly used in contemporary organizations as a tool for digitizing systems and reconfiguring key functions such as human resource management (HRM). However, not all employees exposed to AI-driven HRM digitization benefit equally in their capacity to generate innovative outcomes. Drawing on self-determination theory and integrating perspectives from organizational learning, this study advances a moderated mediation model that explains how and when AI-enabled HRM digitization fosters innovation. We propose that, while AI-driven HRM practices can stimulate the metacognitive strategies of employees, the extent to which these strategies translate into enhanced innovation depends critically on their tacit knowledge awareness. In this proposal, we highlight the paradoxical nature of algorithmic systems: they may serve as autonomy-supportive tools that encourage exploration and self-directed learning or as autonomy-controlling mechanisms that intensify monitoring and reduce intrinsic motivation. Using a multi-wave, multi-source field study of Chinese employees and supervisors (<em>N</em> = 347), we found that AI-driven HRM positively predicts innovation through metacognitive strategies, but only when employees demonstrate high levels of tacit knowledge awareness. When awareness is low, AI-HRM digitization fails to enhance innovation, which reveals the limitations of digital HRM effectiveness. These findings underscore that tacit knowledge awareness fundamentally conditions how AI-driven HRM digitization supports or undermines innovation, which advances theory by extending self-determination perspectives to technology-mediated HRM. Thus, this study discusses the theoretical and practical implications for designing AI-enabled HRM systems that balance algorithmic control with employee autonomy.</div></div>","PeriodicalId":46792,"journal":{"name":"Journal of Innovation & Knowledge","volume":"13 ","pages":"Article 100919"},"PeriodicalIF":15.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI adoption in HR: AI trust in digitally lagging organisations","authors":"Alessia Zoppelletto ,&nbsp;Ludovico Bullini Orlandi ,&nbsp;Eleonora Veglianti ,&nbsp;Cecilia Rossignoli","doi":"10.1016/j.jik.2025.100912","DOIUrl":"10.1016/j.jik.2025.100912","url":null,"abstract":"<div><div>Artificial intelligence (AI) holds transformative potential for human resources (HR), yet its adoption remains limited, particularly within the broader context of digital transformation (DT). Although trust is widely recognised as a critical enabler of AI adoption, little is known about the organisational conditions under which this trust develops, especially in firms with low digital maturity. This study investigates how configurations of organisational factors, namely technology, digital skills, culture supporting DT and HR’s involvement in DT initiatives, can shape trust in AI adoption within HR practices. The methodological approach followed two steps: (1) the questionnaire design was validated through insights derived from interviews conducted during a case study analysis, and (2) a fuzzy-set qualitative comparative analysis using survey data was employed. The findings contribute to shedding light on trust enablers and hindering factors that positively influence AI adoption in HR practices. Results show that trust can emerge even in digitally less mature firms when HR functions are strategically involved in broader DT initiatives. Conversely, HR digital skills and functional involvement together with cultural readiness can foster trust independently without top-down managerial involvement. These findings challenge conventional assumptions that digital maturity and leadership engagement are conditions for fostering trust in the adoption of new technologies. By uncovering multiple pathways to trust, this study contributes theoretically by framing trust as a configurational, organisational-level outcome. This work aims to advance the discourse on technological innovation in HR, providing valuable insights for practitioners and scholars to support digitally lagging organisations navigating the challenges of AI adoption.</div></div>","PeriodicalId":46792,"journal":{"name":"Journal of Innovation & Knowledge","volume":"13 ","pages":"Article 100912"},"PeriodicalIF":15.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From fragmentation to interoperability: How semantic models transform environmental, social, governance (ESG) reporting, knowledge, and sustainability governance","authors":"Annas Vijaya ,&nbsp;Faris Dzaudan Qadri ,&nbsp;Linda Salma Angreani ,&nbsp;Hendro Wicaksono","doi":"10.1016/j.jik.2025.100924","DOIUrl":"10.1016/j.jik.2025.100924","url":null,"abstract":"<div><div>Environmental, social, and governance (ESG) reporting faces persistent challenges, including fragmented standards, inconsistent metrics, misalignment with global sustainability goals, and limited stakeholder usability. Numerous studies prove that ontology-based solutions can address several challenges that occur during ESG reporting activities. Although semantic technologies offer valuable benefits for ESG reporting, their utilization in this field remains constrained. Most ontology-based solutions remain in developmental stages, and they are not broadly utilized since organizations lack an understanding of how these tools would help address their reporting problems. This study performs a systematic literature review (SLR) that investigates 19 peer-reviewed studies obtained from Scopus and Web of Science under Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) standards. The SLR identifies critical gaps: (1) existing ontology-driven solutions can address key problems in current ESG reporting; (2) quantitative evaluation methods are rarely integrated with semantic tools, limiting actionable insights; and (3) alignment with evolving standards like the Sustainable Development Goals (SDGs) remains superficial. Based on the SLR insights, this research develops a novel framework through SLR findings by combining ontology-driven methods with quantitative assessment techniques. The framework achieves standardization of various reporting standards through an ESG ontology system that maps essential concepts to build an extensive taxonomy. SDG targets become mutually compatible through established SDG ontologies to allow businesses to measure their activities against international sustainability goals. Fuzzy Multi-Criteria Decision-Making (MCDM) techniques used in combination with an ESG maturity model create quantitative measures to assess ESG performance. The method produces measurable performance indicators that are supported by clear semantic links that allow valid benchmark assessments combined with better data unification and improved decision-making capabilities. The research creates operational frameworks that enable ESG information interoperability, which advance sustainability governance innovation and guide ESG ontology transformations.</div></div>","PeriodicalId":46792,"journal":{"name":"Journal of Innovation & Knowledge","volume":"13 ","pages":"Article 100924"},"PeriodicalIF":15.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Review and comparison of the current and desired status of the organization's infrastructure for implementing knowledge management strategies","authors":"Reza Rostamzadeh ,&nbsp;Taher Najari ,&nbsp;Dalia Streimikienė ,&nbsp;Hero Isavi","doi":"10.1016/j.jik.2025.100923","DOIUrl":"10.1016/j.jik.2025.100923","url":null,"abstract":"<div><div>Revised: Given the increasing emphasis on knowledge management (KM) in academic circles and the growing body of research literature in this field, the researcher conducted a meta-analysis using the Scopus database. The review focused on data extracted between 1996 and 2024 and included functional, graphical, and content analyses. An initial search of Scopus retrieved 2495 scientific documents. The researcher then filtered the results using the database tools for publication year, document type, Scopus classification, and language. After preprocessing, 175 scientific documents remained for inclusion in the meta-synthesis. The researcher performed data visualization and analysis in RStudio, VOSviewer, and MAXQDA software. The main contributions of this research are as follows: (1) identifying the most influential authors, countries, journals, universities, and articles within the field; (2) mapping the dimensions of the field and identifying keyword gaps through content analysis; and (3) conducting thematic analysis and pinpointing central components.</div></div>","PeriodicalId":46792,"journal":{"name":"Journal of Innovation & Knowledge","volume":"13 ","pages":"Article 100923"},"PeriodicalIF":15.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}