Pub Date : 2026-01-27DOI: 10.1016/j.jjimei.2026.100389
Anusha Mini Selvan , Sahayaselvi Susainathan , Hesil Jerda George , Satyanarayana Parayitam , Sabiha Fazalbhoy , Shamshad Ahamed Shaik
This study aims to unfold relationships between green innovation (GI), green entrepreneurial behavior (GEB), and sustainable business performance (SBP). A conceptual model was developed by incorporating AI adoption, creativity, and curiosity as antecedents to green innovation (GI) by entrepreneurs. Further, the relationship between GI, green entrepreneurial intention (GEI), GEB, SBP. In addition to direct effects, AI adoption as a moderator between curiosity, GI, GEI, and between GEB and SBP. To test these hypothesized relationships, data were collected from 550 entrepreneurs from eleven districts in the Southern part of India (Tamil Nadu) was analyzed. After checking the measurement model, the structural model was assessed with partial least squares – structural equation modeling [PLS-SEM]. The results indicated (i) positive impact of AI adoption, creativity and curiosity on GI, and (ii) AI adoption and GI as significant predictors of GEI. Further, the results supported the positive influence of GEI on GEB, which in turn significantly influened SBP. Findings reveal that AI adoption strengthened the relationship between (i) creativity and GI, (ii) GI and GEI, and (iii) GEB and SBP. This study exends the theory of planned behavior (TPB) by adding AI adoption and green innovation as predictors of green intention. Most importantly, this study illustrates how ecological concerns transform and shape their traditional entrepreneurial intentions and behaviors in the context of sustainable development. Further, the findings supported integrating AI adoption with GEB for sustainable business growth and make significant contribution to the literature. This study provides detailed insights for policymakers, local governments, and entrepreneurs interested in promoting sustainable business growth.
{"title":"The synergy between Artificial Intelligence adoption and green entrepreneurship for sustainable business growth","authors":"Anusha Mini Selvan , Sahayaselvi Susainathan , Hesil Jerda George , Satyanarayana Parayitam , Sabiha Fazalbhoy , Shamshad Ahamed Shaik","doi":"10.1016/j.jjimei.2026.100389","DOIUrl":"10.1016/j.jjimei.2026.100389","url":null,"abstract":"<div><div>This study aims to unfold relationships between green innovation (GI), green entrepreneurial behavior (GEB), and sustainable business performance (SBP). A conceptual model was developed by incorporating AI adoption, creativity, and curiosity as antecedents to green innovation (GI) by entrepreneurs. Further, the relationship between GI, green entrepreneurial intention (GEI), GEB, SBP. In addition to direct effects, AI adoption as a moderator between curiosity, GI, GEI, and between GEB and SBP. To test these hypothesized relationships, data were collected from 550 entrepreneurs from eleven districts in the Southern part of India (Tamil Nadu) was analyzed. After checking the measurement model, the structural model was assessed with partial least squares – structural equation modeling [PLS-SEM]. The results indicated (i) positive impact of AI adoption, creativity and curiosity on GI, and (ii) AI adoption and GI as significant predictors of GEI. Further, the results supported the positive influence of GEI on GEB, which in turn significantly influened SBP. Findings reveal that AI adoption strengthened the relationship between (i) creativity and GI, (ii) GI and GEI, and (iii) GEB and SBP. This study exends the theory of planned behavior (TPB) by adding AI adoption and green innovation as predictors of green intention. Most importantly, this study illustrates how ecological concerns transform and shape their traditional entrepreneurial intentions and behaviors in the context of sustainable development. Further, the findings supported integrating AI adoption with GEB for sustainable business growth and make significant contribution to the literature. This study provides detailed insights for policymakers, local governments, and entrepreneurs interested in promoting sustainable business growth.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"6 1","pages":"Article 100389"},"PeriodicalIF":0.0,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077529","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}
Pub Date : 2026-01-23DOI: 10.1016/j.jjimei.2026.100388
Nutt Jaturat, Khahan Na-Nan, Bowei Hu
As artificial intelligence (AI) continues to transform industries, ensuring AI responsibility has become critical for ethical governance. Despite the growing number of frameworks emphasizing transparency, accountability, and sustainability, a standardized measurement tool remains lacking. This study develops and validates a seven-dimensional AI Responsibility framework encompassing Privacy and Security, Transparency and Accountability, Impact on Employment, Sustainability, User-Centered Design, Social Impact, and Innovation and Adaptation. Using Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA), the study confirms the framework’s construct validity and reliability. The results indicate strong model fit, with all constructs exceeding recommended thresholds for composite reliability (CR) and average variance extracted (AVE). The study contributes to AI ethics research by offering an empirically validated measurement instrument. Practically, the framework serves as a benchmarking tool for organizations and policymakers to assess AI governance strategies and regulatory compliance. As AI adoption continues to expand, this framework provides a structured approach to fostering trust, accountability, and responsible AI deployment.
{"title":"Measuring AI responsibility: A cross-country validation of a multidimensional framework","authors":"Nutt Jaturat, Khahan Na-Nan, Bowei Hu","doi":"10.1016/j.jjimei.2026.100388","DOIUrl":"10.1016/j.jjimei.2026.100388","url":null,"abstract":"<div><div>As artificial intelligence (AI) continues to transform industries, ensuring AI responsibility has become critical for ethical governance. Despite the growing number of frameworks emphasizing transparency, accountability, and sustainability, a standardized measurement tool remains lacking. This study develops and validates a seven-dimensional AI Responsibility framework encompassing Privacy and Security, Transparency and Accountability, Impact on Employment, Sustainability, User-Centered Design, Social Impact, and Innovation and Adaptation. Using Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA), the study confirms the framework’s construct validity and reliability. The results indicate strong model fit, with all constructs exceeding recommended thresholds for composite reliability (CR) and average variance extracted (AVE). The study contributes to AI ethics research by offering an empirically validated measurement instrument. Practically, the framework serves as a benchmarking tool for organizations and policymakers to assess AI governance strategies and regulatory compliance. As AI adoption continues to expand, this framework provides a structured approach to fostering trust, accountability, and responsible AI deployment.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"6 1","pages":"Article 100388"},"PeriodicalIF":0.0,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026131","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}
Pub Date : 2026-01-14DOI: 10.1016/j.jjimei.2025.100386
Karuturi Soumya , Karuturi Sai Sharat , Nune Sreenivas
The need for timely monitoring and management of the authenticity of academic certificates is increasing owing to the easy availability of computational facilities and network connectivity. This study is aimed to develop a means to verify the authenticity of an academic certificate data, such as; sender, receiver and digital signatures, using a blockchain ecosystem. Globally, the number of tertiary education students and graduates in various universities are constantly increasing every year; therefore the need for easy Web – based verification of degree certificates is also generating new business opportunities worldwide. Proof of certification of academic documents by easy means at low cost is not only necessary for the students but is equally required by the employers for a quick and trustworthy verification of the academic documents of the applicants during the recruitment process globally. Thus, a two-server-based framework is designed to support the multiple devices to train a local model using local data, and the gradients of the local model can be later sent to a central server that aggregates them to create a global model. Extensive experimental results confirm that BP can achieve efficient collusion resistance and verifiability of academic certificates results with a straightforward solution that demands the exploration of plausible business models.
{"title":"Academic certificate fraud detection by Web-based intelligent access control system: An effective role of Blockchain technology","authors":"Karuturi Soumya , Karuturi Sai Sharat , Nune Sreenivas","doi":"10.1016/j.jjimei.2025.100386","DOIUrl":"10.1016/j.jjimei.2025.100386","url":null,"abstract":"<div><div>The need for timely monitoring and management of the authenticity of academic certificates is increasing owing to the easy availability of computational facilities and network connectivity. This study is aimed to develop a means to verify the authenticity of an academic certificate data, such as; sender, receiver and digital signatures, using a blockchain ecosystem. Globally, the number of tertiary education students and graduates in various universities are constantly increasing every year; therefore the need for easy Web – based verification of degree certificates is also generating new business opportunities worldwide. Proof of certification of academic documents by easy means at low cost is not only necessary for the students but is equally required by the employers for a quick and trustworthy verification of the academic documents of the applicants during the recruitment process globally. Thus, a two-server-based framework is designed to support the multiple devices to train a local model using local data, and the gradients of the local model can be later sent to a central server that aggregates them to create a global model. Extensive experimental results confirm that BP can achieve efficient collusion resistance and verifiability of academic certificates results with a straightforward solution that demands the exploration of plausible business models.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"6 1","pages":"Article 100386"},"PeriodicalIF":0.0,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976557","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}
Pub Date : 2026-01-06DOI: 10.1016/j.jjimei.2025.100387
Julian Schwierzy , Robert Dehghan , Sebastian Schmidt , Nils Grashof , Hanna Hottenrott , Michael Woywode
Understanding the diffusion of emerging technologies is essential for capturing the benefits of innovation. Yet, traditional science, technology, and innovation (ST&I) indicators are often limited in measuring technology adoption. This study investigates the potential of analyzing corporate websites through web mining and machine learning to measure the adoption of additive manufacturing (AM) technologies. Furthermore, it examines how regional ST&I indicators — specifically patents and publications — shape AM adoption patterns. Despite still being niche, AM adoption in Germany doubled from 0.37% (2022) to 0.74% (2023) of firms. Regional web-based adoption hot spots largely align with patent and publication activity. In addition, our regression analyses reveal a positive and statistically significant relationship between these indicators and AM diffusion based on our AI-based web indicator. These results underline the potential of WebAI methods to complement traditional ST&I indicators.
{"title":"Mapping technology diffusion with AI: A web-based approach for tracking additive manufacturing adoption","authors":"Julian Schwierzy , Robert Dehghan , Sebastian Schmidt , Nils Grashof , Hanna Hottenrott , Michael Woywode","doi":"10.1016/j.jjimei.2025.100387","DOIUrl":"10.1016/j.jjimei.2025.100387","url":null,"abstract":"<div><div>Understanding the diffusion of emerging technologies is essential for capturing the benefits of innovation. Yet, traditional science, technology, and innovation (ST&I) indicators are often limited in measuring technology adoption. This study investigates the potential of analyzing corporate websites through web mining and machine learning to measure the adoption of additive manufacturing (AM) technologies. Furthermore, it examines how regional ST&I indicators — specifically patents and publications — shape AM adoption patterns. Despite still being niche, AM adoption in Germany doubled from 0.37% (2022) to 0.74% (2023) of firms. Regional web-based adoption hot spots largely align with patent and publication activity. In addition, our regression analyses reveal a positive and statistically significant relationship between these indicators and AM diffusion based on our AI-based web indicator. These results underline the potential of WebAI methods to complement traditional ST&I indicators.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"6 1","pages":"Article 100387"},"PeriodicalIF":0.0,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925286","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}
Pub Date : 2025-11-29DOI: 10.1016/j.jjimei.2025.100384
Ratna Juita , Dedi I. Inan , Budi Santoso
This study addresses the critical yet fragmented understanding of digital market adoption among underserved MSMEs in developing countries. Despite increasing technology access, these vulnerable communities face significant adoption barriers beyond infrastructure availability. Extending the Technology-Organization-Environment (TOE) framework with individual-level factors, this research investigates the key drivers of digital market adoption intention in this context. Analysing data from 151 underserved MSMEs through partial least squares structural equation modelling (PLS-SEM), the study reveals three key contributions. First, self-efficacy exhibits dual mediating roles: it fully mediates the relationship between facilitating conditions and adoption intention and competitively mediates the relationship between technology anxiety and adoption intention, suggesting that while confidence mitigates anxiety’s negative influence, anxiety can paradoxically drive “desperation adoption” under survival pressures. Second, trust acts as both an enabler and a moderator, influencing the effects of technology anxiety, organizational size, and competitive pressure on adoption. Third, these mediation and moderation mechanisms collectively enhance the model’s explanatory power (R² = 0.76), demonstrating that psychological readiness determines how MSMEs interpret and act upon external supports. The findings underscore that digital adoption in underserved contexts requires interventions that build self-efficacy, foster trust, and align infrastructural investment with psychological empowerment to achieve inclusive digital transformation for underserved MSMEs.
{"title":"Digital market adoption by underserved MSMEs in developing countries: Mediation and moderation by self-efficacy and trust","authors":"Ratna Juita , Dedi I. Inan , Budi Santoso","doi":"10.1016/j.jjimei.2025.100384","DOIUrl":"10.1016/j.jjimei.2025.100384","url":null,"abstract":"<div><div>This study addresses the critical yet fragmented understanding of digital market adoption among underserved MSMEs in developing countries. Despite increasing technology access, these vulnerable communities face significant adoption barriers beyond infrastructure availability. Extending the Technology-Organization-Environment (TOE) framework with individual-level factors, this research investigates the key drivers of digital market adoption intention in this context. Analysing data from 151 underserved MSMEs through partial least squares structural equation modelling (PLS-SEM), the study reveals three key contributions. First, self-efficacy exhibits dual mediating roles: it fully mediates the relationship between facilitating conditions and adoption intention and competitively mediates the relationship between technology anxiety and adoption intention, suggesting that while confidence mitigates anxiety’s negative influence, anxiety can paradoxically drive “desperation adoption” under survival pressures. Second, trust acts as both an enabler and a moderator, influencing the effects of technology anxiety, organizational size, and competitive pressure on adoption. Third, these mediation and moderation mechanisms collectively enhance the model’s explanatory power (R² = 0.76), demonstrating that psychological readiness determines how MSMEs interpret and act upon external supports. The findings underscore that digital adoption in underserved contexts requires interventions that build self-efficacy, foster trust, and align infrastructural investment with psychological empowerment to achieve inclusive digital transformation for underserved MSMEs.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"6 1","pages":"Article 100384"},"PeriodicalIF":0.0,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618354","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}
Pub Date : 2025-11-17DOI: 10.1016/j.jjimei.2025.100383
Carlos Guachamín-Chicaiza , Freddy Hernán Villota-González , Virgilio Zúñiga-Grajeda
Defects in automotive windshields (e.g., breakages, bubbles, cracks) compromise safety, increase production costs, and reduce product quality. This study integrates machine learning techniques and probabilistic modeling to optimize defect classification and derive prescriptive strategies for windshield manufacturing. A dataset of 1151 samples was analyzed through: (i) descriptive and correlational analysis of categorical (Thickness, Paint, Color, Tone, Sanding) and continuous (Weight, Area) variables; (ii) clustering and t-SNE for group identification; (iii) a stacking ensemble (MLP, Gradient Boosting, Random Forest, SVM, CatBoost) for classification; and (iv) probabilistic modeling with Shannon entropy for uncertainty assessment. Descriptive analysis revealed several important patterns. Area showed a bimodal distribution (IQR = 1.27–12.76 m², maximum = 39.89 m²), with Breaks more frequent in smaller surfaces and Cracked defects in larger ones (H = 111.86, p < 0.001, η² = 0.024). Thickness <5 mm was linked to higher Breaks (H = 19.16, padj = 1.8 × 10⁻⁴, η² = 0.022). Correlational analysis identified a moderate association between Tone and Color (Cramér’s V = 0.50), while Sanding was largely independent (V ≤ 0.20). Kruskal–Wallis tests confirmed that Thickness (H = 240.10, p < 0.001) and Tone (H = 136.18, p < 0.001) were significantly associated with Weight and Area. Clustering (k = 3, silhouette = 0.27) differentiated groups characterized by low weights/small areas with Breaks, larger areas with Cracked, and heavier compact units with Bubbles. The ensemble achieved a weighted F1-score of 0.83 on the hold-out test, with AUC = 0.96 (Breaks), 0.98 (Bubbles), 0.91 (Cracked). Its performance followed the same tendencies suggested by the exploratory phase, where nonlinear relationships between Thickness, Weight, and Area were already apparent. Building on this, probabilistic modeling refined the insights by detecting critical sensitivity thresholds: Breaks >60 % below 20 kg, with a high-risk zone at 11.3–17.2 kg (η² = 0.028), and Bubbles slightly increasing in heavy windshields >60–70 kg (≤15 %). Together, these results show a coherent narrative: exploratory analyses highlighted patterns, the model aligned with those trends, and probabilistic methods quantified the operational limits where defects become most likely. Based on this integrated analysis, three interventions were proposed: (i) structural reinforcement of thin glass (<5 mm), (ii) implementation of automated handling for large-area windshields, and (iii) ergonomic protocols to minimize operator-related defects. Overall, the complementarity between exploratory, modeling, and probabilistic approaches not only validated defect mechanisms but also provided prescriptive strategies for quality control. Nevertheless, limitations include the reliance on data from a single
{"title":"Machine learning and probabilistic analysis strategies for defect reduction in windshield manufacturing","authors":"Carlos Guachamín-Chicaiza , Freddy Hernán Villota-González , Virgilio Zúñiga-Grajeda","doi":"10.1016/j.jjimei.2025.100383","DOIUrl":"10.1016/j.jjimei.2025.100383","url":null,"abstract":"<div><div>Defects in automotive windshields (e.g., breakages, bubbles, cracks) compromise safety, increase production costs, and reduce product quality. This study integrates machine learning techniques and probabilistic modeling to optimize defect classification and derive prescriptive strategies for windshield manufacturing. A dataset of 1151 samples was analyzed through: (i) descriptive and correlational analysis of categorical (Thickness, Paint, Color, Tone, Sanding) and continuous (Weight, Area) variables; (ii) clustering and t-SNE for group identification; (iii) a stacking ensemble (MLP, Gradient Boosting, Random Forest, SVM, CatBoost) for classification; and (iv) probabilistic modeling with Shannon entropy for uncertainty assessment. Descriptive analysis revealed several important patterns. Area showed a bimodal distribution (IQR = 1.27–12.76 m², maximum = 39.89 m²), with Breaks more frequent in smaller surfaces and Cracked defects in larger ones (<em>H</em> = 111.86, <em>p</em> < 0.001, η² = 0.024). Thickness <5 mm was linked to higher Breaks (<em>H</em> = 19.16, <em>padj</em> = 1.8 × 10⁻⁴, η² = 0.022). Correlational analysis identified a moderate association between Tone and Color (Cramér’s <em>V</em> = 0.50), while Sanding was largely independent (<em>V</em> ≤ 0.20). Kruskal–Wallis tests confirmed that Thickness (<em>H</em> = 240.10, <em>p</em> < 0.001) and Tone (<em>H</em> = 136.18, <em>p</em> < 0.001) were significantly associated with Weight and Area. Clustering (<em>k</em> = 3, silhouette = 0.27) differentiated groups characterized by low weights/small areas with Breaks, larger areas with Cracked, and heavier compact units with Bubbles. The ensemble achieved a weighted F1-score of 0.83 on the hold-out test, with AUC = 0.96 (Breaks), 0.98 (Bubbles), 0.91 (Cracked). Its performance followed the same tendencies suggested by the exploratory phase, where nonlinear relationships between Thickness, Weight, and Area were already apparent. Building on this, probabilistic modeling refined the insights by detecting critical sensitivity thresholds: Breaks >60 % below 20 kg, with a high-risk zone at 11.3–17.2 kg (η² = 0.028), and Bubbles slightly increasing in heavy windshields >60–70 kg (≤15 %). Together, these results show a coherent narrative: exploratory analyses highlighted patterns, the model aligned with those trends, and probabilistic methods quantified the operational limits where defects become most likely. Based on this integrated analysis, three interventions were proposed: (i) structural reinforcement of thin glass (<5 mm), (ii) implementation of automated handling for large-area windshields, and (iii) ergonomic protocols to minimize operator-related defects. Overall, the complementarity between exploratory, modeling, and probabilistic approaches not only validated defect mechanisms but also provided prescriptive strategies for quality control. Nevertheless, limitations include the reliance on data from a single","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100383"},"PeriodicalIF":0.0,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145570936","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}
Pub Date : 2025-11-15DOI: 10.1016/j.jjimei.2025.100382
Yong Zhu, Gang Li
Environmental policy is a critical tool for countries to establish principles and guidelines for effective environmental governance. This paper explores the evolution of environmental policy themes and stakeholder networks in China’s governance framework over a decade. Based on a dataset of 1,153 policy documents from 31 provinces, this study applies natural language processing for tokenization and utilizes unsupervised and supervised machine learning techniques — Dynamic Topic Modeling for topic detection and Named Entity Recognition for stakeholder extraction. Through spatio-temporal analysis and network analysis, this innovative approach provides new insights into governance dynamics and establishing a novel research paradigm for the academic community. Findings indicate a significant transition from a government-centric approach to a collaborative multi-stakeholder governance model that enhances data-driven decision-making and offers practical recommendations for policymakers.
{"title":"Analyzing the evolution of environmental policy themes and governance stakeholder networks: A computational approach","authors":"Yong Zhu, Gang Li","doi":"10.1016/j.jjimei.2025.100382","DOIUrl":"10.1016/j.jjimei.2025.100382","url":null,"abstract":"<div><div>Environmental policy is a critical tool for countries to establish principles and guidelines for effective environmental governance. This paper explores the evolution of environmental policy themes and stakeholder networks in China’s governance framework over a decade. Based on a dataset of 1,153 policy documents from 31 provinces, this study applies natural language processing for tokenization and utilizes unsupervised and supervised machine learning techniques — Dynamic Topic Modeling for topic detection and Named Entity Recognition for stakeholder extraction. Through spatio-temporal analysis and network analysis, this innovative approach provides new insights into governance dynamics and establishing a novel research paradigm for the academic community. Findings indicate a significant transition from a government-centric approach to a collaborative multi-stakeholder governance model that enhances data-driven decision-making and offers practical recommendations for policymakers.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100382"},"PeriodicalIF":0.0,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519050","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}
This study examines the influence of Green Transformational Leadership (GTL) on Environmental Performance Outcomes (EPO) in Thailand's Eastern Economic Corridor (EEC) industries, with particular attention to the mediating roles of Green Organizational Culture Management (GOCM) and Knowledge Management (KM). Drawing on the Natural Resource-Based View (NRBV), a quantitative survey was conducted with 312 industrial firms using a multi-phase data collection process. The analysis indicates that green transformational leadership has a significant direct effect on environmental performance outcomes (β = 0.240, p < 0.001), while both green organizational culture management and knowledge management partially mediate this relationship. Knowledge management demonstrates a stronger indirect effect (β = 0.085, p < 0.001) compared to green organizational culture management (β = 0.038, p = 0.022), suggesting that knowledge-based systems provide more immediate pathways for translating leadership intent into environmental outcomes. The six demographic and positional control variables showed no significant influence on environmental performance outcome, indicating that the main effects are not driven by respondent characteristics. These findings support the applicability of the Natural Resource-Based View in an emerging-economy context and highlight the complementary roles of culture and knowledge in shaping environmental performance. The study advances theoretical understanding by integrating green transformational leadership, green organizational culture management, and knowledge management into a single framework and offers sector-relevant implications for industries seeking to align leadership development, cultural practices, and knowledge systems with sustainability goals.
本研究探讨了绿色变革型领导（GTL）对泰国东部经济走廊（EEC）产业环境绩效结果（EPO）的影响，特别关注绿色组织文化管理（GOCM）和知识管理（KM）的中介作用。基于自然资源基础观点（NRBV），采用多阶段数据收集过程对312家工业企业进行了定量调查。分析表明，绿色变革型领导对环境绩效结果有显著的直接影响（β = 0.240, p < 0.001），而绿色组织文化管理和知识管理在这一关系中起到部分中介作用。与绿色组织文化管理（β = 0.038, p = 0.022）相比，知识管理表现出更强的间接效应（β = 0.085, p < 0.001），这表明基于知识的系统为将领导意图转化为环境结果提供了更直接的途径。6个人口统计学和地理位置控制变量对环境绩效结果没有显著影响，表明主要影响不是由被调查者特征驱动的。这些发现支持了自然资源基础观点在新兴经济体背景下的适用性，并强调了文化和知识在塑造环境绩效方面的互补作用。该研究通过将绿色变革型领导、绿色组织文化管理和知识管理整合到一个单一框架中，推进了理论理解，并为寻求将领导力发展、文化实践和知识系统与可持续发展目标相结合的行业提供了与行业相关的启示。
{"title":"Driving sustainability in emerging economies: Leadership, culture, and knowledge management in environmental performance","authors":"Nhatphaphat Juicharoen , Khahan Na-Nan , Sureerut Inmor , Kanakarn Phanniphong , Xinyu Wang","doi":"10.1016/j.jjimei.2025.100381","DOIUrl":"10.1016/j.jjimei.2025.100381","url":null,"abstract":"<div><div>This study examines the influence of Green Transformational Leadership (GTL) on Environmental Performance Outcomes (EPO) in Thailand's Eastern Economic Corridor (EEC) industries, with particular attention to the mediating roles of Green Organizational Culture Management (GOCM) and Knowledge Management (KM). Drawing on the Natural Resource-Based View (NRBV), a quantitative survey was conducted with 312 industrial firms using a multi-phase data collection process. The analysis indicates that green transformational leadership has a significant direct effect on environmental performance outcomes (β = 0.240, <em>p</em> < 0.001), while both green organizational culture management and knowledge management partially mediate this relationship. Knowledge management demonstrates a stronger indirect effect (β = 0.085, <em>p</em> < 0.001) compared to green organizational culture management (β = 0.038, <em>p</em> = 0.022), suggesting that knowledge-based systems provide more immediate pathways for translating leadership intent into environmental outcomes. The six demographic and positional control variables showed no significant influence on environmental performance outcome, indicating that the main effects are not driven by respondent characteristics. These findings support the applicability of the Natural Resource-Based View in an emerging-economy context and highlight the complementary roles of culture and knowledge in shaping environmental performance. The study advances theoretical understanding by integrating green transformational leadership, green organizational culture management, and knowledge management into a single framework and offers sector-relevant implications for industries seeking to align leadership development, cultural practices, and knowledge systems with sustainability goals.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100381"},"PeriodicalIF":0.0,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415381","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}
Pub Date : 2025-10-17DOI: 10.1016/j.jjimei.2025.100377
Acheampong Owusu
Using the resource-based theory as the lens, this research proposes a conceptual model to explore the determinants of AI in the Ghanaian banking sector and also examine its impact on the OpEx of the banks. The study adopted a quantitative research approach with a survey method to collect data from 331 CIOs/IS/IT Managers/Data Scientists/Business Analysts and other knowledgeable managers in the Ghanaian banks who were sampled via stratified and purposive sampling techniques. The data analysis was done via partial least squares structural equation modelling (PLS-SEM). The findings revealed the determinants of AI adoption in the Ghanaian banks are Absorptive Capacity, Agility and Capabilities of the banks. Also, it was established from the empirical results that the adoption of AI enhances OpEX of the banks. The determinants obtained in this study would lay a foundation for future research which could be incorporated into a new theoretical model of AI adoption.
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This study examined resistance to service robots among younger, educated Thai consumers in chain restaurants and addressed the research gaps to better understand technology adoption in non-Western markets. Data were analyzed from 325 Thai consumers using structural equation modeling. Five significant resistance factors were identified as interaction discomfort (β = 0.361), service robot personality (β = 0.217), warmth competence (β = 0.170), perceived service failure (β = 0.169), and inflexibility (β = -0.166). The counterintuitive negative effect of inflexibility suggested that predictable, standardized robot behavior may reduce resistance in Thailand, challenging Western assumptions about customization. Our model demonstrated strong explanatory power (R² = 0.586), extending innovation resistance theory (IRT) to service robotics by identifying culturally specific barriers in an emerging market. The findings suggested that prioritizing improved robot-customer interactions while leveraging predictability would be a service advantage in Thai cultural contexts.
{"title":"Robot resistance in the land of smiles: Unraveling the behavior of Thai consumers toward restaurant service robots","authors":"Noptanit Chotisarn , Nontouch Srisuksa , Rachanon Taweephol , Panuschagone Simakhajornboon , Thadathibesra Phuthong","doi":"10.1016/j.jjimei.2025.100379","DOIUrl":"10.1016/j.jjimei.2025.100379","url":null,"abstract":"<div><div>This study examined resistance to service robots among younger, educated Thai consumers in chain restaurants and addressed the research gaps to better understand technology adoption in non-Western markets. Data were analyzed from 325 Thai consumers using structural equation modeling. Five significant resistance factors were identified as interaction discomfort (β = 0.361), service robot personality (β = 0.217), warmth competence (β = 0.170), perceived service failure (β = 0.169), and inflexibility (β = -0.166). The counterintuitive negative effect of inflexibility suggested that predictable, standardized robot behavior may reduce resistance in Thailand, challenging Western assumptions about customization. Our model demonstrated strong explanatory power (R² = 0.586), extending innovation resistance theory (IRT) to service robotics by identifying culturally specific barriers in an emerging market. The findings suggested that prioritizing improved robot-customer interactions while leveraging predictability would be a service advantage in Thai cultural contexts.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100379"},"PeriodicalIF":0.0,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145323990","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}
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