Pub Date : 2025-01-17DOI: 10.1016/j.techsoc.2025.102820
Seokbeom Kwon , Alan L. Porter
Corporate research has been a primary driver of recent advances in Machine Learning and Artificial Intelligence (ML/AI). The present study contends that firms' prominent role in advancing the ML/AI research field is partly attributed to their use of exclusive data for ML/AI research. Using data on nearly 8000 preprints of ML/AI research papers archived in arXiv and the performance of their proposed algorithms, we found multifaceted evidence that corporate ML/AI research has exhibited a particularly significant citation impact compared to non-corporate research. Importantly, we showed that the significance of corporate research is more pronounced when it originates from the use of exclusive data. We argue that firms' use of exclusive data has been instrumental in not only encouraging their research on ML/AI, but also enhancing the research impact, which we call the “dual role” of the data in corporate research on ML/AI. In light of the policy concern regarding the potential anticompetitive implications of firms' utilization of data exclusivity in the evolving landscape of ML/AI, our conclusion calls for a comprehensive policy discourse on the consequences of firms' exclusive use of data for their ML/AI research within broader dimensions of societal welfare, including innovation and competition.
{"title":"Use of exclusive data for corporate research on machine learning and artificial intelligence: Implications for innovation and competition policy","authors":"Seokbeom Kwon , Alan L. Porter","doi":"10.1016/j.techsoc.2025.102820","DOIUrl":"10.1016/j.techsoc.2025.102820","url":null,"abstract":"<div><div>Corporate research has been a primary driver of recent advances in Machine Learning and Artificial Intelligence (ML/AI). The present study contends that firms' prominent role in advancing the ML/AI research field is partly attributed to their use of exclusive data for ML/AI research. Using data on nearly 8000 preprints of ML/AI research papers archived in arXiv and the performance of their proposed algorithms, we found multifaceted evidence that corporate ML/AI research has exhibited a particularly significant citation impact compared to non-corporate research. Importantly, we showed that the significance of corporate research is more pronounced when it originates from the use of exclusive data. We argue that firms' use of exclusive data has been instrumental in not only encouraging their research on ML/AI, but also enhancing the research impact, which we call the “dual role” of the data in corporate research on ML/AI. In light of the policy concern regarding the potential anticompetitive implications of firms' utilization of data exclusivity in the evolving landscape of ML/AI, our conclusion calls for a comprehensive policy discourse on the consequences of firms' exclusive use of data for their ML/AI research within broader dimensions of societal welfare, including innovation and competition.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"81 ","pages":"Article 102820"},"PeriodicalIF":10.1,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143170938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-17DOI: 10.1016/j.techsoc.2025.102821
M.A. Hannan , Mahendhiran S. Nair , Pervaiz K. Ahmed , Santha Vaithilingam , Safat B. Wali , M.S. Reza , Sayem M. Abu , Pin Jern Ker , R.A. Begum , Hwai Chyuan Ong , Denny K.S. Ng , Gilsoo Jang
Global warming poses a significant threat to the planet, driven largely by carbon emissions from fossil fuels. Despite international commitments and efforts to reduce carbon emissions, current initiatives have fallen short of achieving the targets set. Recognizing this gap, the transition to hydrogen energy (HE) as an alternative to fossil fuel emerges as an essential and transformative strategy for achieving emission reduction goals and securing a sustainable energy solution. This study introduces a novel, value-based approach with the primary aim of creating a sustainable HE ecosystem framework that spans three pillars-economic, social, and environmental, aligned with the United Nations Sustainable Development Goals (UN-SDGs). This new framework called the 8R-8I ROV framework introduces the notion of return-on value (ROV) using 8R nature-based value and 8I enablers within the hydrogen energy ecosystem. The study provides a comprehensive characterization of the enablers required for a robust HE ecosystem. These enablers are necessary for ensuring both tangible and intangible outcomes across the three pillars, consequently enhancing value creation for all stakeholders. Through this analysis, the study provides insights into the pivotal role of the enablers within the HE ecosystem, facilitating this transition, and ultimately contributing to the achievement of sustainable development goals.
{"title":"Return on values of hydrogen energy transitions: A perspective on the conceptual framework","authors":"M.A. Hannan , Mahendhiran S. Nair , Pervaiz K. Ahmed , Santha Vaithilingam , Safat B. Wali , M.S. Reza , Sayem M. Abu , Pin Jern Ker , R.A. Begum , Hwai Chyuan Ong , Denny K.S. Ng , Gilsoo Jang","doi":"10.1016/j.techsoc.2025.102821","DOIUrl":"10.1016/j.techsoc.2025.102821","url":null,"abstract":"<div><div>Global warming poses a significant threat to the planet, driven largely by carbon emissions from fossil fuels. Despite international commitments and efforts to reduce carbon emissions, current initiatives have fallen short of achieving the targets set. Recognizing this gap, the transition to hydrogen energy (HE) as an alternative to fossil fuel emerges as an essential and transformative strategy for achieving emission reduction goals and securing a sustainable energy solution. This study introduces a novel, value-based approach with the primary aim of creating a sustainable HE ecosystem framework that spans three pillars-economic, social, and environmental, aligned with the United Nations Sustainable Development Goals (UN-SDGs). This new framework called the 8R-8I ROV framework introduces the notion of return-on value (ROV) using 8R nature-based value and 8I enablers within the hydrogen energy ecosystem. The study provides a comprehensive characterization of the enablers required for a robust HE ecosystem. These enablers are necessary for ensuring both tangible and intangible outcomes across the three pillars, consequently enhancing value creation for all stakeholders. Through this analysis, the study provides insights into the pivotal role of the enablers within the HE ecosystem, facilitating this transition, and ultimately contributing to the achievement of sustainable development goals.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"81 ","pages":"Article 102821"},"PeriodicalIF":10.1,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143170435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-16DOI: 10.1016/j.techsoc.2025.102818
Ivan Kekez , Lode Lauwaert , Nina Begičević Ređep
This paper presents a systematic review of 64 papers using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) of research on bias and discrimination in the context of using Artificial Intelligence (AI). Specifically, while limiting the scope to research in HRM, it aims to answer three questions that are relevant to the research community. The first question is whether research papers define the terms 'bias' and 'discrimination', and if so how. Second, given that there are different forms of bias and discrimination, the question is exactly which ones are being investigated. Are there any forms of bias and discrimination that are underrepresented? The third question is whether a negativity bias exists in research on bias and discrimination in the context of AI. The answers to the first two questions point to some research problems. The review shows that in a substantial number of papers, the terms 'bias' and 'discrimination' are not or hardly defined. Furthermore, there is a disproportionate focus among researchers on bias and discrimination related to skin tone (racism) and gender (sexism). In the discussion, we provide reasons why this is undesirable for both scientific and extratheoretical reasons. The answer to the last question is negative. There is a relatively good balance between research that zooms in on the positive effects of AI on bias and discrimination, and research that deals with AI leading to (more) bias and discrimination.
{"title":"Is artificial intelligence (AI) research biased and conceptually vague? A systematic review of research on bias and discrimination in the context of using AI in human resource management","authors":"Ivan Kekez , Lode Lauwaert , Nina Begičević Ređep","doi":"10.1016/j.techsoc.2025.102818","DOIUrl":"10.1016/j.techsoc.2025.102818","url":null,"abstract":"<div><div>This paper presents a systematic review of 64 papers using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) of research on bias and discrimination in the context of using Artificial Intelligence (AI). Specifically, while limiting the scope to research in HRM, it aims to answer three questions that are relevant to the research community. The first question is whether research papers define the terms 'bias' and 'discrimination', and if so how. Second, given that there are different forms of bias and discrimination, the question is exactly which ones are being investigated. Are there any forms of bias and discrimination that are underrepresented? The third question is whether a negativity bias exists in research on bias and discrimination in the context of AI. The answers to the first two questions point to some research problems. The review shows that in a substantial number of papers, the terms 'bias' and 'discrimination' are not or hardly defined. Furthermore, there is a disproportionate focus among researchers on bias and discrimination related to skin tone (racism) and gender (sexism). In the discussion, we provide reasons why this is undesirable for both scientific and extratheoretical reasons. The answer to the last question is negative. There is a relatively good balance between research that zooms in on the positive effects of AI on bias and discrimination, and research that deals with AI leading to (more) bias and discrimination.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"81 ","pages":"Article 102818"},"PeriodicalIF":10.1,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The concept of coopetition - simultaneous collaboration and competition between organizations to achieve mutually beneficial outcomes - plays a pivotal role in shaping business performance, particularly during periods of rapid technological advancements. This is especially evident the manufacturing sector, where innovation and competitive dynamics intersect with economic and social forces. The current academic discourse predominantly focuses on the qualitative identification and analysis of coopetition attributes, leaving a significant gap for large-scale quantitative studies to enable empirical assessment. This study aims to examine the significance of three groups of coopetition attributes for coopetition performance classified into two strategic (dynamics, paradoxicality), six relational (asymmetry, complexity, coopetition intensity, mutual dependence, strength, tensions), and five behavioral attributes (competition intensity, conflict, formality, investments, trust). Using data from 1216 manufacturing firms in Poland and employing a generalized Covariance based Structural Equation Model (CB-SEM), this study offers nuanced insights to the global discourse at the intersection of technological change and social dynamics. The results indicate that the strategic attribute paradoxicality, the relational attribute strength, and most of the behavioral attributes (trust, competition intensity, investments, formality) positively impact coopetition performance. Additionally, a significant negative impact of the strategic attribute dynamics was demonstrated, while no significant influence was identified for the remaining relational attributes (asymmetry, tensions) as well as the behavioral attribute conflict. Diverging from prior qualitative approaches, this study offers data-driven insights for decision-makers navigating societal and technological change, highlighting which attributes should be stimulated to enhance coopetition performance while minimizing the level of dynamics within coopetition strategies.
{"title":"Decoding coopetition performance using impactful coopetition attributes: Evidence from manufacturing companies","authors":"Patrycja Klimas , Arkadiusz Kawa , Karina Sachpazidu , Sylwia Stańczyk , Katharina Brenk , Dominik K. Kanbach","doi":"10.1016/j.techsoc.2025.102819","DOIUrl":"10.1016/j.techsoc.2025.102819","url":null,"abstract":"<div><div>The concept of coopetition - simultaneous collaboration and competition between organizations to achieve mutually beneficial outcomes - plays a pivotal role in shaping business performance, particularly during periods of rapid technological advancements. This is especially evident the manufacturing sector, where innovation and competitive dynamics intersect with economic and social forces. The current academic discourse predominantly focuses on the qualitative identification and analysis of coopetition attributes, leaving a significant gap for large-scale quantitative studies to enable empirical assessment. This study aims to examine the significance of three groups of coopetition attributes for coopetition performance classified into two strategic (<em>dynamics</em>, <em>paradoxicality</em>), six relational (<em>asymmetry</em>, <em>complexity</em>, <em>coopetition intensity</em>, <em>mutual dependence</em>, <em>strength</em>, <em>tensions</em>), and five behavioral attributes (<em>competition intensity</em>, <em>conflict</em>, <em>formality</em>, <em>investments</em>, <em>trust</em>). Using data from 1216 manufacturing firms in Poland and employing a generalized Covariance based Structural Equation Model (CB-SEM), this study offers nuanced insights to the global discourse at the intersection of technological change and social dynamics. The results indicate that the strategic attribute <em>paradoxicality</em>, the relational attribute <em>strength</em>, and most of the behavioral attributes (<em>trust, competition intensity, investments, formality</em>) positively impact coopetition performance. Additionally, a significant negative impact of the strategic attribute <em>dynamics</em> was demonstrated, while no significant influence was identified for the remaining relational attributes (<em>asymmetry, tensions</em>) as well as the behavioral attribute <em>conflict</em>. Diverging from prior qualitative approaches, this study offers data-driven insights for decision-makers navigating societal and technological change, highlighting which attributes should be stimulated to enhance coopetition performance while minimizing the level of dynamics within coopetition strategies.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"81 ","pages":"Article 102819"},"PeriodicalIF":10.1,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-16DOI: 10.1016/j.techsoc.2024.102809
Oihana Basilio Ruiz de Apodaca , Vicente J Montes Gan , Fernando Moreno-Brieva
The literature has explained how technology adoption and usage is affected by many socio-technical issues and how the emergence and evolution of networks is key for innovation. However, empirical analysis of interactions between leadership networks and new artificial intelligence-driven technologies is scarce. To shed new light on leadership communities around the adoption of an existing and a new technology related to leadership networks, we analyze the level of socio-digital engagement of a non-hierarchical network of individuals associated with a prestigious Spanish Foundation in the context of pressing social challenges. We apply a methodology that combines quantitative and qualitative methods, sentiment analysis, and insider research to analyze the strategy applied by the Foundation. Our main results show that the existence of strong, trust-based relationships and the introduction of a well-designed AI-based tool may only have a limited effect on the adoption of technology and the evolution of a network. Specifically, in the case studied, the network of leaders around the Foundation comprised peer leadership networks but not collective leadership networks focused on action on social challenges.
{"title":"Non-hierarchic leadership collaboration: Exploring the adoption of AI-driven social networking for addressing social challenges in an extra-organizational environment","authors":"Oihana Basilio Ruiz de Apodaca , Vicente J Montes Gan , Fernando Moreno-Brieva","doi":"10.1016/j.techsoc.2024.102809","DOIUrl":"10.1016/j.techsoc.2024.102809","url":null,"abstract":"<div><div>The literature has explained how technology adoption and usage is affected by many socio-technical issues and how the emergence and evolution of networks is key for innovation. However, empirical analysis of interactions between leadership networks and new artificial intelligence-driven technologies is scarce. To shed new light on leadership communities around the adoption of an existing and a new technology related to leadership networks, we analyze the level of socio-digital engagement of a non-hierarchical network of individuals associated with a prestigious Spanish Foundation in the context of pressing social challenges. We apply a methodology that combines quantitative and qualitative methods, sentiment analysis, and insider research to analyze the strategy applied by the Foundation. Our main results show that the existence of strong, trust-based relationships and the introduction of a well-designed AI-based tool may only have a limited effect on the adoption of technology and the evolution of a network. Specifically, in the case studied, the network of leaders around the Foundation comprised peer leadership networks but not collective leadership networks focused on action on social challenges.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"81 ","pages":"Article 102809"},"PeriodicalIF":10.1,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143170937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-16DOI: 10.1016/j.techsoc.2025.102817
Jian Li , Jingdi Zhao , Jinsong Huang
Consumers have a significant demand for receiving services in secret consumption, and choosing artificial intelligence (AI) to provide confidential services is becoming an option for them. However, the underlying mechanism behind their choice of AI service provider in secret consumption is not clear. Based on social avoidance theory, this paper finds that consumers appreciate AI in the context of secret consumption. They wish to hide their information and reduce social ties with service providers, thus preferring AI service providers with nonsocial attributes over human service providers. The underlying mechanism for this phenomenon is the need for social avoidance during secret consumption, which increases the value of AI's nonsocial attribute.
{"title":"Social avoidance needs boost AI's nonsocial attribute valuation in secret consumption","authors":"Jian Li , Jingdi Zhao , Jinsong Huang","doi":"10.1016/j.techsoc.2025.102817","DOIUrl":"10.1016/j.techsoc.2025.102817","url":null,"abstract":"<div><div>Consumers have a significant demand for receiving services in secret consumption, and choosing artificial intelligence (AI) to provide confidential services is becoming an option for them. However, the underlying mechanism behind their choice of AI service provider in secret consumption is not clear. Based on social avoidance theory, this paper finds that consumers appreciate AI in the context of secret consumption. They wish to hide their information and reduce social ties with service providers, thus preferring AI service providers with nonsocial attributes over human service providers. The underlying mechanism for this phenomenon is the need for social avoidance during secret consumption, which increases the value of AI's nonsocial attribute.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"81 ","pages":"Article 102817"},"PeriodicalIF":10.1,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143170436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-13DOI: 10.1016/j.techsoc.2025.102816
Jindan Gong , Maria Xylia , Claudia Strambo , Björn Nykvist , Sirin Celik
Electrification, digitalization and automation are three trends driving the low-carbon transition of the transport sector. For the transition to be just, it is essential to ensure that those affected by these trends view the changes they bring as acceptable and fair. Transport policy development, however, mostly remains expert-driven. To explore just transitions in the context of transport electrification, digitalization and automation, we use Sweden as a case to analyse how taxi and truck drivers perceive the implications of these trends on six aspects of their quality of working life: skill requirements, work organization, job security, identity, safety and health. We also explore how these perceptions vary across dimensions of age, gender and geography. We found several perceived challenges related to skill requirements and identity, though drivers also see the potential for the trends to improve their work environment, safety and performance. However, these potential benefits are not taken for granted. The perceived implications may also raise potential distributional, procedural and recognition injustices, for instance regarding costs of reskilling, drivers' autonomy and the recognition of drivers' knowledge. We conclude that the technologies can have both positive and negative implications, and it is rather institutional arrangements, social processes, and broader societal and industrial shifts that cause transport workers to question their future in this sector. Incorporating drivers' insights into decision-making can enhance the drivers’ quality of working life and wellbeing while preserving their dignity.
{"title":"What happened to the driver? Implications of electrification, digitalization, and automation on truck and taxi drivers","authors":"Jindan Gong , Maria Xylia , Claudia Strambo , Björn Nykvist , Sirin Celik","doi":"10.1016/j.techsoc.2025.102816","DOIUrl":"10.1016/j.techsoc.2025.102816","url":null,"abstract":"<div><div>Electrification, digitalization and automation are three trends driving the low-carbon transition of the transport sector. For the transition to be just, it is essential to ensure that those affected by these trends view the changes they bring as acceptable and fair. Transport policy development, however, mostly remains expert-driven. To explore just transitions in the context of transport electrification, digitalization and automation, we use Sweden as a case to analyse how taxi and truck drivers perceive the implications of these trends on six aspects of their quality of working life: skill requirements, work organization, job security, identity, safety and health. We also explore how these perceptions vary across dimensions of age, gender and geography. We found several perceived challenges related to skill requirements and identity, though drivers also see the potential for the trends to improve their work environment, safety and performance. However, these potential benefits are not taken for granted. The perceived implications may also raise potential distributional, procedural and recognition injustices, for instance regarding costs of reskilling, drivers' autonomy and the recognition of drivers' knowledge. We conclude that the technologies can have both positive and negative implications, and it is rather institutional arrangements, social processes, and broader societal and industrial shifts that cause transport workers to question their future in this sector. Incorporating drivers' insights into decision-making can enhance the drivers’ quality of working life and wellbeing while preserving their dignity.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"81 ","pages":"Article 102816"},"PeriodicalIF":10.1,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143170936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-11DOI: 10.1016/j.techsoc.2025.102815
Mohsen Khezri , Jamal Mamkhezri
This study aims to deepen our understanding of green entrepreneurship and its effects on ecological footprints, focusing on fishing grounds and CO₂ emissions—areas often underexplored in environmental research. Utilizing panel data from 2002 to 2018 across 14 countries, this research investigates the environmental impacts of eleven distinct entrepreneurial activities. Methodologically, the study incorporates a variety of control variables, including GDP per capita, trade openness, energy intensity, natural resource rents, and urbanization, to ensure robust analysis. Key findings illustrate the dual aspects of the Kuznets curve: 1) A positive squared GDP per capita coefficient of 0.514 indicates increasing ecological impacts from fishing as economic growth progresses. In contrast, a negative coefficient of −0.140 for CO2 emissions suggests diminishing impacts with further economic development. 2) The results reveal that entrepreneurial activities have a differential impact: reducing the ecological footprint associated with fishing activities while increasing CO2 emissions. 3) The relationship between GDP and ecological footprints varies by a country's economic status and the intensity of entrepreneurial activity, highlighting how different indices such as internal market dynamics and physical and services infrastructure interact uniquely with these environmental outcomes. The study underscores the importance of tailored economic and environmental policies to mitigate the diverse impacts of entrepreneurship on the ecosystem.
{"title":"Evaluating the ecological footprints of Entrepreneurial activities: Insights from a cross-country assessment","authors":"Mohsen Khezri , Jamal Mamkhezri","doi":"10.1016/j.techsoc.2025.102815","DOIUrl":"10.1016/j.techsoc.2025.102815","url":null,"abstract":"<div><div>This study aims to deepen our understanding of green entrepreneurship and its effects on ecological footprints, focusing on fishing grounds and CO₂ emissions—areas often underexplored in environmental research. Utilizing panel data from 2002 to 2018 across 14 countries, this research investigates the environmental impacts of eleven distinct entrepreneurial activities. Methodologically, the study incorporates a variety of control variables, including GDP per capita, trade openness, energy intensity, natural resource rents, and urbanization, to ensure robust analysis. Key findings illustrate the dual aspects of the Kuznets curve: 1) A positive squared GDP per capita coefficient of 0.514 indicates increasing ecological impacts from fishing as economic growth progresses. In contrast, a negative coefficient of −0.140 for CO2 emissions suggests diminishing impacts with further economic development. 2) The results reveal that entrepreneurial activities have a differential impact: reducing the ecological footprint associated with fishing activities while increasing CO2 emissions. 3) The relationship between GDP and ecological footprints varies by a country's economic status and the intensity of entrepreneurial activity, highlighting how different indices such as internal market dynamics and physical and services infrastructure interact uniquely with these environmental outcomes. The study underscores the importance of tailored economic and environmental policies to mitigate the diverse impacts of entrepreneurship on the ecosystem.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"81 ","pages":"Article 102815"},"PeriodicalIF":10.1,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143170934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-09DOI: 10.1016/j.techsoc.2025.102813
Jens Peter Andersen , Lise Degn , Rachel Fishberg , Ebbe K. Graversen , Serge P.J.M. Horbach , Evanthia Kalpazidou Schmidt , Jesper W. Schneider , Mads P. Sørensen
This study explores the use of generative AI (GenAI) and research integrity assessments of use cases by researchers, including PhD students, at Danish universities. Conducted through a survey sent to all Danish researchers from January to February 2024, the study received 2534 responses and evaluated 32 GenAI use cases across five research phases: idea generation, research design, data collection, data analysis, and writing/reporting. Respondents reported on their own and colleagues' GenAI usage. They also assessed whether the practices in the use cases were considered good research practice. Through an explorative factor analysis, we identified three clusters of perception: "GenAI as a work horse", "GenAI as a language assistant only", and "GenAI as a research accelerator". The findings further show varied opinions on GenAI's research integrity implications. Language editing and data analysis were generally viewed positively, whereas experiment design and peer review tasks faced more criticism. Controversial areas included image creation/modification and synthetic data, with comments highlighting the need for critical and reflexive use of GenAI. Usage differed by main research area, with technical and quantitative sciences reporting slightly higher usage and more positive assessments. Junior researchers used GenAI more than senior colleagues, while no significant gender differences were observed. The study underscores the need for adaptable, discipline-specific guidelines for GenAI use in research, developed collaboratively with experts to align with diverse research practices and minimize ethical and practical misalignment.
{"title":"Generative Artificial Intelligence (GenAI) in the research process – A survey of researchers’ practices and perceptions","authors":"Jens Peter Andersen , Lise Degn , Rachel Fishberg , Ebbe K. Graversen , Serge P.J.M. Horbach , Evanthia Kalpazidou Schmidt , Jesper W. Schneider , Mads P. Sørensen","doi":"10.1016/j.techsoc.2025.102813","DOIUrl":"10.1016/j.techsoc.2025.102813","url":null,"abstract":"<div><div>This study explores the use of generative AI (GenAI) and research integrity assessments of use cases by researchers, including PhD students, at Danish universities. Conducted through a survey sent to all Danish researchers from January to February 2024, the study received 2534 responses and evaluated 32 GenAI use cases across five research phases: idea generation, research design, data collection, data analysis, and writing/reporting. Respondents reported on their own and colleagues' GenAI usage. They also assessed whether the practices in the use cases were considered good research practice. Through an explorative factor analysis, we identified three clusters of perception: \"GenAI as a work horse\", \"GenAI as a language assistant only\", and \"GenAI as a research accelerator\". The findings further show varied opinions on GenAI's research integrity implications. Language editing and data analysis were generally viewed positively, whereas experiment design and peer review tasks faced more criticism. Controversial areas included image creation/modification and synthetic data, with comments highlighting the need for critical and reflexive use of GenAI. Usage differed by main research area, with technical and quantitative sciences reporting slightly higher usage and more positive assessments. Junior researchers used GenAI more than senior colleagues, while no significant gender differences were observed. The study underscores the need for adaptable, discipline-specific guidelines for GenAI use in research, developed collaboratively with experts to align with diverse research practices and minimize ethical and practical misalignment.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"81 ","pages":"Article 102813"},"PeriodicalIF":10.1,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143170935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-09DOI: 10.1016/j.techsoc.2025.102814
Vernika Agarwal , Palak Verma , Giulio Ferrigno
The education sector has experienced transformative shifts with increased integration of artificial intelligence (AI) and Learning Analytics in higher education, notably within universities. This evolution necessitates the integration of Education 5.0 with Industry 5.0, especially in emerging economies like India, where teaching professionals play an essential part in facilitating this transition. Despite the opportunities for skills enhancement, significant obstacles impede the sustainable technological empowerment of academicians. This study recognizes these challenges in the context of Indian higher education and explores solutions to accelerate the acceptance of Education 5.0. Utilizing a mixed-methods approach, semi-structured interviews with 14 academicians were conducted, followed by thematic analysis using NVIVO and ranking via the Best-worst Multi-criteria Decision-Making Process (BWM). The research indicates that resolving these challenges can streamline the transition to AI-enabled educational technologies. The study highlights the significance of university management in formulating targeted policies and training programs that minimize these challenges, ultimately enhancing the educational infrastructure and fostering a technologically proficient academic workforce. Theoretically, this research enriches the discourse on technology empowerment in education by mapping the interplay between educational advancements and organizational change, offering an understanding that can be applied to similar contexts globally.
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