Pub Date : 2026-01-08DOI: 10.1016/j.techsoc.2026.103233
Zuxu Chen , Yu Song
To better address artificial intelligence challenges, a rational assessment of its impacts is essential. However, when estimating the influence of artificial intelligence, most studies have overlooked sectoral heterogeneity and regional competition, which are prevalent in reality. This paper constructs an analytical framework based on the GTAP-E model and input-output analysis to more effectively forecast artificial intelligence's intricate effects. The results show that both China and the US are estimated to achieve better GDP growth during 2025–2035, but the US growth rate is higher. Rising AI adoption in developed countries lowers production costs and prices, impacting exports from China. Environmentally, despite producing more, China's CO2 emissions growth rate is significantly lower than expected, demonstrates that artificial intelligence has great potential in helping China reduce emissions. China's imports of embodied CO2 resulting from the export of energy-intensive products will be reduced. In contrast, the US, which may popularize artificial intelligence earlier, is reducing its CO2 emission intensity more slowly than China by 2035. Besides, with the growth of demand resulting from artificial intelligence, the US will export more embodied CO2 emissions overseas.
{"title":"The potential impact of artificial intelligence on CO2 emissions: A comparison between China and the US","authors":"Zuxu Chen , Yu Song","doi":"10.1016/j.techsoc.2026.103233","DOIUrl":"10.1016/j.techsoc.2026.103233","url":null,"abstract":"<div><div>To better address artificial intelligence challenges, a rational assessment of its impacts is essential. However, when estimating the influence of artificial intelligence, most studies have overlooked sectoral heterogeneity and regional competition, which are prevalent in reality. This paper constructs an analytical framework based on the GTAP-E model and input-output analysis to more effectively forecast artificial intelligence's intricate effects. The results show that both China and the US are estimated to achieve better GDP growth during 2025–2035, but the US growth rate is higher. Rising AI adoption in developed countries lowers production costs and prices, impacting exports from China. Environmentally, despite producing more, China's CO<sub>2</sub> emissions growth rate is significantly lower than expected, demonstrates that artificial intelligence has great potential in helping China reduce emissions. China's imports of embodied CO<sub>2</sub> resulting from the export of energy-intensive products will be reduced. In contrast, the US, which may popularize artificial intelligence earlier, is reducing its CO<sub>2</sub> emission intensity more slowly than China by 2035. Besides, with the growth of demand resulting from artificial intelligence, the US will export more embodied CO<sub>2</sub> emissions overseas.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"85 ","pages":"Article 103233"},"PeriodicalIF":12.5,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924202","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 : 2026-01-08DOI: 10.1016/j.techsoc.2026.103231
Fengxiu Zhou , Yinfeng Chen , Chien-Chiang Lee
Amid the concurrent trends of global climate governance and digital transformation, digital service trade networks (DSTNs) have become instrumental in reducing carbon emissions and strengthening national competitiveness. Using panel data from 38 OECD and BRICS countries between 2010 and 2022, this study applies social network analysis to characterize the evolution of the global DSTN and empirically investigates how countries’ embeddedness within this network—conceptualized as participation and dominance—affects carbon emissions. The results demonstrate that deeper network embeddedness significantly mitigates emissions, with a one-unit increase in participation reducing emissions by 0.2 %–0.3 %, and a comparable rise in dominance leading to a reduction of 0.3 %–0.8 %. The carbon emission reduction effects exhibit spatial and temporal heterogeneity among OECD countries and in the pre-pandemic period. Further quantile regression results show that this effect is nonlinear. Mechanism tests reveal two distinct pathways through which embeddedness operates—participation fosters industrial scaling, whereas dominance promotes optimization of the energy structure, with synergistic effects further enhancing the reduction in emissions. Spatial econometric models also confirm significant positive spillovers, reducing emission intensity in neighboring economies by 0.6 %–24.8 %. This study proposes a digital-green synergy framework for climate governance, underscoring the importance of harmonized digital trade policies, facilitated technology diffusion, and integrated low-carbon value chains to advance global carbon neutrality.
{"title":"Leveraging digital service trade for a low-carbon future: The roles of network embedding, spillovers, and policy pathways","authors":"Fengxiu Zhou , Yinfeng Chen , Chien-Chiang Lee","doi":"10.1016/j.techsoc.2026.103231","DOIUrl":"10.1016/j.techsoc.2026.103231","url":null,"abstract":"<div><div>Amid the concurrent trends of global climate governance and digital transformation, digital service trade networks (DSTNs) have become instrumental in reducing carbon emissions and strengthening national competitiveness. Using panel data from 38 OECD and BRICS countries between 2010 and 2022, this study applies social network analysis to characterize the evolution of the global DSTN and empirically investigates how countries’ embeddedness within this network—conceptualized as participation and dominance—affects carbon emissions. The results demonstrate that deeper network embeddedness significantly mitigates emissions, with a one-unit increase in participation reducing emissions by 0.2 %–0.3 %, and a comparable rise in dominance leading to a reduction of 0.3 %–0.8 %. The carbon emission reduction effects exhibit spatial and temporal heterogeneity among OECD countries and in the pre-pandemic period. Further quantile regression results show that this effect is nonlinear. Mechanism tests reveal two distinct pathways through which embeddedness operates—participation fosters industrial scaling, whereas dominance promotes optimization of the energy structure, with synergistic effects further enhancing the reduction in emissions. Spatial econometric models also confirm significant positive spillovers, reducing emission intensity in neighboring economies by 0.6 %–24.8 %. This study proposes a digital-green synergy framework for climate governance, underscoring the importance of harmonized digital trade policies, facilitated technology diffusion, and integrated low-carbon value chains to advance global carbon neutrality.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"85 ","pages":"Article 103231"},"PeriodicalIF":12.5,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145975893","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 : 2026-01-08DOI: 10.1016/j.techsoc.2026.103232
Wooi Seong Kam , Solon Magrizos , Michael Christofi
There is a dearth of knowledge on the use of generative Artificial Intelligence (AI) for social marketing, in particular blood donor recruitment and retention. This study aims to investigate the impact of AI-generated image on blood donation intention employing a 2 (AI-generated vs. non-AI-generated images) x 2 (Human vs AI disclaimer) factorial experiment. AI-generated and non-AI-generated images are comparably effective, suggesting the acceptability of AI-generated image for blood donation marketing and the possible role of homophily in driving blood donation intention. AI disclaimer produces negative bias and has negative interaction effect on both image types, suggesting the ability of AI disclaimer in activating respondents’ persuasion knowledge and posing a threat to their anthropocentric beliefs. This novel research contributes to the modelling of constructs for blood donation intention using an integrated approach resulting in an empirical conceptual framework which lays the foundation for future social marketing research.
{"title":"To AI or not to AI? The impact of generative AI image on blood donation intentions","authors":"Wooi Seong Kam , Solon Magrizos , Michael Christofi","doi":"10.1016/j.techsoc.2026.103232","DOIUrl":"10.1016/j.techsoc.2026.103232","url":null,"abstract":"<div><div>There is a dearth of knowledge on the use of generative Artificial Intelligence (AI) for social marketing, in particular blood donor recruitment and retention. This study aims to investigate the impact of AI-generated image on blood donation intention employing a 2 (AI-generated vs. non-AI-generated images) x 2 (Human vs AI disclaimer) factorial experiment. AI-generated and non-AI-generated images are comparably effective, suggesting the acceptability of AI-generated image for blood donation marketing and the possible role of homophily in driving blood donation intention. AI disclaimer produces negative bias and has negative interaction effect on both image types, suggesting the ability of AI disclaimer in activating respondents’ persuasion knowledge and posing a threat to their anthropocentric beliefs. This novel research contributes to the modelling of constructs for blood donation intention using an integrated approach resulting in an empirical conceptual framework which lays the foundation for future social marketing research.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"85 ","pages":"Article 103232"},"PeriodicalIF":12.5,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145975858","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}
Conversational AI companions—such as Replika and Character.AI—are increasingly adopted to provide emotional support, yet their psychological effects remain underexplored. This study investigates whether the use of AI companions is associated with enhanced well-being, and whether these associations are moderated by social network/support and loneliness. We analysed cross-sectional data from 14,721 Japanese adults participating in nationwide internet panel surveys conducted in December 2024 and January 2025. Well-being was assessed across three domains: evaluative (life satisfaction), hedonic (happiness), and eudaimonic (purpose and meaning in life). AI use was categorized as either companion or non-companion. Moderators included social network/support (measured via the Lubben Social Network Scale, LSNS-6) and loneliness (UCLA Loneliness Scale). Multivariable linear regression and restricted cubic spline models were used to assess associations and effect modification. Use of AI companions was significantly associated with higher scores across all well-being domains. In contrast, non-companion AI use showed weaker or inconsistent associations. A U-shaped moderation pattern emerged for friend-based social network/support: the benefits of AI companions were most pronounced among those with moderate levels of social connection and attenuated among those with either very high or very low levels. The strongest positive associations were observed among individuals reporting high loneliness. These findings suggest that AI companions may offer emotional and psychological benefits, particularly for individuals with unmet social and emotional needs or moderate social embeddedness. Future research should explore causal mechanisms and develop design strategies that promote well-being without impairing real-world social engagement.
{"title":"AI companions and subjective well-being: Moderation by social connectedness and loneliness","authors":"Atsushi Nakagomi , Yasuko Akutsu , Mika Yasuoka , Noriyuki Abe , Shiichi Ihara , Taisuke Teroh , Takahiro Tabuchi","doi":"10.1016/j.techsoc.2026.103229","DOIUrl":"10.1016/j.techsoc.2026.103229","url":null,"abstract":"<div><div>Conversational AI companions—such as Replika and Character.AI—are increasingly adopted to provide emotional support, yet their psychological effects remain underexplored. This study investigates whether the use of AI companions is associated with enhanced well-being, and whether these associations are moderated by social network/support and loneliness. We analysed cross-sectional data from 14,721 Japanese adults participating in nationwide internet panel surveys conducted in December 2024 and January 2025. Well-being was assessed across three domains: evaluative (life satisfaction), hedonic (happiness), and eudaimonic (purpose and meaning in life). AI use was categorized as either companion or non-companion. Moderators included social network/support (measured via the Lubben Social Network Scale, LSNS-6) and loneliness (UCLA Loneliness Scale). Multivariable linear regression and restricted cubic spline models were used to assess associations and effect modification. Use of AI companions was significantly associated with higher scores across all well-being domains. In contrast, non-companion AI use showed weaker or inconsistent associations. A U-shaped moderation pattern emerged for friend-based social network/support: the benefits of AI companions were most pronounced among those with moderate levels of social connection and attenuated among those with either very high or very low levels. The strongest positive associations were observed among individuals reporting high loneliness. These findings suggest that AI companions may offer emotional and psychological benefits, particularly for individuals with unmet social and emotional needs or moderate social embeddedness. Future research should explore causal mechanisms and develop design strategies that promote well-being without impairing real-world social engagement.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"85 ","pages":"Article 103229"},"PeriodicalIF":12.5,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924201","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 : 2026-01-06DOI: 10.1016/j.techsoc.2026.103228
Zongpu Yang , Usman Mehmood , Abdulateif A. Almulhim , Abdullah A. Aljughaiman
The global economic landscape has been increasingly shaped by technological disruption, demographic pressures, and external shocks such as the COVID-19 pandemic, raising urgent questions about what drives economic resilience (ER) in developing regions like ASEAN. This study investigates the determinants of ER across ten ASEAN countries from 2010 to 2023, focusing on population growth (PG), foreign investment (FI), digital economy (DE), talent (TLN), and technology (TECH). After confirming slope heterogeneity and cross-sectional dependence, unit root tests (CADF, CIPS) and Westerlund cointegration were applied, followed by the Method of Moments Quantile Regression (MMQR) as the main estimator. To account for global shocks and cross-sectional dependence, Augmented Mean Group (AMG) and Common Correlated Effects Mean Group (CCEMG) estimators were employed, while Fixed Effects (FE), Feasible Generalized Least Squares (FGLS), and Driscoll–Kraay errors were used for robustness. The results reveal significant heterogeneity across the ER distribution. FI, TLN, and TECH exhibit rising positive effects at higher quantiles, indicating that more resilient economies benefit more from capital inflows, education quality, and digital readiness. DE has a limited or mixed influence, becoming significant only under certain long-run models, while PG shows weak and inconsistent effects. Contrasts between MMQR and long-run estimators highlight that short-term resilience during shocks such as COVID-19 is shaped by digital infrastructure and institutional capacity, whereas long-run gains depend on regional integration and structural reform. Robustness checks largely affirm these patterns. The study concludes that ASEAN's resilience is shaped by both absorptive capacity and policy responsiveness. It underscores the need for inclusive digitalization, human capital development, and coordinated regional strategies to ensure that economic shocks translate into adaptive, rather than regressive, outcomes. These findings inform targeted reforms that can help ASEAN countries build resilience in a volatile and interconnected global economy.
{"title":"Economic resilience in ASEAN under global shocks: The roles of demography, investment, digital economy, and talent","authors":"Zongpu Yang , Usman Mehmood , Abdulateif A. Almulhim , Abdullah A. Aljughaiman","doi":"10.1016/j.techsoc.2026.103228","DOIUrl":"10.1016/j.techsoc.2026.103228","url":null,"abstract":"<div><div>The global economic landscape has been increasingly shaped by technological disruption, demographic pressures, and external shocks such as the COVID-19 pandemic, raising urgent questions about what drives economic resilience (ER) in developing regions like ASEAN. This study investigates the determinants of ER across ten ASEAN countries from 2010 to 2023, focusing on population growth (PG), foreign investment (FI), digital economy (DE), talent (TLN), and technology (TECH). After confirming slope heterogeneity and cross-sectional dependence, unit root tests (CADF, CIPS) and Westerlund cointegration were applied, followed by the Method of Moments Quantile Regression (MMQR) as the main estimator. To account for global shocks and cross-sectional dependence, Augmented Mean Group (AMG) and Common Correlated Effects Mean Group (CCEMG) estimators were employed, while Fixed Effects (FE), Feasible Generalized Least Squares (FGLS), and Driscoll–Kraay errors were used for robustness. The results reveal significant heterogeneity across the ER distribution. FI, TLN, and TECH exhibit rising positive effects at higher quantiles, indicating that more resilient economies benefit more from capital inflows, education quality, and digital readiness. DE has a limited or mixed influence, becoming significant only under certain long-run models, while PG shows weak and inconsistent effects. Contrasts between MMQR and long-run estimators highlight that short-term resilience during shocks such as COVID-19 is shaped by digital infrastructure and institutional capacity, whereas long-run gains depend on regional integration and structural reform. Robustness checks largely affirm these patterns. The study concludes that ASEAN's resilience is shaped by both absorptive capacity and policy responsiveness. It underscores the need for inclusive digitalization, human capital development, and coordinated regional strategies to ensure that economic shocks translate into adaptive, rather than regressive, outcomes. These findings inform targeted reforms that can help ASEAN countries build resilience in a volatile and interconnected global economy.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"85 ","pages":"Article 103228"},"PeriodicalIF":12.5,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924198","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 : 2026-01-06DOI: 10.1016/j.techsoc.2026.103213
Norzaidi Mohd Daud
The rise of generative artificial intelligence (AI) has created both opportunities and tensions in postgraduate education, where voluntary adoption is shaped not only by technical functionality but also by perceptions of trust, content quality, and academic integrity. This study extends technology adoption theories by proposing a dual-path model that distinguishes the drivers of voluntary adoption from those of techno-resistance—two processes often treated as identical in prior research. Using survey data from 170 postgraduate students in Malaysia, the findings demonstrate that functional trust (confidence in AI’s stability and reliability) significantly predicts voluntary usage (β = 0.168, p < 0.001). In contrast, evaluative trust (confidence in AI’s intellectual adequacy and academic validity) does not reduce resistance. This highlights an asymmetry in the role of trust, where technical dependability promotes adoption, but academic credibility does not automatically diminish resistance. The study also introduces the construct of epistemic utility, defined as the perceived richness, relevance, and scholarly value of AI-generated content. Results show that epistemic utility is the strongest predictor of adoption (β = 0.785, p < 0.001), underscoring students’ emphasis on content quality over technical reliability. Moreover, while system reliability reduces techno-resistance (β = −0.176, p = 0.034), adoption and resistance stem from distinct antecedents. Significantly, voluntary usage improves academic performance (β = 0.270) more than resistance hinders it (β = −0.210). Together, these findings advance theory by clarifying trust differentiation and introducing epistemic utility as a critical lens for understanding postgraduate engagement with AI.
生成式人工智能（AI）的兴起在研究生教育中既创造了机会，也带来了紧张，在研究生教育中，自愿采用不仅受到技术功能的影响，还受到信任、内容质量和学术诚信的影响。本研究扩展了技术采用理论，提出了一个双路径模型，将自愿采用的驱动因素与技术抵抗的驱动因素区分开来，这两个过程在之前的研究中通常被视为相同。使用来自马来西亚170名研究生的调查数据，研究结果表明，功能信任（对人工智能稳定性和可靠性的信心）显着预测自愿使用（β = 0.168, p < 0.001）。相比之下，评估性信任（对人工智能智力充分性和学术有效性的信心）并没有减少阻力。这凸显了信任角色的不对称，技术上的可靠性促进了采用，但学术上的可信度并不会自动减少抵制。该研究还介绍了认知效用的结构，定义为人工智能生成内容的感知丰富性、相关性和学术价值。结果显示，认知效用是采用的最强预测因子（β = 0.785, p < 0.001），强调学生对内容质量的重视超过技术可靠性。此外，虽然系统可靠性降低了技术阻力（β = - 0.176, p = 0.034），但采用和阻力源于不同的前因。值得注意的是，自愿使用手机能提高学习成绩（β = 0.270），而不使用手机会阻碍学习成绩（β = - 0.210）。总之，这些发现通过澄清信任差异和引入认知效用作为理解研究生参与人工智能的关键视角，推进了理论的发展。
{"title":"Beyond ‘good’ or ‘bad’: Investigating trust and techno-resistance in postgraduate students' voluntary use of AI technologies","authors":"Norzaidi Mohd Daud","doi":"10.1016/j.techsoc.2026.103213","DOIUrl":"10.1016/j.techsoc.2026.103213","url":null,"abstract":"<div><div>The rise of generative artificial intelligence (AI) has created both opportunities and tensions in postgraduate education, where voluntary adoption is shaped not only by technical functionality but also by perceptions of trust, content quality, and academic integrity. This study extends technology adoption theories by proposing a dual-path model that distinguishes the drivers of voluntary adoption from those of techno-resistance—two processes often treated as identical in prior research. Using survey data from 170 postgraduate students in Malaysia, the findings demonstrate that functional trust (confidence in AI’s stability and reliability) significantly predicts voluntary usage (β = 0.168, p < 0.001). In contrast, evaluative trust (confidence in AI’s intellectual adequacy and academic validity) does not reduce resistance. This highlights an asymmetry in the role of trust, where technical dependability promotes adoption, but academic credibility does not automatically diminish resistance. The study also introduces the construct of epistemic utility, defined as the perceived richness, relevance, and scholarly value of AI-generated content. Results show that epistemic utility is the strongest predictor of adoption (β = 0.785, p < 0.001), underscoring students’ emphasis on content quality over technical reliability. Moreover, while system reliability reduces techno-resistance (β = −0.176, p = 0.034), adoption and resistance stem from distinct antecedents. Significantly, voluntary usage improves academic performance (β = 0.270) more than resistance hinders it (β = −0.210). Together, these findings advance theory by clarifying trust differentiation and introducing epistemic utility as a critical lens for understanding postgraduate engagement with AI.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"85 ","pages":"Article 103213"},"PeriodicalIF":12.5,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924181","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 : 2026-01-06DOI: 10.1016/j.techsoc.2026.103224
Xiufeng Zhang , Litao Liu , Shujing Zhang
With the rise of the digital economy, data elements have emerged as a new production factor driving global technological innovation. However, existing research has not systematically examined their influence on enterprise technological innovation or the heterogeneity of these effects. This study investigates the boundaries and optimization pathways through which data elements foster enterprise innovation. Using China’s National Big Data Pilot Policy Zone as a quasi-natural experiment, it analyzes data from listed Chinese enterprises between 2011 and 2024, employing a multi-period difference-in-differences (MPDID) approach. The findings indicate that: (1) the pilot policy enhances enterprises’ integration of data elements into innovation activities, significantly increasing innovation levels. Data elements stimulate both innovation motivation and output, confirming their role as a key production factor; (2) data elements promote innovation through multiple channels, including improved supply chain transparency, reduced coordination costs, and the restructuring of upstream–downstream cooperation, facilitating enterprise innovation; (3) data elements primarily foster independent rather than joint technological innovation, revealing barriers related to data sharing and benefit alignment in cross-organizational collaboration; and (4) the innovation-empowering effects of data elements are mainly reflected on the extensive margin (scale expansion) rather than the intensive margin (efficiency improvement), indicating that enterprises prioritize the quantity over the quality of innovation. This study clarifies the mechanisms and constraints of data-driven technological innovation at the micro level and provides both theoretical and practical guidance for refining big data policies and advancing innovation empowered by data elements.
{"title":"Empowering effect of big data policies on enterprise technological innovation: Evidence from China","authors":"Xiufeng Zhang , Litao Liu , Shujing Zhang","doi":"10.1016/j.techsoc.2026.103224","DOIUrl":"10.1016/j.techsoc.2026.103224","url":null,"abstract":"<div><div>With the rise of the digital economy, data elements have emerged as a new production factor driving global technological innovation. However, existing research has not systematically examined their influence on enterprise technological innovation or the heterogeneity of these effects. This study investigates the boundaries and optimization pathways through which data elements foster enterprise innovation. Using China’s National Big Data Pilot Policy Zone as a quasi-natural experiment, it analyzes data from listed Chinese enterprises between 2011 and 2024, employing a multi-period difference-in-differences (MPDID) approach. The findings indicate that: (1) the pilot policy enhances enterprises’ integration of data elements into innovation activities, significantly increasing innovation levels. Data elements stimulate both innovation motivation and output, confirming their role as a key production factor; (2) data elements promote innovation through multiple channels, including improved supply chain transparency, reduced coordination costs, and the restructuring of upstream–downstream cooperation, facilitating enterprise innovation; (3) data elements primarily foster independent rather than joint technological innovation, revealing barriers related to data sharing and benefit alignment in cross-organizational collaboration; and (4) the innovation-empowering effects of data elements are mainly reflected on the extensive margin (scale expansion) rather than the intensive margin (efficiency improvement), indicating that enterprises prioritize the quantity over the quality of innovation. This study clarifies the mechanisms and constraints of data-driven technological innovation at the micro level and provides both theoretical and practical guidance for refining big data policies and advancing innovation empowered by data elements.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"85 ","pages":"Article 103224"},"PeriodicalIF":12.5,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924205","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 : 2026-01-06DOI: 10.1016/j.techsoc.2026.103222
Kyle S. Herman , Tatiana Gaitan Amortegui , Steve Griffiths
Artificial intelligence (AI) is increasingly invoked as a means of advancing climate and environmental solutions, yet how it shapes environmental technology (ET) development remains poorly understood. This study addresses that gap by asking: To what extent are AI and ETs converging, and which specific domains are driving this integration? To investigate these questions, we have constructed a new dataset of nearly 8000 AI-environmental (AI-ENVI) patents filed at the USPTO from 2003 through 2023. Drawing on this dataset, we perform semantic reclassification followed by forward citation mapping to identify influential innovations and to evaluate prominent domains of technological convergence. We find that three fields dominate: renewable energy optimization, electric vehicles, and fossil fuel efficiency/industrial decarbonization. To probe further, we examine metrics of novelty, disruptiveness, and generality. Novelty is highest in grid-level energy storage optimization; disruptiveness peaks in blockchain-based energy trading; and generality is strongest in the former as well as energy demand forecasting. Evidence that AI applications for incumbent industries attract high forward citations, and that carbon capture and storage (CCS) emerges as a disruptive subcategory, underscores the need for appropriate policy mechanisms to avoid carbon lock-in within the ET–AI nexus.
{"title":"Are AI and environmental technology innovations converging?","authors":"Kyle S. Herman , Tatiana Gaitan Amortegui , Steve Griffiths","doi":"10.1016/j.techsoc.2026.103222","DOIUrl":"10.1016/j.techsoc.2026.103222","url":null,"abstract":"<div><div>Artificial intelligence (AI) is increasingly invoked as a means of advancing climate and environmental solutions, yet how it shapes environmental technology (ET) development remains poorly understood. This study addresses that gap by asking: To what extent are AI and ETs converging, and which specific domains are driving this integration? To investigate these questions, we have constructed a new dataset of nearly 8000 AI-environmental (AI-ENVI) patents filed at the USPTO from 2003 through 2023. Drawing on this dataset, we perform semantic reclassification followed by forward citation mapping to identify influential innovations and to evaluate prominent domains of technological convergence. We find that three fields dominate: renewable energy optimization, electric vehicles, and fossil fuel efficiency/industrial decarbonization. To probe further, we examine metrics of novelty, disruptiveness, and generality. Novelty is highest in grid-level energy storage optimization; disruptiveness peaks in blockchain-based energy trading; and generality is strongest in the former as well as energy demand forecasting. Evidence that AI applications for incumbent industries attract high forward citations, and that carbon capture and storage (CCS) emerges as a disruptive subcategory, underscores the need for appropriate policy mechanisms to avoid carbon lock-in within the ET–AI nexus.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"85 ","pages":"Article 103222"},"PeriodicalIF":12.5,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145975889","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 : 2026-01-05DOI: 10.1016/j.techsoc.2026.103217
Alireza Moghayedi , Kathy Michell , Shalini Urs , Anh Tran , Jim Mason
The rapid digital transformation of urban environments is reshaping how citizens interact with public infrastructure. One emerging innovation is Digitalized Urban Public Facilities (DUPFs). While DUPFs are widely recognized for their operational and technological benefits, their social implications, particularly regarding inclusivity and social sustainability, remain underexplored. This study addresses this gap by examining how DUPF characteristics, user experiences, and socio-demographic profiles interact to shape perceptions of inclusivity and social sustainability. Adopting a multi-method quantitative research design, the study combines descriptive analysis and inferential modeling techniques. Drawing from a comprehensive literature review, a causal model is developed and validated using survey data collected from users across four global case studies. Through structural equation modeling (SEM) and moderation analysis, the findings reveal that DUPFs significantly enhance social sustainability, especially among marginalized and older users, who benefit most from improved accessibility, usability, and service responsiveness. The results further highlight that higher levels of digitalization and accessible information correlate strongly with perceived inclusivity. Moderation effects show that age and marginalization status amplify the positive impacts of DUPFs, while gender and income have minimal moderating influence. This study contributes novel insights into the social value of digital public services and provides actionable guidance for designing inclusive, user-centered DUPFs that advance equity and urban sustainability across diverse communities.
{"title":"Unlocking social sustainability and inclusivity of digitalized urban public Facilities: A Causal model across global case studies","authors":"Alireza Moghayedi , Kathy Michell , Shalini Urs , Anh Tran , Jim Mason","doi":"10.1016/j.techsoc.2026.103217","DOIUrl":"10.1016/j.techsoc.2026.103217","url":null,"abstract":"<div><div>The rapid digital transformation of urban environments is reshaping how citizens interact with public infrastructure. One emerging innovation is Digitalized Urban Public Facilities (DUPFs). While DUPFs are widely recognized for their operational and technological benefits, their social implications, particularly regarding inclusivity and social sustainability, remain underexplored. This study addresses this gap by examining how DUPF characteristics, user experiences, and socio-demographic profiles interact to shape perceptions of inclusivity and social sustainability. Adopting a multi-method quantitative research design, the study combines descriptive analysis and inferential modeling techniques. Drawing from a comprehensive literature review, a causal model is developed and validated using survey data collected from users across four global case studies. Through structural equation modeling (SEM) and moderation analysis, the findings reveal that DUPFs significantly enhance social sustainability, especially among marginalized and older users, who benefit most from improved accessibility, usability, and service responsiveness. The results further highlight that higher levels of digitalization and accessible information correlate strongly with perceived inclusivity. Moderation effects show that age and marginalization status amplify the positive impacts of DUPFs, while gender and income have minimal moderating influence. This study contributes novel insights into the social value of digital public services and provides actionable guidance for designing inclusive, user-centered DUPFs that advance equity and urban sustainability across diverse communities.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"85 ","pages":"Article 103217"},"PeriodicalIF":12.5,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145975857","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 : 2026-01-05DOI: 10.1016/j.techsoc.2026.103220
Wassili Lasarov , Stefan Hoffmann , Yogesh K. Dwivedi
Generative artificial intelligence (GenAI) tools have attracted worldwide attention, yet the drivers of their adoption remain insufficiently understood. This study develops and tests a comprehensive model of GenAI adoption that integrates two central value dimensions – functional value and parasocial value – alongside four classes of individual background factors: personality traits, hopes and fears regarding AI, beliefs about developer responsibility, and trust in GenAI. Using partial least squares structural equation modeling (PLS-SEM) with data from 638 participants, we examine both direct and mediated effects on adoption intention and conduct subgroup analyses for users with and without prior GenAI experience (Study 1). To capture adoption dynamics over time, we complement this analysis with a three-month follow-up study of 227 participants from the original sample (Study 2). Results show that functional value positively predicts adoption intention among both adopters and non-adopters, whereas parasocial value predicts adoption intention only among adopters. In addition, background factors indirectly shape adoption through their influence on perceived values. By demonstrating the differential role of functional versus parasocial value, introducing four classes of background factors, and incorporating a longitudinal design, this research advances understanding of GenAI acceptance and contributes to broader discussions of responsible innovation. The findings also carry implications for organizations and policymakers, who must encourage adoption while safeguarding against risks such as user manipulation or dependency.
{"title":"With a little help from my artificial friend: Functional and parasocial value in ChatGPT use","authors":"Wassili Lasarov , Stefan Hoffmann , Yogesh K. Dwivedi","doi":"10.1016/j.techsoc.2026.103220","DOIUrl":"10.1016/j.techsoc.2026.103220","url":null,"abstract":"<div><div>Generative artificial intelligence (GenAI) tools have attracted worldwide attention, yet the drivers of their adoption remain insufficiently understood. This study develops and tests a comprehensive model of GenAI adoption that integrates two central value dimensions – functional value and parasocial value – alongside four classes of individual background factors: personality traits, hopes and fears regarding AI, beliefs about developer responsibility, and trust in GenAI. Using partial least squares structural equation modeling (PLS-SEM) with data from 638 participants, we examine both direct and mediated effects on adoption intention and conduct subgroup analyses for users with and without prior GenAI experience (Study 1). To capture adoption dynamics over time, we complement this analysis with a three-month follow-up study of 227 participants from the original sample (Study 2). Results show that functional value positively predicts adoption intention among both adopters and non-adopters, whereas parasocial value predicts adoption intention only among adopters. In addition, background factors indirectly shape adoption through their influence on perceived values. By demonstrating the differential role of functional versus parasocial value, introducing four classes of background factors, and incorporating a longitudinal design, this research advances understanding of GenAI acceptance and contributes to broader discussions of responsible innovation. The findings also carry implications for organizations and policymakers, who must encourage adoption while safeguarding against risks such as user manipulation or dependency.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"85 ","pages":"Article 103220"},"PeriodicalIF":12.5,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924182","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}
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