Pub Date : 2025-12-13DOI: 10.1016/j.techsoc.2025.103203
Yun Zhong , Han Yan , Ziqian Xia
Against the backdrop of intensifying global climate change, corporate greenwashing behavior is attracting widespread attention from regulators and academia. This paper investigates whether the climate physical risk faced by a customer is transmitted through the supply chain, influencing the supplier's selective disclosure of carbon information. Using a sample of Chinese A-share listed companies from 2008 to 2023, we find a significant result: Suppliers exhibit a markedly reduced level of carbon greenwashing when their principal customers are exposed to more severe climate physical risks. Mechanism analysis suggests that this inhibitory effect primarily operates through increased scrutiny on carbon reduction verification and a decline in the supplier management's future expectations. Furthermore, this effect is stronger under specific conditions: when customer concentration is higher, the customer's digital technology level is more advanced, and the supplier's financial flexibility is poorer. Collectively, these findings not only provide new evidence on the cross-firm transmission mechanism of climate risk but also offer important micro-level implications for achieving global climate governance goals.
{"title":"Who is lifting the green veil? Climate physical risks and supply chain spillovers of corporate carbon greenwashing","authors":"Yun Zhong , Han Yan , Ziqian Xia","doi":"10.1016/j.techsoc.2025.103203","DOIUrl":"10.1016/j.techsoc.2025.103203","url":null,"abstract":"<div><div>Against the backdrop of intensifying global climate change, corporate greenwashing behavior is attracting widespread attention from regulators and academia. This paper investigates whether the climate physical risk faced by a customer is transmitted through the supply chain, influencing the supplier's selective disclosure of carbon information. Using a sample of Chinese A-share listed companies from 2008 to 2023, we find a significant result: Suppliers exhibit a markedly reduced level of carbon greenwashing when their principal customers are exposed to more severe climate physical risks. Mechanism analysis suggests that this inhibitory effect primarily operates through increased scrutiny on carbon reduction verification and a decline in the supplier management's future expectations. Furthermore, this effect is stronger under specific conditions: when customer concentration is higher, the customer's digital technology level is more advanced, and the supplier's financial flexibility is poorer. Collectively, these findings not only provide new evidence on the cross-firm transmission mechanism of climate risk but also offer important micro-level implications for achieving global climate governance goals.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"85 ","pages":"Article 103203"},"PeriodicalIF":12.5,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796947","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 : 2025-12-13DOI: 10.1016/j.techsoc.2025.103199
Raphael Gonda , Jaehun Park
In the wake of the emergence of ChatGPT as a transformative tool, effective policies and regulations for its integration into research and education are vital. This paper addresses challenges in the rapidly evolving AI landscape by identifying key events and discussions. To that end, the current study extracted insights from the academic community that had come during the early stages of ChatGPT's adoption. A dataset of 84,706 sentences sourced from Twitter (X.com) users in the research and academic community and collected over the course of eight months between November 2022 and June 2023 were examined using topic modeling and aspect-based sentiment analysis to explore prevailing reactions and perceptions. Nine key themes such as academic writing, coding, and time-saving tasks were identified. Strong sentiments emerged around policy debates, the issue of data security, and evolving research practices. Further, a causal analysis was performed to identify discussions and events that had triggered shifts in public sentiment. Examples include temporary restrictions on generative AI, new institutional policies, and legislative efforts to ensure responsible AI integration. This paper provides a timeline-linked perspective on how the academic community, thus far, has responded to generative AI. The findings can inform pragmatic, context-sensitive policies that foster innovation while safeguarding academic values.
{"title":"Charting the ChatGPT landscape: Insights from academic discourse on Twitter","authors":"Raphael Gonda , Jaehun Park","doi":"10.1016/j.techsoc.2025.103199","DOIUrl":"10.1016/j.techsoc.2025.103199","url":null,"abstract":"<div><div>In the wake of the emergence of ChatGPT as a transformative tool, effective policies and regulations for its integration into research and education are vital. This paper addresses challenges in the rapidly evolving AI landscape by identifying key events and discussions. To that end, the current study extracted insights from the academic community that had come during the early stages of ChatGPT's adoption. A dataset of 84,706 sentences sourced from Twitter (X.com) users in the research and academic community and collected over the course of eight months between November 2022 and June 2023 were examined using topic modeling and aspect-based sentiment analysis to explore prevailing reactions and perceptions. Nine key themes such as academic writing, coding, and time-saving tasks were identified. Strong sentiments emerged around policy debates, the issue of data security, and evolving research practices. Further, a causal analysis was performed to identify discussions and events that had triggered shifts in public sentiment. Examples include temporary restrictions on generative AI, new institutional policies, and legislative efforts to ensure responsible AI integration. This paper provides a timeline-linked perspective on how the academic community, thus far, has responded to generative AI. The findings can inform pragmatic, context-sensitive policies that foster innovation while safeguarding academic values.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"85 ","pages":"Article 103199"},"PeriodicalIF":12.5,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839461","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 : 2025-12-13DOI: 10.1016/j.techsoc.2025.103200
Matteo Cristofaro , Pier Luigi Giardino , Jeffrey Muldoon
Artificial intelligence (AI) is shaping entrepreneurial decision making today, increasingly informing opportunities recognition, assessment, and exploitation. Yet prior sector knowledge of entrepreneurs remains a fundamental pillar in these cognitive activities, providing the experiential schemas and contextual understanding that anchor entrepreneurial judgment. This study examines the interaction between two forces – AI-driven analysis and sector knowledge – and their influence on entrepreneurial outcomes, encompassing the recognition, assessment, and exploitation of opportunities. Using a controlled laboratory experiment with 124 entrepreneurs, we manipulate AI usage and measure prior sector knowledge to identify the independent and joint effects of these factors on entrepreneurial decision outcomes. Results show that AI increases the number of opportunities recognized and enhances the depth of opportunity assessment, exploitation, and contextual understanding. At the same time, AI reduces novelty in recognition and innovation in exploitation. Sector knowledge restores this creative dimension, enabling entrepreneurs to integrate intuitive insights with AI-supported deliberation. Entrepreneurs who combine AI and expertise achieve the most balanced outcomes, excelling simultaneously in novelty, depth, contextual understanding, and innovation. These results extend dual-process theories of cognition by demonstrating that prior knowledge conditions how AI reshapes the balance between intuitive and deliberative processes. Practically, the study highlights that the strategic value of AI in entrepreneurship lies not in substituting for human judgment but in complementing it with sector-specific expertise that anchors both originality and analytical rigor.
{"title":"Entrepreneurial decision-making in the age of AI: Sector knowledge at the balance of intuition and analysis","authors":"Matteo Cristofaro , Pier Luigi Giardino , Jeffrey Muldoon","doi":"10.1016/j.techsoc.2025.103200","DOIUrl":"10.1016/j.techsoc.2025.103200","url":null,"abstract":"<div><div>Artificial intelligence (AI) is shaping entrepreneurial decision making today, increasingly informing opportunities recognition, assessment, and exploitation. Yet prior sector knowledge of entrepreneurs remains a fundamental pillar in these cognitive activities, providing the experiential schemas and contextual understanding that anchor entrepreneurial judgment. This study examines the interaction between two forces – AI-driven analysis and sector knowledge – and their influence on entrepreneurial outcomes, encompassing the recognition, assessment, and exploitation of opportunities. Using a controlled laboratory experiment with 124 entrepreneurs, we manipulate AI usage and measure prior sector knowledge to identify the independent and joint effects of these factors on entrepreneurial decision outcomes. Results show that AI increases the number of opportunities recognized and enhances the depth of opportunity assessment, exploitation, and contextual understanding. At the same time, AI reduces novelty in recognition and innovation in exploitation. Sector knowledge restores this creative dimension, enabling entrepreneurs to integrate intuitive insights with AI-supported deliberation. Entrepreneurs who combine AI and expertise achieve the most balanced outcomes, excelling simultaneously in novelty, depth, contextual understanding, and innovation. These results extend dual-process theories of cognition by demonstrating that prior knowledge conditions how AI reshapes the balance between intuitive and deliberative processes. Practically, the study highlights that the strategic value of AI in entrepreneurship lies not in substituting for human judgment but in complementing it with sector-specific expertise that anchors both originality and analytical rigor.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"85 ","pages":"Article 103200"},"PeriodicalIF":12.5,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796997","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 : 2025-12-13DOI: 10.1016/j.techsoc.2025.103198
Cristina Voinea , Sebastian Porsdam Mann , Julian Savulescu , Brian D. Earp
Like the arrival of calculators in 1970s classrooms, large language models (LLMs) provoke both fears of intellectual deskilling and hopes of more efficient learning. In this paper we analyze the calculator analogy, arguing that while it is a useful starting point to understand the potential impact of LLMs in education, it is ultimately insufficient. We show where the analogy holds and, just as importantly, where its limitations reveal the unique pedagogical challenges posed by LLMs. These challenges arise from fundamental differences in how calculators and LLMs mediate learning, reflecting the distinct affordances of each technology. We argue that because of their affordances, realizing the educational potential of LLMs calls for cultivating epistemic virtues suited to human–AI interaction, such as patience, reflective engagement, or intellectual vigilance and humility. Equally, LLM design must actively foster these virtues through features like built-in prompts, feedback loops or reflective questions, to name just a few.
{"title":"The calculator analogy: Epistemic virtues for using LLMs","authors":"Cristina Voinea , Sebastian Porsdam Mann , Julian Savulescu , Brian D. Earp","doi":"10.1016/j.techsoc.2025.103198","DOIUrl":"10.1016/j.techsoc.2025.103198","url":null,"abstract":"<div><div>Like the arrival of calculators in 1970s classrooms, large language models (LLMs) provoke both fears of intellectual deskilling and hopes of more efficient learning. In this paper we analyze the calculator analogy, arguing that while it is a useful starting point to understand the potential impact of LLMs in education, it is ultimately insufficient. We show where the analogy holds and, just as importantly, where its limitations reveal the unique pedagogical challenges posed by LLMs. These challenges arise from fundamental differences in how calculators and LLMs mediate learning, reflecting the distinct affordances of each technology. We argue that because of their affordances, realizing the educational potential of LLMs calls for cultivating epistemic virtues suited to human–AI interaction, such as patience, reflective engagement, or intellectual vigilance and humility. Equally, LLM design must actively foster these virtues through features like built-in prompts, feedback loops or reflective questions, to name just a few.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"85 ","pages":"Article 103198"},"PeriodicalIF":12.5,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796998","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 : 2025-12-12DOI: 10.1016/j.techsoc.2025.103197
Yuhang Li , Chunhao Ma , Lisai Yu
Amid the rapid advancement of generative artificial intelligence (AI), public perceptions and emotional attitudes toward AI have become increasingly complex. Existing research has predominantly focused on micro-level aspects such as risk perception and technology acceptance, with limited attention to its connections with national identity and cultural sentiment. Adopting a nationalism perspective, this study employs computational social science methods to examine whether and how nationalistic expressions emerge in public discussions on the Chinese large language model DeepSeek on the social media platform Weibo. We developed a lexicon-based methodology for nationalism, structured around five dimensions: national pride, national revival, anti-foreign, techno-nationalism, and cultural nationalism. Leveraging large-scale text mining and the Analysis of Topic Model Networks (ANTMN), we identify two distinct discursive clusters named the Utility cluster and the Sociopolitical cluster, and further conducted a comparative analysis of how nationalism was discursively articulated within each cluster. The results show that discussions of DeepSeek prominently reflect nationalistic sentiment, with techno-nationalism emerging as the most salient dimension. Significant structural differences were observed between clusters in the ways nationalism is articulated. This study expands the theoretical scope of AI public opinion research, proposes a quantifiable framework for analyzing nationalism, and offers new empirical insights into the national symbolism and collective emotions embedded in contemporary AI technologies.
{"title":"Artificial intelligence and digital Nationalism: A social media discourse analysis","authors":"Yuhang Li , Chunhao Ma , Lisai Yu","doi":"10.1016/j.techsoc.2025.103197","DOIUrl":"10.1016/j.techsoc.2025.103197","url":null,"abstract":"<div><div>Amid the rapid advancement of generative artificial intelligence (AI), public perceptions and emotional attitudes toward AI have become increasingly complex. Existing research has predominantly focused on micro-level aspects such as risk perception and technology acceptance, with limited attention to its connections with national identity and cultural sentiment. Adopting a nationalism perspective, this study employs computational social science methods to examine whether and how nationalistic expressions emerge in public discussions on the Chinese large language model DeepSeek on the social media platform Weibo. We developed a lexicon-based methodology for nationalism, structured around five dimensions: national pride, national revival, anti-foreign, techno-nationalism, and cultural nationalism. Leveraging large-scale text mining and the Analysis of Topic Model Networks (ANTMN), we identify two distinct discursive clusters named the Utility cluster and the Sociopolitical cluster, and further conducted a comparative analysis of how nationalism was discursively articulated within each cluster. The results show that discussions of DeepSeek prominently reflect nationalistic sentiment, with techno-nationalism emerging as the most salient dimension. Significant structural differences were observed between clusters in the ways nationalism is articulated. This study expands the theoretical scope of AI public opinion research, proposes a quantifiable framework for analyzing nationalism, and offers new empirical insights into the national symbolism and collective emotions embedded in contemporary AI technologies.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"85 ","pages":"Article 103197"},"PeriodicalIF":12.5,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796952","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 : 2025-12-12DOI: 10.1016/j.techsoc.2025.103193
Dejian Yu , Aoqiu Shen , Wenjin Zuo
Firms face serious challenges in predicting the direction of innovation and identifying strategic partners in rapidly evolving technological fields, as traditional single-layer network models tend to ignore the synergies between inter-firm relationships and cross-field technology diffusion. To address this limitation, we combine the firm perspective with bi-layer patent networks to analyze synergistic technology diffusion links and predict the direction of innovation frontiers. We use a bi-layer diffusion-based patent network model that dynamically combines firm-level technology similarity networks with technology topic co-occurrence networks. By employing diffusion-based network analysis, the model quantifies the propagation of technological elements across organizational and disciplinary boundaries and identifies emerging technological directions and collaboration opportunities. The model is empirically validated on a dataset in the field of "Blockchain + Audit" to effectively predict future research directions and recommend potential technology development fields and partners for firms. This study provides valuable insights into the diffusion process of technology elements in bi-layer networks, and provides strategic insights to guide firms’ innovation decisions in dynamic technological environments.
{"title":"Cross-layer diffusion in patent networks: Forecasting innovation through enterprise–technology synergy","authors":"Dejian Yu , Aoqiu Shen , Wenjin Zuo","doi":"10.1016/j.techsoc.2025.103193","DOIUrl":"10.1016/j.techsoc.2025.103193","url":null,"abstract":"<div><div>Firms face serious challenges in predicting the direction of innovation and identifying strategic partners in rapidly evolving technological fields, as traditional single-layer network models tend to ignore the synergies between inter-firm relationships and cross-field technology diffusion. To address this limitation, we combine the firm perspective with bi-layer patent networks to analyze synergistic technology diffusion links and predict the direction of innovation frontiers. We use a bi-layer diffusion-based patent network model that dynamically combines firm-level technology similarity networks with technology topic co-occurrence networks. By employing diffusion-based network analysis, the model quantifies the propagation of technological elements across organizational and disciplinary boundaries and identifies emerging technological directions and collaboration opportunities. The model is empirically validated on a dataset in the field of \"Blockchain + Audit\" to effectively predict future research directions and recommend potential technology development fields and partners for firms. This study provides valuable insights into the diffusion process of technology elements in bi-layer networks, and provides strategic insights to guide firms’ innovation decisions in dynamic technological environments.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"85 ","pages":"Article 103193"},"PeriodicalIF":12.5,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796951","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 : 2025-12-11DOI: 10.1016/j.techsoc.2025.103192
Jinbao Wen , Xiang Yu , Wei Yang
This study examines the spatio-temporal shifts, driving mechanisms, and structural resilience of Global Innovation Networks (GINs) by leveraging transnational patent data from the World Intellectual Property Organization (2003–2023). Through Social Network Analysis (SNA), Temporal Exponential Random Graph Models (TERGM), and Network Resilience Assessment Modeling (NRAM), we deliver a dynamic and multi-level analysis of GINs. Findings indicate that GINs maintain small-world properties and a stable core-periphery architecture, while experiencing a marked eastward shift in influential nodes. The traditional Western-centered core has expanded to incorporate emerging economies such as China, India, and South Korea, signaling a decentralization of global innovation activity. TERGM results reveal multi-level drivers: endogenous structures such as reciprocity and triadic closure guide self-organization; actor attributes exhibit asymmetric effects, where patent protection strength, political stability, and market size attract innovation inflows, whereas economic scale and trade promote outflows; exogenous proximities show cultural similarity fosters connections, while geographic and administrative distances act as barriers. Notably, knowledge distance's constraining role weakens when accounting for structural embeddedness. NRAM assessments show that GIN resilience has strengthened over time, with improved tolerance to both targeted and random disruptions. Yet systemic vulnerability persists through a limited set of core nations (including the US, China, and Germany)—whose failure may trigger broad instability. By incorporating endogenous dynamics, seldom-studied exogenous factors, and resilience into a unified framework, this research advances GIN theory and offers strategic insights for governance and global patent planning amid systemic uncertainties.
{"title":"Spatio-temporal shifts, driving mechanisms, and resilience dynamics: Unraveling the evolution of global innovation networks (2003–2023)","authors":"Jinbao Wen , Xiang Yu , Wei Yang","doi":"10.1016/j.techsoc.2025.103192","DOIUrl":"10.1016/j.techsoc.2025.103192","url":null,"abstract":"<div><div>This study examines the spatio-temporal shifts, driving mechanisms, and structural resilience of Global Innovation Networks (GINs) by leveraging transnational patent data from the World Intellectual Property Organization (2003–2023). Through Social Network Analysis (SNA), Temporal Exponential Random Graph Models (TERGM), and Network Resilience Assessment Modeling (NRAM), we deliver a dynamic and multi-level analysis of GINs. Findings indicate that GINs maintain small-world properties and a stable core-periphery architecture, while experiencing a marked eastward shift in influential nodes. The traditional Western-centered core has expanded to incorporate emerging economies such as China, India, and South Korea, signaling a decentralization of global innovation activity. TERGM results reveal multi-level drivers: endogenous structures such as reciprocity and triadic closure guide self-organization; actor attributes exhibit asymmetric effects, where patent protection strength, political stability, and market size attract innovation inflows, whereas economic scale and trade promote outflows; exogenous proximities show cultural similarity fosters connections, while geographic and administrative distances act as barriers. Notably, knowledge distance's constraining role weakens when accounting for structural embeddedness. NRAM assessments show that GIN resilience has strengthened over time, with improved tolerance to both targeted and random disruptions. Yet systemic vulnerability persists through a limited set of core nations (including the US, China, and Germany)—whose failure may trigger broad instability. By incorporating endogenous dynamics, seldom-studied exogenous factors, and resilience into a unified framework, this research advances GIN theory and offers strategic insights for governance and global patent planning amid systemic uncertainties.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"85 ","pages":"Article 103192"},"PeriodicalIF":12.5,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145746922","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 : 2025-12-11DOI: 10.1016/j.techsoc.2025.103189
Yingwei (Wayne) Xu , Christina G. Chi , Dogan Gursoy , Ruiying (Raine) Cai
Building on Gestalt theory, this research introduces a holistic framework for defining artificial intelligence (AI) anthropomorphism, which guides the development of a parsimonious Scale of AI Anthropomorphism (SAIA). Through a systematic literature review, seven focus group discussions, and rigorous six-step scale development procedures, the research conceptualizes and validates a multidimensional framework that captures both external and internal human-like traits, including virtues and vices of anthropomorphic AI. Based on 2944 valid responses from five studies, a 40-item, six-dimensional parsimonious SAIA was developed, namely, human-like appearance, cognitive competency, adaptive capacity, social intelligence, morality, and fallibility. The scale demonstrated strong psychometric properties through comprehensive qualitative and quantitative validation. The SAIA serves as a robust tool for assessing the anthropomorphism of both tangible (e.g., robots) and intangible (e.g., chatbots) AI across normal service and service failure contexts.
{"title":"Rethinking AI anthropomorphism: A holistic conceptualization and scale across AI systems and service contexts","authors":"Yingwei (Wayne) Xu , Christina G. Chi , Dogan Gursoy , Ruiying (Raine) Cai","doi":"10.1016/j.techsoc.2025.103189","DOIUrl":"10.1016/j.techsoc.2025.103189","url":null,"abstract":"<div><div>Building on Gestalt theory, this research introduces a holistic framework for defining artificial intelligence (AI) anthropomorphism, which guides the development of a parsimonious Scale of AI Anthropomorphism (SAIA). Through a systematic literature review, seven focus group discussions, and rigorous six-step scale development procedures, the research conceptualizes and validates a multidimensional framework that captures both external and internal human-like traits, including virtues and vices of anthropomorphic AI. Based on 2944 valid responses from five studies, a 40-item, six-dimensional parsimonious SAIA was developed, namely, human-like appearance, cognitive competency, adaptive capacity, social intelligence, morality, and fallibility. The scale demonstrated strong psychometric properties through comprehensive qualitative and quantitative validation. The SAIA serves as a robust tool for assessing the anthropomorphism of both tangible (e.g., robots) and intangible (e.g., chatbots) AI across normal service and service failure contexts.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"85 ","pages":"Article 103189"},"PeriodicalIF":12.5,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796950","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 : 2025-12-11DOI: 10.1016/j.techsoc.2025.103196
Xiaodi Liu , Kum Fai Yuen , Miao Su , Xueqin Wang
Human–technology interactions are often infused with paradoxes. This study adopts a paradox perspective to examine the adoption of consumer-facing service technologies (CFSTs). Specifically, we explore how experienced tension and paradoxical mindset influence users' adoption intention through two paradoxical perceptual pathways: functional perceptions (efficiency versus inefficiency) and emotional perceptions (enjoyment versus exhaustion), with learning paradox acting as a mediating mechanism (Study 1). Contextual variations between human- and technology-dominant settings are also examined (Study 2). Data were collected through an online survey of 350 participants and analysed using structural equation modelling. Study 1 shows that both experienced tension and paradoxical mindset shape the paradoxical perceptions. Efficiency and enjoyment positively affect adoption, whereas inefficiency and exhaustion exert non-significant or negative effects. Nevertheless, all perceptions influence adoption positively through the learning paradox, which shows that users transform contradictory experiences into adaptive engagement. Study 2 provides further evidence of contextual differences: paradoxical perceptions are more pronounced in technology-dominant service contexts (e.g., autonomous delivery robot) than in human-dominant contexts (e.g., self-service locker).
{"title":"Paradoxical adoption of consumer-facing service technologies: Investigating the role of mindset, learning paradox, and technological context","authors":"Xiaodi Liu , Kum Fai Yuen , Miao Su , Xueqin Wang","doi":"10.1016/j.techsoc.2025.103196","DOIUrl":"10.1016/j.techsoc.2025.103196","url":null,"abstract":"<div><div>Human–technology interactions are often infused with paradoxes. This study adopts a paradox perspective to examine the adoption of consumer-facing service technologies (CFSTs). Specifically, we explore how experienced tension and paradoxical mindset influence users' adoption intention through two paradoxical perceptual pathways: functional perceptions (efficiency versus inefficiency) and emotional perceptions (enjoyment versus exhaustion), with learning paradox acting as a mediating mechanism (Study 1). Contextual variations between human- and technology-dominant settings are also examined (Study 2). Data were collected through an online survey of 350 participants and analysed using structural equation modelling. Study 1 shows that both experienced tension and paradoxical mindset shape the paradoxical perceptions. Efficiency and enjoyment positively affect adoption, whereas inefficiency and exhaustion exert non-significant or negative effects. Nevertheless, all perceptions influence adoption positively through the learning paradox, which shows that users transform contradictory experiences into adaptive engagement. Study 2 provides further evidence of contextual differences: paradoxical perceptions are more pronounced in technology-dominant service contexts (e.g., autonomous delivery robot) than in human-dominant contexts (e.g., self-service locker).</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"85 ","pages":"Article 103196"},"PeriodicalIF":12.5,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796999","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 : 2025-12-11DOI: 10.1016/j.techsoc.2025.103194
Vernika Agarwal , Zoubida Benmamoun , Misbah Anjum , Kaliyan Mathiyazhagan , Michael Akim , Marco Pironti
The pace at which cities are getting smart creates many dilemmas in trade-offs among technological advancement, sustainability and equity. As cities adopt artificial intelligence (AI), the Metaverse and other related technologies, they face persistent challenges of governance, infrastructure, digital equality, and environmental concerns. Although research is being done on the benefits of having smart cities, information on the challenges that restrain the implementation of smart cities is still at a nascent stage. This study uses the Best-Worst Method (BWM) that is a multi-criteria decision-making instrument to evaluate key urban innovation problems as to whether they are economically, socially, environmentally and technologically sustainable. This is performed with the use of systematized knowledge management methods, i.e., expert interviews, stakeholder workshops, and regular validation of the prioritization criteria identified and ranking the most crucial issues. The research contributes to the existing body of knowledge on smart cities by providing a clear decision-making structure that places the strategies of urban innovation in the context of the philosophy of knowledge-based sustainability.
{"title":"Urban innovation dilemmas: Tackling the challenges for urban growth in smart city","authors":"Vernika Agarwal , Zoubida Benmamoun , Misbah Anjum , Kaliyan Mathiyazhagan , Michael Akim , Marco Pironti","doi":"10.1016/j.techsoc.2025.103194","DOIUrl":"10.1016/j.techsoc.2025.103194","url":null,"abstract":"<div><div>The pace at which cities are getting smart creates many dilemmas in trade-offs among technological advancement, sustainability and equity. As cities adopt artificial intelligence (AI), the Metaverse and other related technologies, they face persistent challenges of governance, infrastructure, digital equality, and environmental concerns. Although research is being done on the benefits of having smart cities, information on the challenges that restrain the implementation of smart cities is still at a nascent stage. This study uses the Best-Worst Method (BWM) that is a multi-criteria decision-making instrument to evaluate key urban innovation problems as to whether they are economically, socially, environmentally and technologically sustainable. This is performed with the use of systematized knowledge management methods, i.e., expert interviews, stakeholder workshops, and regular validation of the prioritization criteria identified and ranking the most crucial issues. The research contributes to the existing body of knowledge on smart cities by providing a clear decision-making structure that places the strategies of urban innovation in the context of the philosophy of knowledge-based sustainability.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"85 ","pages":"Article 103194"},"PeriodicalIF":12.5,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796949","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号