Pub Date : 2025-01-15DOI: 10.1016/j.techfore.2025.123973
Wei Jiang , Yanhui Hu , Xiangyu Zhao
As the core driving force of the industry, artificial intelligence (AI) has brought technological progress that has a profound impact on carbon emission reduction. In this paper, the quantile unit root test, quantile cointegration test, quantile Granger-causality test, and quantile-on-quantile (QQ) technique are used to study the relationship between the AI index and carbon price in nine carbon emission trading schemes (ETS) in China from the start-up to July 26, 2022. In general, our empirical results show that in all nine ETSs, the AI index is the Granger cause of carbon prices at all quantiles, except for the Chongqing ETS; its causal relationship only exists in the extremely high tail. The QQ approach shows that there is a nonlinear relationship between the AI index and carbon prices, especially in the extremely low and extremely high tails. However, there are differences between markets and between different quantiles of AI and carbon prices within each market. Our results have important practical significance for policymakers, carbon emission enterprises, and investors.
{"title":"The impact of artificial intelligence on carbon market in China: Evidence from quantile-on-quantile regression approach","authors":"Wei Jiang , Yanhui Hu , Xiangyu Zhao","doi":"10.1016/j.techfore.2025.123973","DOIUrl":"10.1016/j.techfore.2025.123973","url":null,"abstract":"<div><div>As the core driving force of the industry, artificial intelligence (AI) has brought technological progress that has a profound impact on carbon emission reduction. In this paper, the quantile unit root test, quantile cointegration test, quantile Granger-causality test, and quantile-on-quantile (QQ) technique are used to study the relationship between the AI index and carbon price in nine carbon emission trading schemes (ETS) in China from the start-up to July 26, 2022. In general, our empirical results show that in all nine ETSs, the AI index is the Granger cause of carbon prices at all quantiles, except for the Chongqing ETS; its causal relationship only exists in the extremely high tail. The QQ approach shows that there is a nonlinear relationship between the AI index and carbon prices, especially in the extremely low and extremely high tails. However, there are differences between markets and between different quantiles of AI and carbon prices within each market. Our results have important practical significance for policymakers, carbon emission enterprises, and investors.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"212 ","pages":"Article 123973"},"PeriodicalIF":12.9,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-13DOI: 10.1016/j.techfore.2025.123980
Jinjin Hu , Dong Huo , Delin Wu , Chunhong Yang
This study investigates whether and how corporate agglomeration networks influence breakthrough innovation. Using a sample of 3097 firms, the research first demonstrates that corporate agglomeration networks facilitate breakthrough innovation, with digital technology adoption playing a synergistic role in this process. Through polynomial regression and mechanism analysis, the study further reveals that corporate agglomeration networks impact breakthrough innovation primarily through two mechanisms: information flow effect and cash flow effect. The impact of corporate agglomeration networks and digital technology adoption on breakthrough innovation exhibits heterogeneity across different technological industries and ownership types. This research enriches the study of corporate agglomeration networks and breakthrough innovation from both spatial and social perspectives, providing insights for corporate agglomeration and innovation policy.
{"title":"The effect of corporate agglomeration networks on breakthrough innovation—Evidence from China","authors":"Jinjin Hu , Dong Huo , Delin Wu , Chunhong Yang","doi":"10.1016/j.techfore.2025.123980","DOIUrl":"10.1016/j.techfore.2025.123980","url":null,"abstract":"<div><div>This study investigates whether and how corporate agglomeration networks influence breakthrough innovation. Using a sample of 3097 firms, the research first demonstrates that corporate agglomeration networks facilitate breakthrough innovation, with digital technology adoption playing a synergistic role in this process. Through polynomial regression and mechanism analysis, the study further reveals that corporate agglomeration networks impact breakthrough innovation primarily through two mechanisms: information flow effect and cash flow effect. The impact of corporate agglomeration networks and digital technology adoption on breakthrough innovation exhibits heterogeneity across different technological industries and ownership types. This research enriches the study of corporate agglomeration networks and breakthrough innovation from both spatial and social perspectives, providing insights for corporate agglomeration and innovation policy.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"212 ","pages":"Article 123980"},"PeriodicalIF":12.9,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-11DOI: 10.1016/j.techfore.2025.123984
Na Li, Yuxiang Chris Zhao, Jundong Zhang, Ying Yan, Qi Huang
With the spread of crowdsourcing models, open innovation communities are able to collect ideas quickly, but it is still a major challenge to select high-quality ideas from a large number of user contributions. Adopting a nonlinear and complex perspective, this study employs a stochastic cusp catastrophe model to shed light on idea selection. We utilised a unique dataset from 12 years of LEGO IDEAS to track the idea selection process over a six-month period at weekly intervals. First, we applied machine learning methods to identify three key factors influencing idea selection: “view,” “comment,” and “update.” Then, we performed a coordinate transformation based on the equilibrium surface in the cusp catastrophe model to build an idea selected catastrophe model. This model illustrates that idea selection in open innovation communities exhibits discontinuous catastrophe. Through catastrophe analysis, we categorized ideas into four types, each with distinct managerial value, highlighting the importance of focusing on “promising ideas” near the selection threshold. Furthermore, adjusting control variables along feasible paths can make previously unselected ideas into selected ones, facilitating the identification of high-quality ideas. Our research contributes to the existing literature on idea selection in open innovation communities, and provides practical insights for innovation managers.
{"title":"Exploring idea selection in open innovation communities: A stochastic cusp catastrophe model perspective","authors":"Na Li, Yuxiang Chris Zhao, Jundong Zhang, Ying Yan, Qi Huang","doi":"10.1016/j.techfore.2025.123984","DOIUrl":"10.1016/j.techfore.2025.123984","url":null,"abstract":"<div><div>With the spread of crowdsourcing models, open innovation communities are able to collect ideas quickly, but it is still a major challenge to select high-quality ideas from a large number of user contributions. Adopting a nonlinear and complex perspective, this study employs a stochastic cusp catastrophe model to shed light on idea selection. We utilised a unique dataset from 12 years of LEGO IDEAS to track the idea selection process over a six-month period at weekly intervals. First, we applied machine learning methods to identify three key factors influencing idea selection: “view,” “comment,” and “update.” Then, we performed a coordinate transformation based on the equilibrium surface in the cusp catastrophe model to build an idea selected catastrophe model. This model illustrates that idea selection in open innovation communities exhibits discontinuous catastrophe. Through catastrophe analysis, we categorized ideas into four types, each with distinct managerial value, highlighting the importance of focusing on “promising ideas” near the selection threshold. Furthermore, adjusting control variables along feasible paths can make previously unselected ideas into selected ones, facilitating the identification of high-quality ideas. Our research contributes to the existing literature on idea selection in open innovation communities, and provides practical insights for innovation managers.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"212 ","pages":"Article 123984"},"PeriodicalIF":12.9,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-11DOI: 10.1016/j.techfore.2025.123982
Thomas Draschbacher , Michael Rachinger , Mats Engwall
Bottlenecks have recently emerged as one of the key objects of inquiry in research on innovation ecosystems. The broader literature is split into two streams on technological and strategic bottlenecks, relying on the implicit assumption that strategic bottlenecks emerge from technological bottlenecks. In practice, however, many ecosystems get “stuck” in the transition from technological to strategic bottlenecks. This results in the formation of hybrid bottlenecks that combine the features of both technological and strategic bottlenecks. The existing recommendations regarding strategies that can be used to address bottlenecks fail to explain actors' strategic responses in these situations. We address this gap by conducting an exploratory multiple case study of strategies actors apply to address the hybrid bottleneck of public charging infrastructure in the innovation ecosystem of battery electric vehicles. We combine resource dependence theory and resource-based theory to show how actors combine different strategies to address hybrid bottlenecks based on how heavily they depend on the availability of bottleneck resources to create value in the innovation ecosystem, their expectations about the future value of these resources, and the ambiguity and uncertainty of the ecosystem's future evolution.
{"title":"To solve or to occupy: Addressing hybrid bottlenecks in innovation ecosystems","authors":"Thomas Draschbacher , Michael Rachinger , Mats Engwall","doi":"10.1016/j.techfore.2025.123982","DOIUrl":"10.1016/j.techfore.2025.123982","url":null,"abstract":"<div><div>Bottlenecks have recently emerged as one of the key objects of inquiry in research on innovation ecosystems. The broader literature is split into two streams on technological and strategic bottlenecks, relying on the implicit assumption that strategic bottlenecks emerge from technological bottlenecks. In practice, however, many ecosystems get “stuck” in the transition from technological to strategic bottlenecks. This results in the formation of hybrid bottlenecks that combine the features of both technological and strategic bottlenecks. The existing recommendations regarding strategies that can be used to address bottlenecks fail to explain actors' strategic responses in these situations. We address this gap by conducting an exploratory multiple case study of strategies actors apply to address the hybrid bottleneck of public charging infrastructure in the innovation ecosystem of battery electric vehicles. We combine resource dependence theory and resource-based theory to show how actors combine different strategies to address hybrid bottlenecks based on how heavily they depend on the availability of bottleneck resources to create value in the innovation ecosystem, their expectations about the future value of these resources, and the ambiguity and uncertainty of the ecosystem's future evolution.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"212 ","pages":"Article 123982"},"PeriodicalIF":12.9,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-10DOI: 10.1016/j.techfore.2024.123964
Guangyi Pan , Mengying Yang , Hao Tan , Hao Yang , Jintao Zhang
In this perspective paper, we argue that the broader economic, political, and geo-strategic considerations leading to a nationalist approach in the development, deployment, and use of COVID-19 vaccines remain largely unexplored in the existing literature. We propose to expand and reframe the current discourse on vaccine nationalism (VN). This involves examining nationalist practices and policies beyond merely securing vaccine access during the global COVID-19 vaccine shortage. We seek to identify the core characteristics of this nationalist approach to COVID-19 vaccines by drawing on existing nationalism literature. We then examine the root causes of vaccine nationalism from three distinctive yet interrelated perspectives, each aimed at uncovering its root causes: national security, technological catch-up, and rising geo-strategic competition in technology and ideology. Notably, our analysis of VN draws extensively on the vaccine-related policies and practices observed in China. By considering these perspectives and their interplay, we contend that a more holistic and nuanced understanding of vaccine nationalism can be achieved.
{"title":"Reconceptualizing vaccine nationalism: A multi-perspective analysis on security, technology, and global competition","authors":"Guangyi Pan , Mengying Yang , Hao Tan , Hao Yang , Jintao Zhang","doi":"10.1016/j.techfore.2024.123964","DOIUrl":"10.1016/j.techfore.2024.123964","url":null,"abstract":"<div><div>In this perspective paper, we argue that the broader economic, political, and geo-strategic considerations leading to a nationalist approach in the development, deployment, and use of COVID-19 vaccines remain largely unexplored in the existing literature. We propose to expand and reframe the current discourse on vaccine nationalism (VN). This involves examining nationalist practices and policies beyond merely securing vaccine access during the global COVID-19 vaccine shortage. We seek to identify the core characteristics of this nationalist approach to COVID-19 vaccines by drawing on existing nationalism literature. We then examine the root causes of vaccine nationalism from three distinctive yet interrelated perspectives, each aimed at uncovering its root causes: national security, technological catch-up, and rising geo-strategic competition in technology and ideology. Notably, our analysis of VN draws extensively on the vaccine-related policies and practices observed in China. By considering these perspectives and their interplay, we contend that a more holistic and nuanced understanding of vaccine nationalism can be achieved.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"212 ","pages":"Article 123964"},"PeriodicalIF":12.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-10DOI: 10.1016/j.techfore.2025.123983
Ting Pan, Boqiang Lin
Improving energy efficiency (EE) is an important way to realize the low-carbon transformation of heavily polluting enterprises (HPEs). Green credit policy (GCP) environmental governance has emerged as a key energy-saving strategy. The impact and primary mechanisms of GCP on HPEs' EE are examined in this research using a DID model and data from a Chinese corporate tax survey. It also compares how the decisions of relevant stakeholders from local governments, financial markets, and the public affect the correlation between GCP and EE. Research has found that: (1) HPEs' EE can be successfully increased with GCP. (2) The positive relationship between GCP and EE can be positively regulated by the degree of financial market growth and public environmental awareness, while local government environmental regulations play a negative regulatory role. (3) The GCP improves EE through three main channels: reducing energy use, improving environmental information disclosure, and encouraging technological innovation. (4) In areas with high marketization, high energy intensity, and large firms, the impact of GCP is more significant. By assessing the impact of GCP implementation, the paper offers specific recommendations for enhancing the green finance system and encouraging sustainable green development of businesses.
{"title":"Impact of green credit policy on energy efficiency: Empirical evidence from heavily polluting enterprises","authors":"Ting Pan, Boqiang Lin","doi":"10.1016/j.techfore.2025.123983","DOIUrl":"10.1016/j.techfore.2025.123983","url":null,"abstract":"<div><div>Improving energy efficiency (EE) is an important way to realize the low-carbon transformation of heavily polluting enterprises (HPEs). Green credit policy (GCP) environmental governance has emerged as a key energy-saving strategy. The impact and primary mechanisms of GCP on HPEs' EE are examined in this research using a DID model and data from a Chinese corporate tax survey. It also compares how the decisions of relevant stakeholders from local governments, financial markets, and the public affect the correlation between GCP and EE. Research has found that: (1) HPEs' EE can be successfully increased with GCP. (2) The positive relationship between GCP and EE can be positively regulated by the degree of financial market growth and public environmental awareness, while local government environmental regulations play a negative regulatory role. (3) The GCP improves EE through three main channels: reducing energy use, improving environmental information disclosure, and encouraging technological innovation. (4) In areas with high marketization, high energy intensity, and large firms, the impact of GCP is more significant. By assessing the impact of GCP implementation, the paper offers specific recommendations for enhancing the green finance system and encouraging sustainable green development of businesses.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"212 ","pages":"Article 123983"},"PeriodicalIF":12.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-07DOI: 10.1016/j.techfore.2025.123979
Riyan Hao, Chunqing Li
To explore the formation mechanism of consumers' experiences with AI chatbots, this study, grounded in the assemblage theory, focuses on the centrality of “interaction.” We selected AI chatbot conversational quality as the antecedent, with perceived chatbot competence/warmth, consumer creative self-efficacy, and rapport as mediators. Structural equation modeling and fuzzy-set qualitative comparative analysis (fsQCA) set qualitative comparative analysis were employed to test the theoretical model. The research results indicate that AI chatbot conversation quality and its various dimensions positively promote consumers' experiences with AI chatbots. The competence expansion path represented by “perceived competence - creative self-efficacy” and the emotional extension path represented by “perceived warmth - rapport” each play a chain mediation role in this process. The results of fsQCA support these findings and identify different antecedent configurations for enhancing consumers' experiences with AI chatbots. Moreover, they demonstrate the significant role of emotional factors in shaping a satisfying consumers' experience with AI chatbots over both short and long-time spans. The conclusions offer practical guidance for improving chatbot services and advancing human-chatbot interaction.
{"title":"How AI chatbots shape satisfactory experiences: A combined perspective of competence expansion and emotional extension","authors":"Riyan Hao, Chunqing Li","doi":"10.1016/j.techfore.2025.123979","DOIUrl":"10.1016/j.techfore.2025.123979","url":null,"abstract":"<div><div>To explore the formation mechanism of consumers' experiences with AI chatbots, this study, grounded in the assemblage theory, focuses on the centrality of “interaction.” We selected AI chatbot conversational quality as the antecedent, with perceived chatbot competence/warmth, consumer creative self-efficacy, and rapport as mediators. Structural equation modeling and fuzzy-set qualitative comparative analysis (fsQCA) set qualitative comparative analysis were employed to test the theoretical model. The research results indicate that AI chatbot conversation quality and its various dimensions positively promote consumers' experiences with AI chatbots. The competence expansion path represented by “perceived competence - creative self-efficacy” and the emotional extension path represented by “perceived warmth - rapport” each play a chain mediation role in this process. The results of fsQCA support these findings and identify different antecedent configurations for enhancing consumers' experiences with AI chatbots. Moreover, they demonstrate the significant role of emotional factors in shaping a satisfying consumers' experience with AI chatbots over both short and long-time spans. The conclusions offer practical guidance for improving chatbot services and advancing human-chatbot interaction.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"212 ","pages":"Article 123979"},"PeriodicalIF":12.9,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-07DOI: 10.1016/j.techfore.2024.123966
Ziye Zhang , Lijie Feng , Jinfeng Wang , Weiyu Zhao , Jingbo Yan
Flywheel energy storage (FES) technology, as one of the most promising energy storage technologies, has rapidly developed. It is essential to analyze the evolution path of advanced technology in this field and to predict its development trend and direction. However, some limitations remain in the existing research, which only uses a single feature to analyze technological innovation, fails to consider the development characteristics of technological innovation, and disregards the whole process analysis of the development trend of FES technology and the prediction of future development trends. Therefore, this study proposes a framework for technology evolution path identification and analysis that uses multisource data and incorporates citation and text features to monitor the evolution trend of FES technology and predict the future development direction of this technology. First, text and citation feature vectors from multisource data are extracted using shallow neural network embedding technology and then fused and spliced to obtain high-dimensional vectors that represent documents. Second, the time series of academic papers and patents filed in the last two decades are divided by the change point detection algorithm. Third, the Latent Dirichlet Allocation (LDA) model is applied to identify the topics of academic papers and patent data in different periods, and the cosine similarity calculation method is employed to construct the technical evolution path based on academic papers and patent data. Last, the gap between science and technology is analyzed, and the future development direction of FES technology is clarified.
{"title":"Identification of technology innovation path based on multi-feature vector fusion: The case of flywheel energy storage technology","authors":"Ziye Zhang , Lijie Feng , Jinfeng Wang , Weiyu Zhao , Jingbo Yan","doi":"10.1016/j.techfore.2024.123966","DOIUrl":"10.1016/j.techfore.2024.123966","url":null,"abstract":"<div><div>Flywheel energy storage (FES) technology, as one of the most promising energy storage technologies, has rapidly developed. It is essential to analyze the evolution path of advanced technology in this field and to predict its development trend and direction. However, some limitations remain in the existing research, which only uses a single feature to analyze technological innovation, fails to consider the development characteristics of technological innovation, and disregards the whole process analysis of the development trend of FES technology and the prediction of future development trends. Therefore, this study proposes a framework for technology evolution path identification and analysis that uses multisource data and incorporates citation and text features to monitor the evolution trend of FES technology and predict the future development direction of this technology. First, text and citation feature vectors from multisource data are extracted using shallow neural network embedding technology and then fused and spliced to obtain high-dimensional vectors that represent documents. Second, the time series of academic papers and patents filed in the last two decades are divided by the change point detection algorithm. Third, the Latent Dirichlet Allocation (LDA) model is applied to identify the topics of academic papers and patent data in different periods, and the cosine similarity calculation method is employed to construct the technical evolution path based on academic papers and patent data. Last, the gap between science and technology is analyzed, and the future development direction of FES technology is clarified.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"212 ","pages":"Article 123966"},"PeriodicalIF":12.9,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-06DOI: 10.1016/j.techfore.2025.123975
Simon Dang , Sara Quach , Robin E. Roberts
Most AI device adoption research prioritize immediate factors such as user needs and device functionality, while the complex and dynamic nature of time and individual differences in temporal perspectives are less frequently examined. This study addresses the impact of time in terms of individual differences on AI adoption behaviors, specifically highlighting how different time perspectives influence individuals' decision-making regarding AI device adoption. Machine learning techniques and structural equation modeling were employed to analyze how decision-making varies across temporal dimensions among adopters of AI smart speakers. The results show that individuals, regardless of being future- or present-oriented, show a preference for reasons supporting adoption over reasons against it, indicating a predominant cost-benefit consideration. No direct effects of time perspectives on adoption intentions were noted; rather, the influence of time perspectives is mediated through reasoning processes. Among examined sociodemographic factors, prior experience influences attitude and intentions positively, whereas education level significantly moderates the relationship between a future time perspective and the intention to adopt AI. This paper enriches the AI adoption literature by uniquely combining Behavioral Reasoning Theory with Time Perspective Theory, offering novel insights into the mediation role of reasoning processes in the relationship between time perspectives and adoption intentions.
{"title":"How time fuels AI device adoption: A contextual model enriched by machine learning","authors":"Simon Dang , Sara Quach , Robin E. Roberts","doi":"10.1016/j.techfore.2025.123975","DOIUrl":"10.1016/j.techfore.2025.123975","url":null,"abstract":"<div><div>Most AI device adoption research prioritize immediate factors such as user needs and device functionality, while the complex and dynamic nature of time and individual differences in temporal perspectives are less frequently examined. This study addresses the impact of time in terms of individual differences on AI adoption behaviors, specifically highlighting how different time perspectives influence individuals' decision-making regarding AI device adoption. Machine learning techniques and structural equation modeling were employed to analyze how decision-making varies across temporal dimensions among adopters of AI smart speakers. The results show that individuals, regardless of being future- or present-oriented, show a preference for reasons supporting adoption over reasons against it, indicating a predominant cost-benefit consideration. No direct effects of time perspectives on adoption intentions were noted; rather, the influence of time perspectives is mediated through reasoning processes. Among examined sociodemographic factors, prior experience influences attitude and intentions positively, whereas education level significantly moderates the relationship between a future time perspective and the intention to adopt AI. This paper enriches the AI adoption literature by uniquely combining Behavioral Reasoning Theory with Time Perspective Theory, offering novel insights into the mediation role of reasoning processes in the relationship between time perspectives and adoption intentions.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"212 ","pages":"Article 123975"},"PeriodicalIF":12.9,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-06DOI: 10.1016/j.techfore.2025.123977
Makhmoor Bashir , M. Muzamil Naqshbandi , Sudeepta Pradhan
The existing body of literature on work from anywhere (WFA) has shed light on various advantages for organizations, such as the ability to recruit and utilize talent on a global scale, the ability to address immigration challenges, and the potential for increased productivity. Additionally, employees are afforded the opportunity to enjoy geographical flexibility. However, very few studies have investigated the negative consequences of WFA on organizational outcomes. Drawing on the flexible firm theory, this study proposes a theoretical model to posit the direct and indirect impacts of WFA on knowledge hiding, socialization and distrust. Further, knowledge management capability is proposed as a moderator in the relationship between WFA and knowledge hiding. The hypotheses were tested on a sample of 340 respondents from Indian information technology firms in India. Interestingly, the findings highlight an insignificant impact of WFA on knowledge hiding. However, the impact of WFA on distrust and socialization was positive and significant. Additionally, the results from mediation and moderation were significant. The findings of this study provide valuable new insights in understanding the consequences of WFA and should lead to further dialogue and fuel further studies.
{"title":"How ‘work from anywhere’ impacts knowledge hiding, distrust, and socialization: The role of knowledge infrastructure","authors":"Makhmoor Bashir , M. Muzamil Naqshbandi , Sudeepta Pradhan","doi":"10.1016/j.techfore.2025.123977","DOIUrl":"10.1016/j.techfore.2025.123977","url":null,"abstract":"<div><div>The existing body of literature on work from anywhere (WFA) has shed light on various advantages for organizations, such as the ability to recruit and utilize talent on a global scale, the ability to address immigration challenges, and the potential for increased productivity. Additionally, employees are afforded the opportunity to enjoy geographical flexibility. However, very few studies have investigated the negative consequences of WFA on organizational outcomes. Drawing on the flexible firm theory, this study proposes a theoretical model to posit the direct and indirect impacts of WFA on knowledge hiding, socialization and distrust. Further, knowledge management capability is proposed as a moderator in the relationship between WFA and knowledge hiding. The hypotheses were tested on a sample of 340 respondents from Indian information technology firms in India. Interestingly, the findings highlight an insignificant impact of WFA on knowledge hiding. However, the impact of WFA on distrust and socialization was positive and significant. Additionally, the results from mediation and moderation were significant. The findings of this study provide valuable new insights in understanding the consequences of WFA and should lead to further dialogue and fuel further studies.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"212 ","pages":"Article 123977"},"PeriodicalIF":12.9,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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