Pub Date : 2026-04-01Epub Date: 2026-01-22DOI: 10.1016/j.techfore.2026.124545
Jun-Phil Uhm , Kun Chang , Sanghoon Kim , Hyun-Woo Lee
Olympic virtual series made its inaugural debut as the first Olympic-licensed virtual sports, yet whether audiences will perceive them as legitimate official Olympic events is unknown. Drawing on the environmental psychology model and transportation theory, we examined the relationship between the audience's perceived virtual gaming atmosphere, sense of presence, positive emotions, and perceived legitimacy of the Olympic virtual series. 339 Olympic virtual series spectators were included in the analysis of a serial mediation model using PROCESS macro. The findings revealed that game atmosphere was a significant factor in establishing audiences' sense of presence, positive emotion, and perceived legitimacy. The mediation effects of presence and positive emotion on the relationship between the game atmosphere and perceived legitimacy were also significant. This study contributed to the media communication literature and provided useful practical implications for the International Olympic Committee regarding how to enhance spectators' Olympic digital experience.
{"title":"Digitalization of the Olympics and legitimacy of the Olympic virtual series: An environmental psychology perspective","authors":"Jun-Phil Uhm , Kun Chang , Sanghoon Kim , Hyun-Woo Lee","doi":"10.1016/j.techfore.2026.124545","DOIUrl":"10.1016/j.techfore.2026.124545","url":null,"abstract":"<div><div>Olympic virtual series made its inaugural debut as the first Olympic-licensed virtual sports, yet whether audiences will perceive them as legitimate official Olympic events is unknown. Drawing on the environmental psychology model and transportation theory, we examined the relationship between the audience's perceived virtual gaming atmosphere, sense of presence, positive emotions, and perceived legitimacy of the Olympic virtual series. 339 Olympic virtual series spectators were included in the analysis of a serial mediation model using PROCESS macro. The findings revealed that game atmosphere was a significant factor in establishing audiences' sense of presence, positive emotion, and perceived legitimacy. The mediation effects of presence and positive emotion on the relationship between the game atmosphere and perceived legitimacy were also significant. This study contributed to the media communication literature and provided useful practical implications for the International Olympic Committee regarding how to enhance spectators' Olympic digital experience.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"225 ","pages":"Article 124545"},"PeriodicalIF":13.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038281","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 : 2026-04-01Epub Date: 2026-01-12DOI: 10.1016/j.techfore.2025.124512
René Abreu-Ledón , Darkys E. Luján-García , Pedro Garrido-Vega , Jose A.D. Machuca , Yodaira Borroto-Pentón
Technological advances—and, more recently, the COVID-19 pandemic—have accelerated the growth of online purchasing, prompting a surge in empirical research on online consumer behavior. A substantial share of these studies rely on structural equation modeling (SEM) for data analysis. However, inadequate or improper application of SEM can produce unreliable results and misleading conclusions, undermining scientific progress and managerial decision-making. To address this critical concern, the present study provides, to the best of our knowledge, the first comprehensive assessment of SEM applications—encompassing both covariance-based structural equation modeling (CB-SEM) and partial least squares structural equation modeling (PLS-SEM)— in online purchase intention (OPI) research. Our review covers 120 empirical articles published between 2000 and 2023 and reveals that methodological requirements of SEM are often overlooked, which risks invalidating both theoretical contributions and managerial implications. In response, we offer practical recommendations and a results-based guide to assist researchers and reviewers in enhancing the rigor, reliability, and decision-oriented value of SEM studies in this field.
{"title":"Evaluating the use of structural equation modeling in online purchase intention research: A comprehensive review (2000−2023)","authors":"René Abreu-Ledón , Darkys E. Luján-García , Pedro Garrido-Vega , Jose A.D. Machuca , Yodaira Borroto-Pentón","doi":"10.1016/j.techfore.2025.124512","DOIUrl":"10.1016/j.techfore.2025.124512","url":null,"abstract":"<div><div>Technological advances—and, more recently, the COVID-19 pandemic—have accelerated the growth of online purchasing, prompting a surge in empirical research on online consumer behavior. A substantial share of these studies rely on structural equation modeling (SEM) for data analysis. However, inadequate or improper application of SEM can produce unreliable results and misleading conclusions, undermining scientific progress and managerial decision-making. To address this critical concern, the present study provides, to the best of our knowledge, the first comprehensive assessment of SEM applications—encompassing both covariance-based structural equation modeling (CB-SEM) and partial least squares structural equation modeling (PLS-SEM)— in online purchase intention (OPI) research. Our review covers 120 empirical articles published between 2000 and 2023 and reveals that methodological requirements of SEM are often overlooked, which risks invalidating both theoretical contributions and managerial implications. In response, we offer practical recommendations and a results-based guide to assist researchers and reviewers in enhancing the rigor, reliability, and decision-oriented value of SEM studies in this field.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"225 ","pages":"Article 124512"},"PeriodicalIF":13.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980365","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 : 2026-04-01Epub Date: 2026-01-08DOI: 10.1016/j.techfore.2025.124515
Yu Su , Xuan Feng
This study is positioned within Responsible AI practice in energy markets, which exhibit inherent volatility and complexity. We integrate classical and modern machine learning techniques for enhanced energy price correlation forecasting. Principal Component Analysis (PCA) is employed for dimensionality reduction to identify underlying factors driving energy price correlations, leveraging its interpretability as a key analytical advantage. Long Short-Term Memory (LSTM) networks are then introduced for time-series modeling of energy prices and their inter-correlations.
Using a controlled simulation experiment, we empirically compare PCA-based and LSTM approaches in predicting energy price co-movements. While PCA provides transparent insights into correlation structure with low computation cost, LSTM achieves higher predictive accuracy (8.7% lower MES, 11.4% lower MAE) by capturing nonlinear temporal dependencies. The analysis highlights a governance-performance trade-off between PCA's interpretability and deep learning's precision, suggesting that model choice should be aligned with institutional capacity, regulatory requirements, and deployment constraints. These findings have significant implications for a technology-driven circular economy transitions, demonstrating how improved predictive modeling can enhance renewable integration and energy efficiency in energy markets.
{"title":"Machine learning approaches to predicting energy price correlation: From a responsible AI perspective","authors":"Yu Su , Xuan Feng","doi":"10.1016/j.techfore.2025.124515","DOIUrl":"10.1016/j.techfore.2025.124515","url":null,"abstract":"<div><div>This study is positioned within Responsible AI practice in energy markets, which exhibit inherent volatility and complexity. We integrate classical and modern machine learning techniques for enhanced energy price correlation forecasting. Principal Component Analysis (PCA) is employed for dimensionality reduction to identify underlying factors driving energy price correlations, leveraging its interpretability as a key analytical advantage. Long Short-Term Memory (LSTM) networks are then introduced for time-series modeling of energy prices and their inter-correlations.</div><div>Using a controlled simulation experiment, we empirically compare PCA-based and LSTM approaches in predicting energy price co-movements. While PCA provides transparent insights into correlation structure with low computation cost, LSTM achieves higher predictive accuracy (8.7% lower MES, 11.4% lower MAE) by capturing nonlinear temporal dependencies. The analysis highlights a governance-performance trade-off between PCA's interpretability and deep learning's precision, suggesting that model choice should be aligned with institutional capacity, regulatory requirements, and deployment constraints. These findings have significant implications for a technology-driven circular economy transitions, demonstrating how improved predictive modeling can enhance renewable integration and energy efficiency in energy markets.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"225 ","pages":"Article 124515"},"PeriodicalIF":13.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929257","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 : 2026-04-01Epub Date: 2026-01-20DOI: 10.1016/j.techfore.2026.124555
Marta Leocata , Giulia Livieri , Silvia Morlacchi , Fausto Corvino , Franco Flandoli , Alberto Eugenio Ermenegildo Pirni
Household adoption of rooftop photovoltaic (PV) systems is central to the green energy transition, yet diffusion depends on social influence and behavioral biases, as well as payback economics. This study develops a parsimonious Markovian model in which households move sequentially from being unengaged (“Carbon”) to informed, to planning, and finally to adoption (“Green”). Transition rates are micro-founded by two mechanisms: (i) social contagion/communication, proxied by the current share of adopters, and (ii) economic profitability, proxied by payback time computed from a Net Present Value framework. Novel to this diffusion setting, bounded rationality is introduced via hyperbolic discounting, creating a procrastination loop that delays adoption even when PV is economically attractive in a long-run perspective. Calibrated on the Italian residential PV diffusion path (2006–2020) and assessed in national and regional applications, the model reproduces observed trajectories and enables forward-looking scenario analysis (2020–2026). Results show that policies yielding similar payback improvements can produce different outcomes once present bias is accounted for and that behaviorally informed intervention are stronger. The findings contribute a micro-to-macro bridge between behavioral economics and technology diffusion modeling and imply that effective policy portfolios (and PV business models) should complement incentives with commitment devices and social-norm peer strategies to accelerate PV uptake and its spillover emissions benefits.
{"title":"Understanding the householder solar panel consumer: A Markovian model and its societal implications","authors":"Marta Leocata , Giulia Livieri , Silvia Morlacchi , Fausto Corvino , Franco Flandoli , Alberto Eugenio Ermenegildo Pirni","doi":"10.1016/j.techfore.2026.124555","DOIUrl":"10.1016/j.techfore.2026.124555","url":null,"abstract":"<div><div>Household adoption of rooftop photovoltaic (PV) systems is central to the green energy transition, yet diffusion depends on social influence and behavioral biases, as well as payback economics. This study develops a parsimonious Markovian model in which households move sequentially from being unengaged (“Carbon”) to informed, to planning, and finally to adoption (“Green”). Transition rates are micro-founded by two mechanisms: (i) social contagion/communication, proxied by the current share of adopters, and (ii) economic profitability, proxied by payback time computed from a Net Present Value framework. Novel to this diffusion setting, bounded rationality is introduced via hyperbolic discounting, creating a procrastination loop that delays adoption even when PV is economically attractive in a long-run perspective. Calibrated on the Italian residential PV diffusion path (2006–2020) and assessed in national and regional applications, the model reproduces observed trajectories and enables forward-looking scenario analysis (2020–2026). Results show that policies yielding similar payback improvements can produce different outcomes once present bias is accounted for and that behaviorally informed intervention are stronger. The findings contribute a micro-to-macro bridge between behavioral economics and technology diffusion modeling and imply that effective policy portfolios (and PV business models) should complement incentives with commitment devices and social-norm peer strategies to accelerate PV uptake and its spillover emissions benefits.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"225 ","pages":"Article 124555"},"PeriodicalIF":13.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038276","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 : 2026-04-01Epub Date: 2026-01-07DOI: 10.1016/j.techfore.2025.124488
Likun Cao , James Evans
This study offers a new perspective on the depth-versus-breadth debate in innovation strategy by modeling inventive search within dynamic collective knowledge systems and underscoring the importance of timing for technological impact. Using frontier machine learning to project patent citation networks in hyperbolic space, we analyze 4.9 million U.S. patents to examine how search strategies give rise to distinct temporal patterns of impact accumulation. We find that inventions based on deep search, which relies on a specialized understanding of the complex structure of recombination, drive higher short-term impact through early adoption within specialized communities, but face diminishing returns as innovations become “locked-in” with limited diffusion potential. Conversely, when inventions are grounded in broad search that spans disparate domains, they encounter initial resistance but achieve wider diffusion and greater long-term impact by reaching cognitively diverse audiences. Individual inventions require both depth and breadth for stable impact. Organizations can strategically balance approaches across multiple inventions: using depth to build reliable technological infrastructure while pursuing breadth to expand applications. We advance innovation theory by demonstrating how deep and broad search strategies distinctly shape the timing and trajectory of technological impact, and how individual inventors and organizations can leverage these mechanisms to balance exploitation and exploration.
{"title":"Deep versus broad technology search and the timing of innovation impact","authors":"Likun Cao , James Evans","doi":"10.1016/j.techfore.2025.124488","DOIUrl":"10.1016/j.techfore.2025.124488","url":null,"abstract":"<div><div>This study offers a new perspective on the depth-versus-breadth debate in innovation strategy by modeling inventive search within dynamic collective knowledge systems and underscoring the importance of timing for technological impact. Using frontier machine learning to project patent citation networks in hyperbolic space, we analyze 4.9 million U.S. patents to examine how search strategies give rise to distinct temporal patterns of impact accumulation. We find that inventions based on deep search, which relies on a specialized understanding of the complex structure of recombination, drive higher short-term impact through early adoption within specialized communities, but face diminishing returns as innovations become “locked-in” with limited diffusion potential. Conversely, when inventions are grounded in broad search that spans disparate domains, they encounter initial resistance but achieve wider diffusion and greater long-term impact by reaching cognitively diverse audiences. Individual inventions require both depth and breadth for stable impact. Organizations can strategically balance approaches across multiple inventions: using depth to build reliable technological infrastructure while pursuing breadth to expand applications. We advance innovation theory by demonstrating how deep and broad search strategies distinctly shape the timing and trajectory of technological impact, and how individual inventors and organizations can leverage these mechanisms to balance exploitation and exploration.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"225 ","pages":"Article 124488"},"PeriodicalIF":13.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904108","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}
This study utilizes Cognitive Dissonance Theory to empirically investigate how ‘AI washing’, the discrepancy between AI narratives and actual capabilities, affects the corporate technological gap. Using panel data from China's A-share listed firms (2007–2022), the findings establish a significant inverted U-shaped relationship between ‘AI washing’ and the technological gap. Mediation analysis confirms this relationship is channeled through both internal R&D investment and industry-level R&D investment. Moderation analysis reveals that strong AI-enabled participatory learning capability flattens the inverted U-curve, indicating earlier corrective action. Conversely, high investor sentiment is shown to steepen the curve. Furthermore, the nonlinear effect is subdued for firms in national AI pilot zones or high-technology-intensive industries. This research advances ‘AI washing’ literature through quantitative analysis, extends Cognitive Dissonance Theory to the domain of technology strategy, and offers empirical insights for responsible AI governance.
{"title":"Unveiling AI washing: Bridging corporate technological gaps through a cognitive dissonance lens","authors":"Zhe Sun , Yujun Wen , Liang Zhao , Intesar Almugren , Aradhana Galgotia","doi":"10.1016/j.techfore.2025.124511","DOIUrl":"10.1016/j.techfore.2025.124511","url":null,"abstract":"<div><div>This study utilizes Cognitive Dissonance Theory to empirically investigate how ‘AI washing’, the discrepancy between AI narratives and actual capabilities, affects the corporate technological gap. Using panel data from China's A-share listed firms (2007–2022), the findings establish a significant inverted U-shaped relationship between ‘AI washing’ and the technological gap. Mediation analysis confirms this relationship is channeled through both internal R&D investment and industry-level R&D investment. Moderation analysis reveals that strong AI-enabled participatory learning capability flattens the inverted U-curve, indicating earlier corrective action. Conversely, high investor sentiment is shown to steepen the curve. Furthermore, the nonlinear effect is subdued for firms in national AI pilot zones or high-technology-intensive industries. This research advances ‘AI washing’ literature through quantitative analysis, extends Cognitive Dissonance Theory to the domain of technology strategy, and offers empirical insights for responsible AI governance.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"225 ","pages":"Article 124511"},"PeriodicalIF":13.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904110","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}
Today, companies increasingly utilize customer-driven approaches like Quality Function Deployment (QFD) in new product development (NPD) to differentiate their products from competitors and satisfy customer needs and demands. Despite QFD's many capabilities in product design based on customer opinions, its practical implementation faces numerous challenges, such as heavy reliance on personal opinions for identifying and assessing the importance of customer requirements (CRs) and engineering characteristics (ECs), as well as the difficulty of understanding interactions between them. This problem worsens when there are connections and correlations between the identified requirements and characteristics. Given the benefits of extracting CRs from product reviews over traditional methods such as questionnaires and interviews, this study proposes a data-driven framework that combines data mining techniques and multi-attribute decision-making to tackle these issues. In this framework, online customer reviews (OCRs) are considered at all stages of NPD to maximize customer involvement, and association rule mining (ARM) is employed to discover causal relationships and weights among CRs, ECs, and their interactions. Additionally, by applying the Fuzzy Cognitive Map (FCM) method and integrating it with QFD, the relationships between CRs and ECs are analyzed, and CRs are prioritized accordingly. To demonstrate the practical application of this data-driven development framework, it is applied to the development of a mobile phone product using OCRs from Amazon.
{"title":"A data-driven framework for new product development: Integrating QFD and FCM based on online customer reviews","authors":"Romina Raafat , Jalil Heidary Dahooie , Edwin Garces , Tugrul Daim","doi":"10.1016/j.techfore.2025.124523","DOIUrl":"10.1016/j.techfore.2025.124523","url":null,"abstract":"<div><div>Today, companies increasingly utilize customer-driven approaches like Quality Function Deployment (QFD) in new product development (NPD) to differentiate their products from competitors and satisfy customer needs and demands. Despite QFD's many capabilities in product design based on customer opinions, its practical implementation faces numerous challenges, such as heavy reliance on personal opinions for identifying and assessing the importance of customer requirements (CRs) and engineering characteristics (ECs), as well as the difficulty of understanding interactions between them. This problem worsens when there are connections and correlations between the identified requirements and characteristics. Given the benefits of extracting CRs from product reviews over traditional methods such as questionnaires and interviews, this study proposes a data-driven framework that combines data mining techniques and multi-attribute decision-making to tackle these issues. In this framework, online customer reviews (OCRs) are considered at all stages of NPD to maximize customer involvement, and association rule mining (ARM) is employed to discover causal relationships and weights among CRs, ECs, and their interactions. Additionally, by applying the Fuzzy Cognitive Map (FCM) method and integrating it with QFD, the relationships between CRs and ECs are analyzed, and CRs are prioritized accordingly. To demonstrate the practical application of this data-driven development framework, it is applied to the development of a mobile phone product using OCRs from Amazon.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"225 ","pages":"Article 124523"},"PeriodicalIF":13.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980362","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 : 2026-04-01Epub Date: 2026-01-16DOI: 10.1016/j.techfore.2026.124537
Bing Li , Kun Ding , Vincent Larivière
This study investigates the relationship between interdisciplinary research and technological change, using paper-patent citations. Technological change is defined as the extent to which a technology consolidates or disrupts existing technologies, patents can be categorized into three types: destabilizing, consolidating, and moderating. Drawing on all journal articles published in 2002 and indexed in the Web of Science database and all patents from the USPTO, our study reveals that research papers have a relatively minor impact on destabilizing patents compared to moderating and consolidating patents. Particularly noteworthy is the significant contribution of papers in the field of Biomedical Research to technological advancements. Analyzing distinct dimensions of interdisciplinary research—variety, balance, disparity, and the Rao-Stirling index—we find that destabilizing patent citations decrease with both variety and the Rao-Stirling index increase. The correlation with balance exhibits a U-shaped relationship, but we observe no significant relationship with disparity. Moreover, consolidating patent citations demonstrate an increase with disparity and the Rao-Stirling index, while showing a decrease in relation to balance, but no negligible association is found with variety. Moderate patent citations increase with both variety and balance, and decrease with both disparity and the Rao-Stirling index.
本研究以论文专利引文为工具，探讨跨学科研究与技术变革之间的关系。技术变革被定义为一项技术巩固或破坏现有技术的程度，专利可以分为三种类型：不稳定、巩固和缓和。通过对Web of Science数据库收录的2002年发表的所有期刊文章和美国专利商标局的所有专利进行分析，我们的研究表明，与缓和和巩固专利相比，研究论文对不稳定专利的影响相对较小。特别值得注意的是生物医学研究领域的论文对技术进步的重大贡献。通过对跨学科研究的多样性、平衡性、差异性和Rao-Stirling指数进行分析，我们发现不稳定专利引用随多样性和Rao-Stirling指数的增加而减少。与平衡呈u型相关，与差异无显著相关。合并专利被引率随差异和Rao-Stirling指数的增加而增加，随平衡而降低，但与多样性的关系不容忽视。中等专利被引量随多样性和平衡性而增加，随差异和Rao-Stirling指数而减少。
{"title":"Interdisciplinary research and technological change","authors":"Bing Li , Kun Ding , Vincent Larivière","doi":"10.1016/j.techfore.2026.124537","DOIUrl":"10.1016/j.techfore.2026.124537","url":null,"abstract":"<div><div>This study investigates the relationship between interdisciplinary research and technological change, using paper-patent citations. Technological change is defined as the extent to which a technology consolidates or disrupts existing technologies, patents can be categorized into three types: destabilizing, consolidating, and moderating. Drawing on all journal articles published in 2002 and indexed in the Web of Science database and all patents from the USPTO, our study reveals that research papers have a relatively minor impact on destabilizing patents compared to moderating and consolidating patents. Particularly noteworthy is the significant contribution of papers in the field of Biomedical Research to technological advancements. Analyzing distinct dimensions of interdisciplinary research—variety, balance, disparity, and the Rao-Stirling index—we find that destabilizing patent citations decrease with both variety and the Rao-Stirling index increase. The correlation with balance exhibits a U-shaped relationship, but we observe no significant relationship with disparity. Moreover, consolidating patent citations demonstrate an increase with disparity and the Rao-Stirling index, while showing a decrease in relation to balance, but no negligible association is found with variety. Moderate patent citations increase with both variety and balance, and decrease with both disparity and the Rao-Stirling index.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"225 ","pages":"Article 124537"},"PeriodicalIF":13.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980364","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 : 2026-04-01Epub Date: 2026-01-07DOI: 10.1016/j.techfore.2025.124509
Mingxue Wei , Suraksha Gupta , Xiaoping Yang , Yichuan Wang
The rapid advancement of medical technologies has increased electronic obsolescence and e-waste, yet research on this issue within the medical sector remains limited. Existing studies mainly emphasize environmental and health risks from improper disposal, overlooking the role of consumers, particularly elderly individuals with chronic conditions who depend on wearable health devices. This study addresses this gap by examining how sustainable consumption of wearable healthcare devices among senior citizens can help reduce e-waste and support net-zero goals. Using a mixed-methods design, we conducted 20 semi-structured interviews and surveyed 647 senior citizens, analysing the data through Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings reveal that user experience strongly drives sustainable consumption. Moreover, user experience is shaped by design leadership, technology leadership, and brand leadership. Several factors, including self-health management efficacy, perceived severity, perceived vulnerability, and disclosure policy, moderate these relationships, while regulation shows no significant moderating effect. The study highlights the importance of enhancing user experience and leadership attributes in wearable healthcare devices to promote sustainable consumption and mitigate e-waste among elderly users.
{"title":"Sustainable consumption of wearable healthcare devices and its relevance to the net-zero agenda: Evidence from senior citizens","authors":"Mingxue Wei , Suraksha Gupta , Xiaoping Yang , Yichuan Wang","doi":"10.1016/j.techfore.2025.124509","DOIUrl":"10.1016/j.techfore.2025.124509","url":null,"abstract":"<div><div>The rapid advancement of medical technologies has increased electronic obsolescence and e-waste, yet research on this issue within the medical sector remains limited. Existing studies mainly emphasize environmental and health risks from improper disposal, overlooking the role of consumers, particularly elderly individuals with chronic conditions who depend on wearable health devices. This study addresses this gap by examining how sustainable consumption of wearable healthcare devices among senior citizens can help reduce e-waste and support net-zero goals. Using a mixed-methods design, we conducted 20 semi-structured interviews and surveyed 647 senior citizens, analysing the data through Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings reveal that user experience strongly drives sustainable consumption. Moreover, user experience is shaped by design leadership, technology leadership, and brand leadership. Several factors, including self-health management efficacy, perceived severity, perceived vulnerability, and disclosure policy, moderate these relationships, while regulation shows no significant moderating effect. The study highlights the importance of enhancing user experience and leadership attributes in wearable healthcare devices to promote sustainable consumption and mitigate e-waste among elderly users.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"225 ","pages":"Article 124509"},"PeriodicalIF":13.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929255","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}
{"title":"Corrigendum to “Digital technology adoption and SC recoverability. The mediating role of relationship transparency and SC production risk management capabilities” [Technol. Forecast. Soc. Change volume 218, September 2025, 124219 https://doi.org/10.1016/j.techfore.2025.124219]","authors":"Nidhi Singh , Usama Awan , Sarah Basahel , Rsha Alghafes","doi":"10.1016/j.techfore.2026.124549","DOIUrl":"10.1016/j.techfore.2026.124549","url":null,"abstract":"","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"225 ","pages":"Article 124549"},"PeriodicalIF":13.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189574","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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