Technological Forecasting and Social Change最新文献

{"title":"Unlocking the black box: Enhancing human-AI collaboration in high-stakes healthcare scenarios through explainable AI","authors":"Reda Hassan ,&nbsp;Nhien Nguyen ,&nbsp;Stine Rasdal Finserås ,&nbsp;Lars Adde ,&nbsp;Inga Strümke ,&nbsp;Ragnhild Støen","doi":"10.1016/j.techfore.2025.124265","DOIUrl":"10.1016/j.techfore.2025.124265","url":null,"abstract":"<div><div>Despite the advanced predictive capabilities of artificial intelligence (AI) systems, their inherent opacity often leaves users confused about the rationale behind their outputs. We investigate the challenge of AI opacity, which undermines user trust and the effectiveness of clinical judgment in healthcare. We demonstrate how human experts make judgment in high-stakes scenarios where their judgment diverges from AI predictions, emphasizing the need for explainability to enhance clinical judgment and trust in AI systems. We used a scenario-based methodology, conducting 28 semi-structured interviews and observations with clinicians from Norway and Egypt. Our analysis revealed that, during the process of forming judgments, human experts engage in AI interrogation practices when faced with opaque AI systems. Obtaining explainability from AI systems leads to increased interrogation practices aimed at gaining a deeper understanding of AI predictions. With the introduction of explainable AI (XAI), experts demonstrate greater trust in the AI system, show a readiness to learn from AI, and may reconsider or update their initial judgments when they contradict AI predictions.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"219 ","pages":"Article 124265"},"PeriodicalIF":12.9,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685749","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":"How do farmers' cognition and social networks affect the adoption behavior of straw-returning technology? Evidence from the black soil region of Northeast China","authors":"Zhanlu Zou,&nbsp;Jianjun Jin,&nbsp;Jie Yang,&nbsp;Dan Liu,&nbsp;Xuan Zhang","doi":"10.1016/j.techfore.2025.124282","DOIUrl":"10.1016/j.techfore.2025.124282","url":null,"abstract":"<div><div>Understanding factors influencing farmers' adoption of straw-returning technology is key to reducing straw burning and shaping agricultural policies. However, research on this topic remains underdeveloped. This study aims to investigate the impact of subjective cognition and social networks on farmers' adoption behavior of straw-returning technology in the black soil region of Northeast China. The results show that over 50 % of farmers in the study area have adopted straw-returning technology. Farmers generally have a moderate level of subjective cognition towards this technology, while the strength of their social networks is low. Subjective cognition and social networks are the main factors influencing farmers' straw-returning technology adoption behavior. Enhanced economic benefit cognition (increasing crop yield and reduce input costs) can significantly promote their adoption behavior. Farmers with stronger influence asymmetry (who have relatives and friends working in government or who have joined scientific organizations and cooperatives) are more likely to adopt this technology. Additionally, sociodemographic characteristics also play a role in influencing adoption behavior. The findings and policy implications of this study can help policymakers to guide farmers to adopt straw-returning technology more effectively, thereby reducing resource waste in agricultural production.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"219 ","pages":"Article 124282"},"PeriodicalIF":12.9,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680440","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":"Applying machine learning to predict production capacity for engineer-to-order products: Learning from wind turbine industry","authors":"Yanlan Mao ,&nbsp;Jan Holmström ,&nbsp;Yang Cheng","doi":"10.1016/j.techfore.2025.124295","DOIUrl":"10.1016/j.techfore.2025.124295","url":null,"abstract":"<div><div>To shorten lead times, engineer-to-order (ETO) companies develop production capacity plans before finalising product designs. However, the production capacity planning of ETO products tends to be highly unpredictable due to factors such as changes in customer requirements, leading to discrepancies between actual demand and planned capacity. Despite the enormous body of literature on capacity planning, there is a lack of research in the context of ETO. Especially, the literature on the use of data-driven methods, such as machine learning (ML) for production capacity prediction, is sparse. Recognising this potential, this study focuses on early production capacity prediction for ETO products using ML and aims to improve the accuracy of production capacity planning. In this paper, design science research is employed in a real company to develop a ML implementation framework. We find that the stacking model outperforms other three models, demonstrating the feasibility of using ML methods to predict production capacity early in dynamic environments. The developed artefact demonstrates a method for employing ML to predict production capacity for ETO products within a real-world problem domain. Furthermore, the challenges encountered during the ML implementation are discussed based on the proposed artefact, and corresponding suggestions are provided for practitioners.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"219 ","pages":"Article 124295"},"PeriodicalIF":12.9,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680438","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":"Transforming supply chain operations: Unveiling the path ahead by leveraging artificial intelligence (AI) to drive the shift towards carbon neutrality (CN)","authors":"Li Zheng ,&nbsp;Rong Zhou ,&nbsp;Nidhi Singh ,&nbsp;Muhammad Zafar Yaqub ,&nbsp;Saeed Badghish","doi":"10.1016/j.techfore.2025.124280","DOIUrl":"10.1016/j.techfore.2025.124280","url":null,"abstract":"<div><div>The negative impact of carbon emissions has received greater attention and is a significant challenge. Recent research has focused on promoting CN and achieving CN goals through the use of advanced techniques. While there is some debate on the use and influence of different technologies, there is a limited exploration of the use of specific technologies, such as AI, in achieving CN, particularly in the supply chain (SC) domain. This research paper examines the intersection between AI-led CN and SC, exploring the multifaceted aspects that define this evolving landscape. This paper aims to unravel the complexities and outcomes surrounding the use of AI-led disruptive technologies to achieve CN in SC operations. The authors conducted a comprehensive review of 47 relevant studies to facilitate a critical synthesis of the literature. Based on an extensive literature review, the study identified various antecedents and consequences of AI-led CN goals, spanning a geographical expanse under the broad themes of Sustainable Energy, Digital and Technological Transformation, Biomass Conversion, Waste Management, and Carbon Forecasting and Accounting in the SC domain. The findings revealed several antecedents to AI-led CN goals, including discussion on energy supply issues, intelligent systems, access to green financial markets, and slag waste optimization, as well as a few consequences of such implementation, such as AI-based methods to monitor slag waste, biomass concerns, and carbon neutrality, among others. The study also developed a framework, highlighting various AI-led CN strategies at the optimization and operational stages, as well as several AI-led innovations and their importance. The study offers a few policy suggestions, such as designing emission reduction policies, encouraging public-private partnerships, developing a regulatory framework to promote the industrial transformation of intelligent buildings, and managing uncertainties related to slag waste, biomass, energy systems, and other SC issues to ensure proper implementation of AI-led CN systems in the SC domain.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"219 ","pages":"Article 124280"},"PeriodicalIF":12.9,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680439","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":"An interpretable two-stage adaptive deep learning model for humanitarian aid information prediction and emergency response support","authors":"Yi Feng ,&nbsp;Xinwei Wang ,&nbsp;Dujuan Wang ,&nbsp;Yunqiang Yin ,&nbsp;Joshua Ignatius","doi":"10.1016/j.techfore.2025.124293","DOIUrl":"10.1016/j.techfore.2025.124293","url":null,"abstract":"<div><div>Diverse modes of information in social media posts during emergency responses collectively present an opportunity to advance artificial intelligence (AI) technologies to promote the integration of AI in humanitarian aid operations. To accurately identify humanitarian aid information and its categories, and to facilitate effective emergency responses, we first designed a two-stage humanitarian aid information prediction framework (THAIP). The first stage identifies humanitarian aid information and the second stage predicts the specific categories of information. We then developed an interpretable two-stage adaptive deep learning model (ITADL) based on THAIP, which adaptively determines the optimal data modality, model structure, and parameters based on the tasks at different stages. When applied to a real-world dataset from the social media platform Twitter in the context of emergency response, THAIP and ITADL achieved superior performance compared to models using a single-stage framework and several other deep learning models. Furthermore, the responses predicted by ITADL are interpreted at both global and local levels, enhancing the model's interpretability and providing valuable decision support for humanitarian aid planning and emergency response.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"219 ","pages":"Article 124293"},"PeriodicalIF":12.9,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680441","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":"Does individuals' pro-environmental behavior in the virtual world influence their perceived human-nature connection? The green consumption effect","authors":"Jian Gao ,&nbsp;Liyu Tang ,&nbsp;Jianguo Wang","doi":"10.1016/j.techfore.2025.124296","DOIUrl":"10.1016/j.techfore.2025.124296","url":null,"abstract":"<div><div>As virtual platforms increasingly shape individual behavior and social engagement, understanding the sustainability implications of digitally mediated experiences has become a critical research frontier. As exemplified by Ant Forest, this study examines the role of virtual pro-environmental behavior (VPEB) in shaping individuals' perceived human–nature connection (HNC), a foundational construct in environmental psychology and sustainability science. On the basis of a scenario-based survey, the findings reveal that VPEB positively influences perceived HNC, with this effect mediated by the emotional experience of warm glow, driven by enhanced perceptions of social worth. Furthermore, the study identifies psychological ownership as a significant moderator, whereby users who have a sense of ownership over virtual worlds exhibit a stronger linkage between VPEB and perceived HNC. By examining the function of behavioral and affective mechanisms within virtual worlds, this study advances the understanding of the psychological experience underpinning individuals' perceived HNC in virtual environments. In doing so, this research offers theoretical insights into encouraging sustainability engagement via immersive technologies and provides managerial implications for the design of technology-driven interventions that foster individuals' ecological awareness.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"219 ","pages":"Article 124296"},"PeriodicalIF":12.9,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672311","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":"From oligarchy to decentralization: Network structures and collaboration on digital platforms","authors":"Chao Liu","doi":"10.1016/j.techfore.2025.124286","DOIUrl":"10.1016/j.techfore.2025.124286","url":null,"abstract":"<div><div>The potential of peer production on digital platforms—leveraging collective intelligence and decentralized collaboration—has gained increasing attention from organizations. However, peer production is not purely non-hierarchical, and its models and effectiveness vary significantly. While some platforms successfully engage participants and harness collective intelligence, others struggle with involvement and evolve into oligarchical structures. Existing research has largely overlooked the variety of peer production models and the influence of network embeddedness on these variations. This study addresses this gap by examining peer production projects on GitHub and analyzing how network structures affect decentralized collaboration. This is particularly important because participant anonymity on digital platforms obscures social cues that typically signal trust and reputation. The findings reveal that social networks play a vital role in signaling status and reputation. Specifically, network brokerage—manifested through structural holes—positively impacts decentralized collaboration, while higher network cohesion and centrality are linked to reduced collaboration. This research advances theoretical understanding by uncovering the diversity of peer production models and highlighting the critical role of network structures in shaping decentralized collaboration on digital platforms. It also offers practical insights for organizations aiming to enhance collaborative environments and suggests promising directions for future research in this area.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"219 ","pages":"Article 124286"},"PeriodicalIF":12.9,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672309","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":"Revolutionising retail: A review of Intelligent Unmanned Convenience Stores and their market implications","authors":"Tanu Priya Kohli ,&nbsp;Smitu Malhotra ,&nbsp;Venugopal Pingali","doi":"10.1016/j.techfore.2025.124273","DOIUrl":"10.1016/j.techfore.2025.124273","url":null,"abstract":"<div><div>Intelligent Unmanned Convenience Stores (IUCVSs, hereafter) introduce a fresh, intelligent service providing customers with an innovative shopping experience making use of technologies such as computer vision, Artificial Intelligence, automated payment systems, smart entry and exit systems, and more that eliminate lines and direct interaction with retail staff. This integrated suite of digital technologies functions as a silicon shopkeeper, automating the responsibilities typically carried out by human personnel in traditional convenience store operations. Although there has been a growing emphasis on research over the past decade, the literature concerning IUCVSs still lacks coherence, being dispersed across diverse contexts. Therefore, the current moment offers a favourable opportunity to evaluate the field by establishing the foundation for forthcoming research to delineate the extent of IUCVSs. The present research provides a glimpse into the existing body of literature examining consumer receptiveness to technological interfaces in retail as opposed to human interfaces by empirically investigating the emergence and growth of IUCVSs. Searching on SCOPUS and the Web of Science has yielded a final set of 106 articles relevant to IUCVSs. Therefore, by utilising the TCCM review framework, this study offers an in-depth understanding of the prevailing theories, countries shaping the contexts, the crucial variables as typical characteristics, and the various research approaches employed in IUCVSs research from 1996 to 2024. This study contributes to the existing literature by conducting a comprehensive review of these articles, a task previously unexplored by scholars, and presents an integrated conceptual framework for IUCVSs by amalgamating the insights gleaned from such studies. This study concludes by discussing various theoretical and managerial implications and suggesting potential avenues for future research directions using the TCCM framework.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"219 ","pages":"Article 124273"},"PeriodicalIF":12.9,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672200","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":"Stimulus-organism-response framework of decision-makers intention to adopt generative AI to replace entry-level jobs: The moderating impact of personality traits","authors":"Mohammad Dalvi-Esfahani ,&nbsp;Hajar Barati-Ahmadabadi ,&nbsp;T. Ramayah ,&nbsp;Jason J. Turner ,&nbsp;Noorminshah A. Iahad ,&nbsp;Nasrin Azar","doi":"10.1016/j.techfore.2025.124291","DOIUrl":"10.1016/j.techfore.2025.124291","url":null,"abstract":"<div><div>This study was motivated by the limited research on the adoption of Generative Artificial Intelligence (GenAI) in the workplace. Based on the Stimulus-Organism-Response (S-O-R) framework, we developed a model to assess the factors influencing decision-makers' intention to adopt GenAI as a substitute for entry-level jobs in financial institutions. To test the hypotheses, we collected survey data from 335 respondents in Malaysian financial institutions and analyzed it using partial least squares structural equation modeling. The findings indicate that trust in GenAI significantly affects decision-makers' intention to adopt it as an alternative solution to human positions. Trust, in turn, was found to be positively influenced by constructs from the Theory of Effective Use (transparent interaction, informed action, and representational fidelity) as well as AI literacy, which reflects users' ability to evaluate and interact with AI. The results also show that personality traits, particularly conscientiousness, moderate the relationship between trust and adoption intention, highlighting the importance of individual differences in GenAI usage. Collectively, our findings extend the S-O-R framework by revealing how both cognitive and affective factors shape GenAI adoption behavior. The study also offers practical implications for GenAI stakeholders, especially about the vital role of trust-building strategies in fostering AI adoption.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"219 ","pages":"Article 124291"},"PeriodicalIF":12.9,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672312","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":"Sustainable futures: What's driving France's eco-revolution?","authors":"Kishwar Ali ,&nbsp;Qingyu Zhang ,&nbsp;Francesco Paolo Appio","doi":"10.1016/j.techfore.2025.124284","DOIUrl":"10.1016/j.techfore.2025.124284","url":null,"abstract":"<div><div>This study explores the interplay between industrial robot adoption, green bonds, climate patents, and economic governance in reducing the greenhouse gas emissions intensity of energy consumption (GIEC) in France. Using advanced econometric methodologies, including Kernel Regularized Least Squares and Bayesian Neural Networks, we analyze quarterly data from 2008 to 2019 to uncover both direct and moderating effects. The findings reveal that industrial robots, green bonds, and climate-focused patents significantly reduce GIEC, while household energy consumption has an adverse effect. The moderating role of economic governance in France is pivotal; amplifying emissions from green finance and technology, while curbing those from household energy use, emphasizing the need for targeted, environmentally aligned governance strategies. Sensitivity analyses confirm these patterns across alternative specifications. By revealing the sector-specific channels through which technology, finance, and governance jointly deliver—or derail—decarbonisation, the study sharpens ecological modernisation theory and equips managers and policymakers with an integrated, evidence-based blueprint for aligning industrial efficiency, green finance, and institutional reform to meet national and Paris-Agreement climate goals.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"219 ","pages":"Article 124284"},"PeriodicalIF":12.9,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672201","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}