Technological Forecasting and Social Change最新文献

{"title":"Cross-border knowledge transfer and the innovation performance of developing economy small and medium enterprises: A moderated mediation effect of industry networks and localization of knowledge","authors":"","doi":"10.1016/j.techfore.2024.123702","DOIUrl":"10.1016/j.techfore.2024.123702","url":null,"abstract":"<div><p>Despite the importance and empirical evidence associated with cross-border knowledge transfer (CBKT) research, there remain scanty explanations of the direct and indirect effects of CBKT on innovation performance (IP) especially within the context of SMEs in the developing economies. Based on the knowledge-based view theorization, this research sought to close this gap by investigating (i) the impact of CBKT on the innovation performance of developing economy SMEs, and (ii) the moderated mediation effect of industry networks (IN) and localization of knowledge (LK) on the CBKT-IN relationship. The data analyzed from 371 SME owners-managers and managers revealed the direct effects of CBKT on IP, CBKT on LK, and LK on IP. The study also confirmed the mediation effect of LK on the CBKT and IP relationship and found that IN has an antecedent effect on CBKT, as well as a moderating role in the CBKT-LK relationship. This research offers a theoretical explanation of IN and LK as mechanisms and structures that support the CBKT-IP relationship in developing economy SMEs. Also, I suggest that SMEs in developing economies must direct their business relationship with their foreign business partners to capture the knowledge that aligns with strategic innovation outcomes.</p></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142058085","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":"What motivates consumers to adopt controversial green mobility innovations? The case of shared e-bikes and e-scooters","authors":"","doi":"10.1016/j.techfore.2024.123694","DOIUrl":"10.1016/j.techfore.2024.123694","url":null,"abstract":"<div><p>Green innovations sometimes have a debatable environmental impact, which could be related to how their adopters use them. This paper examines five factors encompassing a consumer trait of being innovative and various motivations that could help further understand the adoption of controversial green innovations. Mainly, this paper examines the use decision and users' behavioral intentions in two green innovations that have received mixed opinions – shared e-bikes and e-scooters, and how they are related to a consumer trait (eco-innovativeness) and consumer motivations (instrumental, environmental, hedonic, and symbolic). We surveyed shared e-bike and e-scooter users (<em>n</em> = 337) and non-users (<em>n</em> = 1001) in Sweden and applied structural equation modeling to test the relationship between the factors and adoption. Results show that eco-innovativeness and motivations affect use decisions and behavioral intentions differently. Specifically, environmental motivations negatively affect use decisions but are positively significant in future behavioral intentions. Hedonic motivations have the strongest effects, which could help explain the controversy surrounding shared e-bikes and e-scooters. Theoretically, we contribute to understanding how traits and motivations are significant in the diffusion of green innovations. The paper shows that despite being mainly promoted as good for the environment, other motivations could significantly drive the adoption of novel green products. Practically, this indicates that promotion and public policies surrounding green innovations should not be limited to communicating their functional and pro-environmental attributes.</p></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S004016252400492X/pdfft?md5=4e03f4906840ba5eef131f5ead6dc0eb&pid=1-s2.0-S004016252400492X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142050230","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}

{"title":"Exam scheduling under pandemic conditions: A mathematical model and decision support system","authors":"","doi":"10.1016/j.techfore.2024.123687","DOIUrl":"10.1016/j.techfore.2024.123687","url":null,"abstract":"<div><p>The scheduling of university exams is a complex task that involves various constraints such as administrative limits, pedagogical needs, student volume, and different courses. The emergence of Covid-19 and future pandemics has added new constraints related to infection prevention and contact tracing. To address these challenges, this study proposes a multi-objective mathematical model that considers university resources, reduced classroom occupancy, and minimized student interaction. The model aims to minimize violations of pandemic-related constraints and categorize exams by difficulty. To facilitate scheduling for entire faculties or universities, a Genetic Algorithm based web-based decision support system is developed. With these tools, the study successfully created an optimal schedule for eight departments simultaneously.</p></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142058084","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":"Toward low carbon development through digital economy: A new perspective of factor market distortion","authors":"","doi":"10.1016/j.techfore.2024.123685","DOIUrl":"10.1016/j.techfore.2024.123685","url":null,"abstract":"<div><p>The development and application of digital technology has brought profound influences on our economic development. It is therefore urgent to measure such impact on low carbon development. This study aims to measure the impact of digital economy development among Chinese provinces by using an intermediary effects model and conducting heterogeneity analysis. The results show that digital economy can facilitate low carbon development in China by alleviating capital and labor market distortions. Industrial digitization has the greatest impact, while digital industrialization has the smallest impact. Moreover, from a region perspective, digital economy is more effective to promote low carbon development in those eastern regions, while it is critical to improve the digital economy development environment in those central and western regions. Finally, several policy recommendations are raised by considering the Chinese realities.</p></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142050229","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":"Sowing the seeds for sustainability: A business model innovation perspective on artificial intelligence in green technology startups","authors":"","doi":"10.1016/j.techfore.2024.123653","DOIUrl":"10.1016/j.techfore.2024.123653","url":null,"abstract":"<div><p>In today's data-driven era, ubiquitous concern about environmental issues pushes more startups to engage in business model innovation that promotes environmentally friendly technologies. The goal of these startups is to create technology-based products and services that enhance environmental sustainability. In this context, artificial intelligence promises to be a key instrument to create, capture, and deliver value. However, the existing literature lacks a deep understanding of how startups using AI innovate their business models to achieve a positive environmental impact. Therefore, this paper investigates how green technology startups utilize AI from a business model innovation perspective for environmental sustainability. We conduct a qualitative, exploratory multiple-case study using the Eisenhardt methodology, based on interview data analyzed using qualitative content analysis. We derive five predominant manifestations for AI-driven business model innovation and identify archetypical connections between business model dimensions. Further, we establish three overarching archetypical associations among the cases. In doing so, we contribute to theory and practice by providing a deeper account of how green technology startups attempt to maximize their positive environmental impact through AI. The results of this study also highlight how business model innovation driven by AI can support society in securing a more environmentally sustainable future.</p></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0040162524004517/pdfft?md5=73c95c16aa4dfa12d0131dd8e576f5ce&pid=1-s2.0-S0040162524004517-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142050231","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}

{"title":"Short-term air pollution prediction using graph convolutional neural networks","authors":"","doi":"10.1016/j.techfore.2024.123684","DOIUrl":"10.1016/j.techfore.2024.123684","url":null,"abstract":"<div><p>Pollution is a major concern in the present day, causing multiple illnesses and deaths, specifically in developing countries in Asia and Africa. While it has drawn worldwide attention as governments try to issue laws to meet certain criteria for air pollution levels, pollution concentration forecasting has become a major challenge. Particularly, short term forecasting will help to gain information regarding concentrations of harmful pollutants for the upcoming hours and enable better decision-making with regards to controlling air pollution. In this paper, we investigate spatio-temporal graph-based models to determine the best methods for spatial and temporal analysis of data. The models have the additional capacity to perform multi-variate predictions of correlated data, i.e., predicting multiple pollutant concentrations simultaneously, thus requiring lower computational efforts. A real-world pollution dataset measured over Delhi, India, is used to comparing the proposed models with baselines, which shows the Spatio-Temporal Graph Convolution Neural Network (STGCN) models to be performing better than others. For a better understanding of model architectures with the most effective strategies for spatial and temporal data analysis, three models, namely STGCN-A, STGCN-B, STGCN-C have been developed. The models have been compared with 6 other baselines over multiple forecasting horizons of 1 h, 24 h, and 48 h timesteps using various metrics such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percent error (MAPE). On the PM_2.5 dataset of Delhi, STGCN-B achieves a performance of 10.53 MAE, 6.92 RMSE and 25.25 MAPE for a 1 h forecast, while STGCN-C achieves 20.18 MAE, 14.73 RMSE and 55.45 MAPE for a 24 h forecast. In general, both structures achieve similar results, with STGCN-C being better in many cases. They are further analysed through observation-prediction graphs and Taylor diagrams, which give an insight into our findings. The models are additionally validated on a benchmark real-world dataset from California, USA for better understanding of the spatio-temporal relations and model performances on a more stable dataset, where STGCN-C performs best for PM_2.5 with 4.30 RMSE, 1.98 MAE, 25.96 MAPE for 1 h predictions for univariate data and 3.63 RMSE, 1.88 MAE and 25.91 MAPE in multivariate forecasting. The developed spatio-temporal graph-based models hold promising applications in urban air quality management, aiding policymakers in implementing targeted interventions to mitigate pollution-related health risks. Furthermore, these models can support public health agencies by providing timely and accurate forecasts of pollutant concentrations, enabling proactive measures to safeguard community well-being. Our study showcases the efficacy of spatio-temporal graph-based models in accurately forecasting air pollutant concentrations, with particular emphasis on short-term predictions. By leveragin","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142040833","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":"Public perceptions and acceptance of artificial intelligence humanoid bots/robots: Evidence from Turkey","authors":"","doi":"10.1016/j.techfore.2024.123678","DOIUrl":"10.1016/j.techfore.2024.123678","url":null,"abstract":"<div><p>The development of artificial intelligence (AI) humanoid bots/robots (AIHB/R), which integrate AI with robot technology to emulate human traits, has swiftly evolved and found applications in several sectors like healthcare, education, home support, industrial production, and entertainment. AIHB/R's decision-making process has become more complex due to incorporating AI, a fascinating element affecting public perceptions and attitudes towards these robots. This paper investigates this topic in the Turkish context based on data collected from 485 adults. A general finding is that participants have positive perceptions and attitudes towards (AIHB/R) and some concerns. We found significant gender differences between male and female participants regarding the use of AIHB/R in care and treatment services (CTS), administration and justice (AJ), and in the future (FC). No gender difference was found when using religious practice (RP). The educational level statistically correlated significantly with the total scale scores and the four sub-domains. CTS has a positive and moderately significant effect on accepting AIHB/R applications. The R² value for this domain showed the highest explanatory power of the model with 0.829.</p></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142040846","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":"Scarcity and market design: How effective matching can promote the peer-to-peer sharing economy","authors":"","doi":"10.1016/j.techfore.2024.123686","DOIUrl":"10.1016/j.techfore.2024.123686","url":null,"abstract":"<div><p>Scarcity is an essential issue in service marketing. This study examined scarcity in a game-theoretic setting. It focused on the role of customer evaluations, through satisfaction ratings, on a technology-enabled peer-to-peer sharing economy platform in achieving stable provider–user matching. The model shows that an appropriate market design employing a user-proposing version of Gale and Shapley's (1962) deferred acceptance algorithm for a ride-sharing platform where many users approach a few providers can help avoid congestion and ensure stable matching. To manage scarcity, the model uses a customer satisfaction rating system that modifies the widely used batch matching algorithm by matching highly rated riders much faster than their low-rated peers. A platform's ability to offer time-bound stable matching for highly-rated users during scarcity is likely to help it maintain a competitive edge.</p></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142040830","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":"The nature, causes, and effects of skepticism on technology diffusion","authors":"","doi":"10.1016/j.techfore.2024.123663","DOIUrl":"10.1016/j.techfore.2024.123663","url":null,"abstract":"<div><p>Although skepticism is involved in technical change and scientific revolutions, surprisingly, the literature lacks a systematic analysis of the different forms, causes, and roles of skepticism in the diffusion of innovation.</p><p>This paper defines, identifies and models different types and causes of skepticism and their role in technology adoption. The paper identifies and studies: skepticism involving the characteristics of the technology and the producer; skepticism that induces to disbelieve market signal; and comparative skepticism i.e., skepticism produced by an unbalance relationship between the perceived complexity of the problem and the solution.</p><p>Among the theoretical findings of the paper and regarding skepticism on market signals, we found that the non-differentiability and oscillation of diffusion rates occur if individuals use the information on diffusion rates as proxy of the probability of technology working and modify this probability according to their skepticism.</p></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S004016252400461X/pdfft?md5=c7464f907296b48c87b77b53aa8cd721&pid=1-s2.0-S004016252400461X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142040831","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}

{"title":"Developing a strategic roadmap towards integration in Industry 4.0: A dynamic capabilities theory perspective","authors":"","doi":"10.1016/j.techfore.2024.123679","DOIUrl":"10.1016/j.techfore.2024.123679","url":null,"abstract":"<div><p>In recent years, the topic of the fourth industrial revolution has been discussed by industries and organizations. Currently, this topic has evolved beyond conceptualization, focusing more on challenges, capabilities, and benefits. This research aims to identify dynamic capabilities within the context of Industry 4.0. Employing literature review and content analysis, we utilized grounded theory to identify capabilities. Validation was achieved through feedback from 11 scholars using questionnaires and the Delphi method. The analysis confirmed the presence of 26 dynamic capabilities essential for Industry 4.0. Subsequently, the Dynamic Capabilities theory was applied to categorize these capabilities into ‘<em>sensing</em>,’ ‘<em>seizing</em>,’ and ‘<em>reconfiguration</em>.’ As a result, this study establishes a roadmap model that prioritizes capabilities for Industry 4.0 integration across these categories. It organizes capabilities based on priority for implementation and within each category, highlighting the most crucial ones determined by scholars' scores. This model provides a comprehensive understanding of the path towards integration in Industry 4.0.</p></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142040827","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}