Pub Date : 2024-10-14DOI: 10.1016/j.techfore.2024.123802
Xuanmei Cheng , Fangting Ge , Mark Xu , Ying Li
This study examines the nexus between urban economic resilience and the heat island effect in China. It also scrutinizes the potential mediating role of digital technology and the moderating effects of talent gathering and regional advantage, based on panel data from three metropolitan regions in China for the period 2006–2022. Its findings indicate that urban economic resilience significantly mitigates the heat island effect, digital technology mediates this relationship, and the moderators talent gathering and regional advantage significantly influence this relationship. Additionally, the spatial spillover effects of urban economic resilience on the heat island effect were found to be positive in these regions. Based on the empirical findings, this study offers valuable insights for policymaking, namely, that resilient urban economies are better equipped to adapt to climate change and environmental challenges, which enables them to implement effective mitigation measures for the heat island effect and influence broader regional climate dynamics.
{"title":"The heat island effect, digital technology, and urban economic resilience: Evidence from China","authors":"Xuanmei Cheng , Fangting Ge , Mark Xu , Ying Li","doi":"10.1016/j.techfore.2024.123802","DOIUrl":"10.1016/j.techfore.2024.123802","url":null,"abstract":"<div><div>This study examines the nexus between urban economic resilience and the heat island effect in China. It also scrutinizes the potential mediating role of digital technology and the moderating effects of talent gathering and regional advantage, based on panel data from three metropolitan regions in China for the period 2006–2022. Its findings indicate that urban economic resilience significantly mitigates the heat island effect, digital technology mediates this relationship, and the moderators talent gathering and regional advantage significantly influence this relationship. Additionally, the spatial spillover effects of urban economic resilience on the heat island effect were found to be positive in these regions. Based on the empirical findings, this study offers valuable insights for policymaking, namely, that resilient urban economies are better equipped to adapt to climate change and environmental challenges, which enables them to implement effective mitigation measures for the heat island effect and influence broader regional climate dynamics.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"209 ","pages":"Article 123802"},"PeriodicalIF":12.9,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433552","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 : 2024-10-10DOI: 10.1016/j.techfore.2024.123792
Faisal Mahmood , Younes Ben Zaied , Mohammad Zoynul Abedin
This paper evaluates the predictive performance of various machine learning models in economic forecasting using cross-validation and bootstrap bagging techniques. Focusing on a key area in economic forecasting, this study compares these models using cross-validation and bootstrap bagging techniques. The study uses a detailed dataset of green bonds issuing organizations from 30 regions of China from 2014 to 2022. The results indicate varying levels of efficacy among the models, with the deep multi-layer perceptron (DMLP) model showing better performance in accuracy and generalizability. When equipped with cross-validation, the k-nearest neighbor (KNN) model performed best among the five models. However, the decision tree is observed to be the best model when the bootstrap bagging technique is applied to all the five models. These findings highlight the potential of machine learning models to enhance economic forecasting accuracy, providing valuable insights for managers and economists in selecting suitable predictive models. The research contributes to understanding predictive modeling in economics, offering insights into applying machine learning techniques for accurate and reliable economic forecasting.
{"title":"Role of green finance instruments in shaping economic cycles","authors":"Faisal Mahmood , Younes Ben Zaied , Mohammad Zoynul Abedin","doi":"10.1016/j.techfore.2024.123792","DOIUrl":"10.1016/j.techfore.2024.123792","url":null,"abstract":"<div><div>This paper evaluates the predictive performance of various machine learning models in economic forecasting using cross-validation and bootstrap bagging techniques. Focusing on a key area in economic forecasting, this study compares these models using cross-validation and bootstrap bagging techniques. The study uses a detailed dataset of green bonds issuing organizations from 30 regions of China from 2014 to 2022. The results indicate varying levels of efficacy among the models, with the deep multi-layer perceptron (DMLP) model showing better performance in accuracy and generalizability. When equipped with cross-validation, the k-nearest neighbor (KNN) model performed best among the five models. However, the decision tree is observed to be the best model when the bootstrap bagging technique is applied to all the five models. These findings highlight the potential of machine learning models to enhance economic forecasting accuracy, providing valuable insights for managers and economists in selecting suitable predictive models. The research contributes to understanding predictive modeling in economics, offering insights into applying machine learning techniques for accurate and reliable economic forecasting.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"209 ","pages":"Article 123792"},"PeriodicalIF":12.9,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427378","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 : 2024-10-09DOI: 10.1016/j.techfore.2024.123789
Bao Wu , Kangjun Ren , Yao Fu , Defeng He , Mengmeng Pan
While scholars have largely confirmed that target firms respond proactively to institutional investor ESG activism, a wide range of studies pay little attention to green responses beyond organizational boundaries. Based on a dataset comprising 8557 firm-year observations of Chinese publicly listed manufacturing firms from 2012 to 2021, we find that institutional investor ESG activism also prompts firms to improve green supply chain management performance. In industries with a higher level of technological integration, firms are more likely to enhance green supply chain management performance at the ESG requests of institutional investors. Firms with greater technological influence are also more inclined to adopt such a collaborative-based green response. Both the moderating effects of technological integration and technological influence are more pronounced for firms equipped with digital intelligence. Our findings provide novel implications for firms seeking to address shareholder environmental concerns through green supply chain management in the context of digital intelligence.
{"title":"Institutional investor ESG activism and green supply chain management performance: Exploring contingent roles of technological interdependences in different digital intelligence contexts","authors":"Bao Wu , Kangjun Ren , Yao Fu , Defeng He , Mengmeng Pan","doi":"10.1016/j.techfore.2024.123789","DOIUrl":"10.1016/j.techfore.2024.123789","url":null,"abstract":"<div><div>While scholars have largely confirmed that target firms respond proactively to institutional investor ESG activism, a wide range of studies pay little attention to green responses beyond organizational boundaries. Based on a dataset comprising 8557 firm-year observations of Chinese publicly listed manufacturing firms from 2012 to 2021, we find that institutional investor ESG activism also prompts firms to improve green supply chain management performance. In industries with a higher level of technological integration, firms are more likely to enhance green supply chain management performance at the ESG requests of institutional investors. Firms with greater technological influence are also more inclined to adopt such a collaborative-based green response. Both the moderating effects of technological integration and technological influence are more pronounced for firms equipped with digital intelligence. Our findings provide novel implications for firms seeking to address shareholder environmental concerns through green supply chain management in the context of digital intelligence.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"209 ","pages":"Article 123789"},"PeriodicalIF":12.9,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427386","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}
Despite extensive research on the impact of policy on objective outcomes of performance, the effects on subjective perceptions remain relatively unexplored, yet these are critical in shaping public behaviors and influencing policy makings. To address this gap, we investigate the impact of China's smart city pilots on both objective and subjective environmental performance, examining the underlying mechanisms involved. The empirical illustration is based on rich micro-level data and a difference-in-differences approach. Our results suggest that smart city initiatives have a negative and statistically significant impact on pollution. This reduction is facilitated through the spread of digital technologies and the increased adoption of energy technologies. However, smart city initiatives also reinforce subjective perceptions of environmental degradation. We find that information transmission, measured using the level of educational attainment, internet use and migration, plays an important role in shaping these subjective perceptions. Our study contributes to the literature on smart cities and research on gaps between objective outcomes and subjective perceptions, as well as information transmission theories, while our results offer multiple policy implications.
{"title":"Subjective perceptions versus objective outcomes: Assessing the impact of smart city pilots on environmental quality in China","authors":"Wenyin Cheng , Xin Ouyang , Anqi Yu , Zhiyang Shen , Michael Vardanyan","doi":"10.1016/j.techfore.2024.123799","DOIUrl":"10.1016/j.techfore.2024.123799","url":null,"abstract":"<div><div>Despite extensive research on the impact of policy on objective outcomes of performance, the effects on subjective perceptions remain relatively unexplored, yet these are critical in shaping public behaviors and influencing policy makings. To address this gap, we investigate the impact of China's smart city pilots on both objective and subjective environmental performance, examining the underlying mechanisms involved. The empirical illustration is based on rich micro-level data and a difference-in-differences approach. Our results suggest that smart city initiatives have a negative and statistically significant impact on pollution. This reduction is facilitated through the spread of digital technologies and the increased adoption of energy technologies. However, smart city initiatives also reinforce subjective perceptions of environmental degradation. We find that information transmission, measured using the level of educational attainment, internet use and migration, plays an important role in shaping these subjective perceptions. Our study contributes to the literature on smart cities and research on gaps between objective outcomes and subjective perceptions, as well as information transmission theories, while our results offer multiple policy implications.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"209 ","pages":"Article 123799"},"PeriodicalIF":12.9,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427384","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 : 2024-10-08DOI: 10.1016/j.techfore.2024.123800
Kim Wilkins , Ksenia Ivanova , Helen Marshall , Lisa Bennett , Joanne Anderton
This research article argues for the effectiveness of storytelling techniques not only for communicating complex systems, but also for analysing complex systems and modelling outcomes. This kind of analysis and modelling of impacts is vital in strategic decision making and technology foresight. Strategy engages at many points with complex sociotechnical systems. Disruptive technological impact does not necessarily inhere in the technological objects or capabilities themselves. Rather, disruption arises from a convergence of factors including social, human, and ethical considerations. These factors are manifold, difficult (if not impossible) to predict, and—when it comes to the actions of individuals—informed by unknowable subjective personal histories, experiences, and current circumstances. Faced with such opaque and constantly shifting contextual factors, foreseeing technological impact presents challenges that are difficult to surmount. We show how the techniques of storytelling shed light on those challenges.
{"title":"Stories and systems: Exploring technological impact in complex systems through creative writing techniques","authors":"Kim Wilkins , Ksenia Ivanova , Helen Marshall , Lisa Bennett , Joanne Anderton","doi":"10.1016/j.techfore.2024.123800","DOIUrl":"10.1016/j.techfore.2024.123800","url":null,"abstract":"<div><div>This research article argues for the effectiveness of storytelling techniques not only for communicating complex systems, but also for analysing complex systems and modelling outcomes. This kind of analysis and modelling of impacts is vital in strategic decision making and technology foresight. Strategy engages at many points with complex sociotechnical systems. Disruptive technological impact does not necessarily inhere in the technological objects or capabilities themselves. Rather, disruption arises from a convergence of factors including social, human, and ethical considerations. These factors are manifold, difficult (if not impossible) to predict, and—when it comes to the actions of individuals—informed by unknowable subjective personal histories, experiences, and current circumstances. Faced with such opaque and constantly shifting contextual factors, foreseeing technological impact presents challenges that are difficult to surmount. We show how the techniques of storytelling shed light on those challenges.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"209 ","pages":"Article 123800"},"PeriodicalIF":12.9,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427382","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 : 2024-10-08DOI: 10.1016/j.techfore.2024.123794
Tianyu Hou , Liang Zhang , Julie Juan Li , Bin Chong , Yanzi Wu
While numerous studies have investigated the influence of knowledge search strategies on the impact of patented inventions, these studies predominantly focus on an invention's technological value. The economic value dimension has received less attention. This study draws on category-spanning and recombinant search literature to examine how knowledge search affects the economic value of patented inventions and compares this impact with that on technological value. Through an analysis of the knowledge search of a large sample of 1,998,504 U.S. utility patents, we find that knowledge search depth enhances an invention's economic value but negatively impacts its technological value. In contrast, knowledge search scope boosts an invention's technological value but diminishes its economic value. Moreover, knowledge relatedness, i.e., the extent to which the knowledge components being recombined are similar, has significant moderating effects. We conclude with a discussion of the theoretical and practical implications of our findings.
{"title":"The quest for valuable inventions: Knowledge search and the value of patented inventions","authors":"Tianyu Hou , Liang Zhang , Julie Juan Li , Bin Chong , Yanzi Wu","doi":"10.1016/j.techfore.2024.123794","DOIUrl":"10.1016/j.techfore.2024.123794","url":null,"abstract":"<div><div>While numerous studies have investigated the influence of knowledge search strategies on the impact of patented inventions, these studies predominantly focus on an invention's technological value. The economic value dimension has received less attention. This study draws on category-spanning and recombinant search literature to examine how knowledge search affects the economic value of patented inventions and compares this impact with that on technological value. Through an analysis of the knowledge search of a large sample of 1,998,504 U.S. utility patents, we find that knowledge search depth enhances an invention's economic value but negatively impacts its technological value. In contrast, knowledge search scope boosts an invention's technological value but diminishes its economic value. Moreover, knowledge relatedness, i.e., the extent to which the knowledge components being recombined are similar, has significant moderating effects. We conclude with a discussion of the theoretical and practical implications of our findings.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"209 ","pages":"Article 123794"},"PeriodicalIF":12.9,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427383","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 : 2024-10-08DOI: 10.1016/j.techfore.2024.123796
Ruben Akse
Socio-technical innovations are necessary to establish a transition towards sustainable infrastructural systems. Actors developing and implementing these innovations experience considerable uncertainty whether innovations will technically work, are beneficiary for societal goals and how other actors will behave during the innovation process. Such uncertainties hamper the (successful) introduction of innovations, as actors struggle with handling uncertainty. There is a research gap that explains how public and private actors make specific choices regarding uncertainty interactively. Therefore, this paper has systematically reviewed literature on the interactions of actors in uncertain innovation processes. In total fifty-three articles out 2909 have been included in the full review. Based on these articles, a conceptual model has been proposed how actors experience, respond to, and consequently make decisions under uncertainty, in a cyclical interaction process with other actors. This process is influenced by uncertainty competencies (actor-specific characteristics), as well as uncertainty settings (formal and informal governance rules). The conceptual model will inform further research on the role of uncertainty in multi-actor innovation processes, and how actor competencies and uncertainty settings can be improved to stimulate a sustainability transition by socio-technical innovations.
{"title":"Towards a conceptual model of uncertainty management for socio-technical innovations: A systematic review","authors":"Ruben Akse","doi":"10.1016/j.techfore.2024.123796","DOIUrl":"10.1016/j.techfore.2024.123796","url":null,"abstract":"<div><div>Socio-technical innovations are necessary to establish a transition towards sustainable infrastructural systems. Actors developing and implementing these innovations experience considerable uncertainty whether innovations will technically work, are beneficiary for societal goals and how other actors will behave during the innovation process. Such uncertainties hamper the (successful) introduction of innovations, as actors struggle with handling uncertainty. There is a research gap that explains how public and private actors make specific choices regarding uncertainty interactively. Therefore, this paper has systematically reviewed literature on the interactions of actors in uncertain innovation processes. In total fifty-three articles out 2909 have been included in the full review. Based on these articles, a conceptual model has been proposed how actors experience, respond to, and consequently make decisions under uncertainty, in a cyclical interaction process with other actors. This process is influenced by uncertainty competencies (actor-specific characteristics), as well as uncertainty settings (formal and informal governance rules). The conceptual model will inform further research on the role of uncertainty in multi-actor innovation processes, and how actor competencies and uncertainty settings can be improved to stimulate a sustainability transition by socio-technical innovations.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"209 ","pages":"Article 123796"},"PeriodicalIF":12.9,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427381","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}
Defined as the merging of social and environmental sustainability into corporate operations, sustainable entrepreneurship has embraced data science more and more to improve operational effectiveness and decision-making. Using statistics, machine learning, and computer science to uncover insights from challenging datasets, this interdisciplinary method blends the ideas of sustainability with sophisticated data analysis approaches. Our research supports the choice of this issue by stressing the urgent requirement of sophisticated analytical instruments to negotiate the complexity of sustainable business practices. We compare our proposed model against Logistic Regression, Feedforward Neural Networks, and Support Vector Machines (SVMs). This not only shows how better CNN models are for certain uses but also highlights the general possibilities of data science in promoting sustainability in business. Our results highlight the transforming ability of sophisticated machine learning methods in promoting informed, sustainable decision-making and supporting the more general conversation on sustainable business.
{"title":"Data science in sustainable entrepreneurship: A multidisciplinary field of applications","authors":"Brij B. Gupta , Akshat Gaurav , Varsha Arya , Wadee Alhalabi","doi":"10.1016/j.techfore.2024.123798","DOIUrl":"10.1016/j.techfore.2024.123798","url":null,"abstract":"<div><div>Defined as the merging of social and environmental sustainability into corporate operations, sustainable entrepreneurship has embraced data science more and more to improve operational effectiveness and decision-making. Using statistics, machine learning, and computer science to uncover insights from challenging datasets, this interdisciplinary method blends the ideas of sustainability with sophisticated data analysis approaches. Our research supports the choice of this issue by stressing the urgent requirement of sophisticated analytical instruments to negotiate the complexity of sustainable business practices. We compare our proposed model against Logistic Regression, Feedforward Neural Networks, and Support Vector Machines (SVMs). This not only shows how better CNN models are for certain uses but also highlights the general possibilities of data science in promoting sustainability in business. Our results highlight the transforming ability of sophisticated machine learning methods in promoting informed, sustainable decision-making and supporting the more general conversation on sustainable business.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"209 ","pages":"Article 123798"},"PeriodicalIF":12.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427385","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 : 2024-10-04DOI: 10.1016/j.techfore.2024.123801
Liang Ma , Peng Yu , Xin Zhang , Gaoshan Wang , Feifei Hao
Although the use of generative artificial intelligence (AI) within organizations is becoming increasingly common, research on how to enhance employees' competitive advantage through generative AI use within organizations is very limited. Using a resource-based view model, this study investigates the relationship between generative AI use and employees' competitive advantage, as well as the moderating role of perceived organization support. From an analysis of data from 264 employees from 200 organizations, it is found that work-related generative AI use has a positive effect on employee boundary spanning, which contributes to employee competitive advantage. Secondly, work-related generative AI use also has a positive effect on employee agility, including employee resilience and employee adaptability, which further contributes to employee competitive advantage. However, work-related generative AI use has a positive effect on employee proactivity, while the effect of employee proactivity on employee competitive advantage is not significant. Thirdly, perceived organizational support can enhance the effect between employee boundary spanning and employee competitive advantage. However, it is interesting to observe that perceived organizational support enhances the effect between employee adaptability and employee competitive advantage, while weakening the effect between employee proactivity and employee competitive advantage. It does not exert a moderating effect between employee resilience and employee competitive advantage. These findings can help deepen the current understanding of the relationship between generative AI use in the organization and employee competitive advantage, and provide suggestions for business managers on how to use generative AI to improve employee competitive advantage.
{"title":"How AI use in organizations contributes to employee competitive advantage: The moderating role of perceived organization support","authors":"Liang Ma , Peng Yu , Xin Zhang , Gaoshan Wang , Feifei Hao","doi":"10.1016/j.techfore.2024.123801","DOIUrl":"10.1016/j.techfore.2024.123801","url":null,"abstract":"<div><div>Although the use of generative artificial intelligence (AI) within organizations is becoming increasingly common, research on how to enhance employees' competitive advantage through generative AI use within organizations is very limited. Using a resource-based view model, this study investigates the relationship between generative AI use and employees' competitive advantage, as well as the moderating role of perceived organization support. From an analysis of data from 264 employees from 200 organizations, it is found that work-related generative AI use has a positive effect on employee boundary spanning, which contributes to employee competitive advantage. Secondly, work-related generative AI use also has a positive effect on employee agility, including employee resilience and employee adaptability, which further contributes to employee competitive advantage. However, work-related generative AI use has a positive effect on employee proactivity, while the effect of employee proactivity on employee competitive advantage is not significant. Thirdly, perceived organizational support can enhance the effect between employee boundary spanning and employee competitive advantage. However, it is interesting to observe that perceived organizational support enhances the effect between employee adaptability and employee competitive advantage, while weakening the effect between employee proactivity and employee competitive advantage. It does not exert a moderating effect between employee resilience and employee competitive advantage. These findings can help deepen the current understanding of the relationship between generative AI use in the organization and employee competitive advantage, and provide suggestions for business managers on how to use generative AI to improve employee competitive advantage.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"209 ","pages":"Article 123801"},"PeriodicalIF":12.9,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427380","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 : 2024-10-03DOI: 10.1016/j.techfore.2024.123592
Dalton Alexandre Kai , Edson Pinheiro de Lima , Guilherme Brittes Benitez
This research explores how startups oriented to Industry 4.0 mature their concepts, products, and business across evolutionary lifecycles in the innovation ecosystem. We examine how startups mature their business by through four lifecycle stages: Ideation, MVP, Traction, and Consolidation, by collaborating with other actors in the innovation ecosystem. We adopt a social cognitive perspective using its four core properties: intentionally, forethought, self-reactiveness, and self-reflectiveness to explain how startups acquire knowledge to become self-effective in mastering skills and competencies required to develop their solutions. We employ a qualitative and longitudinal case study spanning five years within an innovation ecosystem, including more than 30 follow-up sessions, 27 semi-structured interviews, and a survey with 120 startups. Our findings reveal that startups in their initial stages exhibit more open, transparent, and exploratory behavior towards ecosystem actors. As they evolve, they become more specific and formal throughout their lifecycle, interacting, co-creating, and adding value among various groups or stakeholders, ultimately reaching a multi-sided platform governance structure. We also present a framework illustrating how startups typically relate to other ecosystem actors to mature their businesses. Our results can help entrepreneurs overcome the stages of a startup's lifecycle and achieve consolidation in the market.
{"title":"A social cognitive perspective in innovation ecosystems: Understanding startups from ideation to consolidation in industry 4.0 era","authors":"Dalton Alexandre Kai , Edson Pinheiro de Lima , Guilherme Brittes Benitez","doi":"10.1016/j.techfore.2024.123592","DOIUrl":"10.1016/j.techfore.2024.123592","url":null,"abstract":"<div><div>This research explores how startups oriented to Industry 4.0 mature their concepts, products, and business across evolutionary lifecycles in the innovation ecosystem. We examine how startups mature their business by through four lifecycle stages: Ideation, MVP, Traction, and Consolidation, by collaborating with other actors in the innovation ecosystem. We adopt a social cognitive perspective using its four core properties: intentionally, forethought, self-reactiveness, and self-reflectiveness to explain how startups acquire knowledge to become self-effective in mastering skills and competencies required to develop their solutions. We employ a qualitative and longitudinal case study spanning five years within an innovation ecosystem, including more than 30 follow-up sessions, 27 semi-structured interviews, and a survey with 120 startups. Our findings reveal that startups in their initial stages exhibit more open, transparent, and exploratory behavior towards ecosystem actors. As they evolve, they become more specific and formal throughout their lifecycle, interacting, co-creating, and adding value among various groups or stakeholders, ultimately reaching a multi-sided platform governance structure. We also present a framework illustrating how startups typically relate to other ecosystem actors to mature their businesses. Our results can help entrepreneurs overcome the stages of a startup's lifecycle and achieve consolidation in the market.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"209 ","pages":"Article 123592"},"PeriodicalIF":12.9,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427897","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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