{"title":"Is institutional pressure the driver for green business model innovation of SMEs? Mediating and moderating roles of regional innovation intermediaries","authors":"Min-Jae Lee , Hyeseung Choi , Taewoo Roh","doi":"10.1016/j.techfore.2024.123814","DOIUrl":"10.1016/j.techfore.2024.123814","url":null,"abstract":"<div><div>While extant research has concentrated on the promotion of regional innovation, it has predominantly adopted a knowledge-based approach. However, there has been a paucity of inquiry into the collective influence of institutional pressure and regional innovation intermediaries (RIIs) on the green business model innovation (GBMI) of small-and-medium-sized enterprises (SMEs). This study aims to identify the mechanisms through which SMEs enhance their GBMI, arguing that RIIs' multifaceted response to institutional pressures can act as both a mediator and a moderator. The empirical results, based on data collected from 176 South Korean SMEs in collaboration with Technoparks, indicate that institutional pressures, specifically regulatory and normative pressures, positively impact GBMI. Although RIIs demonstrate a positive mediating effect on the relationship between institutional pressures and GBMI, the moderating effect of RII yielded a mixed result: positive moderation with regulatory pressure and negative with normative pressure. The findings of our study suggest that RIIs should cultivate a nuanced understanding of the distinctive attributes of each institutional pressure to bolster the GBMI of SMEs.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"209 ","pages":"Article 123814"},"PeriodicalIF":12.9,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536216","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}
Prapti Maharjan , Mara Hauck , Arjan Kirkels , Benjamin Buettner , Heleen de Coninck
{"title":"Deriving experience curves: A structured and critical approach applied to PV sector","authors":"Prapti Maharjan , Mara Hauck , Arjan Kirkels , Benjamin Buettner , Heleen de Coninck","doi":"10.1016/j.techfore.2024.123795","DOIUrl":"10.1016/j.techfore.2024.123795","url":null,"abstract":"<div><div>Experience curves are widely used for cost estimates in energy-economy models and are proposed as a forecasting tool for projecting the future environmental impact of emerging technologies. However, further application is limited by data availability and methodological challenges related to modelling the dynamic relationship between cost, different kinds of learning, and scale effects. This paper systematically compares existing experience curves using empirical data from the PV sector. We compare the cost forecast of the assessed experience curves, derive the learning rates over different periods, and draw parallels to the environmental experience curve. Our results show that the single-factor experience curve (SEFC) is the most stable model, showing consistent performance across different technological eras, train-test splits and validation methods. Two-factor and multi-factor experience curves exhibit higher sensitivity, with their performance metrics varying significantly based on the data subsets used. Diagnostic tests are important to examine the robustness of the results. For the environmental experience curve, data quality and model explanatory power are lower, yet there is potential for its applicability in projecting environmental impact and energy use. Policymakers and modellers should consider the specific technological era when using learning rates for decision-making. Our findings indicate that learning-by-doing provides a steady learning rate across all experience curves. In the early stages of technological maturity, cost reductions in the PV industry are driven by learning-by-innovation, which is later dominated by economies of scale.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"209 ","pages":"Article 123795"},"PeriodicalIF":12.9,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536215","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}
Nicole Cecchele Lago , Arthur Marcon , Jose Luis Duarte Ribeiro , Daniel de Abreu Pereira Uhr , Yasmin Olteanu , Klaus Fichter
{"title":"Quantifying the impact of inbound open innovation","authors":"Nicole Cecchele Lago , Arthur Marcon , Jose Luis Duarte Ribeiro , Daniel de Abreu Pereira Uhr , Yasmin Olteanu , Klaus Fichter","doi":"10.1016/j.techfore.2024.123817","DOIUrl":"10.1016/j.techfore.2024.123817","url":null,"abstract":"<div><div>Within the dynamic context of innovation ecosystems and their actors, this study quantifies the impact of engagement in inbound open innovation on startups. Using data from a 2021 German innovation survey, which included responses from 1512 startups, we examined how cooperation with actors from the innovation ecosystem influences startup innovation in terms of business models, processes, products/services, and technologies. We assessed these impacts using the Propensity Score Matching method with the Kernel Tricube estimator. Our results show that higher levels of inbound open innovation positively affect startups' business models, processes, and technological innovation, but the impact on their product and service innovation was not statistically significant in this particular context. These findings underscore the critical roles of other startups, companies, and research institutions in fostering startup innovation, as they can provide the resources and support that are so crucial to help them navigate the complexities of innovation development and commercialization. They also encourage startups to engage in collaborative activities and help them to make informed decisions about inbound open innovation. Our study offers valuable insights for policymakers, entrepreneurs, and researchers.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"209 ","pages":"Article 123817"},"PeriodicalIF":12.9,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536205","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 impact of carbon transition risk concerns on stock market cycles: Evidence from China","authors":"Qin Luo , Xinjie Lu , Dengshi Huang , Qing Zeng","doi":"10.1016/j.techfore.2024.123827","DOIUrl":"10.1016/j.techfore.2024.123827","url":null,"abstract":"<div><div>This study constructs a measure of the Carbon Transition Risk Concern (CTRC) index using a textual method. Then, the paper investigates the ability of the CTRC to influence stock market cycles (volatility) in China. The out-of-sample results indicate that the CTRC significantly increases the predictive accuracy. More importantly, the CTRC contains unique information, even considering the macroeconomic variables and economic policy uncertainty. The channel analysis also suggests that the CTRC primarily affects the stock market cycles through the discount rate channel rather than the cash flow channel. Overall, this study uncovers the predictive capability of the CTRC for stock market volatility in China, offering a fresh perspective for investors and policymakers to enhance their understanding of stock market cycles.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"209 ","pages":"Article 123827"},"PeriodicalIF":12.9,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535828","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 gig economy's secret weapon: ChatGPT","authors":"Ali Nawaz Khan , Naseer Abbas Khan","doi":"10.1016/j.techfore.2024.123808","DOIUrl":"10.1016/j.techfore.2024.123808","url":null,"abstract":"<div><div>ChatGPT has the potential to transform the gig economy by amplifying worker efficiency and productivity. This study aims to examine the effect of innovative use of ChatGPT on gig worker performance using a mediating role of gig worker attitudes towards ChatGPT. This study also aims to determine the moderating role of gig worker agility in the association between the innovative application of ChatGPT and gig worker performance. This study employed a time lag approach involving two time waves and the final sample for this study was 418 gig workers. The data was collected via an online survey that involved the use of questionnaires which were developed on a five point Likert scale. The findings of this study indicate that the innovative use of ChatGPT has a significant positive direct influence on gig worker attitudes towards ChatGPT use and further on the level of performance of the gig workers. The results further confirmed the mediation effect of gig worker attitudes towards ChatGPT. Moreover, gig workers' agility significantly moderated the relationship between innovative use of ChatGPT and gig worker performance. These insights have important implications for individuals and organizations looking to meet the challenges and opportunities of the gig economy.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"209 ","pages":"Article 123808"},"PeriodicalIF":12.9,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536204","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}
Maryam Mazaheri, Jaime Bonnin Roca, Arjan Markus, Elena M. Tur, Bob Walrave
{"title":"Maturity assessment of green patent clusters: Methodological implications","authors":"Maryam Mazaheri, Jaime Bonnin Roca, Arjan Markus, Elena M. Tur, Bob Walrave","doi":"10.1016/j.techfore.2024.123813","DOIUrl":"10.1016/j.techfore.2024.123813","url":null,"abstract":"<div><div>Patents are one of the most widely used tools to analyze environmental technologies. Organizations such as the World Intellectual Property Organization and OECD have developed search strategies to retrieve green patents based on their patent classification. These classifications divide patents into clusters, which are aligned with different sustainability goals. In this paper, we take advantage of this to analyze the distribution of patents across 1.221 patent classes within six clusters defined by OECD's ENV-TECH classification. We also assess the maturity stage of each patent class by fitting two commonly used S-curve models, namely logistic and Gompertz. We find that (a) most patent classes are still in a relatively early stage of the technology life cycle and (b) considerable heterogeneity exists in the distribution of patents, both within and across clusters. We discuss the methodological implications of our results and provide recommendations for scholars, drawing on green patent analyses, to conduct future work on environmental technologies.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"209 ","pages":"Article 123813"},"PeriodicalIF":12.9,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536201","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":"Digital technologies, labor market flows and training: Evidence from Italian employer-employee data","authors":"Valeria Cirillo , Andrea Mina , Andrea Ricci","doi":"10.1016/j.techfore.2024.123735","DOIUrl":"10.1016/j.techfore.2024.123735","url":null,"abstract":"<div><div>New technologies can shape the production process by affecting the way in which inputs are embedded in the organization, their quality, and their use. Using an original employer-employee dataset that merges firm-level data on digital technology adoption and other characteristics of production with employee-level data on worker entry and exit rates from the administrative archive of the Italian Ministry of Labor, this paper explores the effects of new digital technologies on labor flows in the Italian economy. Using a Difference-in-Difference approach, we show that digital technologies lead to an increase in the firm-level hiring rate – particularly for young workers - and reduce the firm-level separation rate. We also find that digital technologies are positively associated with workplace training, proxied by the share of trained employees and the amount of training costs per employee. Furthermore, we explore the heterogeneity of effects related to different technologies (robots, cybersecurity and IoT). Our results are confirmed through several robustness checks.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"209 ","pages":"Article 123735"},"PeriodicalIF":12.9,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536202","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":"Big data and machine learning-based decision support system to reshape the vaticination of insurance claims","authors":"Rachana Jaiswal , Shashank Gupta , Aviral Kumar Tiwari","doi":"10.1016/j.techfore.2024.123829","DOIUrl":"10.1016/j.techfore.2024.123829","url":null,"abstract":"<div><div>Based on actuarial science theory, decision-making theory, and anonymous big data, this study employs machine learning to advance insurance claim forecasting, aiming to enhance pricing accuracy, mitigate adverse selection risks, and optimize operational efficiency for improved customer satisfaction and global competitiveness. The study utilized the Boruta algorithm with LightGBM for feature selection, analyzing a 57-dimensional dataset and identifying an optimal subset of 24 features. The improved LightGBM model achieved superior results (AUC ∼ 0.9272 and accuracy ∼ 92.94 %), surpassing other models evaluated. Beyond operational improvements, the proposed model holds the potential to contribute to various United Nations SDGs, such as promoting financial inclusion (SDG 1; SDG 10), reducing fraud, improving public safety (SDG 3; SDG 11; SDG 13), and encouraging sustainable practices (SDG 9; SDG 11). By utilizing data-driven insights to make more informed and accurate decisions, insurance companies can provide better services to their policyholders and contribute to a more equitable and sustainable society.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"209 ","pages":"Article 123829"},"PeriodicalIF":12.9,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536203","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":"Emission-smart advertising: Balancing performance with CO2 emissions in digital advertising","authors":"Nadr El Hana , Galina Kondrateva , Silvia Martin","doi":"10.1016/j.techfore.2024.123818","DOIUrl":"10.1016/j.techfore.2024.123818","url":null,"abstract":"<div><div>The environmental impact of digital advertising is the subject of debate. Effective media planning and content management are crucial for capturing audience attention and achieving objectives such as brand recognition, increased traffic, user engagement, and loyalty building. However, few studies highlight the risks of prioritizing economic gains over ecological sustainability and the massive tons of CO2 emissions that an average digital ad campaign generates. This study makes a significant contribution to the literature by introducing the novel framework of emission-smart advertising that balances the performance of digital advertising and CO2 emissions reduction. A two-phase methodology combined a qualitative study and the Delphi method. Based on our findings, we elaborate on innovative and rarely discussed theoretical, managerial, and community contributions. These contributions concern solutions merging media performance evaluation and reduction of CO2 emissions of digital ad campaigns, as well as the optimization process at all stages, including energy saving, choice, formats, data, and key performance indicators.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"209 ","pages":"Article 123818"},"PeriodicalIF":12.9,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535823","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}
Jaime Roberto Pohlmann , Jose Luis Duarte Ribeiro , Carla Schwengber ten Caten , Sabrina da Rosa Pojo Santos
{"title":"A micro and meso analysis of the role of principal investigators in entrepreneurial university ecosystems","authors":"Jaime Roberto Pohlmann , Jose Luis Duarte Ribeiro , Carla Schwengber ten Caten , Sabrina da Rosa Pojo Santos","doi":"10.1016/j.techfore.2024.123797","DOIUrl":"10.1016/j.techfore.2024.123797","url":null,"abstract":"<div><div>This study investigates the Principal Investigator's (PI's) role in enhancing Technology Transfer (TT) culture within a University Ecosystem (UE). We propose an approach for diagnosing TT culture within a UE at the micro- and meso-levels. The approach employs a survey to assess the UE's maturity regarding the core competencies of a PI. The approach: (i) assesses UE's maturity and categorises its researchers regarding the PI's core competencies, (ii) assesses researchers' resources and capabilities, and (iii) assesses the UE's TT culture. We applied the proposed approach to a large Brazilian university for testing and validation. The findings offer a comprehensive overview of UE's TT culture and highlight the PI's pivotal role in establishing a robust UE. As a contribution, we offer a practical approach to diagnosing a university ecosystem's TT culture through micro and meso-level analysis, advancing research on PI's role in aligning a UE to the Entrepreneurial University (EU) concept.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"209 ","pages":"Article 123797"},"PeriodicalIF":12.9,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142444547","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}