Technological Forecasting and Social Change最新文献_第6页

Perceived worth of human capital across IT jobseekers in the digital era 数字时代 IT 求职者对人力资本价值的看法

IF 12.9 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2024-10-22 DOI: 10.1016/j.techfore.2024.123819

Elena Shakina , Natalia V. Volkova , Sofia Paklina

In an era of technological transformation in labor markets, jobseekers navigate a new landscape of wage uncertainty during the hiring process. Not disclosing wage expectations typically leads to subsequent wage bargaining, which can impact the job matching process and the final agreed-upon wage. This study delves into when jobseekers are more inclined to disclose wage expectations in their online CVs, considering the interplay of human capital accumulation (education, professional skills, work experience) and job-related signals (mobility, job commitment, work schedule/mode). Analyzing over 152,000 anonymous online CVs of tech professionals from 2016 to 2020 in one of the Eastern European countries, the study uses the OLS model with Heckman correction for sample selection bias. The findings reveal trends in wage expectation disclosure. Younger IT jobseekers, along with those who are more educated, skilled, and mobile, tend toward wage bargaining, seldom disclosing wage expectations. Similar effect holds for women jobseekers. Conversely, older jobseekers with basic skills, seeking full-time roles and clear work schedules, are more likely to voluntarily post wage expectations. This research extends compensation literature by focusing on online CVs and exploring a broader range of indicators, reflecting the significant impact of technology-driven social changes on jobseeker behavior in modern labor markets.

在劳动力市场技术变革的时代，求职者在招聘过程中要面对工资不确定的新情况。不透露工资预期通常会导致随后的工资讨价还价，从而影响工作匹配过程和最终商定的工资。本研究考虑到人力资本积累（教育、专业技能、工作经验）和工作相关信号（流动性、工作承诺、工作时间表/模式）的相互作用，深入研究求职者何时更倾向于在其在线简历中披露工资预期。本研究分析了东欧某国科技专业人士在 2016 年至 2020 年期间的 152,000 多份匿名在线简历，使用了 OLS 模型，并对样本选择偏差进行了赫克曼校正。研究结果揭示了工资期望披露的趋势。年轻的 IT 求职者，以及受教育程度更高、技能更强、流动性更大的求职者，倾向于讨价还价，很少披露工资期望值。女性求职者也有类似的情况。相反，拥有基本技能、寻求全职工作和明确工作时间表的年长求职者更有可能自愿公布工资预期。这项研究以在线简历为重点，探索了更广泛的指标，从而扩展了报酬方面的文献，反映了技术驱动的社会变革对现代劳动力市场求职者行为的重大影响。

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引用次数: 0

Is institutional pressure the driver for green business model innovation of SMEs? Mediating and moderating roles of regional innovation intermediaries 制度压力是中小企业绿色商业模式创新的驱动力吗？区域创新中介机构的中介和调节作用

IF 12.9 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2024-10-21 DOI: 10.1016/j.techfore.2024.123814

Min-Jae Lee , Hyeseung Choi , Taewoo Roh

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.

虽然现有研究集中于促进区域创新，但主要采用的是以知识为基础的方法。然而，关于制度压力和区域创新中介机构（RIIs）对中小型企业（SMEs）绿色商业模式创新（GBMI）的集体影响的研究却很少。本研究旨在确定中小型企业加强其绿色商业模式创新的机制，认为区域创新中介机构对制度压力的多方面反应既可以起到中介作用，也可以起到调节作用。基于与科技园合作收集的 176 家韩国中小企业的数据，实证结果表明，制度压力，特别是监管和规范压力，对 GBMI 有积极影响。虽然 RII 对制度压力和 GBMI 之间的关系有积极的中介作用，但 RII 的调节作用却产生了好坏参半的结果：与监管压力的调节作用为正，与规范压力的调节作用为负。我们的研究结果表明，风险投资机构应深入了解每种制度压力的独特属性，以促进中小企业的GBMI。

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引用次数: 0

Deriving experience curves: A structured and critical approach applied to PV sector 得出经验曲线：适用于光伏行业的结构化批判方法

IF 12.9 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2024-10-21 DOI: 10.1016/j.techfore.2024.123795

Prapti Maharjan , Mara Hauck , Arjan Kirkels , Benjamin Buettner , Heleen de Coninck

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.

经验曲线被广泛用于能源经济模型中的成本估算，并被提议作为预测新兴技术未来环境影响的预测工具。然而，由于数据可用性以及成本、不同类型的学习和规模效应之间动态关系建模方法上的挑战，经验曲线的进一步应用受到了限制。本文利用光伏行业的经验数据，系统地比较了现有的经验曲线。我们比较了所评估的经验曲线的成本预测，得出了不同时期的学习率，并与环境经验曲线进行了比较。我们的结果表明，单因素经验曲线（SEFC）是最稳定的模型，在不同的技术时代、训练-测试分割和验证方法中表现出一致的性能。双因素和多因素经验曲线表现出更高的灵敏度，其性能指标因所使用的数据子集而有显著差异。诊断测试对于检查结果的稳健性非常重要。环境经验曲线的数据质量和模型解释能力较低，但在预测环境影响和能源使用方面仍有应用潜力。决策者和建模者在使用学习率进行决策时，应考虑特定的技术时代。我们的研究结果表明，在所有经验曲线上，边干边学都能提供稳定的学习率。在技术成熟的早期阶段，光伏产业的成本降低是由边创新边学习驱动的，后来则由规模经济主导。

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引用次数: 0

Quantifying the impact of inbound open innovation 量化入境开放式创新的影响

IF 12.9 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2024-10-21 DOI: 10.1016/j.techfore.2024.123817

Nicole Cecchele Lago , Arthur Marcon , Jose Luis Duarte Ribeiro , Daniel de Abreu Pereira Uhr , Yasmin Olteanu , Klaus Fichter

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.

在创新生态系统及其参与者的动态背景下，本研究量化了参与对内开放式创新对初创企业的影响。利用 2021 年德国创新调查的数据（其中包括 1512 家初创企业的回复），我们研究了与创新生态系统参与者的合作如何在商业模式、流程、产品/服务和技术方面影响初创企业的创新。我们使用倾向得分匹配法和 Kernel Tricube 估计器评估了这些影响。我们的结果表明，较高水平的入境开放式创新会对初创企业的商业模式、流程和技术创新产生积极影响，但在这种特定情况下，对其产品和服务创新的影响在统计学上并不显著。这些发现强调了其他初创企业、公司和研究机构在促进初创企业创新方面的关键作用，因为它们可以提供资源和支持，而这些资源和支持对于帮助初创企业应对创新发展和商业化的复杂性至关重要。它们还鼓励初创企业参与合作活动，并帮助它们就入境开放式创新做出明智的决策。我们的研究为政策制定者、企业家和研究人员提供了宝贵的见解。

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引用次数: 0

The impact of carbon transition risk concerns on stock market cycles: Evidence from China 碳转型风险担忧对股市周期的影响：来自中国的证据

IF 12.9 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2024-10-21 DOI: 10.1016/j.techfore.2024.123827

Qin Luo , Xinjie Lu , Dengshi Huang , Qing Zeng

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.

本研究采用文本方法构建了碳过渡风险关注指数（CTRC）的衡量指标。然后，本文研究了 CTRC 对中国股市周期（波动性）的影响能力。样本外结果表明，CTRC 显著提高了预测准确性。更重要的是，即使考虑到宏观经济变量和经济政策的不确定性，CTRC 也包含独特的信息。渠道分析还表明，CTRC 主要通过贴现率渠道而非现金流渠道影响股市周期。总之，本研究揭示了 CTRC 对中国股市波动的预测能力，为投资者和政策制定者加强对股市周期的理解提供了一个全新的视角。

引用次数: 0

The gig economy's secret weapon: ChatGPT 打工经济的秘密武器：ChatGPT

IF 12.9 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2024-10-20 DOI: 10.1016/j.techfore.2024.123808

Ali Nawaz Khan , Naseer Abbas Khan

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.

ChatGPT 有可能通过提高工人的效率和生产力来改变临时工经济。本研究旨在利用 "临时工 "对 ChatGPT 的态度这一中介作用，研究创新使用 ChatGPT 对 "临时工 "绩效的影响。本研究还旨在确定 "临时工 "的敏捷性在创新应用 ChatGPT 与 "临时工 "绩效之间的关联中的调节作用。本研究采用了时滞法，包括两个时间波，最终样本为 418 名临时工。数据是通过在线调查收集的，其中涉及使用五点李克特量表编制的问卷。研究结果表明，ChatGPT 的创新使用对临时工使用 ChatGPT 的态度有显著的正向直接影响，并进一步影响临时工的绩效水平。研究结果进一步证实了临时工对 ChatGPT 态度的中介效应。此外，临时工的敏捷性在很大程度上调节了创新使用 ChatGPT 与临时工绩效之间的关系。这些见解对希望迎接临时工经济的挑战和机遇的个人和组织具有重要意义。

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引用次数: 0

Maturity assessment of green patent clusters: Methodological implications 绿色专利集群的成熟度评估：方法论意义

IF 12.9 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2024-10-19 DOI: 10.1016/j.techfore.2024.123813

Maryam Mazaheri, Jaime Bonnin Roca, Arjan Markus, Elena M. Tur, Bob Walrave

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.

专利是分析环境技术最广泛使用的工具之一。世界知识产权组织和经合组织等组织已经制定了检索策略，根据专利分类检索绿色专利。这些分类法将专利分为多个群组，这些群组与不同的可持续发展目标相一致。在本文中，我们利用这一优势分析了经合组织 ENV-TECH 分类所定义的六个群组中 1.221 个专利类别的专利分布情况。我们还通过拟合两种常用的 S 曲线模型（即逻辑模型和 Gompertz 模型）来评估每个专利类别所处的成熟阶段。我们发现：(a) 大部分专利类别仍处于技术生命周期的相对早期阶段；(b) 专利分布存在相当大的异质性，无论是在集群内部还是在集群之间。我们讨论了研究结果的方法论意义，并为学者们借鉴绿色专利分析方法今后开展环境技术研究提供了建议。

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引用次数: 0

Digital technologies, labor market flows and training: Evidence from Italian employer-employee data 数字技术、劳动力市场流动和培训：来自意大利雇主-雇员数据的证据

IF 12.9 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2024-10-19 DOI: 10.1016/j.techfore.2024.123735

Valeria Cirillo , Andrea Mina , Andrea Ricci

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.

新技术可以通过影响投入在组织中的嵌入方式、质量和使用来塑造生产流程。本文使用了一个原始的雇主-雇员数据集，该数据集将企业层面的数字技术采用和其他生产特征数据与来自意大利劳动部行政档案的雇员层面的工人入职率和离职率数据合并在一起，探讨了新数字技术对意大利经济中劳动力流动的影响。利用差分法，我们发现数字技术提高了企业层面的雇佣率，尤其是年轻工人的雇佣率，并降低了企业层面的离职率。我们还发现，数字技术与工作场所的培训（以受过培训的员工比例和每名员工的培训成本为指标）呈正相关。此外，我们还探讨了与不同技术（机器人、网络安全和物联网）相关的异质性效应。我们的结果通过几项稳健性检查得到了证实。

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引用次数: 0

Big data and machine learning-based decision support system to reshape the vaticination of insurance claims 基于大数据和机器学习的决策支持系统，重塑保险理赔流程

IF 12.9 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2024-10-19 DOI: 10.1016/j.techfore.2024.123829

Rachana Jaiswal , Shashank Gupta , Aviral Kumar Tiwari

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.

本研究基于精算科学理论、决策理论和匿名大数据，利用机器学习推进保险索赔预测，旨在提高定价准确性、降低逆向选择风险、优化运营效率，从而提高客户满意度和全球竞争力。该研究利用 Boruta 算法和 LightGBM 进行特征选择，分析了 57 维数据集，并确定了 24 个特征的最佳子集。改进后的 LightGBM 模型取得了优异的结果（AUC ∼ 0.9272，准确率 ∼ 92.94 %），超过了其他评估模型。除了业务改进之外，所提出的模型还有可能为联合国的各种可持续发展目标做出贡献，如促进金融包容性（可持续发展目标 1；可持续发展目标 10）、减少欺诈、改善公共安全（可持续发展目标 3；可持续发展目标 11；可持续发展目标 13）以及鼓励可持续实践（可持续发展目标 9；可持续发展目标 11）。通过利用数据驱动的洞察力做出更明智、更准确的决策，保险公司可以为其保单持有人提供更好的服务，并为一个更加公平和可持续的社会做出贡献。

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引用次数: 0

Emission-smart advertising: Balancing performance with CO2 emissions in digital advertising 排放智能广告：平衡数字广告的性能与二氧化碳排放量

IF 12.9 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2024-10-19 DOI: 10.1016/j.techfore.2024.123818

Nadr El Hana , Galina Kondrateva , Silvia Martin

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.

数字广告对环境的影响一直是人们争论的话题。有效的媒体规划和内容管理对于吸引受众注意力，实现品牌认知、增加流量、用户参与和建立忠诚度等目标至关重要。然而，很少有研究强调经济收益优先于生态可持续性的风险，以及一个普通的数字广告活动所产生的大量二氧化碳排放。本研究引入了新颖的排放智能广告框架，平衡了数字广告和二氧化碳减排之间的关系，为相关文献做出了重要贡献。研究采用了定性研究和德尔菲法相结合的两阶段方法。在研究结果的基础上，我们阐述了在理论、管理和社区方面鲜有讨论的创新性贡献。这些贡献涉及将数字广告活动的媒体性能评估与减少二氧化碳排放相结合的解决方案，以及各个阶段的优化过程，包括节能、选择、格式、数据和关键性能指标。

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引用次数: 0