Journal of Economy and Technology最新文献_第3页

Quantitative modeling of M&A success probability: Integrating macroeconomic volatility and temporal factors through survival analysis 并购成功概率的定量建模：通过生存分析整合宏观经济波动和时间因素

Journal of Economy and Technology

Pub Date : 2025-01-07 DOI: 10.1016/j.ject.2024.12.002

Dan Xu

This paper delves into the intricate dynamics of the likelihood of merger and acquisition (M&A) completion in China and scrutinizes the influence of global and domestic economic conditions through survival analysis. By utilizing data from 3227 domestic M&A transactions from 1998 to 2024, this study employs quantitative survival analysis and the Cox proportional hazards model to evaluate how economic indicators shape M&A success rates. Notably, increases in the industrial production index (IPI) and producer price index (PPI) are positively associated with an increased likelihood of completion, reflecting how economic expansion fosters financial stability, strengthens firm capacity, and facilitates deal finalization. In contrast, rising global policy uncertainty, as captured by the global economic policy uncertainty (EPU) index, significantly reduces the likelihood of M&A completion by amplifying valuation ambiguity, negotiation frictions, and regulatory risks. Unexpectedly, the global economic growth—represented by the global real economic activity (GREA) index—correlates with a decreased likelihood of success in domestic M&A, potentially due to a shift in focus toward international opportunities and rising costs of domestic operations. Furthermore, the KaplanMeier estimator of the hazard function reveals a nonlinear curve depicting the likelihood of deal completion over time, emphasizing fluctuations in the probability of success. Our results indicate that the time elapsed from the announcement of a deal can provide crucial information on the ex-ante probability of its success or failure, highlighting the importance of considering the temporal aspect of the deal.

本文深入研究了中国并购可能性（M&；A）完成的复杂动态，并通过生存分析审视了全球和国内经济状况的影响。本研究利用1998 - 2024年国内并购交易3227笔交易的数据，采用定量生存分析和Cox比例风险模型，评估经济指标对并购成功率的影响。值得注意的是，工业生产指数（IPI）和生产者价格指数（PPI）的增加与完成可能性的增加呈正相关，反映了经济扩张如何促进金融稳定，增强企业能力，并促进交易的完成。相反，正如全球经济政策不确定性（EPU）指数所反映的那样，不断上升的全球政策不确定性通过放大估值模糊、谈判摩擦和监管风险，显著降低了并购完成的可能性。出乎意料的是，以全球实体经济活动（GREA）指数为代表的全球经济增长与国内并购成功的可能性下降相关，这可能是由于将重点转向国际机会和国内运营成本上升所致。此外，风险函数的KaplanMeier估计揭示了一条非线性曲线，描绘了交易随时间完成的可能性，强调了成功概率的波动。我们的研究结果表明，从宣布交易开始所经过的时间可以提供交易成功或失败的事前概率的关键信息，突出了考虑交易时间方面的重要性。

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引用次数: 0

Innovative machine learning approaches for complexity in economic forecasting and SME growth: A comprehensive review 针对经济预测和中小企业增长复杂性的创新机器学习方法：全面回顾

Journal of Economy and Technology

Pub Date : 2025-01-04 DOI: 10.1016/j.ject.2025.01.001

Mustafa I. Al-Karkhi , Grzegorz Rza̧dkowski

Economic forecasting and small and medium-sized enterprises (SMEs) growth prediction have become essential tools for guiding policy, business strategy, and economic development in an increasingly data-driven world. This paper reviews recent advancements in economic regression and SME growth forecasts, with a focus on the application of machine learning (ML) techniques. Specifically, the findings highlight that the integration of ensemble methods and deep learning models has achieved significant improvements in prediction accuracy, while interpretability tools such as SHAP and LIME enhance transparency and user trust. It provides a structured analysis of diverse methodologies that includes ensemble methods, deep learning models, and interpretability tools to evaluate their effectiveness and limitations in addressing the complexities of economic and SME data. This review categorizes studies by regional focus to highlight unique challenges in different economic landscapes and the adaptability of various forecasting models. Key challenges—such as imbalanced data, feature selection, and the integration of real-time data—were identified as critical factors for enhancing prediction reliability and applicability. By comparing existing surveys and identifying gaps, this review presents actionable insights and proposes future research directions that emphasize the need for integrative models that combine Explainable Artificial Intelligence (XAI) with cross-regional data fusion for more accurate and adaptable economic forecasts. These integrative models have the potential to achieve greater regional generalizability by the offering of better decision-making tools for policymakers. The findings underscore the transformative role of ML and XAI in economic forecasting and offer valuable guidance for researchers and decision-makers to optimize forecasting models for business growth and economic planning.

经济预测和中小企业（SMEs）增长预测已经成为指导政策、商业战略和经济发展的重要工具。本文回顾了经济回归和中小企业增长预测的最新进展，重点关注机器学习（ML）技术的应用。具体而言，研究结果强调集成方法和深度学习模型的集成在预测精度方面取得了显着提高，而SHAP和LIME等可解释性工具增强了透明度和用户信任。它提供了多种方法的结构化分析，包括集成方法、深度学习模型和可解释性工具，以评估它们在处理经济和中小企业数据复杂性方面的有效性和局限性。本综述按区域重点对研究进行分类，以突出不同经济格局的独特挑战和各种预测模型的适应性。关键挑战，如数据不平衡、特征选择和实时数据的集成，被认为是提高预测可靠性和适用性的关键因素。通过比较现有调查并找出差距，本综述提出了可操作的见解，并提出了未来的研究方向，强调需要将可解释人工智能（XAI）与跨区域数据融合相结合的综合模型，以实现更准确和适应性更强的经济预测。通过为决策者提供更好的决策工具，这些综合模型有可能实现更大的区域通用性。这些发现强调了ML和XAI在经济预测中的变革性作用，并为研究人员和决策者优化预测模型以实现业务增长和经济规划提供了有价值的指导。

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引用次数: 0

Enhancing energy resilience in manufacturing enterprises: A systematic mapping of challenges to strategies 增强制造企业的能源弹性：战略挑战的系统映射

Journal of Economy and Technology

Pub Date : 2025-01-04 DOI: 10.1016/j.ject.2025.01.002

P. Lebepe, T.N.D. Mathaba

An unreliable energy supply disrupts productivity and operational stability in manufacturing enterprises worldwide. Addressing these challenges requires achieving consensus among experts from diverse backgrounds. This study provides a preliminary understanding of mapping challenges to strategies, ensuring each challenge is paired with the most effective solution. By employing a structured and methodological approach, it ensures actionable insights, advancing academic discourse on energy resilience frameworks and their practical application in manufacturing enterprises. The study integrates Fleiss’ Kappa for expert agreement with the CRITIC (Criteria Importance Through Intercriteria Correlation) method for objective strategy weighting, ensuring rigorous evaluation of relevance and importance. Grounded in the 4As energy resilience framework; Availability, Accessibility, Affordability, and Acceptability, the approach ensures adaptability and a balanced alignment of challenges with actionable strategies. Fourteen industry experts validated the framework, prioritizing strategies such as flexible scheduling and renewable energy integration. This study addresses the limitations of traditional methods like Delphi, which require multiple rounds and delay outcomes, by achieving rapid consensus in a single round. Combining Fleiss’ Kappa and CRITIC balances qualitative insights with objective analysis, reducing biases and enhancing reliability. These contributions establish the framework as a novel, scalable, and practical tool for improving energy resilience in diverse manufacturing contexts.

不可靠的能源供应会破坏全球制造企业的生产力和运营稳定性。应对这些挑战需要不同背景的专家达成共识。本研究提供了将挑战映射到策略的初步理解，确保每个挑战都与最有效的解决方案相匹配。通过采用结构化和方法论的方法，它确保了可操作的见解，推进了能源弹性框架的学术论述及其在制造企业中的实际应用。该研究将Fleiss的Kappa专家协议与批评家（通过标准间相关性的标准重要性）方法相结合，用于客观策略加权，确保对相关性和重要性进行严格评估。基于4a能源弹性框架；可用性、可访问性、可负担性和可接受性，该方法确保了可适应性和挑战与可操作策略的平衡对齐。14位行业专家验证了该框架，优先考虑了灵活调度和可再生能源整合等策略。该研究解决了传统方法（如Delphi）需要多轮和延迟结果的局限性，通过在单轮中实现快速共识。结合Fleiss的Kappa和CRITIC平衡定性见解与客观分析，减少偏见和提高可靠性。这些贡献使该框架成为一种新颖的、可扩展的、实用的工具，用于提高不同制造环境下的能源弹性。

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引用次数: 0

User activity to enhance customer lifetime value modeling in contractual streaming industry 用户活动增强合同流媒体行业的客户生命周期价值建模

Journal of Economy and Technology

Pub Date : 2025-01-03 DOI: 10.1016/j.ject.2024.12.001

Eudes Adiba , Maurice Comlan , Eugéne C. Ezin , Nesta Kouzounhoue

This article presents a model for Customer Lifetime Value (CLV) tailored to the subscription-based streaming industry, incorporating both contractual dynamics and user activity. Unlike traditional CLV models that overlook contracts, this semi-Markov model captures the time users remain in specific subscription plans and the transitions between these subscription plans. Using empirical data from the MTN TV platform for a step-by-step implementation, the study identifies key factors influencing subscription cancellations, such as expiration dates and viewing behavior. The results show that longer subscriptions yield higher CLV, with more predictable churn cycles. These findings can guide marketing strategies and resource management to maximize CLV in the streaming sector.

本文提出了一个为基于订阅的流媒体行业量身定制的客户生命周期价值（CLV）模型，该模型结合了合同动态和用户活动。与忽略合约的传统CLV模型不同，这个半马尔可夫模型捕获了用户在特定订阅计划中停留的时间以及这些订阅计划之间的转换。利用MTN TV平台的经验数据逐步实施，该研究确定了影响订阅取消的关键因素，如到期日期和观看行为。结果显示，订阅时间越长，CLV越高，流失周期也越可预测。这些发现可以指导营销策略和资源管理，以最大限度地提高流媒体行业的CLV。

引用次数: 0

ChatGPT and CLT: Investigating differences in multimodal processing ChatGPT和CLT：研究多模态加工的差异

Journal of Economy and Technology

Pub Date : 2024-12-07 DOI: 10.1016/j.ject.2024.11.008

Michael Cahalane, Samuel N. Kirshner

Drawing on construal level theory, recent studies have demonstrated that ChatGPT interprets text inputs from an abstract perspective. However, as ChatGPT has evolved into a multimodal tool, this research examines whether ChatGPT's abstraction bias extends to image-based prompts. In a pre-registered study utilising hierarchical letters, ChatGPT predominantly associated these images with local rather than global letters, suggesting a concrete bias when analysing images. This starkly contrasts human participants who predominantly identified the same images with the global letters, indicating that humans and ChatGPT significantly diverge in image interpretations. Furthermore, while humans generally perceive ChatGPT to be more concrete in image processing, there is a notable discrepancy between this perception and the actual level of concreteness exhibited by ChatGPT in handling image-based tasks. These findings provide insights into the distinct cognitive behaviours of LLMs compared to humans, contributing to an emerging understanding of LLM cognition in the context of multimodal inputs.

根据解释水平理论，最近的研究表明，ChatGPT从抽象的角度解释文本输入。然而，随着ChatGPT已经发展成为一个多模态工具，本研究考察了ChatGPT的抽象偏见是否扩展到基于图像的提示。在一项使用分层字母的预注册研究中，ChatGPT主要将这些图像与局部字母而不是全局字母联系起来，这表明在分析图像时存在具体偏差。这与主要识别相同图像与全球字母的人类参与者形成鲜明对比，表明人类和ChatGPT在图像解释上存在显着差异。此外，虽然人类通常认为ChatGPT在图像处理中更具体，但这种感知与ChatGPT在处理基于图像的任务时所表现出的实际具体水平之间存在显著差异。这些发现提供了法学硕士与人类相比的独特认知行为的见解，有助于在多模态输入背景下对法学硕士认知的新兴理解。

引用次数: 0

Research on the low-carbon transformation of energy consumption under population aging in China 人口老龄化背景下中国能源消费低碳转型研究

Journal of Economy and Technology

Pub Date : 2024-12-02 DOI: 10.1016/j.ject.2024.11.007

Rumei Liu , Lu Tang , Qingrui Liu , Jianing Zhang

The low-carbon transformation of energy consumption is a key path to achieving the carbon emissions peak and carbon neutrality in China. Furthermore, technological innovation and policy regulation are necessary to promote low-carbon transformation of energy consumption, especially considering the issues associated with population aging. According to empirical facts and theoretical analysis, we demonstrate that population aging, technological innovation, and policy regulation affect the low-carbon transformation of energy consumption. Then, we build a PVAR model and conduct an empirical test using provincial panel data in China from 2003 to 2020. The results show that population aging, technological innovation, and policy regulation all contribute to the low-carbon index of energy consumption, and the interaction among the three forms a transmission mechanism to promote the low-carbon transformation of energy use in terms of both the production and consumption. The positive effect of population aging on the low-carbon index of energy consumption exhibits an inverted U-shaped curve that gradually increases at first and then gradually decreases. The positive effect of policy regulations on the low-carbon index of energy consumption follows an L-shaped curve, and the positive effect of technological innovation on the low-carbon index of energy consumption shows a constantly increasing trend. From the perspective of impact intensity, compared with population aging and policy regulation, technological innovation has a higher impact on the low-carbon index of energy consumption. With the rise in population aging, the effects of technological innovation, policy regulation, and technological innovation-policy regulation on the low-carbon index of energy consumption are ranked from low to high intensity. From the perspective of regional heterogeneity, compared with the middle and western regions, the positive effect of technological innovation and policy regulation on the low-carbon index of energy consumption under population aging in the eastern region is more significant. Our findings reveal the value of technological innovation and policy regulation in promoting low-carbon transformation of energy consumption while population aging is increasing in all countries around the world.

能源消费的低碳转型是中国实现碳排放峰值和碳中和的关键途径。此外，促进能源消费的低碳转型需要技术创新和政策调控，特别是考虑到人口老龄化的相关问题。本文通过实证和理论分析，论证了人口老龄化、技术创新和政策调控对能源消费低碳转型的影响。然后，利用2003 - 2020年中国各省面板数据构建PVAR模型并进行实证检验。结果表明，人口老龄化、技术创新和政策调控都对能源消费低碳指数有贡献，三者相互作用形成了一种传导机制，在生产和消费两方面都促进了能源利用方式的低碳转型。人口老龄化对能源消费低碳指数的正向影响呈先逐渐增大后逐渐减小的倒u型曲线。政策规制对能源消费低碳指数的正向效应呈l型曲线，技术创新对能源消费低碳指数的正向效应呈不断增强的趋势。从影响强度来看，与人口老龄化和政策调控相比，技术创新对能耗低碳指标的影响更大。随着人口老龄化的加剧，技术创新、政策调控、技术创新-政策调控对能源消费低碳指数的影响强度由低到高。从区域异质性来看，与中西部地区相比，东部地区技术创新和政策调控对人口老龄化下能源消费低碳指数的正向作用更为显著。我们的研究结果揭示了在世界各国人口老龄化加剧的背景下，技术创新和政策调控在促进能源消费低碳转型中的价值。

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引用次数: 0

Examining the impacts of information and communication technology (ICT) on national development and wellbeing: A global perspective 检查信息和通信技术（ICT）对国家发展和福祉的影响：全球视角

Journal of Economy and Technology

Pub Date : 2024-11-28 DOI: 10.1016/j.ject.2024.11.006

Ming-Yi Wu

The impacts of information and communications technology (ICT) on national development and wellbeing is a current research issue. By integrating four World Bank datasets and World Happiness Report’s global wellbeing dataset, this study analyzes the impacts of ICT on national development and wellbeing in 124 economies. There are several significant findings based on multiple regression analysis. First, Internet, mobile, and broadband subscription rates are significant predictors for logged GDP per capita. Second, broadband subscription rate is a significant predictor for perceptions of corruption. Third, Internet and broadband penetration rates are significant predictors for subjective wellbeing, social support and healthy life expectancy. Finally, fixed broadband and fixed telephone subscription rates are significant predictors for freedom to make life choices. Interestingly, fixed telephone subscription rate inversely predicts freedom to make life choices. The findings of this study bring updated insights into ICT impacts on national development and wellbeing around the world.

信息和通信技术（ICT）对国家发展和福祉的影响是当前的一个研究问题。通过整合世界银行的四个数据集和《世界幸福报告》的全球幸福数据集，本研究分析了信息通信技术对124个经济体的国家发展和幸福的影响。基于多元回归分析，有几个显著的发现。首先，互联网、移动和宽带订阅率是人均GDP的重要预测指标。其次，宽带订阅率是腐败程度的重要预测指标。第三，互联网和宽带普及率是主观幸福感、社会支持和健康预期寿命的重要预测因子。最后，固定宽带和固定电话订阅率是生活选择自由的重要预测指标。有趣的是，固定电话订阅率与生活选择的自由度成反比。这项研究的结果为信息通信技术对全球国家发展和福祉的影响提供了最新的见解。

引用次数: 0

An innovative way of analyzing COVID topics with LLM 用LLM分析COVID主题的创新方法

Journal of Economy and Technology

Pub Date : 2024-11-19 DOI: 10.1016/j.ject.2024.11.004

Fahim Sufi

In the aftermath of the COVID-19 pandemic, international landscapes have been profoundly reshaped, with shifts in political alliances, economic priorities, and socio-cultural norms. Such evolutions, reflected in the vast expanse of digital conversations, particularly on Twitter, necessitate advanced tools for analysis given their impact on policy and strategy. In this context, the presented study underscores the indispensability of Artificial Intelligence (AI) in discerning intricate patterns from voluminous and multifaceted Twitter data on COVID-19. Through an innovative methodology leveraging AI modalities such as language detection, sentiment analysis, topic analysis, Large Language Model (LLM), regression, clustering, this study distills textual features from 152,070 multilingual tweets across 58 languages, spanning 645 days from 15 July 2021–20 April 2023. Our analyses, automatically identify five pivotal COVID-19 discussion topics and expound on four critical factors—tweet language, retweet count, and positive and negative sentiments—that significantly influence these conversations. In essence, the paper's contributions lie in: 1) unveiling an AI-centric autonomous methodology for deep insights into COVID-19 discussions; 2) empirically validating this approach using a diverse, multilingual dataset that resulted in five key discussion areas; and 3) presenting 52 nuanced AI-generated observations that detail factors influencing these discussions. Comparative literature suggests that our approach offers unparalleled depth in AI-driven analytics related to COVID-19 discourse. In summary, this paper underline the pressing need to harness the power of AI-based Tweet analytics as an indispensable tool in formulating strategic decisions pertaining to disaster responses.

在2019冠状病毒病大流行之后，国际格局发生了深刻的变化，政治联盟、经济重点和社会文化规范发生了变化。这种演变反映在数字对话（尤其是Twitter）的广泛应用上，鉴于它们对政策和战略的影响，需要先进的分析工具。在此背景下，本研究强调了人工智能（AI）在从关于COVID-19的大量和多方面的推特数据中识别复杂模式方面的不可或缺性。通过一种创新的方法，利用人工智能模式，如语言检测、情感分析、主题分析、大型语言模型（LLM）、回归、聚类，本研究从58种语言的152,070条多语种推文中提取文本特征，时间跨度为2021年7月15日至2023年4月20日的645天。我们的分析自动识别了五个关键的COVID-19讨论主题，并阐述了四个关键因素——推文语言、转发数量以及积极和消极情绪——这些因素对这些对话产生了重大影响。从本质上讲，本文的贡献在于：1)揭示了一种以人工智能为中心的自主方法，以深入了解COVID-19的讨论；2)使用多样化的多语言数据集对该方法进行实证验证，得出五个关键讨论领域；3)提出52个细致入微的人工智能观察结果，详细说明影响这些讨论的因素。比较文献表明，我们的方法在与COVID-19话语相关的人工智能驱动分析方面提供了无与伦比的深度。总之，本文强调迫切需要利用基于人工智能的推特分析的力量，将其作为制定与灾害应对有关的战略决策的不可或缺的工具。

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引用次数: 0

Unveiling the three-dimensional ecological footprint dynamics in the era of technological revolution 揭示技术革命时代的三维生态足迹动态

Journal of Economy and Technology

Pub Date : 2024-11-19 DOI: 10.1016/j.ject.2024.11.005

Muhammed Ashiq Villanthenkodath

This study integrates technological innovation and economic growth into the environmental degradation function for India. Unlike prior studies, it uses the three-dimensional ecological footprint as a measure of environmental quality for the period span from 1980 to 2022. Empirically, the study employs the Auto-Regressive Distributed Lag (ARDL) model alongside various cointegration regression methods. The results indicate that technological innovation has a positive and significant impact in the long run; however, it exhibits a negative and significant effect in the short run. This finding suggests that, though technological innovation may contribute to environmental degradation over time by increasing the three-dimensional ecological footprint, it can enhance environmental quality in the short term by reducing it. Additionally, the study confirms the Environmental Kuznets Curve (EKC) hypothesis concerning the three-dimensional ecological footprint in the long run, while it does not find support for this relationship in the short run. Finally, the study recommends comprehensive technology and economy-related policies to foster the path to sustainable development.

本文将技术创新和经济增长整合到印度的环境退化函数中。与之前的研究不同，它使用三维生态足迹作为1980年至2022年期间环境质量的衡量标准。在实证上，本研究采用了自回归分布滞后（ARDL）模型和各种协整回归方法。结果表明：技术创新在长期内具有显著的正向影响；然而，它在短期内表现出负面和显著的影响。这一发现表明，尽管随着时间的推移，技术创新可能会通过增加三维生态足迹而导致环境退化，但它可以通过减少三维生态足迹而在短期内提高环境质量。此外，该研究在长期内证实了三维生态足迹的环境库兹涅茨曲线（EKC）假设，但在短期内没有发现这种关系的支持。最后，该研究建议采取综合的技术和经济政策，以促进可持续发展的道路。

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引用次数: 0

The role of parental circumstances and luck in shaping socioeconomic success: A simulation-based analysis of talent 父母环境和运气在塑造社会经济成功中的作用：基于模拟的人才分析

Journal of Economy and Technology

Pub Date : 2024-11-16 DOI: 10.1016/j.ject.2024.11.003

Hana Hebishima , Shin-ichi Inage

This study investigates the interplay between parental circumstances, talent, and luck in shaping long-term socioeconomic success through an agent-based simulation model. Building on prior research on the influence of socioeconomic status (SES) and parental circumstances on educational outcomes, the model simulates how parental circumstances enhance innate talent through education and examines how this talent interacts with luck throughout an individual’s life. The simulation is divided into two phases: an educational phase and a working phase. Our results reveal that while parental circumstances and education amplify talent and increase the potential for success, cumulative luck plays the most decisive role in determining savings at age 60. A strong positive correlation is observed between cumulative luck and lifetime savings, whereas the direct influence of talent, even when enhanced through education, remains limited. Additionally, favorable parental circumstances elevate baseline savings, even for individuals experiencing misfortune, underscoring the importance of early educational advantages. These findings highlight that although talent and parental support are essential for fostering success, luck ultimately dominates in shaping financial outcomes. The study offers critical policy insights, advocating for equitable access to education and strategic investments to mitigate the disproportionate impact of luck, promote social mobility, and reduce structural inequalities across generations.

本研究通过基于主体的模拟模型调查了父母环境、天赋和运气在塑造长期社会经济成功中的相互作用。在先前关于社会经济地位（SES）和父母环境对教育结果影响的研究的基础上，该模型模拟了父母环境如何通过教育增强天赋，并研究了这种天赋在个人一生中如何与运气相互作用。模拟分为两个阶段：教育阶段和工作阶段。我们的研究结果显示，虽然父母的环境和教育可以放大天赋，增加成功的潜力，但累积的运气在决定60岁时的储蓄方面起着最决定性的作用。累积的运气和一生的储蓄之间存在很强的正相关关系，而天赋的直接影响，即使通过教育得到增强，仍然是有限的。此外，良好的父母环境提高了基本储蓄，即使对那些经历不幸的人来说也是如此，这强调了早期教育优势的重要性。这些发现强调，虽然天赋和父母的支持对成功至关重要，但运气最终在决定财务结果方面占主导地位。该研究提供了重要的政策见解，倡导公平的教育机会和战略投资，以减轻运气的不成比例影响，促进社会流动性，减少代际间的结构性不平等。

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引用次数: 0