Journal of Economy and Technology最新文献

{"title":"Remanufacturing facility installation decisions under product sourcing cost uncertainties: A real options approach","authors":"Mohammad Ahnaf Sadat,&nbsp;K. Jo Min","doi":"10.1016/j.ject.2025.02.003","DOIUrl":"10.1016/j.ject.2025.02.003","url":null,"abstract":"<div><div>In this paper, we investigate the strategic decision-making process of a Maintenance Repair and Overhaul (MRO) company considering the installation of a remanufacturing facility under product sourcing cost uncertainties (e.g., purchasing new products from third-party, and remanufacturing used ones). We consider the remanufacturing costs to consist of constant and variable portions. The variable portion is the acquisition cost of used products, which we consider to be correlated with the new product's purchasing costs. Assuming an indefinite lifespan for the remanufacturing facility and equivalent pricing and customer valuation for remanufactured and new products, we employ the real options approach and the quasi-analytical method for problem modeling and solution derivation. The study reveals that the decision to install a remanufacturing facility is influenced by various cost combinations rather than a single threshold. We derive and show the procedure to obtain these cost combinations. Significantly, we discover that unit-based variable subsidies, such as tax exemptions, can effectively reduce this cost threshold, making remanufacturing a more viable option. This insight is crucial for policymakers and businesses, highlighting the role of government incentives in promoting sustainable remanufacturing practices and contributing to the understanding of remanufacturing as a financially viable and sustainable strategy.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 123-142"},"PeriodicalIF":0.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Event driven technology intelligence","authors":"Song-Kyoo Kim","doi":"10.1016/j.ject.2025.01.004","DOIUrl":"10.1016/j.ject.2025.01.004","url":null,"abstract":"<div><div>The Event Driven Technology Intelligence (EDTI) model offers a structured approach to navigating the complexities of technological innovation. This research aims to summarize the core concepts and phases of the EDTI model while also exploring its significance in various industries. By expanding on the traditional Hype-cycle model to include seven phases, EDTI guides companies from concept innovation through product execution. Crucial to this model are the Moments-of-Event which represent key breakthroughs necessary for advancing technologies. By integrating these insights, EDTI enhances decision-making, fosters innovation, and mitigates risks associated with technology obsolescence, ultimately helping organizations adapt to changing consumer demands and maintain competitiveness in dynamic markets. In the rapidly evolving landscape of technology, the EDTI model emerges as a vital tool for organizations striving to remain competitive. This model flourishes in a climate marked by global competition and the constant demand for innovation. Key to its success is the integration of critical events and technological breakthroughs, which allow businesses to effectively manage and leverage emerging technologies.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 143-150"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federated learning and information sharing between competitors with different training effectiveness","authors":"Jiajun Meng ,&nbsp;Jing Chen ,&nbsp;Dongfang Zhao","doi":"10.1016/j.ject.2024.12.003","DOIUrl":"10.1016/j.ject.2024.12.003","url":null,"abstract":"<div><div>Federated Learning (FL) is an innovative technique that allows multiple firms to collaborate in training machine learning models while preserving data privacy. This is especially important in industries where data is sensitive or subject to regulations like the General Data Protection Regulation (GDPR). Despite its substantial benefits, the adoption of FL in competitive markets faces significant challenges, particularly due to concerns about training effectiveness and price competition. In practice, data from different firms may not be independently and identically distributed (non-IID) and heterogenous, which can lead to differences in model training effectiveness when aggregated through FL. This paper explores how initial product quality, data volume, and training effectiveness affect the formation of FL. We develop a theoretical model to analyze firms’ decisions between adopting machine learning (ML) independently or collaborating through FL. Our results show that when the initial product quality is high, FL can never be formed. Moreover, when the initial product quality is low, and when data volume is low and firms’ training effectiveness differences are small, FL is more likely to form. This is because the competition intensification effect is dominated by the market expansion effect of FL. However, when there is a significant difference in training effectiveness, firms are less likely to adopt FL due to concerns about competitive disadvantage (i.e., the market expansion effect is dominated by the competition intensification effect). This paper contributes to the literature on FL by addressing the strategic decisions firms face in competitive markets and providing insights into how FL designers and policymakers can encourage the formation of FL.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 1-9"},"PeriodicalIF":0.0,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From innovation capability to green innovation capability: Evidence from Chinese big tech firms","authors":"Martín Hernani-Merino ,&nbsp;Jorge Tello-Gamarra ,&nbsp;David Mayorga ,&nbsp;Julio Zevallos","doi":"10.1016/j.ject.2025.01.003","DOIUrl":"10.1016/j.ject.2025.01.003","url":null,"abstract":"<div><div>Innovation capability as a source of competitive advantage for firms is a consolidated topic in the literature. However, there is still little evidence about the green characteristics that innovation capability incorporates. This article aims to identify the innovation capability and analyze the existence of green variables in said capability. We conducted a multiple case study comprising four Big Tech firms from the Chinese technology industry. The results show that each of the capabilities involved in the innovation capability (technological, operational, managerial, marketing, and learning) demonstrates a green variable. As a second result, we define the concept of green innovation capability as a repertoire of abilities, skills, knowledge, and routines for the firm to design, produce, and transact green products and services. Furthermore, we propose the concepts of the capabilities that form the green innovation capability.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 95-108"},"PeriodicalIF":0.0,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative modeling of M&A success probability: Integrating macroeconomic volatility and temporal factors through survival analysis","authors":"Dan Xu","doi":"10.1016/j.ject.2024.12.002","DOIUrl":"10.1016/j.ject.2024.12.002","url":null,"abstract":"<div><div>This paper delves into the intricate dynamics of the likelihood of merger and acquisition (M&amp;A) completion in China and scrutinizes the influence of global and domestic economic conditions through survival analysis. By utilizing data from 3227 domestic M&amp;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&amp;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&amp;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&amp;A, potentially due to a shift in focus toward international opportunities and rising costs of domestic operations. Furthermore, the Kaplan<img>Meier 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.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 202-222"},"PeriodicalIF":0.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Innovative machine learning approaches for complexity in economic forecasting and SME growth: A comprehensive review","authors":"Mustafa I. Al-Karkhi ,&nbsp;Grzegorz Rza̧dkowski","doi":"10.1016/j.ject.2025.01.001","DOIUrl":"10.1016/j.ject.2025.01.001","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 109-122"},"PeriodicalIF":0.0,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143600456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing energy resilience in manufacturing enterprises: A systematic mapping of challenges to strategies","authors":"P. Lebepe,&nbsp;T.N.D. Mathaba","doi":"10.1016/j.ject.2025.01.002","DOIUrl":"10.1016/j.ject.2025.01.002","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 82-94"},"PeriodicalIF":0.0,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"User activity to enhance customer lifetime value modeling in contractual streaming industry","authors":"Eudes Adiba ,&nbsp;Maurice Comlan ,&nbsp;Eugéne C. Ezin ,&nbsp;Nesta Kouzounhoue","doi":"10.1016/j.ject.2024.12.001","DOIUrl":"10.1016/j.ject.2024.12.001","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 69-81"},"PeriodicalIF":0.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ChatGPT and CLT: Investigating differences in multimodal processing","authors":"Michael Cahalane,&nbsp;Samuel N. Kirshner","doi":"10.1016/j.ject.2024.11.008","DOIUrl":"10.1016/j.ject.2024.11.008","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 10-21"},"PeriodicalIF":0.0,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on the low-carbon transformation of energy consumption under population aging in China","authors":"Rumei Liu ,&nbsp;Lu Tang ,&nbsp;Qingrui Liu ,&nbsp;Jianing Zhang","doi":"10.1016/j.ject.2024.11.007","DOIUrl":"10.1016/j.ject.2024.11.007","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 151-165"},"PeriodicalIF":0.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}