Journal of Economy and Technology最新文献

{"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}

{"title":"Examining the impacts of information and communication technology (ICT) on national development and wellbeing: A global perspective","authors":"Ming-Yi Wu","doi":"10.1016/j.ject.2024.11.006","DOIUrl":"10.1016/j.ject.2024.11.006","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 190-201"},"PeriodicalIF":0.0,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642982","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":"An innovative way of analyzing COVID topics with LLM","authors":"Fahim Sufi","doi":"10.1016/j.ject.2024.11.004","DOIUrl":"10.1016/j.ject.2024.11.004","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"4 ","pages":"Pages 35-56"},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of parental circumstances and luck in shaping socioeconomic success: A simulation-based analysis of talent","authors":"Hana Hebishima ,&nbsp;Shin-ichi Inage","doi":"10.1016/j.ject.2024.11.003","DOIUrl":"10.1016/j.ject.2024.11.003","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 223-236"},"PeriodicalIF":0.0,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792511","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}