Pub Date : 2025-06-18DOI: 10.1016/j.ject.2025.06.001
Pei Xue , Tianshun Li
We introduce an estimation framework utilizing a Supervised Variational Autoencoder (SVAE) to address challenges posed by high-dimensional socioeconomic data. Unlike classical linear dimensionality reduction methods, such as PCA and Lasso regression, the proposed SVAE effectively captures complex nonlinear interactions through supervised latent representation learning. Empirical analyses using comprehensive cross-country data from the World Bank (196 countries, 1997–2023) demonstrate the SVAE framework’s superior predictive accuracy, interpretability, and robustness in forecasting GDP growth, highlighting its potential for policy evaluation and macroeconomic forecasting.
{"title":"A supervised variational autoencoder framework for dimensionality reduction and predictive modeling in high-dimensional socioeconomic data","authors":"Pei Xue , Tianshun Li","doi":"10.1016/j.ject.2025.06.001","DOIUrl":"10.1016/j.ject.2025.06.001","url":null,"abstract":"<div><div>We introduce an estimation framework utilizing a Supervised Variational Autoencoder (SVAE) to address challenges posed by high-dimensional socioeconomic data. Unlike classical linear dimensionality reduction methods, such as PCA and Lasso regression, the proposed SVAE effectively captures complex nonlinear interactions through supervised latent representation learning. Empirical analyses using comprehensive cross-country data from the World Bank (196 countries, 1997–2023) demonstrate the SVAE framework’s superior predictive accuracy, interpretability, and robustness in forecasting GDP growth, highlighting its potential for policy evaluation and macroeconomic forecasting.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"4 ","pages":"Pages 9-19"},"PeriodicalIF":0.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579703","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}
Pub Date : 2025-06-02DOI: 10.1016/j.ject.2025.05.003
Katsiaryna Bahamazava
In this paper, we introduce a framework to evaluate the economic implications of algorithmic bias specifically for the emergency response systems that incorporate AI. Unlike existing research, which mostly addresses technical or ethical aspects in isolation, our approach integrates economic theory with algorithmic fairness to quantify and systematically analyze how biases in data quality and algorithm design impact resource allocation efficiency, response time equity, healthcare outcomes, and social welfare. Using explicit modeling of emergency-specific variables, which includes time sensitivity and urgency, we demonstrate that biases substantially exacerbate demographic disparities. This could lead to delayed emergency responses, inefficient resource utilization, worsened health outcomes, and significant welfare losses. Our numerical simulations further illustrate the economic viability and effectiveness of bias mitigation strategies, such as fairness-constrained optimization and improved data representativeness, in simultaneously enhancing equity and economic efficiency. The framework presented provides policymakers, healthcare providers, and AI developers with actionable insights and a robust economic rationale for deploying equitable AI-driven solutions in emergency management contexts.
{"title":"Implications of algorithmic bias in AI-driven emergency response systems","authors":"Katsiaryna Bahamazava","doi":"10.1016/j.ject.2025.05.003","DOIUrl":"10.1016/j.ject.2025.05.003","url":null,"abstract":"<div><div>In this paper, we introduce a framework to evaluate the economic implications of algorithmic bias specifically for the emergency response systems that incorporate AI. Unlike existing research, which mostly addresses technical or ethical aspects in isolation, our approach integrates economic theory with algorithmic fairness to quantify and systematically analyze how biases in data quality and algorithm design impact resource allocation efficiency, response time equity, healthcare outcomes, and social welfare. Using explicit modeling of emergency-specific variables, which includes time sensitivity and urgency, we demonstrate that biases substantially exacerbate demographic disparities. This could lead to delayed emergency responses, inefficient resource utilization, worsened health outcomes, and significant welfare losses. Our numerical simulations further illustrate the economic viability and effectiveness of bias mitigation strategies, such as fairness-constrained optimization and improved data representativeness, in simultaneously enhancing equity and economic efficiency. The framework presented provides policymakers, healthcare providers, and AI developers with actionable insights and a robust economic rationale for deploying equitable AI-driven solutions in emergency management contexts.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"4 ","pages":"Pages 20-34"},"PeriodicalIF":0.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623529","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}
Pub Date : 2025-05-13DOI: 10.1016/j.ject.2025.02.002
Jiajun Meng , Jing Chen , Dongfang Zhao
{"title":"Corrigendum to “Federated learning and information sharing between competitors with different training effectiveness” [J. Econ. Technol. (2025) 1–9]","authors":"Jiajun Meng , Jing Chen , Dongfang Zhao","doi":"10.1016/j.ject.2025.02.002","DOIUrl":"10.1016/j.ject.2025.02.002","url":null,"abstract":"","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Page 282"},"PeriodicalIF":0.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942066","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}
Pub Date : 2025-05-11DOI: 10.1016/j.ject.2025.04.001
Axel Stock , Minoo Talebi Ashoori
In this paper, we study utilizing a game theoretic model, how variety seeking triggered by customer boredom may affect a firm’s rewards program, pricing strategy and profits. Customer Boredom is conceptualized as a utility loss resulting from the purchase of a previously consumed brand. We analyze a two period model where two firms compete for a market of forward looking consumers by selling horizontally differentiated brands. When making the purchase decision in the second period, consumers trade off the utility loss from boredom with the benefits from obtaining the reward offered. In our analysis, we interestingly find that depending on consumers’ discount factor, firm profits either strictly increase or follow a u-shaped relationship with customer boredom. We also consider the case when brands differ in quality and show that under some conditions the high-quality firm’s profits decline while the low quality firm benefits when variety seeking due to boredom increases.
{"title":"The impact of customer boredom on the efficacy of a rewards","authors":"Axel Stock , Minoo Talebi Ashoori","doi":"10.1016/j.ject.2025.04.001","DOIUrl":"10.1016/j.ject.2025.04.001","url":null,"abstract":"<div><div>In this paper, we study utilizing a game theoretic model, how variety seeking triggered by customer boredom may affect a firm’s rewards program, pricing strategy and profits. Customer Boredom is conceptualized as a utility loss resulting from the purchase of a previously consumed brand. We analyze a two period model where two firms compete for a market of forward looking consumers by selling horizontally differentiated brands. When making the purchase decision in the second period, consumers trade off the utility loss from boredom with the benefits from obtaining the reward offered. In our analysis, we interestingly find that depending on consumers’ discount factor, firm profits either strictly increase or follow a u-shaped relationship with customer boredom. We also consider the case when brands differ in quality and show that under some conditions the high-quality firm’s profits decline while the low quality firm benefits when variety seeking due to boredom increases.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 299-313"},"PeriodicalIF":0.0,"publicationDate":"2025-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069348","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}
Blockchain technology, by revolutionizing the way businesses use data, is shifting the cost-responsiveness frontier. While the most popular application of blockchain is cryptocurrency, nowadays it is touching many other businesses including the food and beverage industry. This paper is a short survey in assessing the usefulness of blockchain technology in the food and beverage supply chain, with a narrow focus on the impact on the product recalls. While recalls are crucial in the food and beverage industry, as they deal with public health, they happen frequently and therefore an efficient and responsive recall process is essential. This paper investigates whether US companies utilizing blockchain technology experience shorter recall durations. Data from Food and Drug Administration (FDA) recall datasets, specifically targeting companies implementing blockchain technology, are analyzed using statistical analysis methods. The results reveal that companies adopting blockchain technology have significantly shorter recall times, demonstrating their usefulness in food and beverage recalls, along with its other advantages. This study highlights the potential of blockchain in improving recall management within the food and drink industry and provides applicable insights for food and beverage supply chain managers.
{"title":"Blockchains effects on responsiveness to recalls in the food and beverage industry","authors":"Abbas Keramati , Bethany Siau , Tyler Bellitto , Jafar Heydari , Tanya Panchal","doi":"10.1016/j.ject.2025.05.001","DOIUrl":"10.1016/j.ject.2025.05.001","url":null,"abstract":"<div><div>Blockchain technology, by revolutionizing the way businesses use data, is shifting the cost-responsiveness frontier. While the most popular application of blockchain is cryptocurrency, nowadays it is touching many other businesses including the food and beverage industry. This paper is a short survey in assessing the usefulness of blockchain technology in the food and beverage supply chain, with a narrow focus on the impact on the product recalls. While recalls are crucial in the food and beverage industry, as they deal with public health, they happen frequently and therefore an efficient and responsive recall process is essential. This paper investigates whether US companies utilizing blockchain technology experience shorter recall durations. Data from Food and Drug Administration (FDA) recall datasets, specifically targeting companies implementing blockchain technology, are analyzed using statistical analysis methods. The results reveal that companies adopting blockchain technology have significantly shorter recall times, demonstrating their usefulness in food and beverage recalls, along with its other advantages. This study highlights the potential of blockchain in improving recall management within the food and drink industry and provides applicable insights for food and beverage supply chain managers.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 283-298"},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068898","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}
Pub Date : 2025-02-22DOI: 10.1016/j.ject.2025.02.001
Chunyu Yuan , Dongfang Zhao , Sos S. Agaian
Accurate segmentation of skin lesions from dermoscopic images is crucial for early skin cancer detection, yet variations in lesion appearance and image artifacts present challenges. This study proposes an efficient deep learning model, UCM-NetV2, to improve accuracy and computational efficiency. UCM-NetV2 enhances the UCM-Net architecture with a novel "cyber-structure" com- bining Multilayer Perceptron and CNN layers, improving prediction accuracy while maintaining an ultra-lightweight design with only 0.046 million parameters. Evaluations on the ISIC2017 and ISIC2018 datasets demonstrate that UCM-NetV2 outperforms existing methods in accuracy and com- putational efficiency, achieving up to 67 times faster inference speeds than U-Net and requiring less than 0.04 GFLOPs. These advancements make skin lesion analysis more accessible, particularly in resource-limited settings, enabling proactive skin health monitoring and facilitating teledermatology. To foster further innovation in mobile health diagnostics, the source code for UCM-NetV2 is on https://github.com/chunyuyuan/UCMV2-Net.
{"title":"UCM-NetV2: An efficient and accurate deep learning model for skin lesion segmentation","authors":"Chunyu Yuan , Dongfang Zhao , Sos S. Agaian","doi":"10.1016/j.ject.2025.02.001","DOIUrl":"10.1016/j.ject.2025.02.001","url":null,"abstract":"<div><div>Accurate segmentation of skin lesions from dermoscopic images is crucial for early skin cancer detection, yet variations in lesion appearance and image artifacts present challenges. This study proposes an efficient deep learning model, UCM-NetV2, to improve accuracy and computational efficiency. UCM-NetV2 enhances the UCM-Net architecture with a novel \"cyber-structure\" com- bining Multilayer Perceptron and CNN layers, improving prediction accuracy while maintaining an ultra-lightweight design with only 0.046 million parameters. Evaluations on the ISIC2017 and ISIC2018 datasets demonstrate that UCM-NetV2 outperforms existing methods in accuracy and com- putational efficiency, achieving up to 67 times faster inference speeds than U-Net and requiring less than 0.04 GFLOPs. These advancements make skin lesion analysis more accessible, particularly in resource-limited settings, enabling proactive skin health monitoring and facilitating teledermatology. To foster further innovation in mobile health diagnostics, the source code for UCM-NetV2 is on <span><span>https://github.com/chunyuyuan/UCMV2-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 251-263"},"PeriodicalIF":0.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143854789","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}
Pub Date : 2025-02-17DOI: 10.1016/j.ject.2025.02.003
Mohammad Ahnaf Sadat, K. Jo Min
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.
{"title":"Remanufacturing facility installation decisions under product sourcing cost uncertainties: A real options approach","authors":"Mohammad Ahnaf Sadat, 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}
Pub Date : 2025-02-01DOI: 10.1016/j.ject.2025.01.004
Song-Kyoo Kim
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.
{"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}
Pub Date : 2025-01-14DOI: 10.1016/j.ject.2024.12.003
Jiajun Meng , Jing Chen , Dongfang Zhao
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.
{"title":"Federated learning and information sharing between competitors with different training effectiveness","authors":"Jiajun Meng , Jing Chen , 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}
Pub Date : 2025-01-11DOI: 10.1016/j.ject.2025.01.003
Martín Hernani-Merino , Jorge Tello-Gamarra , David Mayorga , Julio Zevallos
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.
{"title":"From innovation capability to green innovation capability: Evidence from Chinese big tech firms","authors":"Martín Hernani-Merino , Jorge Tello-Gamarra , David Mayorga , 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}
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