3. Producción
Browse
6 results
Search Results
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, A novel two-phase approach to computing a regional social progress index(Springer, 2020-01-01)In recent decades, concerns have emerged regarding the fact that standard macroeconomic statistics (such as gross domestic product) do not provide a sufficiently detailed and accurate picture of societal progress and well-being and of people’s true quality of life. This has further translated into concerns regarding the design of related public policies and whether these actually have the intended impact in practice. One of the first steps in bridging the gap between well-being metrics and policy intervention is the development of improved well-being measures. The calculation of a regional Social Progress Index (SPI) has been on the policymakers’ agenda for quite some time, as it is used to assist in the proposal of strategies that would create the conditions for all individuals in a society to reach their full potential, enhancing and sustaining the quality of their lives, while reducing regional inequalities. In this manuscript, we show a novel way to calculate a regional SPI under a two-phase approach. In the first phase, we aggregate the item-level information into subfactor-level indices and the subfactor-level indices into a factor-level index using an objective general index (OGI); in the second phase, we use the factor-level indices to obtain the regional SPI through a pure data envelopment analysis (DEA) approach. We further apply the method developed to analyse a single period of social progress in Peru. The manuscript is a contribution to the practical measurement of social progress. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Expert decision-making: a Markovian approach to studying the agency problem(Elsevier, 2021-12-01)In this paper, we study the agency problem in an organisation within a Markovian framework. More specifically, the paper presents the case of a principal imposing an incentive-control structure upon an agent to force him to follow the principal's interests for which he was hired, against the tendency of the agent to follow his own interests. Findings point toward the principal's difficulty in controlling the behaviour of the agent through incentives and monitoring; instead, best results are obtained when hiring agents who care for their reputation and refrain from unprofessional behaviours. The implication is that if we consider that it might be difficult to identify this characteristic at the time of the agent's hiring, the best criterion will be to look for low levels of greed in the agent. This conclusion goes in some way against current practices of looking for aggressive agents for the generation of higher profits. Nevertheless, it should be noted that these potential benefits might actually fade away if the agent follows his own interests, instead of the principal's. Another interesting result points to the restricted, although necessary, role of monitoring to control the agent's behaviour, a result that goes against current research interests on measures of corporate governance. The paper is a contribution to expert decision-making. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A DEA and random forest regression approach to studying bank efficiency and corporate governance(Palgrave Macmillan, 2021-05-10)We employ Data Envelopment Analysis to estimate the new technical, new cost, and new profit efficiency of Indian banks over the period 2008–2018. Then, we use Random Forest Regression to examine the impact of corporate governance (Board Size, Board Independence, Duality, Gender Diversity, and Board Meetings), bank characteristics (Return on Assets, Size, and Equity to Total Assets), and other characteristics (Ownership and Years) on bank efficiency. Among others, we found that board characteristics play a significant role particularly in new profit efficiency; therefore, policymakers and regulators should consider Board Size, Board Independence, Board Meetings, and Duality while framing guidelines for enhancing bank new profit efficiency. We also found that Board Independence plays a vital role in bank new cost efficiency, while Gender Diversity contributes to both new technical and new cost efficiency. This study makes methodological contributions by employing Machine Learning based Random Forest Regression in tandem with Data Envelopment Analysis under a two-phase model to examine corporate governance and bank efficiency, which is a pioneering attempt. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Enhancing enterprise investment efficiency through artificial intelligence: The role of accounting information transparency(Elsevier Ltd, 2024-12-01)In the post-COVID-19 era, with global economic recovery as a critical goal, the rapid development of artificial intelligence (AI) has emerged as a key driver of economic growth and transformation. AI not only acts as a powerful catalyst for economic development but also significantly impacts enterprise investment efficiency (EIE). This paper explores the influence of AI on EIE, with a focus on the role of accounting information transparency. Using data from Shanghai and Shenzhen A-share listed enterprises between 2010 and 2021, the findings demonstrate that AI development significantly enhances EIE. These results are confirmed through robustness tests, including variable substitution, and addressing endogeneity and sample limitations. Mechanism analysis reveals that AI improves EIE by increasing the transparency of accounting information. Additionally, heterogeneity analysis shows that AI has a greater impact on the investment efficiency of high-tech and technology-intensive enterprises, non-state-owned enterprises, and those located in highly urbanised areas, such as ‘Broadband China’ pilot cities. This paper examines how AI development affects EIE through the lens of enterprise accounting information transparency, offering actionable insights for enhancing accounting disclosures and serving as a valuable resource for enterprises navigating the technological transformation of the modern era. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Role of the Mass Vaccination Programme in Combating the COVID-19 Pandemic: An LSTM-Based Analysis of COVID-19 Confirmed Cases(Elsevier Ltd, 2023-03-01)The COVID-19 virus has impacted all facets of our lives. As a global response to this threat, vaccination programmes have been initiated and administered in numerous nations. The question remains, however, as to whether mass vaccination programmes result in a decrease in the number of confirmed COVID-19 cases. In this study, we aim to predict the future number of COVID-19 confirmed cases for the top ten countries with the highest number of vaccinations in the world. A well-known Deep Learning method for time series analysis, namely, the Long Short-Term Memory (LSTM) networks, is applied as the prediction method. Using three evaluation metrics, i.e., Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), we found that the model built by using LSTM networks could give a good prediction of the future number and trend of COVID-19 confirmed cases in the considered countries. Two different scenarios are employed, namely: ‘All Time’, which includes all historical data; and ‘Before Vaccination’, which excludes data collected after the mass vaccination programme began. The average MAPE scores for the ‘All Time’ and ‘Before Vaccination’ scenarios are 5.977% and 10.388%, respectively. Overall, the results show that the mass vaccination programme has a positive impact on decreasing and controlling the spread of the COVID-19 disease in those countries, as evidenced by decreasing future trends after the programme was implemented. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Critical Analysis of the Integration of Blockchain and Artificial Intelligence for Supply Chain(Springer, 2023-08-01)The integration between blockchain and artificial intelligence (AI) has gained a lot of attention in recent years, especially since such integration can improve security, efficiency, and productivity of applications in business environments characterised by volatility, uncertainty, complexity, and ambiguity. In particular, supply chain is one of the areas that have been shown to benefit tremendously from blockchain and AI, by enhancing information and process resilience, enabling faster and more cost-efficient delivery of products, and augmenting products’ traceability, among others. This paper performs a state-of-the-art review of blockchain and AI in the field of supply chains. More specifically, we sought to answer the following three principal questions: Q1—What are the current studies on the integration of blockchain and AI in supply chain?, Q2—What are the current blockchain and AI use cases in supply chain?, and Q3—What are the potential research directions for future studies involving the integration of blockchain and AI? The analysis performed in this paper has identified relevant research studies that have contributed both conceptually and empirically to the expansion and accumulation of intellectual wealth in the supply chain discipline through the integration of blockchain and AI.