The Ecuadorian electricity sector encompasses generation, transmission, distribution and sales. Since the change of the Constitution in Ecuador in 2008, the sector has opted to employ a centralized model. The present research aims to measure the efficiency level of the Ecuadorian electricity sector during the period 2012–2021, using a DEA-NETWORK methodology, which allows examining and integrating each of the phases defined above through intermediate inputs, which are inputs in subsequent phases and outputs of some other phases. These intermediate inputs are essential for analyzing efficiency from a global view of the system. For research purposes, the Ecuadorian electricity sector was divided into 9 planning zones. The results revealed that the efficiency of zones 6 and 8 had the greatest impact on the overall efficiency of the Ecuadorian electricity sector during the period 2012–2015. On the other hand, the distribution phase is the most efficient with an index of 0.9605, followed by sales with an index of 0.6251. It is also concluded that the most inefficient phases are generation and transmission, thus verifying the problems caused by the use of a centralized model.

This study examines the aggregate consumption function of Saudi Arabia from 2000 to 2022, focusing on identifying key determinants of household consumption and evaluating the impacts of disposable income, household wealth, government expenditure, interest rates, and oil revenues. the research uses advanced econometric methods, including the autoregressive distributed lag (ARDL) model and Johansen cointegration test, to analyze the relationships among these variables. the findings reveal that disposable income, household wealth, and government expenditure significantly and positively influence consumption, whereas interest rates show a negative correlation. oil revenues also play a critical role, reflecting the country’s economic reliance on oil. the study highlights the necessity for economic diversification to reduce the impact of oil price volatility on household income and consumption stability. The results offer crucial insights for policymakers, emphasizing the need for strategies that enhance household income and wealth, maintain robust public sector spending, and effectively manage interest rates. these findings also support the importance of consistent and predictable income sources for sustaining consumption. additionally, this study suggests directions for future research, including developing sophisticated forecasting models to predict consumption trends and exploring other influencing factors such as demographic shifts and technological progress.

Rapidly changing business environments and fierce competition are making it increasingly difficult for modern companies to maintain competitive advantage and accomplish business longevity. This study can fill the research gap in mission research and longevity research, and provides implications on what form and content of mission should be selected when determining the direction of a company’s corporate strategy. Although a company’s mission is a communication tool that represents the company’s strategic priorities and unique values, it has rarely been considered an important factor in business longevity. This study conducts a content analysis of the mission statements of 43 companies in the Henokiens Association to clarify the linkage between a company’s mission and business longevity and the configurations of long-lived firms’ missions. Our results show most long-lived firms have clear missions and perceptions of familism expansion. The firms’ past, present, and future additions to their concern for products, business growth, unique philosophy, and stakeholders are highlighted in their mission statements. Therefore, the main theoretical contribution of focusing on the corporate mission as a factor of business longevity in this study is not only a new approach to the longevity factor, but also the discovery of new values of the mission in strategic management research. The practical contribution of this study is that it reveals that companies seeking long-term competitive advantage in the market need to design, possess, and share a high-quality mission from a long-term perspective and instill the ideology of extended familyism. It can also provide hints about strategic priorities for small, family-run businesses facing threats to their survival.

The economy of Pakistan has faced many challenges due to COVID-19, leading to numerous systemic failures and leaving it struggling to recover. This research aims to shed light on the specific challenges faced by Pakistani textile companies during the pandemic. Comprehensive data was collected from one hundred fifty-three textile managers in Pakistan. Upon examining the impact of COVID-19 on businesses, it has been found that the most pressing issues revolved around working capital and strategies for generating new sales. Interestingly, many of these businesses were well-prepared in the digital realm, readily embracing digital knowledge and seizing opportunities by pivoting to the production of personal protective equipment (PPE) and N95 masks. This study aims to evaluate the early consequences of COVID-19 on Pakistan’s textile industry. Considering the scarcity of research on these challenges and opportunities, our work contributes to a better understanding of the hurdles the textile sector faces. Furthermore, it sets the groundwork for future research in this domain. It provides valuable insights for textile businesses, enabling them to align their strategies with the ever-evolving digital marketing landscape.

Purpose: This study investigates the mediating effect of Environmental Attachment (EA) among consumers in an emerging market, concentrating on the impact of two key factors: Green Environmental Awareness (GEA) and Sense of Responsibility (SOR) on Sustainable Product Consumption (SPC). Design/methodology/approach: A thorough online survey was carried out with Google Docs and distributed to 304 Pakistani consumers who now use or are considering purchasing sustainable or green products. Structural Equation Modeling (SEM) was used to rigorously test the suggested model utilizing a non-probability sampling technique, specifically the stratified purposive sampling approach. Findings: Green environmental awareness (GEA) and a sense of responsibility (SOR) have been shown to have a substantial impact on creating environmental attachment (EA) in both existing and potential customers of sustainable products. The findings of this study also revealed that environmental attachment (EA) plays an important role as a mediator in the links between green environmental awareness (GEA) and the consumption of sustainable goods (SPC), as well as between a sense of responsibility (SOR) and SPC. Despite this, it is crucial to note that the projected direct effect of GEA on SPC was shown to be statistically insignificant. This conclusion implies that additional factors outside the scope of this study may influence the relationship between GEA and SPC. Research limitations/implications: It is vital to highlight that the focus of this study is on an online sample of consumers near Punjab, Pakistan. Future studies should look at other parts of Pakistan to acquire a more complete picture of sustainable consumption trends. Furthermore, our findings suggest that characteristics impacting sustainable consumption, such as Green Environmental Awareness (GEA) and Sense of Responsibility (SOR), may differ among countries. As a result, performing a comparison analysis involving two or more countries could provide valuable insights into projecting sustainable product consumption among current and potential sustainable product customers. Originality/Value: This study contributes to the literature by investigating the factors of sustainable consumption using the lens of the Norm Activation Model theory (NAM), notably Green Environmental Awareness (GEA) and Sense of Responsibility (SOR), to predict sustainable product consumption. The findings are important for promoting long-term goals in Pakistan and provide a framework that can be applied in other emerging markets.

The purpose of this paper is to explore the performance of ridge regression and the random forest model improved by genetic algorithm in predicting the Boston house price data set and conduct a comparative analysis. To achieve it, the data is divided into training set and test set according to the ratio of 70-30. The RidgeCV library is used to select the best regularization parameter for the Ridge regression model, and for the random forest model, the genetic algorithm is used to optimize the model's hyperparameters. The result shows that compared with ridge regression, the random forest model improved by genetic algorithm can perform better in the regression problem of Boston house prices.