Twenty-two tomato (Solanum lycopersicum L.) genotypes were examined for correlation and path analysis in the randomized block design under open field conditions. Total fruit yield showed a significant positive correlation with the number of fruits per plant, average fruit weight, lycopene content, and percent seedling survival in the field at both the genotypic and phenotypic levels. A strong correlation between these characters revealed that selection based on these characters would consequently improve the total fruit yield. Path analysis showed that the number of fruits per plant, average fruit weight, percent seedling survival in the nursery, and number of locules per fruit exhibited high positive direct phenotypic effects on total fruit yield, whereas the number of fruits per plant, average fruit weight, percent seedling survival in the field, and pollen viability had very high positive direct genotypic effects. Therefore, to increase the yield, it would be profitable to prioritize these traits in the selection program.

In the rapidly evolving landscape of China’s pharmaceutical industry, this study investigates how pharmaceutical enterprises can achieve profitable sales innovation amid the process of digital transformation. Grounded in the Affordance theory, it posits that the positive impact of digital transformation on sales innovation is driven by the affordance afforded by digital technology and ubiquity. The research focuses on A-share pharmaceutical companies in China, utilizing data from 2012 to 2022 and employing multiple regression analysis to examine the influence of digital transformation on corporate sales innovation. The results demonstrate a significant positive effect of digital transformation on sales innovation. The study further categorizes digital transformation into technological affordance and ubiquity affordance, separately validating their roles in promoting sales innovation. Moreover, by considering synergistic effects, the research unveils the intricate relationship between digital transformation and corporate innovation performance. The findings provide a fresh perspective on understanding how digital technology propels sales innovation and offer concrete guidance for the digital transformation practices in the pharmaceutical industry.

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.

The major goal of decisions made by a business organization is to enhance business performance. These days, owners, managers and other stakeholders are seeking for opportunities of modelling and automating decisions by analysing the most recent data with the help of artificial intelligence (AI). This study outlines a simple theoretical model framework using internal and external information on current and potential clients and performing calculations followed by immediate updating of contracting probabilities after each sales attempt. This can help increase sales efficiency, revenues, and profits in an easily programmable way and serve as a basis for focusing on the most promising deals customising personal offers of best-selling products for each potential client. The search for new customers is supported by the continuous and systematic collection and analysis of external and internal statistical data, organising them into a unified database, and using a decision support model based on it. As an illustration, the paper presents a fictitious model setup and simulations for an insurance company considering different regions, age groups and genders of clients when analysing probabilities of contracting, average sales and profits per contract. The elements of the model, however, can be generalised or adjusted to any sector. Results show that dynamic targeting strategies based on model calculations and most current information outperform static or non-targeted actions. The process from data to decision-making to improve business performance and the decision itself can be easily algorithmised. The feedback of the results into the model carries the potential for automated self-learning and self-correction. The proposed framework can serve as a basis for a self-sustaining artificial business intelligence system.

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.

This study aims to discover the relationship between growth sales, capital structure, and corporate governance on financial performance of energy and basic material sector public companies in Indonesia. Financial performance is observed from 2 aspects: market performance (Tobin’s Q) and profitability performance (ROA). The population in this study is firms in the energy and basic material sector on Indonesia Stock Exchange. The total population is 248 firms. 39 firms were selected as samples. The data is obtained from the annual report which starts from the period 2018 to 2022. A total of the population was determined as samples by purposive sampling method. Data analysis using panel data regression. The result shows: 1) Growth Sales have a significant influence on market performance; however, it does not have a significant effect on profitability performance. 2) Capital Structure significantly influences market and profitability performance 3) Corporate governance significantly influences market and profitability performance. Suggestions for companies that must strive to increase sales, maintain good corporate governance and pay attention to the company’s capital structure in a balanced manner.