Most researchers hold the role of dynamic capability (DC) as a key factor of achieving sustainable entrepreneurship performance (SEP) and Organizational Performance (OP) with the competitive advantage of knowledge management practices (KMPs). However, the effects of KMPs with SEP and OP are still under discussion. Moreover, the ambidexterity view, exploring the direct impact of KMPs mediated by dynamic capabilities (DC), gives evidence of sustainable entrepreneurship and organizational performance. Measuring the gathered data of 480 textile-based SME entrepreneurs and organizations through structural equation modeling, the study’s finding shows that KMPs positively and significantly influence sustainable entrepreneurship and organizational performance. Furthermore, this study explores the partial mediation of dynamic capabilities among KMPs with SEP and organization performance. At the same time, opportunity recognition is a moderator that strengthens the relationship between DCs with sustainable entrepreneurship and organizational performance. The study is limited to male textile-based SMEs and can be enhanced by female entrepreneurs. Furthermore, this research contributes to entrepreneurship students by encouraging better research work in current and existing studies, and practically assists government and private policymakers and business practitioners to formulate better strategies for the sustainability and economic growth of a country.

In the era of increasing power demand and the power industry's deregulation, it has become essential to transfer reliable and cost-effective electricity to end-consumer. Different studies have been conferring about the electricity crises worldwide, specifically in underdeveloped countries, such as Pakistan. Particularly, the consumer tariff policies concerning distribution companies (DISCOS) arise as the most crucial factors in tariff rates (peak and off-peak hours). In this research work, we have analyzed price data of peak hours using the gray wolf optimization (GWO) in comparison with particle swarm optimization (PSO) and genetic algorithm (GA) techniques, which shows the net outcome of the proposed procedure for the sequence to see the productive pricing efficiency of DISCOS. In addition, tariffs have been formulated by monthly oil adjustments that are explained with 11 different cases for different DISCO. The best lower obtained values of 9.0031 and 9.1450 show that the proposed technique is superior to other optimization algorithms.