PurposeThis research study investigates the factors that influence e-customer satisfaction, e-trust, perceived value and consumers repurchase intention in the context of the B2C e-commerce segment. It investigates the mediation effect of e-customer satisfaction, e-trust and perceived value on repurchase intention. It also examines the moderating role of prior online experience.Design/methodology/approachBased on the adapted questionnaire, pre-recruited enumerators collected the data from five leading business universities of Karachi. They distributed 425 questionnaires and received 415 questionnaires. The study has used Partial Least Square-Structure Equation Modeling (PLS-SEM) technique for data analysis.FindingsWe have tested 20 hypotheses, of which our results do not support five, including two direct, two mediating. Our results support all the direct hypotheses except the following two: (1) delivery service affects e-satisfaction (2) customer services quality effect on trust. We did not find support for the following two mediating hypotheses (1) e-satisfaction mediates delivery services and repurchase intention, (2) service quality mediates customers' service quality and repurchase intention. Our results do not support one moderating relationship. Prior online experience moderates e-perceived value and repurchase intention.Research limitations/implicationsThis research provides valuable information to the online retailers of B2C e-commerce, which can help them make strategies based on their consumers' behavior and encourage them to make repeat purchases from online retailing stores. It allows future researchers to replicate the model in cross-cultural studies in different product categories.Originality/valueWe have examined the moderating effect of prior online experience between (e-satisfaction, e-trust and perceived value) on the repurchase intention.
The Accelerated Particle Swarm Optimization (APSO) algorithm is an efficient and the easiest to implement variant of the famous Particle Swarm Optimization (PSO) algorithm. PSO and its variant APSO have been implemented on the famous Short-Term Hydrothermal Scheduling (STHTS) problem in recent research, and they have shown promising results. The APSO algorithm can be further modified to enhance its optimizing capability by deploying dynamic search space squeezing. This paper presents the implementation of the improved APSO algorithm that is based on dynamic search space squeezing, on the short-term hydrothermal scheduling problem. To give a quantitative comparison, a true statistical comparison based on comparing means is also presented to draw conclusions.
The Cascaded Short-Term Hydrothermal Scheduling (CSTHTS) problem is a highly non-linear, multi-modal, non-convex, and NP-hard optimization problem that has been solved by conventional and metaheuristic algorithms in the past. As the CSTHTS problem falls under the category of applied operational research, therefore, the work is still on-going to find new algorithms and variants of the existing algorithms that would better approximate the optimal global solution in a shorter computational time. This article proposes a novel deterministic thermal economic dispatch method embedded with the improved Accelerated Particle Swarm Optimization (APSO) algorithm to infinitesimally reduce the Big O time complexity for the standard benchmark test case of the CSTHTS optimization problem. Then, it discusses and presents the importance of performing standard statistical tests to establish the supremacy of one metaheuristic algorithm over the other one in solving the CSTHTS problem. The results obtained are better than the results of the many state-of-the-art algorithms applied to solve the considered test case of the CSTHTS problem in the literature, and the superiority of the improved APSO algorithm has been established statistically using the parametric independent samples t-test and the non-parametric Mann–Whitney U-test over the other metaheuristic algorithms such as particle swarm optimization in solving the chosen test case of the CSTHTS problem.
The design analysis of a multi‐input converter using an intelligent controller based on fuzzy logic control algorithm is encompassed. The use of dedicated dc–dc converters is going through a transitory phase in smart‐grid applications. Different characteristics of inputs can be combined to give desired output operation by using a multi‐input converter which is more or less the combination of individual converters sharing a common load thus simultaneously transferring power to the load. The converter with the help of an intelligent algorithm will ensure the buffering time for each input thus multiplexing between various inputs according to the set demand and fluctuating conditions on the input side. Such a converter will behave as a power electronic interface between the utility, user and the renewable energy sources. A three‐input cuk converter has been designed for interfacing wind energy sources with the dc load bus and the main grid. Sending power to the main grid incorporates the power quality enhancement features which will be accommodated using the bi‐directional inverter connected with the grid to perform the active filtration. Moreover, three fuzzy logic controllers will set the output currents corresponding to each input current by changing their reference voltages according to the power demand.
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