No abstract

The purpose of this study is to examine the effects of brand attachment on service loyalty to the services provided by financial sector. As one of the extremely valuable assets of every firm is its brand, attachment creates a deep emotional link between the consumers and the brand such that it contributes to the success of brand management process. To this end, the effects of two dimensions of the construct (brand-self connection and brand prominence) on each of the dimensions of service loyalty would be explored. The questionnaire is based on Park et al. (2010) and Sudhahar et al. (2006). The results of structural equations modeling indicated that brand attachment had a significant positive effect on service loyalty. Furthermore, the existed a positive effect on the dimensions of brand attachment-i.e., brand-self connection and brand prominence-and all dimensions of loyalty-i.e., behavioral, attitudinal, cognitive, conative, affective, commitment, and trust). Among them, brand-self connection had the highest effect on cognitive loyalty, trust-based loyalty, and commitment-based loyalty while brand prominence was most effective on affective loyalty, cognitive loyalty, and trust-based loyalty.Because of the increase in the number of institutions in banking sector and the diversity of services they offer, banking managers can take the advantage of using the results of brand attachment's effect on the study variables and enhance the loyalty to their services.

Construction material delivery to post-disaster reconstruction projects is challenging because of the resource and time limitations that follow a large-scale disaster. There is compelling evidence that inadequate planning jeopardises the success of a large number of post-disaster reconstruction projects. Thus, the current study proposes an integrated approach to facilitate the procurement planning of construction materials following a large-scale disaster. The proposed approach clustered the location of construction projects using a differential evolution (DE)-K-prototypes, a new partitional clustering algorithm based on DE and K-prototypes, method. Then, using a permanent matrix prioritises cluster points based on route reliability-affecting factors. The model’s objectives are to minimise the total travel time, maximise the reliability of the route, and minimise the total weighted undelivered materials to projects. In the case of distribution of material through land vehicles, the possibility of breakdowns in the vehicle is considered, allowing for the determination of vehicle breakdown under various scenarios and the minimisation of undelivered materials to projects. As a result of the uncertain character of the disaster, the demands of construction projects are fuzzy, and Jimenez’s method is used to handle it. Due to the complexity of the problem, two algorithms are proposed, a multi-objective evolutionary algorithm based on decomposition (MOEA/D) and a non-dominated sorting genetic algorithm-II (NSGA-II). The results confirm that the proposed MOEA/D has a higher accuracy while NSGA-II has a shorter computational time. By providing new theoretical perspectives on disaster recovery strategies in the construction sector, this study contributes to the growing body of knowledge about disaster recovery strategies in the sector. The findings of this study can be employed to develop an integrated planning system for the delivery of construction materials to post-disaster reconstruction projects in disaster-prone countries.

Abstract

Internet technology has provided an indescribable new way for businesses to attract new customers, track their behaviour, customise services, products, and advertising. Internet technology and the new trend of online shopping have resulted in the establishment of numerous websites to sell products on a daily basis. Products compete to be displayed on the limited pages of a website in online shopping because it has a significant impact on sales. Website designers carefully select which products to display on a page in order to influence the customers’ purchasing decisions. However, concerns regarding appropriate decision making have not been fully addressed. As a result, this study conducts a comprehensive comparative analysis of the performance of ten different metaheuristics. The ant lion optimiser (ALO), Dragonfly algorithm (DA), Grasshopper optimisation algorithm (GOA), Harris hawks optimisation (HHO), Moth-flame optimisation algorithm (MFO), Multi-verse optimiser (MVO), sine cosine algorithm (SCA), Salp Swarm Algorithm (SSA), The whale optimisation algorithm (WOA), and Grey wolf optimiser (GWO) are some of the recent algorithms that were chosen for this study. The results show that the MFO outperforms the other methods in all sizes. MFO has an average normalised objective function of 81%, while ALO has a normalised objective function of 77%. In contrast, HHO has the worst performance of 16%. The study’s findings add new theoretical and practical insights to the growing body of knowledge about e-commerce environments and have implications for planners, policymakers, and managers, particularly in companies where an unplanned advertisement wastes the budget.