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Article
Publication date: 21 August 2023

Seth Ampadu, Yuanchun Jiang, Samuel Adu Gyamfi, Emmanuel Debrah and Eric Amankwa

The purpose of this study is to examine the effect of perceived value of recommended product on consumer’s e-loyalty, based on the proposition of expectation confirmation theory…

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Abstract

Purpose

The purpose of this study is to examine the effect of perceived value of recommended product on consumer’s e-loyalty, based on the proposition of expectation confirmation theory. Vendors’ reputation is tested as the mediator in the perceived value of recommended product and e-loyalty relationship, whereas shopping enjoyment is predicted as the moderator that conditions the perceived value of recommended product and e-loyalty relationship through vendors reputation.

Design/methodology/approach

Data were collected via an online survey platform and through a QR code. Partial least squares analysis, confirmatory factor analysis and structural equation modeling were used to verify the research proposed model.

Findings

The findings revealed that the perceived value of recommended product had a significant positive effect on E-loyalty; in addition, the perceived value of the recommended product and e-loyalty link was partly explained by e-shopper’s confidence in vendor reputation. Therefore, the study established that the direct and indirect relationship between the perceived value of the recommended product and e-loyalty was sensitive and profound to shopping enjoyment.

Originality/value

This study has established that the perceived value of a recommended product can result in consumer loyalty. This has successively provided the e-shop manager and other stakeholders with novel perspectives about why it is necessary to understand consumers’ pre- and postacquisition behavior before recommending certain products to the consumer.

Details

Young Consumers, vol. 24 no. 6
Type: Research Article
ISSN: 1747-3616

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Article
Publication date: 30 April 2020

Mehdi Darbandi, Amir Reza Ramtin and Omid Khold Sharafi

A set of routers that are connected over communication channels can from network-on-chip (NoC). High performance, scalability, modularity and the ability to parallel the structure…

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Abstract

Purpose

A set of routers that are connected over communication channels can from network-on-chip (NoC). High performance, scalability, modularity and the ability to parallel the structure of the communications are some of its advantages. Because of the growing number of cores of NoC, their arrangement has got more valuable. The mapping action is done based on assigning different functional units to different nodes on the NoC, and the way it is done contains a significant effect on implementation and network power utilization. The NoC mapping issue is one of the NP-hard problems. Therefore, for achieving optimal or near-optimal answers, meta-heuristic algorithms are the perfect choices. The purpose of this paper is to design a novel procedure for mapping process cores for reducing communication delays and cost parameters. A multi-objective particle swarm optimization algorithm standing on crowding distance (MOPSO-CD) has been used for this purpose.

Design/methodology/approach

In the proposed approach, in which the two-dimensional mesh topology has been used as base construction, the mapping operation is divided into two stages as follows: allocating the tasks to suitable cores of intellectual property; and plotting the map of these cores in a specific tile on the platform of NoC.

Findings

The proposed method has dramatically improved the related problems and limitations of meta-heuristic algorithms. This algorithm performs better than the particle swarm optimization (PSO) and genetic algorithm in convergence to the Pareto, producing a proficiently divided collection of solving ways and the computational time. The results of the simulation also show that the delay parameter of the proposed method is 1.1 per cent better than the genetic algorithm and 0.5 per cent better than the PSO algorithm. Also, in the communication cost parameter, the proposed method has 2.7 per cent better action than a genetic algorithm and 0.16 per cent better action than the PSO algorithm.

Originality/value

As yet, the MOPSO-CD algorithm has not been used for solving the task mapping issue in the NoC.

Details

International Journal of Pervasive Computing and Communications, vol. 16 no. 2
Type: Research Article
ISSN: 1742-7371

Keywords

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