Evaluating Quality of Matrimonial Websites: Balancing Emotions with Economics

Abstract

There are a plethora of studies evaluating the quality of websites on functional and design-related aspects such as usability and visual parameters. The majority of these studies are related to e-commerce websites where individuals make decision largely relying on economic parameters. However, matrimonial websites are unique, as the decisions involve both economic and non-economic parameters. Therefore, this study aims to propose a framework to evaluate quality of matrimonial websites by incorporating contextual factors and examine differences among different groups of users. This study proffers a website evaluating framework considering non-economic and emotion based factors from the information systems (IS) success model and the search match interaction (SMI) framework. The study proposes a hybrid model of multi-criteria decision-making techniques—namely Fuzzy-AHP and ranking models such as evaluation based on distance from average solution (EDAS), technique for order of preference by similarity to ideal solution (TOPSIS), and complex proportional assessment (COPRAS). The results indicate that the context-specific factors related to search and matchmaking options are the most preferred parameters for evaluation. Males and females have been found to differ in their preferences related to service quality and price. Next, the study compares the performance of three ranking models, namely EDAS, TOPSIS, and COPRAS. The first and second models provide similar results, while the rankings obtained through COPRAS differ slightly. The study contributes towards website evaluation literature by highlighting the importance of contextual factors while evaluating the matrimonial websites and the differences among preferences of the users.

Identifying Alternative Options for Chatbots With Multi-Criteria Decision-Making

“…Twenty subfactors are included in the present research, and analyzing 20 different weight patterns for sensitivity analysis becomes impractical. In such a scenario, the top 10 subfactors can be chosen to see the impact of their changes on the overall results for the sensitive analysis (Sharma et al, 2020).…”