Rating, as a common way of evaluation, is a significant exercise and plays a major role in managerial decision-making in general and in particular online purchasing. The paper aims to discuss these issues.
This study utilizes the theory of social network analysis (SNA) to make a comprehensive evaluation model for rating commodities. Specifically, the paper shows how to apply the network analysis, how it works and what the advantage is. The paper further presents the new model's properties and validates the model's applicability. The paper finally analyzes the results with respect to various dimensions of a movie rating database and report on the insights generated by the model.
Through the designed comparison analysis and the empirical analysis, the model is showed to be better than the traditional ones such as averaging, analytic hierarchy process (AHP) and several mentioned dimension-reduction techniques (DRTs) in terms of consistency and its ability to deal with the missing data.
The new model is solvable in polynomial time and proper for the large-scale data set. Furthermore, this model can also be seen as a data mining method which would be useful to improve insights into customer behavior.
The proposed method enables to give comprehensive rating results which can preserve the rankings implied by all the customers’ ratings, adapt to the database with the missing data and cost a low algorithm time and space.
The authors greatly thank Professor Paolo PIN from University of Siena in Italy for his help. Many thanks for the helpful comments from the associate editor and the two anonymous reviewers.
Li, Y., Wu, C. and Luo, P. (2014), "Rating online commodities by considering consumers’ purchasing networks", Management Decision, Vol. 52 No. 10, pp. 2002-2020. https://doi.org/10.1108/MD-04-2014-0188
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