In the increasingly complex and uncertain decision-making circumstances, interest groups and individuals will deliberately set attributes weight to manipulate the expected ranking of alternatives in order to achieve their benefits. However, it is not easy to change the ranking of alternatives, a certain compensation cost should be paid by decision makers. In previous studies, most scholars only considered the existence of unit compensation cost but ignored the uncertainty of compensation cost, which increased the risk of decision-making. In order to address the research gap, we construct two kinds of uncertainty sets in this work to describe the uncertainty of unit compensation cost more accurately. In addition, a robust strategic weight manipulation model is proposed with the presence of unit compensation cost uncertainty based on the robust optimization method to reduce the risk of the model. Furthermore, the proposed robust optimization model is applied to a numerical simulation of environmental assessment. The results show the applicability of the proposed method. Through comparison analysis and sensitivity analysis, we state that the proposed robust model is more scientific and effective than original model. Finally, some interesting conclusions and future research directions are given.
PurposeWith the continuous development of online shopping, analyzing the competitiveness of products in the fierce market competition is becoming increasingly crucial to position their own product development. However, the information overload brought by the network development makes it getting difficult to obtain the accurate competitiveness information. Therefore, competitiveness analysis research to combine with the perceived helpfulness study needs urgent solution. Furthermore, deviations exist in the three common methods of perceived helpfulness research. Finally, the traditional information fusion analysis only analyzes the advantages and disadvantages of products in competitiveness analysis without taking account of the competitive environment.Design/methodology/approachThis study puts forward a novel prediction model of perceived helpfulness in conjunction of unsupervised learning and sentiment analysis techniques, to conduct the comparison with pros and cons of congeneric products.FindingsThis paper adopts Wilcoxon test to demonstrate the significant rectification of our competitiveness analysis to the traditional methods. It is noted that the positive reviews of the products in this study impact more on product word of mouth and competitiveness than negative ones.Originality/valueTo sum up, the results of this study benefit businesses in locating their dynamic market position with competitors in practice and exploring new method for long-term development strategic planning.
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