Decision-making is always an issue that managers have to deal with. Keenly observing to different preferences of the targets provides useful information for decision-makers who do not require too much information to make decisions. The main purpose is to avoid decision-makers in a dilemma because of too much or opaque information. Based on problem-oriented, this research aims to help decision-makers to develop a macro-vision strategy that fits the needs of different clusters of customers in terms of their favorite restaurants. This research also focuses on providing the rules to rank data sets for decision-makers to make choices for their favorite restaurant.
When the decision-makers need to rethink a new strategic planning, they have to think about whether they want to retain or rebuild their relationship with the old consumers or continue to care for new customers. Furthermore, many of the lecturers show that the relative concept will be more effective than the absolute one. Therefore, based on rough set theory, this research proposes an algorithm of related concepts and sends questionnaires to verify the efficiency of the algorithm.
By feeding the relative order of calculating the ranking rules, we find that it will be more efficient to deal with the faced problems.
The algorithm proposed in this research is applied to the ranking data of food. This research proves that the algorithm is practical and has the potential to reveal important patterns in the data set.
This paper forms part of a special section “Big Data for Social Good and Social Sciences”, guest edited by Miltiadis D. Lytras, Anna Visvizi, Peiquan Jin and Naif Aljohani.
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