AN AGGREGATION OF SURVEY QUESTIONNAIRES IN A FUZZY ENVIRONMENT AND ITS APPLICATION
Dae Young Choi
Department of Management Information Systems, Yuhan Univ., South Korea
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ABSTRACT
In the response process of survey questionnaires, respondents generally express their opinions (assessment) in a fuzzy environment, i.e., assessment with a degree of membership, and in many cases, an aggregation of survey questionnaires is generally computed mathematically. In this paper, we propose a new aggregation method using the FEV (Fuzzy Expected Value) in summarizing survey questionnaires. As an application of proposed aggregation method, to quantify accurately the current state of opinions and the reasons for national IT investment increasing the rate of economic growth, we decided to circulate a survey questionnaire on national IT investment to interested parties throughout the research institute, government, IT industry. Generally, as we have already known and experienced in most of survey questionnaires, there was a little response. Moreover, according to interested parties, the result was biased views on the government IT investment. So, we consider big data analysis on the selection of the proper IT items for national investment. Proposed aggregation method is consisted of 2-phases. In phase 1, keyword selection for the proper IT items based on big data such as Facebook, Twitter, blog, Google, etc. is achieved. This phase is not mandatory (optional and dependent on the characteristic of survey). In phase 2, aggregation by using the FEV is obtained. Proposed aggregation method on survey questionnaires is particularly useful to find current focal issues such as trend, what’s new, etc., on big data such as Facebook, Twitter, blog, Google, etc. Moreover, in many cases, aggregation using the FEV is a better representative value than the arithmetic mean. Generally, the FEV is more suitable than the value of averaging computation in searching for the representative value of fuzzy set.
Keywords: Big Data Analysis, 2-Phase Aggregation, FEV (Fuzzy Expected Value).
Published: 22 November 2019
