Indeterminate Likert scale: feedback based on neutrosophy, its distance measures and clustering algorithm

• Published in: Soft computing (Berlin, Germany) Vol. 24; no. 10; pp. 7459 - 7468
• Main Authors: Kandasamy, Ilanthenral, Kandasamy, W. B. Vasantha, Obbineni, Jagan M., Smarandache, Florentin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2020; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Clustering; Computational Intelligence; Control; Customers; Engineering; Feedback; Fuzzy sets; Likert scale; Mathematical Logic and Foundations; Mechatronics; Memberships; Methodologies and Application; Personality; Quantitative psychology; Questionnaires; Restaurants; Robotics; Social networks; Neutrosophy; Questionnaire; Indeterminate Likert scale; Minimum spanning tree; Clustering algorithm; Triple refined neutrosophic set; Likert scale; Survey; Customer feedback; Neutrosophic logic; Star rating; Indeterminacy; Distance measures
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-019-04372-x

Likert scale is the most widely used psychometric scale for obtaining feedback. The major disadvantage of Likert scale is information distortion and information loss problem that arise due to its ordinal nature and closed format. Real-world responses are mostly inconsistent, imprecise and indeterminate depending on the customers’ emotions. To capture the responses realistically, the concept of neutrosophy (study of neutralities and indeterminacy) is used. Indeterminate Likert scale based on neutrosophy is introduced in this paper. Clustering according to customer feedback is an effective way of classifying customers and targeting them accordingly. Clustering algorithm for feedback obtained using indeterminate Likert scaling is proposed in this paper. While dealing real-world scenarios, indeterminate Likert scaling is better in capturing the responses accurately.