Assessing indoor positioning system: A q-spherical fuzzy rough TOPSIS analysis

Area of Science:

• Decision Sciences
• Information Systems
• Behavioral Economics

Background:

• Understanding employee and customer behavior is crucial for business success.
• Traditional data collection methods may not capture nuanced behavioral patterns.
• Advanced methodologies are needed to enhance data-driven decision-making.

Purpose of the Study:

• To investigate advanced data collection methodologies for understanding location-specific employee and customer behavior.
• To develop and evaluate a novel multi-criteria decision-making framework.
• To identify the most effective technological solution for data collection and behavioral analysis.

Main Methods:

• A comprehensive multi-criteria decision-making (MCDM) framework was employed.
• Evaluation of four technological alternatives based on four distinct criteria.
• Application of the q-spherical fuzzy rough TOPSIS method, incorporating lower/upper set approximation and parameter q (q ≥ 1).

Main Results:

• The proposed q-spherical fuzzy rough TOPSIS method demonstrated effectiveness in selecting optimal technologies.
• The novel approach integrating TOPSIS with q-spherical fuzzy rough set theory provided deeper insights.
• Comparative analysis showed the framework's strength and competitiveness against existing MCDM methodologies.

Conclusions:

• The study successfully presents a robust framework for selecting advanced data collection technologies.
• The research enhances decision-making capabilities in corporate settings through improved behavioral understanding.
• This work contributes to the field of data-driven decision-making by offering a novel and effective MCDM approach.