In-store behavioral analytics technology selection using fuzzy decision making

Abstract

Purpose

With the emerging technologies, collecting and processing data about the behaviors of customers or employees in a specific location has become possible. The purpose of this paper is to evaluate existing data collection technologies.

Design/methodology/approach

Technology evaluation problem is handled as a multi-criteria decision-making (MCDM) problem. In this manner, a decision model containing four criteria and eight sub-criteria and four alternatives are formed. The problem is solved using hesitant analytic hierarchy process (AHP) and trapezoidal fuzzy numbers (TrFN).

Findings

The results show that the most important sub-criteria are: accuracy, quantity, ıntrospective and cost. Decision makers’ evaluate for alternatives, namely wireless fidelity (WiFi), camera, radio-frequency identification and Bluetooth. The best alternative is found as Bluetooth which is followed by WiFi and Camera.

Research limitations/implications

Technology evaluation problem, just like many other MCDM problems are solved using expert evaluations. Thus, the generalizability of the findings is low.

Originality/value

In this paper, technology selection problem has been handled using hesitant AHP for the first time. In addition, the original methodology is extended by using TrFN to represent the expert evaluations in a better way.

Citation

Dogan, O. and Öztaysi, B. (2018), "In-store behavioral analytics technology selection using fuzzy decision making", Journal of Enterprise Information Management, Vol. 31 No. 4, pp. 612-630. https://doi.org/10.1108/JEIM-02-2018-0035