OPM-DMVid: Consumer behavioral modeling for decision making using orthodox process model in video analytics

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Authors: Ajay B. Gadicha, Vijay B. Gadicha, Yudi Supiyanto, Amaludin Arifia, Mayur S. Burange, Ahmed J. Obaid
Cited by: 0
References: 5

Abstract

Purpose: The objective of this research is to investigate the effectiveness of the Orthodox Process Model (OPM) in comprehending consumer behavior and decision-making procedures when it comes to selecting and subscribing to Over-The-Top (OTT) platforms. With the OTT industry constantly expanding and providing a plethora of content options, comprehending the factors that influence consumers’ decision-making is critical for platform providers to draw in and retain subscribers. Approach, methodology, and design: The qualitative and quantitative phases are the two main stages of the investigation. A diverse group of OTT platform users were questioned in-depth during the qualitative phase to learn more about their decision-making procedures, the considerations they take into account when subscribing to these platforms, and their experiences using these platforms. This stage offers thorough and in-depth understandings of the various and multifaceted aspects of consumer behavior. 229 families took part in the study in a classroom setting during the quantitative phase, which involved creating a survey questionnaire. Only 174 households that had purchased the platform were used in the study out of all the data that was gathered. Results The OPM is a good framework for analyzing how customers buy OTT platforms, according to the results. The need for entertainment content is recognized by consumers during the problem recognition stage, which is fueled by things like their discontent with traditional television, their desire for variety, or referrals from friends and family.

Fields of Study & Subjects

Business Process Modeling and Analysis Video Surveillance and Tracking Methods Human Pose and Action Recognition

Publication details

Journal / source
AIP conference proceedings
Volume
3393
Pages
060025–060025
Publication date
1 January 2026
Language
EN