A Model for New Product Development in Business Companies Based on Grounded Theory Approach and Fuzzy Method

Authors

  • Mahyar Nezhadkian Department of Management, Payam Noor University, Iran
  • Seyed Mohammadtaghi Azimi Department of Civil Engineering, Guangzhou University, China
  • Andrea Ferro Department of Science, University of Padova, Italy https://orcid.org/0000-0002-3271-9322
  • Amir Hossein Nafei Department of Industrial Engineering and Management, National Taipei University of Technology, Taiwan, China https://orcid.org/0000-0002-1795-1123

DOI:

https://doi.org/10.47852/bonviewJCCE2202260

Keywords:

new product development, business companies, grounded theory approach, fuzzy method

Abstract

This study aims to provide a model for new product development in business companies with a grounded theory approach and Fuzzy method. The present study was performed in the framework of a qualitative approach using the grounded theory method, and the required data were collected using in-depth interviews with experts. The study experts included university professors in marketing and senior executives in Beijing. The systematic method has three main stages such as open, axial, and selective coding. The obtained results demonstrate qualitative and technical improvement as the fundamental subject, brand and customer as the reason conditions, companies' marketing activities as the strategy, rules transparency and existing standards as the ruling context, managerial–promotional activities as the interfering factors, and new product and trust-building as the consequences in the paradigm model.

 

Received: 24 May 2022 | Revised: 4 July 2022 | Accepted: 11 July 2022

 

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.


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Published

2022-07-12

Issue

Section

Research Articles

How to Cite

Nezhadkian, M., Azimi, S. M., Ferro, A., & Nafei, A. H. (2022). A Model for New Product Development in Business Companies Based on Grounded Theory Approach and Fuzzy Method. Journal of Computational and Cognitive Engineering, 2(2), 124-132. https://doi.org/10.47852/bonviewJCCE2202260