Analysis of Structure-Conduct-Performance Pattern in the Competition of Digital Platforms in  Sports Goods Market

Authors

    Mostafa Mohammadian Department of Physical Education and Sport Sciences, Faculty of Physical Education, Shahrood University of Technology, Shahrood, Iran.
    Hassan Bahrololoum * Department of Physical Education and Sport Sciences, Faculty of Physical Education, Shahrood University of Technology, Shahrood, Iran. bahrololoum@shahroodut.ac.ir
    Reza Andam Associate Professor Department of Physical Education and Sport Sciences, Faculty of Physical Education, Shahrood University of Technology, Shahrood, Iran.
    Ali Dehghani Faculty of Industry and Management, Shahrood University of Technology, Shahrood, Iran
https://doi.org/10.61838/kman.aitech.3.2.8

Keywords:

SCP pattern, sports goods market, digital platform, technology, artificial intelligence

Abstract

ABSTRACT

 

 

Purpose: The main focus of this research is to analyze the SCP model in the competition of digital platforms in the sports goods market.

Design/methodology/approach: This research is applied and descriptive, utilizing a qualitative method to examine the subject. The statistical population includes experts in the fields of the sports industry, human resources, and technology. Sampling was conducted purposefully and using the snowball method. Semi-structured interviews continued until theoretical saturation was reached, and after interviews 12, 13, 14, and 15, it was determined that no new information was being obtained and the interviews were repetitive, so the interviews were stopped. MAXQDA software was used for data analysis.

Findings: The results showed that competition in digital platforms in the sports goods market includes 5 main themes, 11 sub-themes, and 26 selective concepts within the SCP model. The most important components are:  value creation, market forces, platform design, positioning, and company characteristics. These factors are divided into three categories: factors with a positive impact, factors with a negative impact on the success and sustainability of digital platforms in competition, and factors with ambiguous effects.

Originality: The analysis of the SCP model in the competition of digital platforms has extensive impacts on the sports goods market. This study provides a conceptual framework based on the SCP paradigm, which acts as a logical and systematic guide for the empirical examination of digital platform companies’ performance. This study helps managers focus on parameters that assist them in adapting to uncertainties and coping with future shocks that may negatively affect their success, sustainability, and growth. Managers do this by assessing their company’s position in the market and adjusting their strategies, including analyzing entry and exit barriers and understanding the effects of regulatory policies.

 

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Author Biographies

  • Hassan Bahrololoum, Department of Physical Education and Sport Sciences, Faculty of Physical Education, Shahrood University of Technology, Shahrood, Iran.

    Associate Professor Sports Management, Department of Physical Education and Sport Sciences, Faculty of Physical Education, Shahrood University of Technology, Shahrood, Iran.

  • Reza Andam, Associate Professor Department of Physical Education and Sport Sciences, Faculty of Physical Education, Shahrood University of Technology, Shahrood, Iran.

    Associate Professor Sports Management, Department of Physical Education and Sport Sciences, Faculty of Physical Education, Shahrood University of Technology, Shahrood, Iran.

  • Ali Dehghani, Faculty of Industry and Management, Shahrood University of Technology, Shahrood, Iran

    Assistant Professor of Economics, Faculty of Industry and Management, Shahrood University of Technology, Shahrood, Iran

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Published

2025-04-01

How to Cite

Mohammadian, M., Bahrololoum, H., Andam, R., & Dehghani, A. (2025). Analysis of Structure-Conduct-Performance Pattern in the Competition of Digital Platforms in  Sports Goods Market. AI and Tech in Behavioral and Social Sciences, 3(2), 86-112. https://doi.org/10.61838/kman.aitech.3.2.8