International Journal of Nonlinear Analysis and Applications

Designing an intelligent competitive online network model in order to improve business efficiency by using online capabilities in small and medium-sized companies using structural equations.

Document Type : Research Paper

Authors

1 Department of Business Management (Marketing), Zanjan Branch, Islamic Azad University, Zanjan, Iran

2 Department of Civil Engineering (Geotechnics), Islamshahr Branch, Islamic Azad University, Tehran, Iran

Abstract

The statistical population in this research is about 500 managers and employees of companies active in the field of production and distribution of petrochemical products. The sample size was determined through Morgan's Table 217. In this research, we use the Kolmogorov-Smirnov test to ensure the type of distribution of research variables in terms of normality or abnormality. This test is used to check the claim made about the distribution of research variables. When the variable distribution is normal, parametric tests should be used; otherwise, we use non-parametric tests. In the analytical statistics of this research, the univariate linear regression test (t-test) was used to determine the effect of each of the identified factors on loyalty and offline advertising. The measurement tool of this research is a questionnaire that was examined in terms of validity and reliability. After collecting the questionnaires, the data were analyzed using the structural equation modelling method with the partial least squares approach with the help of SPSS21 and Smart PLS2 statistical software. In this way, the use of advanced and up-to-date systems, timely services, providing innovation to identify the needs of customers, discovering creative opportunities in the important market and obtaining new information from customers and suppliers in the market and evaluation are suggested.

Keywords

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International Journal of Nonlinear Analysis and Applications
Volume 16, Issue 11
November 2025
Pages 87-99
  • Receive Date: 13 April 2024
  • Accept Date: 09 June 2024