International Journal of Nonlinear Analysis and Applications

Presenting a structural model of customer behavioral intention in accepting social media marketing

Document Type : Research Paper

Authors

Faculty of Management, Economics and Accounting, Tabriz Branch, Islamic Azad University, Tabriz, Iran

Abstract

This study aimed to provide a structural model of customer intentional behavior in accepting social media marketing. The present study uses an applied (purpose) and survey-analytical research methodology (implementation method) carried out with a mixed (quantitative-qualitative) approach. The first part presented the content analysis method and Delphi technique as factors that influence behavioral intention. The research population in the first part was 6045 documents identified on the science website, and in the second part, 30 social media marketing specialists were targeted by sampling method. The findings of the qualitative section were the identification of 20 factors that influence the behavioral intention of customers, which was the most critical comparative advantage. The statistical population in the quantitative part was users of social networks that the sample size was available by sampling method and based on the unlimited Cochran's formula, 384 people were selected. The data collection tool was a researcher-made questionnaire in the second part. Experts confirmed the validity, and the reliability was confirmed by calculating Cronbach's alpha coefficient. Data analysis was performed by structural equation modeling. The results showed that trust, perceived ease, comparative advantage, adaptability, mental norm, perceived behavioral control, and attitude toward behavior significantly affect customers' behavioral intention in accepting social media marketing. The significant relationship between perceived risk and behavioral intent was not confirmed.

Keywords

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International Journal of Nonlinear Analysis and Applications
Volume 13, Issue 1
March 2022
Pages 4053-4068
  • Receive Date: 12 November 2021
  • Revise Date: 29 December 2021
  • Accept Date: 13 January 2022