Quarterly Publication

Document Type : Original Article

Authors

1 Associate Professor, Department of Business Management, Faculty of Management, Central Tehran Branch of Islamic Azad University, Tehran, Iran.

2 Ph.D. Candidate, Department of Business Management, Faculty of Management, Central Tehran Branch of Islamic Azad University, Tehran, Iran.

3 Professor, Department of Business Management, Faculty of Management, Central Tehran Branch of Islamic Azad University, Tehran, Iran.

10.22050/pbr.2021.282859.1181

Abstract

The purpose of this study is to design and explain the model of online advertising with an image-based marketing approach. In this regard, while reviewing the concepts of online advertising, image-based marketing (GIF marketing) and tourism using confirmatory factor analysis and structural equation modeling, we designed and explained the online advertising model with the gif marketing approach in oil and gas’s Industrial tourism hubs of Iran. The research strategy includes a combined qualitative study of content analysis, granded theory and delphi analysis and quantitative study in survey. The study population in the qualitative part includes experts in the field of advertising focusing on the tourism industry and in the quantitative part all tourists are in the oil and gas’s Industrial tourism hubs of Iran. Normal test and confirmatory factor analysis and structural equation modeling test were used to confirm the components and model. The results showed that the components (causal factors, contexts and outputs) of online advertising model with GIF marketing approach in oil and gas’s Industrial tourism hubs are in order of priority. In order to conduct open interviews and coding, 62 indicators were finally extracted. The results of model validation and model overall fit index (GOF), which has a value of 0.794, showed that the overall fit of the model is desirable and as a result, the overall model is valid and approved. Also, in Q2 index, positive numbers showed more than 0.35, which showed the high predictive power of the model.

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