Presenting a pattern for attracting attention to advertisements on internet sites according to consumer behavior (with a qualitative and quantitative approach)

Document Type : Original Article

Authors

1 Department of business management, kish international branch, Islamis Azad university, kish island, Iran

2 Department of business management, kish international branch, islamic azad university, kish island, iran

10.48308/jbmp.2024.236122.1621

Abstract

The aim of this research is to design a pattern of drawing attention to commercial advertisements on websites, which was done in a mixed method and of the exploratory type in two consecutive qualitative-quantitative stages. In the first stage, the researcher collects the necessary information by examining studies related to the research topic and also in-depth interviews with experts in the field of marketing, and by using the method of grounded theory analysis with Glaser's approach, coding, categorizing and presenting a concept model. In the quantitative stage, the study was conducted using the structural equation modeling method. For this purpose, the data collected from a researcher-made questionnaire based on the proposed model was evaluated using Smart PLS software. After reviewing the previous studies, using exploratory and semi-structured interview tools, qualitative questions were asked from the statistical population of 14 experts and theoretical saturation was achieved. The results of grounded theory showed that the pattern of factors attracting attention to internet advertising includes 19 open codes, 5 central codes and 1 selective code. In the quantitative part, a questionnaire consisting of 19 items was provided to 217 marketing-advertising experts. Based on the results of the structural equation modeling, the value of the significance coefficient t is related to the relationship between the main variables with attracting attention to the forms of advertisements on websites at the 95% confidence level and indicates the significance of their effect on attracting attention to advertisements on websites. Therefore, all variables are identified as main variables.

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History

  • Receive Date: 10 July 2024
  • Revise Date: 17 October 2024
  • Accept Date: 22 October 2024

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