The impact of smartphone advergames characteristics on purchasing intentions: the mediating role of game involvement

  • 184 Views
  • 23 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

This research aimed to investigate the impact of smartphone advergames characteristics on purchasing intentions. Four dimensions were considered (irritation, entertainment, credibility, and informativeness). To achieve this aim, the researchers developed a model based on literature reviews and previous studies; a self-administrated questionnaire was designed and distributed over a convenience sample. The researchers used a quantitative method and a descriptive-analytical approach; the study sample consisted of 600 consumers, and 519 questionnaires were returned with an 86.5% response rate. Various statistical methods, including descriptive, simple linear regression, were used to analyze the data and test the hypotheses. This research’s key findings were that there is a statistically direct impact of irritation, entertainment, credibility, and informativeness on consumers’ purchasing intentions. Moreover, there is a mediating role of game involvement between advergames and consumers’ purchasing intentions. Hence, many Jordanian companies can use smartphone mobile advertising to increase sales and create product brand image among consumers. Smartphone advergaming provides numerous opportunities and challenges for advertisers in the current dynamic business environment.

Acknowledgment
The authors are grateful to the Middle East University, Amman, Jordan, for the full financial support granted to this research project.

view full abstract hide full abstract
    • Figure 1. Research model
    • Table 1. Frequency distribution of the study sample based on “Do you have a smartphone?”
    • Table 2. Frequency distribution of the study sample based on “Do you play online games on your smartphone?”
    • Table 3. Reliability analysis – Cronbach’s Alpha approach
    • Table 4. Distribution of sample based on their playing hours a day
    • Table 5. Distribution of respondents’ age
    • Table 6. Distribution of respondents’ educational level
    • Table 7. Distribution of respondents’ income (JDs)
    • Table 8. Skewness and kurtosis values of each of the study variables
    • Table 9. Collinearity statistics results
    • Table 10. Durbin-Watson statistics results
    • Table 11. Simple linear regression for testing the first hypothesis
    • Table 12. Simple linear regression for testing the second hypothesis
    • Table 13. Simple linear regression for testing the third hypothesis
    • Table 14. Simple linear regression for testing the fourth hypothesis
    • Table 15. Hierarchical linear regression for testing the second main hypothesis
    • Conceptualization
      Rana K. Al-Soluiman, Abdallah Q. Bataineh
    • Formal Analysis
      Rana K. Al-Soluiman
    • Investigation
      Rana K. Al-Soluiman, Hanadi A. Salhab
    • Methodology
      Rana K. Al-Soluiman, Sameer M. Al-Jabaly
    • Writing – original draft
      Rana K. Al-Soluiman
    • Data curation
      Abdallah Q. Bataineh
    • Funding acquisition
      Abdallah Q. Bataineh
    • Project administration
      Abdallah Q. Bataineh
    • Resources
      Abdallah Q. Bataineh, Hanadi A. Salhab
    • Supervision
      Abdallah Q. Bataineh
    • Writing – review & editing
      Abdallah Q. Bataineh
    • Software
      Sameer M. Al-Jabaly
    • Visualization
      Sameer M. Al-Jabaly
    • Validation
      Hanadi A. Salhab