Modeling Factors Affecting Perceived Deception of Advertising in Social Networks with a Structural-Interpretive Approach
he present research aimed at developing a model for perceived social media advertising deception. To attain the aim, the interpretive structural modeling approach was employed. The research sample included all of the lecturers and experts of social media marketing and advertisement field selected by...
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Veröffentlicht in: | مطالعات مدیریت کسب و کار هوشمند 2021-01, Vol.9 (34), p.273-302 |
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Zusammenfassung: | he present research aimed at developing a model for perceived social media advertising deception. To attain the aim, the interpretive structural modeling approach was employed. The research sample included all of the lecturers and experts of social media marketing and advertisement field selected by the purposeful sampling method. Eventually, eight lecturers and experts of social media marketing and advertisement answered the considered questions. The selected experts had at least ten years of experience in studying, teaching, or working in the field of social media. The sampling continued up to the theoretical saturation point. To determine the reliability of the measurement instrument, the ICC value was confirmed in terms of its consistency and absolute agreement. The research results indicated that in relation to the research subject and the proposed model for perceived social media advertising deception, social media advertising attributes had the strongest effect, while the perceived usefulness, customer knowledge, perceived trust, customer attitude, customer attributes, and media attributes were mostly affected by and their own effects were trivial. In addition, the results revealed that the primary factor in the research i.e. social media advertising attributes, was among the influential or driving variables due to its high directional power and low dependency. Other factors were described with high directional power and high dependency. The variables were non-static and so classified as the hybrid variables. |
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ISSN: | 2821-0964 2821-0816 |
DOI: | 10.22054/IMS.2021.56141.1839 |