Author
Listed:
- Rana Meghna
(Business Mangement, Chandigarh University, Mohali, India)
- Arora Nilesh
(Chandigarh University, Mohali, India)
Abstract
The present research has been steered by the growing use of social media in every sphere of human interaction, including the marketing and advertising of brands. Hence, this research intends to develop and test a model of social media advertising with perceived ad personalization, ad incentivization, self-brand congruity, and social influence as major predictors for social media user’s attitude. Further, the study explores the influence of social networking sites involvement and social influence on electronic word of mouth intentions. The study also ascertains the impact of user attitude on e-WoM and further e-WoM on purchase intentions. Data was collected using a structured questionnaire from 456 respondents. Two-step structural equation modelling was applied using AMOS 22.0 to analyse the data. The conceptual model is framed on the grounds of a theory of reasoned action and Elaboration likelihood model. The findings reveal that ad personalization, and social influence plays an important role in framing user attitude towards social media ads. Also, SNS involvement and social influence significantly influence the e-WoM intention of the user which further influences the social media user’s purchase intention. Subsequently, this research will give a prescient model to design an effective social media advertising programme in the social media advertising domain.
Suggested Citation
Rana Meghna & Arora Nilesh, 2022.
"Predicting User Response Behaviour towards Social Media Advertising and e-WoM Antecedents,"
Review of Marketing Science, De Gruyter, vol. 20(1), pages 83-112, September.
Handle:
RePEc:bpj:revmkt:v:20:y:2022:i:1:p:83-112:n:2
DOI: 10.1515/roms-2022-0006
Download full text from publisher
As the access to this document is restricted, you may want to
for a different version of it.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:revmkt:v:20:y:2022:i:1:p:83-112:n:2. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyterbrill.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.