Identification and Classification of the Advertising Texts Features with the Focusing on Consumer Engagement Behavior: A Meta-synthesis Approach

Document Type : Research Paper


1 Prof., Department of Business Management, Faculty of Business Management, College of Management, University of Tehran, Tehran, Iran

2 Associate Prof., Department of Business Management, Faculty of Business Management, College of Management, University of Tehran, Tehran, Iran

3 Ph.D. Candidate, Department of Business Management, Faculty of Business Management, College of Management, University of Tehran, Tehran, Iran


Advertising is an inevitable part of modern capitalist consumer society, and by using advertising content, marketers attempt to provoke consumer engagement and create higher levels of consumer engagement with their brands. Increased consumer engagement is desirable because it is thought to be associated with positive outcomes such as stronger consumer-brand relationships and brand preference, increased satisfaction and loyalty, and more purchases. Advertisements reach consumers through various media such as the Internet (especially social media), radio, television, magazines, and newspapers. Since advertisers use completely different language; there are advantages in creating strange and controversial sentences in unusual ways, as well as communicating with the audience using simple and fluent language. Therefore, in recent years, especially with the significant growth of social media users, it has been inevitable for researchers to focus on the characteristics of engaging content. This study aims to identify and classify the characteristics of brand text messages across various media, such as social, print, and television, that influence conflict behavior, as previously examined in the research. The ultimate goal is to leverage these findings to develop a model for creating engaging content with the identified characteristics.
This study is classified as developmental-applied research, driven by its purpose and qualitative, given the type of data it examines. Furthermore, it adopts an analytical-descriptive approach, collecting qualitative data through the meta-synthesis research method. The articles were extracted from the Google Scholar search engine and were available in Elsevier, Science Direct, Emerald, and Springer electronic databases from 1996 to 2022. First, 86 articles were identified. After assessing parameters such as title, abstract, content, ease of access, and quality of the research method applied to them, the invalid articles were removed. Finally, 23 articles were recognized as suitable for coding were analyzed and reviewed.
In this study, through content analysis, from a total of 86 primary texts, 34 outstanding concepts were finally identified and classified into seven key categories and seven main dimensions or features, including persuasive, information, descriptive, memorability, readability, attention value, and selling power, for Involving texts were identified.
The dimensions of persuasion (comprising five key categories and 20 concepts) were found to have the most significant impact, followed by the dimensions of information (three key categories and 16 concepts) and descriptive (two key categories and 6 concepts), which were ranked next in order of importance.


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