Ad avoidance in the digital context: A systematic literature review and research agenda

背景(考古学) 科学文献 心理学 系统回顾 知识管理 营销 计算机科学 业务 政治学 梅德林 生物 古生物学 法学
作者
Fatih Çelik,Mehmet Safa Çam,Mehmet Ali Köseoğlu
出处
期刊:International Journal of Consumer Studies [Wiley]
卷期号:47 (6): 2071-2105 被引量:82
标识
DOI:10.1111/ijcs.12882
摘要

Abstract The recent growth in digital marketing investments and revenues has attracted the attention of both marketing practitioners and scholars. However, this growth has dramatically increased users' exposure to ad messages, encouraging consumers to avoid them. Therefore, ad avoidance has become a major problem for marketing practitioners. Although researchers have become much more interested in this subject over the past two decades, the body of knowledge on ad avoidance in the digital environment remains fragmented due to the lack of a comprehensive review. Therefore, a holistic overview study is needed that focuses on the big picture and can help researchers to understand the literature comprehensively. This study aims to provide a comprehensive understanding of the topic using a systematic literature review approach on digital ad avoidance. To this end, we provide an in‐depth content analysis of 56 relevant articles published in 31 peer‐reviewed scientific journals up to December 31, 2021. Based on a theories, contexts, characteristics, and methods (TCCM) framework, the study results shed light on ‘what do we know, how do we know, and where should research about digital ad avoidance research be heading?’ Additionally, drawing on the content analysis, we have presented an integrative framework that considers antecedents, outcomes, mediators, and moderators, which can help develop the field systematically and guide future research. By doing so, we think this review meets the need to give an overview of the state‐of‐the‐art scientific body of knowledge on digital ad avoidance and makes important and solid contributions to the literature, practical implications, and future research directions based on the findings.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
科研通AI6.2应助Jason采纳,获得10
1秒前
斯文败类应助Jason采纳,获得10
1秒前
YW发布了新的文献求助10
2秒前
2秒前
Highjump发布了新的文献求助30
2秒前
2秒前
2秒前
指鹿为马完成签到,获得积分10
2秒前
翟不评发布了新的文献求助20
3秒前
3秒前
ouyaya发布了新的文献求助10
3秒前
3秒前
TaiZz发布了新的文献求助10
4秒前
wycx发布了新的文献求助10
4秒前
优秀的素发布了新的文献求助10
4秒前
zrt发布了新的文献求助10
4秒前
SHI发布了新的文献求助10
4秒前
香蕉觅云应助dummy727采纳,获得10
5秒前
5秒前
YYY完成签到,获得积分10
5秒前
5秒前
6秒前
7秒前
8秒前
充电宝应助Yu采纳,获得10
8秒前
小二郎应助心想事成采纳,获得10
9秒前
9秒前
rsy完成签到,获得积分10
9秒前
yyyyou完成签到,获得积分10
9秒前
9秒前
霄学家发布了新的文献求助10
10秒前
王也发布了新的文献求助10
10秒前
ionize完成签到,获得积分10
10秒前
10秒前
11秒前
王大美完成签到,获得积分10
11秒前
开心网络完成签到 ,获得积分10
12秒前
13秒前
13秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
2026年中国辛酸癸酸聚乙二醇甘油酯行业市场现状调查及投资机会研判报告 1000
模型平均及其应用 900
Nondestructive Testing Handbook: Vol. 4, Thermal and Infrared Testing (IR), 4th ed 800
Évora na Idade Média 555
作者名:Kristopher P. Plain,悉尼大学的,目前只能查到其四篇论文,想找到其博士论文 550
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7349914
求助须知:如何正确求助?哪些是违规求助? 8961630
关于积分的说明 19034887
捐赠科研通 6999713
什么是DOI,文献DOI怎么找? 3220814
关于科研通互助平台的介绍 2385581
邀请新用户注册赠送积分活动 2201185