I know what you browsed last night! role of creepiness and persuasion knowledge on click intention of online behavioral advertising

IF 1.5 Q3 MANAGEMENT Decision Pub Date : 2024-08-19 DOI:10.1007/s40622-024-00398-9

Ajay Kumar, Daruri Venkata Srinivas Kumar, R. U. Megha

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Abstract

The Internet has become an integral part of almost every individual’s life. People look forward to the Internet for almost every possible requirement, whether shopping, business, knowledge, entertainment, social interaction, sports, or anything else. One of the most pertinent aspects of the internet is tracking and recording activities that occur online. This tracking allows advertisers to show ads based on individuals’ online behavior. Online behavioral targeting is termed online behavioral advertising (OBA). Existing studies have identified the predictors of customers' behavioral intentions toward OBA attempts based on perceived costs and perceived benefits. This study examines the moderating effect of consumer awareness on OBA acceptance or rejection. The researchers applied a structural equation modeling (SEM) approach with SPSS AMOS to test the model fit, construct reliability and validity, and proposed research hypotheses with field data gathered with the help of a structured questionnaire. The researchers found OBA relevance and OBA credibility to be the significant factors that affect customer acceptance of OBA. Privacy concerns and creepiness were identified as the factors affecting OBA avoidance. The study also established that subjective persuasion knowledge positively moderates the relationship between OBA relevance and OBA acceptance.

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我知道你昨晚浏览了什么！令人毛骨悚然和说服知识对网络行为广告点击意向的影响

互联网几乎已成为每个人生活中不可或缺的一部分。无论是购物、商务、知识、娱乐、社交、体育还是其他任何可能的需求，人们都期待着互联网的出现。互联网最相关的方面之一就是跟踪和记录网上发生的活动。通过这种跟踪，广告商可以根据个人的在线行为展示广告。在线行为定位被称为在线行为广告（OBA）。现有研究已根据感知成本和感知收益确定了客户对 OBA 尝试行为意向的预测因素。本研究探讨了消费者意识对接受或拒绝 OBA 的调节作用。研究人员利用 SPSS AMOS 采用结构方程建模（SEM）方法，通过结构化问卷收集的实地数据，检验了模型的拟合度、构建的可靠性和有效性，并提出了研究假设。研究人员发现，OBA 相关性和 OBA 可信度是影响客户接受 OBA 的重要因素。隐私问题和令人毛骨悚然的感觉被认为是影响顾客回避 OBA 的因素。研究还确定，主观说服知识对 OBA 相关性和 OBA 接受度之间的关系起着积极的调节作用。

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来源期刊

Decision MANAGEMENT-

CiteScore

2.30

自引率

0.00%

发文量

期刊介绍： The aim of the Journal, Decision, is to publish qualitative, quantitative, survey-based, simulation-based research articles at the national and sub-national levels. While there is no stated regional focus of the journal, we are more interested in examining if and how individuals, firms and governments in emerging economies may make decisions differently. Published for the management scholars, business executives and managers, the Journal aims to advance the management research by publishing empirically and theoretically grounded articles in management decision making process. The Editors aim to provide an efficient and high-quality review process to the authors. The Journal accepts submissions in several formats such as original research papers, case studies, review articles and book reviews (book reviews are only by invitation). The Journal welcomes research-based, original and insightful articles on organizational, individual, socio-economic-political, environmental decision making with relevance to theory and practice of business. It also focusses on the managerial decision-making challenges in private, public, private-public partnership and non-profit organizations. The Journal also encourages case studies that provide a rich description of the business or societal contexts in managerial decision-making process including areas – but not limited to – conflict over natural resources, product innovation and copyright laws, legislative or policy change, socio-technical embedding of financial markets, particularly in developing economy, an ethnographic understanding of relations at a workplace, or social network in marketing management, etc. Research topics covered in the Journal include (but not limited to): Finance and Accounting Organizational Theory and Behavior Decision Science Public Policy-Economic Insights Operation Management Innovation and Entrepreneurship Information Technology and Systems Management Optimization and Modelling Supply Chain Management Data Analytics Marketing Management Human Resource Management