Persuasion Theory And Research O'keefe 17.pdf
Srikanth Fonseca
Daniel J. O'Keefe (born 1950) is an American communication and argumentation theory scholar. He is the Owen L. Coon Professor in the Department of Communication Studies at Northwestern University. His research concerns persuasion and argumentation, with a focus on meta-analytic synthesis of research concerning persuasive message effects.[1] This program of work often addresses the question of whether normatively good argumentation contributes to persuasive success.
Today, research on persuasion is a complex and interdisciplinary field that incorporates insights from various academic disciplines, such as economics, marketing, psychology, communication studies, information systems and artificial intelligence. The volume of literature and the numerous theories and models in the field pose challenges for researchers, which can be addressed through systematic reviews or quantitative content analysis. The objective of this paper is to conduct a comprehensive review of the existing literature on persuasion techniques and their potential application in the context of modern online advertising and persuasion. The focus of the review is to identify the most useful insights from the literature for individuals interested in tailoring messages in the digital age.
To achieve this, a concise and organized taxonomy of persuasion techniques will be presented, accompanied by relevant examples, to facilitate the design of effective persuasive messages in online advertising and persuasion efforts. Additionally, the role of language in persuasion and the potential to leverage customer attitudes to enhance message effectiveness is explored. The paper also offers a concise summary of key concepts and theories in persuasion, making it a valuable resource for interdisciplinary and trans-disciplinary researchers. The concept of persuasion techniques is explained, various types of messages that use embedded persuasion are discussed, and the implementation of these persuasive strategies is elaborated upon. The practical guidance provided in this paper is valuable to interdisciplinary researchers, including marketing executives, developers, data scientists and designers who may use persuasive techniques in applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms.
The subsequent sections of this paper are organized as follows: Sect. 2 presents the methodology employed for conducting the literature review. Section 3 provides a comprehensive overview of strategies for designing persuasive messages in the context of marketing, including the classification and description of a wide range of persuasion techniques. Section 4 delves into the principles underlying psychological influence and discusses relevant psychological theories of persuasion. Section 5 focuses on the application of persuasion techniques within a data science framework. Section 6 highlights the main insights gleaned from the paper, while Sect. 7 offers concluding remarks.
The abundance of literature in the field of persuasion research poses a significant challenge. There are many different theories and models that have been developed, each with its own set of concepts, variables and empirical findings. While this is a positive development, as it helps to build a comprehensive understanding of the topic, it can also create challenges for researchers who may struggle to navigate the literature, to stay current with the latest developments and to identify new areas of inquiry.
Moreover, the convergence of persuasion research with computer science and artificial intelligence has become a prominent research area. Often, social science studies in this domain typically presume that all readers possess a background understanding of the persuasion process, which can pose difficulties for non-social science researchers (Jacobucci & Grimm, 2020; Michie et al., 2017). Clear and concise explanations are needed to help these researchers understand persuasion principles and benefit from the integration of computer science and AI in persuasion research. A shared understanding of the persuasion process and the system requirements can facilitate communication and collaboration among stakeholders, including designers, developers, users and others. This can help ensure that everyone is on the same page and working towards a common goal.
The central objective of this paper is to address an existing gap in the literature by providing a comprehensive review targeted at system designers, computer scientists and data scientists who seek to understand persuasion and the underlying psychological mechanisms of persuasion techniques. Specifically, this paper reviews the literature on persuasion techniques and their potential application in the modern context of online advertising and persuasion. To facilitate the design of effective persuasive messages, we present a condensed taxonomy of techniques, accompanied by relevant examples. We integrate the findings from psychological research on reasoning with theories of critical thinking to propose a taxonomy of cognitive errors from a persuasive perspective. This taxonomy serves as a framework that system designers and other practitioners can use to better understand and utilize various persuasion techniques in their work. The role of language in persuasion is explored, along with the examination of how customer attitudes can enhance the effectiveness of persuasive messages.
Furthermore, this paper provides a concise overview of key concepts and theories in persuasion, making it a valuable resource for those seeking to better understand this interdisciplinary field. This may be especially useful for early career researchers or those without a social science background as it provides a comprehensive overview of various persuasion techniques and the underlying psychological processes involved. Relevant concepts and theories are outlined with recommendations for further reading. For coverage, our literature review has been limited to certain literature according to the purpose of the paper (i.e., to provide practical guidance for interdisciplinary researchers who do not have a specific background in the fields of social science, linguistics, psychology or persuasion).
Terms such as persuasion, influence, rhetoric, sales techniques and cognitive biases all relate to the study of how people are influenced to change their attitudes, beliefs or behaviours. This area of study falls under the umbrella of social psychology which is concerned with understanding how individuals interact with others and how social forces shape our thoughts, feelings and behaviours (Taillard, 2000). In the context of marketing, these conceptualizations are relevant because they help marketers understand how to persuade consumers to buy their products or services (Sharma, 1999).
By understanding the principles of persuasion and influence, marketers can design effective advertising campaigns, develop persuasive messaging and use sales techniques that are more likely to result in a positive response from consumers (Akgun et al., 2017). For example, marketers might use rhetorical techniques such as emotional appeals or framing their messages in a particular way to persuade consumers to buy their products (Stiff & Mongeau, 2016). They might also use sales techniques such as scarcity or social proof to create a sense of urgency or to convince consumers that their product is popular and desirable (Cialdini, 2007, p. 237). Overall, the study of persuasion and influence is important for marketing because it helps marketers understand how to effectively communicate with consumers and influence their decisions in a way that benefits their business. For example, digital marketing allows customer engagement through social media, email and other digital channels. Crucially, it contributes to personalization by using machine learning methods to leverage demographical and behavioural data that its easily accessible in online settings.
Machine learning plays a crucial role in digital marketing since its algorithms are used to optimize the selection and placement of ads in front of users (Truong, 2022). This is also known as ad targeting. Machine learning models can be used to analyze user behaviour and preferences to identify the audiences most likely to be interested in a particular product or service. These models can then be used to target ads to these audiences, increasing the chances of engagement and conversion. One important application of machine learning optimization in digital marketing is in predicting the likelihood that a user will click on an ad. Ad click-through rate (CTR) prediction models can be trained on historical data to learn the patterns that are most likely to lead to user engagement with ads. These models can then be used to predict the probability that a given user will click on a particular ad and to adjust the selection and placement of ads in real time to maximize engagement. This gives businesses the opportunity to showcase their products and services on a more personalized level (Truong, 2022).
The integrative review commenced with an extensive literature search conducted through various sources including university library books and web-based engines such as Google Search and Google Scholar. Initially, a limited number of books were searched to identify related concepts which were further explored through reference chasing and citation tracking (i.e., snowballing) to uncover additional relevant publications. To ensure the inclusion of high-quality academic sources, the search was extended to reputable databases including Scopus, ProQuest, SpringerOpen, IEEE Xplore and EBSCO. Search strategies were formulated using relevant phrases and text words, and bibliographic searching was performed by scrutinizing reference lists of articles.
The screening process involved independent review of the titles and abstracts of the search results and other sources. Studies that potentially met the inclusion criteria were further assessed for eligibility based on their full text by the two authors of the paper. The search terms used for title, subject and keyword searches were categorized into two sets: (a) persuasive techniques and (b) persuasion, persuasive language, influence, beliefs, attitudes, intentions, behaviors, rhetoric and advertisement. The search terms were cross-referenced to identify all possible combinations of search terms. The details of the search process, including the number of studies and query search strategies used at each stage of the screening process, are provided in the supplementary information.
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