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Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

A total of 1,022 individuals took part in the survey; participants were asked to fill out the ten item personality measure questionnaire to capture personality traits and the dysfunctional attitude scale (DAS) to measure dysfunctional beliefs and cognitive vulnerabilities. ML classification models using participant profiling information as input were developed to predict the extent to which an individual was influenced by statements that contained different linguistic styles/persuasion techniques. Several ML algorithms were used including support vector machine, LightGBM and Auto-Sklearn to predict the effect of each technique given each individual’s profile (personality, belief system and demographic data).

The findings highlight the importance of incorporating emotion-based variables as model input in predicting the influence of textual statements with embedded persuasion techniques. Across all investigated models, the influence effect could be predicted with an accuracy ranging 53%–70%, indicating the importance of testing multiple ML algorithms in the development of a persuasive communication (PC) system. The classification ability of models was highest when predicting the response to statements using rhetorical devices and flattery persuasion techniques. Contrastingly, techniques such as authority or social proof were less predictable. Adding DAS scale features improved model performance, suggesting they may be important in modelling persuasion.

In this study, the survey was limited to English-speaking countries and largely Western society values. More work is needed to ascertain the efficacy of models for other populations, cultures and languages. Most PC efforts are targeted at groups such as users, clients, shoppers and voters with this study in the communication context of education – further research is required to explore the capability of predictive ML models in other contexts. Finally, long self-reported psychological questionnaires may not be suitable for real-world deployment and could be subject to bias, thus a simpler method needs to be devised to gather user profile data such as using a subset of the most predictive features.

The findings of this study indicate that leveraging richer profiling data in conjunction with ML approaches may assist in the development of enhanced persuasive systems. There are many applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms which can benefit from integrating persuasion communication systems that tailor messaging to the individual – potentially translating into higher economic returns.

This study integrates sets of features that have heretofore not been used together in developing ML-based predictive models of PC. DAS scale data, which relate to dysfunctional beliefs and cognitive vulnerabilities, were assessed for their importance in identifying effective persuasion techniques. Additionally, the work compares a range of persuasion techniques that thus far have only been studied separately. This study also demonstrates the application of various ML methods in predicting the influence of linguistic styles/persuasion techniques within textual statements and show that a robust methodology comparing a range of ML algorithms is important in the discovery of a performant model.

This paper examines the relationship between marketing automation emergence and the marketers' use of heuristics in their decision-making processes. Heuristics play a role for the integration of human decision-making models and automation in augmentation processes, particularly in marketing where automation is widespread.

This study analyzes qualitative data about the impact of marketing automation on the scope of heuristics in decision-making models, and it is based on evidence collected from interviews with twenty-two experienced marketers.

Marketers make extensive use of heuristics to manage their tasks. While the adoption of new automatic marketing tools modify the task environment and field of use of traditional decision-making models, the adoption of heuristics rules with a different scope is essential to defining inputs, interpreting/evaluating outputs and control the marketing automation system.

The paper makes a contribution to research on the relationship between marketing automation and decision-making models. In particular, it proposes the results of in-depth interviews with senior decision makers to assess the impact of marketing automation on the scope of heuristics as decision-making models adopted by marketers.

This article advances a conceptual typology delineating the differences and similarities between business- and social-sector new venture creators. Our classification scheme differentiates business and social entrepreneurs, considering characteristics of social entrepreneurs in a larger entrepreneurial context.Within a conceptual 2x2 typology based on two dimensions: drive (passion vs. business) and desired return (financial ROI vs. social ROI), we identify and classify 80 examples of new venture creators into one of the quadrants of an enterprise model of entrepreneurs. Preliminary results reveal similarities between social and traditional entrepreneurs and differentiate social entrepreneurs in terms of traits, goals, tendencies, and motivational sources.

The purpose of this article was to provide an outline of the citation practices of “Evolving to a new dominant logic for marketing” by Vargo and Lusch (2004) to identify and discuss the most prominent research topics in which citations were used and to suggest future research based on the results of the analysis.

The authors used a comprehensive framework of citation practices based on iterations of previous literature to analyze the relevant literature, which they identified by accessing, systematically and rigorously, every available contribution matching a set of criteria. The authors then categorized these contributions and highlighted the main topics of research interest in each category.

The findings identify some of the factors in the continuous development of SDL, the way this new marketing logic permeated the scientific debate, the infusion of Vargo and Lusch (2004) into several contributions framed in the new logic or justified through it, and a general perception of a default reference. Additionally, the findings highlight the main topics of research interest in each category.

The analysis enabled the detection of the original paper's influence through advances in service studies, pollination into other fields of research and continuous scientific debate. The authors have highlighted several avenues for research and proposed future research directions.

This research analyzed the effects of the spread of the SDL cornerstone article and emphasized the advantage of using an in-depth approach to the analysis of studies through a framework applied to more than 4,600 studies.