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Virtual assistants are increasingly used for persuasive purposes, employing the different modalities of voice and text (or a combination of the two). In this study, the authors compare the persuasiveness of voice-and text-based virtual assistants. The authors argue for perceived human-likeness and cognitive load as underlying mechanisms that can explain why voice- and text-based assistants differ in their persuasive potential by suppressing the activation of consumers' persuasion knowledge.

A pre-registered online-experiment (n = 450) implemented a text-based and two voice-based (with and without interaction history displayed in text) virtual assistants.

Findings show that, contrary to expectations, a text-based assistant is perceived as more human-like compared to a voice-based assistant (regardless of whether the interaction history is displayed), which in turn positively influences brand attitudes and purchase intention. The authors also find that voice as a communication modality can increase persuasion knowledge by being cognitively more demanding in comparison to text.

Simply using voice as a presumably human cue might not suffice to give virtual assistants a human-like appeal. For the development of virtual assistants, it might be beneficial to actively engage consumers to increase awareness of persuasion.

The current study adds to the emergent research stream considering virtual assistants in explicitly exploring modality differences between voice and text (and a combination of the two) and provides insights into the effects of persuasion coming from virtual assistants.

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 study aims to find how can fashion micro-influencers and their electronic word-of-mouth (eWOM) messages increase consumer engagement on social media, focusing on micro-influencers’ influence, typology, eWOM content and consumer engagement.

A total of 20,000 microblogs were collected from Irish fashion micro-influencers and analyzed through keyword classification and content analysis in NVivo. The determinants of eWOM persuasiveness for consumer engagement on social media were investigated based on Sussman and Siegal’s information adoption model.

The study finds that among the four types of micro-influencers, market mavens and their eWOM messages have the highest impact on consumer engagement on social media, and it presents a repetitive and persuasive eWOM model of market mavens to increase consumer participation. Also, the study discovers that micro-influencers’ occasion-related microblogs have an increasing impact on consumer interactions whereas microblogs with brands have a decreasing engagement with consumers on social media.

This study advances prior studies on the relationship between influencers’ eWOM messages and consumer participation on social media by the development of a persuasive eWOM model of micro-influencers to increase consumer engagement and fill in the lack of relevant literature. Also, findings provide actionable insights for marketing communication practitioners to persuade consumers to participate in eWOM communications and establish strong consumer-brand relationships on social media.

This paper aims to investigate the effect of product-related description abstractness/concreteness on perceived trustworthiness and the role of consumer product expertise and shopping-stage mindset in the persuasiveness of abstract vs concrete product descriptions.

Two online experiments were conducted: Study 1 (description abstractness – manipulated between-subject; consumer product expertise, perceived trustworthiness, purchase intent – measured), Study 2 (consumer shopping-stage mindset – manipulated between-subject; description abstractness – manipulated within-subject; consumer product expertise, perceived trustworthiness, abstract/concrete description preference – measured).

The negative effect of the abstractness (abstract descriptions vs the ones supplemented with relevant product details) on description trustworthiness was evidenced in Study 1. Trustworthiness was positively related to purchase intent, especially for high product expertise. Study 2 replicated the effect of product description abstractness on its trustworthiness in terms of two other forms of abstractness (abstract descriptions vs the ones supplemented with irrelevant product details and product benefits vs attributes). The goal-oriented (vs comparative) mindset had a positive effect on the benefit (vs attribute) description preference, especially for high product expertise.

For marketers, the results suggest the positive consequences of presenting concrete information on product attributes and the conditions enhancing the effectiveness of presenting product benefits.

The paper integrates the existing views on consumer response to abstract vs concrete information (lexical abstractness/concreteness, means-end chain theory) and links them to consumer product expertise and shopping-stage mindset.

Reactance theory is applied to investigate consumer responses to “buy local” campaigns initiated by government to counteract the effects of an economic crisis, using the COVID-19 pandemic as an illustrative context.

A conceptual model is developed, aimed at revealing the extent to which “buy local” campaigns – explicitly justified by the need to fight an economic crisis – are likely to lead to (a) compliance (i.e. support for local products/retailers) or (b) freedom restoration (i.e. support for foreign products/retailers). The model is subsequently tested on samples of German (N = 265) and Italian (N = 268) consumers.

“Buy local” campaigns are likely to generate reactance amongst consumers and such reactance can lead to both non-compliance and, albeit less so, freedom restoration outcomes. At the same time, consumer ethnocentrism acts as a countervailing influence by attenuating the effects of generated reactance and its undesirable outcomes.

Psychological reactance theory offers a novel perspective for conceptually approaching the likely responses of consumers towards “buy local” campaigns and the empirical findings support the use of the theory in this context.

Policymakers seeking to encourage consumers to support the local economy during times of an economic crisis need to be aware that “buy local” campaigns may, against their intended communication goals, result in non-compliance as well as consumer responses in the opposite direction. Thus, the reactance-generating potential of such campaigns needs to be explicitly considered at the planning/implementation stage.

The findings confirm the relevance of reactance theory as a conceptual lens for studying the effects of “buy local” campaigns and have important implications for domestic/foreign firms as well as for policy makers seeking to encourage consumers to support the local economy during times of an economic crisis.

We offer a theoretical account of how two types of bricolage influence the entrepreneurial process. The first type involves social relationships or physical or functional assets, and thus pertains to an entrepreneurʼs external resources used in the instantiation of operations of a new venture. The second type pertains to an entrepreneurʼs internal resources‐experiences, credentials, knowledge, and certifications‐which the entrepreneur appropriates, assembles, modifies and deploys in the presentation of a narrative about the entrepreneurial process. We argue that both types of bricolage are essential to the success of a venturing attempt.

The purpose of this paper is to study the impact of lead users (LUs) on innovation success by proposing and validating an integrative model that links LUs, innovation success and knowledge sharing (KS) variables.

The authors research is quantitative in nature and data were collected through a survey conducted among 30 information technology Tunisian companies. The authors preliminary analysis was explored using the “SPSS” software and processed through exploratory factor analysis and regression analysis methods.

Results showed that LUs who possess advanced skills and valuable knowledge can contribute to enhance new successful innovations, and hence, enable companies to gain short and long-term profits. Besides, the authors confirmed that the impact of LUs on innovation success is mediated by the “KS” behavior.

An integrative framework that links LUs, innovation success and a third variable called KS is presented to see beyond the intended importance of this type of user regarding the development of new innovations.

The paper offers new insights to the topic of LUs by emphasizing the role of “KS” variable in strengthening the LUs – innovation success relationship.