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Emphasizing the increasing need for social presence in interpersonal interactions and the irreplaceable aspects of face-to-face communications, this study aims to explore the emotional impact of interpersonal influence on consumers after purchase. As individuals respond differently to others’ feedback (positive and negative/verbal and nonverbal), the author investigates potential moderating factors of the impact of feedback on consumer’s emotions in a postpurchase context.

A qualitative study was conducted using the method of semistructured individual interviews to collect data. The author selected a heterogeneous group of 30 consumers belonging to two categories: 13 adults (including seven women and six men) and 17 adolescents (including eight girls and nine boys). The author took into account this type of sampling in the selection of respondents, as investigating the influence of the respondent’s gender is one of the research objectives.

The thematic content analysis method released a set of propositions the author suggests for future validation: five moderating factors the author established from the literature review (strength of social ties, level of expertise, type of the product and consumer’s age and gender), while four factors sprang from the collected data (consumer’s level of conviction, repetition of the feedback, the feedback’s argumentation and its level of discretion).

The subjectivity of the interviewees’ personal descriptions of their felt internal states affects the accuracy of their responses. In addition, the psychological aspect of the study provoked reluctance and discretion from some respondents. Further research studies could target these limitations to study each identified moderating factor separately and search for the secondary variables that tend to be linked to these factors (e.g. the expertise level is linked to personality variables, such as the perceived level of self-confidence). Furthermore, subsequent studies can go beyond the affective impact of feedback and investigate the behavioral aspect (repurchase intentions).

This study is of great importance in providing more explanations for the reasons why consumers repurchase or abandon a product. The importance of the emotional power of others’ feedback suggests that, when positioning their offers, marketers must ensure that their product has a strong chance of acceptance by consumer’s significant other. In addition, companies must argue their offers, allowing consumers to increase their knowledge about the product. Moreover, interpersonal cues and expertise level are more important competences to find in employees. Who is more than a vendor, for example, to be perceived as having a high level of expertise in his field?

This study stresses the importance of face-to-face interpersonal interaction in a time when social lives are submerged by social media and virtual communications. The findings suggest that offline social power still matters, and its impact is relative to multiple factors that count for consumers. Face-to-face interaction has been viewed as the most efficient way to satisfy individuals’ social needs for connectedness.

This paper provides new insights into the impact of offline interpersonal verbal and nonverbal feedbacks. The feedback-affect process within consumers was explored, and the postpurchase context was precisely emphasized.

Chatbots emerge as a prominent trend within the context of evolving communication settings and enhancing customer experience to improve firms' total quality management strategies. Specifically, users’ initial trust in such chatbots is critical for their adoption. Under the realm of technology acceptance theories, the present research aims to investigate drivers (perceived ease of use, performance expectancy, compatibility, social influence and technology anxiety) and impacts (customer experience and chatbot usage intention) of chatbot initial trust, among Generation Z considered as the more tech-savvy generation, in the particular telecommunication services context.

Research data were collected using an online questionnaire-based survey to test research hypotheses. A sample of 385 students was selected in Tunisia using a convenience sampling technique. Data were then analyzed through structural equation modeling by AMOS 23.

The results highlighted that, except for perceived ease of use and performance expectancy, all determinants have a significant influence on chatbot initial trust (positive impact of social influence and compatibility and negative impact of technology anxiety). Furthermore, chatbot initial trust positively stimulates customer experience with chatbots and chatbot intention of use.

Our results provide particular insights to chatbot developers seeking to enhance trust-building features in these systems and telecommunication operators to better understand user adoption and improve chatbot-based customer interactions among Generation Z in emergent markets.

This paper attempts to consolidate and enrich the existing body of chatbot initial trust literature by emphasizing the role of customer experience with chatbots and technology anxiety, as two pivotal consumer-related factors that have not yet been treated together in one research.

The purpose of this paper is to develop a reliable model that would predict the compressive strength of slurry infiltrated fiber concrete (SIFCON) modified with various supplementary cementitious materials (SCMs) using artificial intelligence approach.

This study engaged the artificial intelligence to predict the compressive strength of SIFCON through deep neural networks (DNN), artificial neural networks, linear regression, regression trees, support vector machine, ensemble trees, Gaussian process regression and neural networks (NN). A thorough data set of 387 samples was gathered from relevant studies. Eleven variables (cement, silica fume, fly ash, metakaolin, steel slag, fine aggregates, steel fiber fraction, steel fiber aspect ratio, superplasticizer, water to binder ratio and curing ages) were taken as input to predict the output (compressive strength). The accuracy and reliability of the developed models were assessed using a variety of performance metrics.

The results showed that the DNN (11-20-20-20-1) predicted the compressive strength of SIFCON better than the other algorithms with R² and mean square error yielding 95.89% and 8.07. The sensitivity analysis revealed that steel fiber, cement, silica fume, steel fiber aspect ratio and superplasticizer are the most vital variables in estimating the compressive strength of SIFCON. Steel fiber contributed the highest value to the SIFCON’s compressive strength with 16.90% impact.

This is a novel technique in predicting the compressive strength of SIFCON optimized with different SCMs using supervised learning algorithms, improving its quality and performance.