This research aims to explore the ways in which the use of WhatsApp for diagonal and lateral communication can improve the achievement of tasks, to what extent it can keep data and information trustworthy and confidential, and in what ways WhatsApp improves the communication of suggestions, instructions, and complaints. The study uses a quantitative research strategy with one independent variable, which is WhatsApp usage in the workplace, and three dependent variables, which are team member communication, employee performance, and confidentiality. To test the proposed research model, the authors conduct an online questionnaire in the United Arab Emirates. Descriptive statistics are used to analyze the quantitative data collected through the questionnaires. The study shows that the use of WhatsApp for communication is positively associated with leader-member exchange (LMX) and team-member exchange (TMX). Both LMX and TMX have a positive correlation with employee performance. WhatsApp is a trusted method to transfer information between team members and between managers and employees. The study also asserts that the use of WhatsApp is an effective tool to improve productivity and performance, and it makes task completion faster. It appears that the study has a limited literature review and lacks previous research on the variables related to data confidentiality and improving team performance. In this case, the study seems to be lacking a thorough examination of prior research related to data confidentiality and its impact on team performance. WhatsApp is a widely used messaging application that offers end-to-end encryption to its users, and this encryption provides a certain level of security and privacy. WhatsApp usage has a positive impact on team performance and productivity. The study presents a concrete understanding of how vertical and horizontal relationships connect the impact of WhatsApp communication on employee performance. The study recommends the use of WhatsApp in the workplace as a safe tool to boost performance and improve productivity and satisfaction.

This analysis integrates the “technology acceptance model (TAM)” with the “use of gratifications theory (U&G)” to develop an embedded model that predicts the use and satisfaction of emotional icons called stickers through WhatsApp. The explanation for combining these two theories is that U&G offers accurate information and a thorough knowledge of use, while TAM theory has been firmly established in several technical implementations. A newly developed hybrid analysis procedure has been applied within this research. Using an artificial neural network (ANN), and the structural equation model (SEM) have been combined. The research also uses the importance-performance map analysis (IPMA) to present each factor’s performance as well as importance. The ANN and IPMA research have both indicated that for sticker use intention, a highly essential predictor is Socialization. An online questionnaire survey was developed to assess the recommended model. The intention to use stickers was significantly affected by “Socialization, Self Presentation, Enjoyment, Novelty, Unique Function, Perceived Ease of Use, and Perceived Usefulness”. The research's main achievement is the convergence of two separate theories into a single conceptualization to accurately calculate the TAM components when it comes to the usage of stickers in WhatsApp. Theoretically, the recommended model provides enough insight for aspects which affect the intention to use stickers with relevance to the socialization’s factors considering interpersonal aspects. Practically, the higher education decision-makers along with professionals would extract variables that are important as compared to others and policies would be developed accordingly. The deep ANN model competence has been analyzed within the research to decide upon the non-linear associations between variables of the theoretical model, methodologically.