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Big data marketing analytics (BDMA) has been discovered to be a key contributing factor to developing necessary marketing capabilities. This research aims to investigate the impact of the technology and information quality of BDMA on the critical marketing capabilities by differentiating between firms with low and high perceived market performance.

The responses were collected from marketing professionals familiar with BDMA in North America (N = 236). The analysis was done with partial least squares-structural equation modelling (PLS-SEM).

The results indicated positive and significant relationships between the information and technology quality as exogenous constructs and the endogenous constructs of the marketing capabilities of marketing planning, implementation and customer relationship management (CRM) with mainly moderate effect sizes. Differences in the path coefficients in the structural model were detected between firms with low and high perceived market performance.

This research indicates the critical role of technology and information quality in developing marketing capabilities. The study discovered heterogeneity in the sample population when using the low and high perceived market performance as the source of potential heterogeneity, the presence of which would likely cause a threat to the validity of the results in case heterogeneity is not considered. Thus, this research builds on previous research by considering this issue.

This study aims to develop and test a research model that explores the empirical relationship between consumer religiosity, brand love and consumer forgiveness. Its objective was to enhance our understanding of the mechanisms that can influence consumers to extend forgiveness to brands in the context of Islamic banking in Tanzania.

The study used a quantitative cross-sectional survey design to gather data from 399 respondents in the Dodoma and Dar-es-salaam regions of Tanzania. A structured questionnaire was used to collect the data, which were subsequently analyzed using structural equation modeling (SEM) with AMOS 21.

The study’s findings revealed that consumer forgiveness is influenced by the level of brand love at an individual level. Additionally, the findings indicate that in the context of Islamic banking, brand love is an emotional behavior that is influenced by the strength of religious beliefs, that is, consumer religiosity. Consequently, the findings highlighted the mediating role of brand love in the proposed relationship between consumer religiosity and consumer forgiveness.

The fact that Islamic banking is guided by Islamic laws (Sharia) and Islamic values means that competitiveness in this sector can be established by serving consumers who are well-versed in Islamic teachings and doctrines. Furthermore, customers who possess a strong understanding of Islamic teachings and doctrines can be an asset to Islamic banks, as they are less likely to switch banks due to service delivery issues.

This empirical study is one of the few attempts to explore the relationship between consumer religiosity, consumer forgiveness and brand love. It expands our understanding of consumer forgiveness by examining the influence of deontological norms (applying norms to assess Islamic banking practices) and teleological evaluation (evaluating Islamic banking practices based on the overall balance of right and wrong expected to occur).

This paper aims to explore students’ intention to use and actual use of the artificial intelligence (AI)-based chatbot such as ChatGPT or Google Bird in the field of higher education in an emerging economic context like Bangladesh.

The present study uses convenience sampling techniques to collect data from the respondents. It applies partial least squares structural equation modeling (PLS-SEM) for analyzing a total of 413 responses to examine the study’s measurement and structural model.

The results explore that perceived ease of use (PEOU) negatively affects intention to adopt AI-powered chatbots (IA), whereas university students’ perceived usefulness (PU) influences their IA positively but insignificantly. Furthermore, time-saving feature (TSF), academic self-efficacy (ASE) and electronic word-of-mouth (EWOM) have a positive and direct impact on their IA. The finding also reveals that students' IA positively and significantly affects their actual use of AI-based chatbot (AU). Precisely, out of the five constructs, the TSF has the strongest impact on students’ intentions to use chatbots.

Students who are not aware of the chatbot usage benefits might ignore these AI-powered language models. On the other hand, developers of chatbots may not be conscious of the crucial drawbacks of their product as per the perceptions of their multiple users. However, the findings transmit a clear message about advantages to users and drawbacks to developers. Therefore, the results will enhance the chatbots’ functionality and usage.

The findings of the study alert the teachers, students and policymakers of higher educational institutions to understand the positive outcomes and to accept AI-powered chatbots such as OpenAI’s ChatGPT. Outcomes also notify the AI-product developers to boost the chatbot’s quality in terms of timeliness, user-friendliness, accuracy and trustworthiness.