This research aims to examine a neural network (artificial intelligence) as an alternative model to examine the neuromarketing phenomenon. Neuromarketing is comparatively new as a technique for designing marketing strategies, especially advertising campaigns. Marketers have used a variety of different neuromarketing tools, for instance functional magnetic resonance imaging (fMRI), eye tracking, electroencephalography (EEG), steady-state probe topography (SSPT), and other expensive gadgets. Similarly, researchers have been using these devices to carry out their studies. Therefore, neuromarketing has been an expensive project for both companies and researchers. We employed 585 human responses and used the neural network (artificial intelligence) technique to examine the predictive consumer buying behavior of an effective advertisement. For this purpose, we employed two neural network applications (artificial intelligence) to examine consumer buying behavior, first taken from a 1–5 Likert scale. A second application was run to examine the predicted consumer buying behavior in light of the neuromarketing phenomenon. The findings suggest that a neural network (artificial intelligence) is a unique, cost-effective, and powerful alternative to traditional neuromarketing tools. This study has significant theoretical and practical implications for future researchers and brand managers in the service and manufacturing sectors.

The aim of research is to analyze the influence of life styles and cultural values on the impulse buying behavior of Pakistani university students and professionals in different organizations. For collection of data, survey technique was used from the students of different universities and organizations by adopting the convenient sampling technique. For analyzing the group differences between the variables of lifestyles and cultural values on impulse buying behavior, it was found that only gender role has the significant differences between male and female students and professionals on the impulse buying behavior. For analyzing the relationships among the variables of life style and cultural values, it was found that significant relationship exists among all the variables. The study also shows that variables of life style and cultural values such as, life satisfaction, security, financial satisfaction, gender role and in-group contact predict the influence on impulse buying behavior of the Pakistani students and professionals.

This chapter explores the integration of Artificial Intelligence (AI), Big Data, and Green Human Resource Management (GHRM) to address mental health challenges in Pakistan's higher education institutions (HEIs). Mental health issues such as stress, anxiety, and burnout significantly impact students and staff, hindering academic performance and well-being. AI-driven tools offer real-time, personalized mental health support, while Big Data enables institutions to identify risks and implement timely interventions. Green HRM complements these technologies by fostering sustainable practices, such as flexible work policies and eco-friendly campuses, that promote mental wellness and environmental responsibility. By combining these approaches, this chapter presents a holistic framework to improve mental health outcomes while aligning with sustainability goals, addressing challenges unique to Pakistan's HEIs, and offering actionable solutions for fostering resilient, supportive educational environments.

In the age of digital marketing transformation, online content plays a pivotal role in shaping consumer behaviors. However, the question of whether user-generated or firm-generated content holds greater significance remains unexplored, particularly in developing countries like Pakistan. This research, conducted with a sample size of 230 respondents, delves into the influence of user-generated content versus firm-generated content on the perceptions, attitudes, and purchase intentions of online shoppers in Pakistan. The study adopts the technology acceptance model (TAM) as its theoretical framework. The study’s findings reveal that individuals engaged in online shopping do not differentiate significantly between the two content sources when assessing perceived utility. Conversely, the research highlights that user-generated content exerts a notably stronger influence on individuals’ attitudes towards advertisements and their perceptions of the company, as compared to content generated by the firm itself. Intriguingly, the analysis demonstrates that the content source does not yield a statistically significant impact on shoppers’ purchase intentions. This paper not only provides practical insights but also contributes to the theoretical understanding of this subject matter within the realm of software engineering and digital marketing research.