This research aims to investigate how artificial intelligence (AI) can be used to improve marketing and brand loyalty. Artificial intelligence (AI) is one of the most revolutionary technologies because it allows computers to independently execute mental skills often reserved for humans, such as problem solving and reasoning. In order to make judgments (like which marketing actions to take) and reason (about those decisions) in real time, machines represent, learn, store, and refine their knowledge progressively based on past experiences and existing information (e.g., predicting customer satisfaction). AI, with its ability to analyze vast datasets, predict customer behaviors, and deliver personalized experiences, is reshaping the way companies approach and enhance their customer loyalty strategies. This exploration delves into the multifaceted role of AI in revolutionizing the realm of customer loyalty, shedding light on its applications, benefits, and the transformative potential it brings to the forefront of modern marketing.

The rapid integration of information and communication technologies (ICT) in higher education has brought about transformative changes to traditional teaching and learning practices. This chapter examines the multifaceted impacts of ICT in the higher education sector. Over the past two decades, ICT has revolutionized the way educational institutions operate, impacting teaching methodologies, student engagement, and administrative processes. In the realm of pedagogy, ICT has facilitated the shift towards student-centered learning, empowering learners with access to diverse digital resources and interactive learning platforms. Moreover, administrative tasks within higher education have been streamlined through the adoption of ICT, resulting in enhanced efficiency in enrollment, record-keeping, and communication between stakeholders. This optimization allows educators to focus more on instructional design and student support, fostering a conducive learning environment.

Purpose of study: This research aims to analyze the mediating effect of corporate social responsibility (CSR) image in the effect of company credibility dimensions (trustworthiness and expertise) on participation intention, in the cause–brand alliance (CBA) context. Methodology: The sample design which is used is purposive sampling with the sample criteria as the stakeholders of the University of Lampung, Indonesia. Data were collected by direct interview. Multiple regression analysis is used to test the hypotheses with 160 university’s stakeholders, using purposive sampling. Result: The results show that trustworthiness and expertise have a directly positive significant effect on CSR image. However, expertise statistically has a greater positive significant effect on consumer participation intention toward the CBA than trustworthiness. This finding is contrary to the contrast-effect theoretical framework (Dean, 2003) and balance theory (Heider, 1958). Newly finding is that CSR image considered a mediating role in the effect of trustworthiness and expertise credibility on participation intention. Implications: This implies that in the context of CBA, the company’s trustworthiness and expertise can be a more useful tool to trigger the positive CSR image in encouraging the stakeholders’ perception to buy the products and services or brand of the company implementing CSR, because CBA practices are considered as a genuine social cause, not as a promotion tool. Novelty/Originality of this study: In this study, the collected data uses the cross-sectional design and the CBA context uses CBA practices implemented by agriculture, education service, beverage industry, and a bank stated-owned enterprise.

In the rapidly evolving landscape of marketing, data-driven decision-making has become paramount for organizations seeking a competitive edge. This chapter presents a comprehensive review of the applications and impact of business intelligence (BI) in the realm of marketing analytics. The research aims to elucidate the multifaceted relationship between BI and marketing analytics, explore the benefits and challenges, and provide insights into emerging trends. The study employs a mixed-method research design involving both qualitative and quantitative approaches, including an extensive literature review and surveys. It examines how BI tools and practices empower marketing professionals with data-driven insights for more effective decision-making and marketing strategy optimization. The chapter discusses key applications, benefits, challenges, and emerging trends in the integration of BI and marketing analytics.

This research delves into the integration and impact of intelligent systems in the realm of Human Resource Management (HRM). Intelligent systems enhance HRM by streamlining processes like recruitment, automating repetitive tasks, and providing data-driven insights for decision-making. AI facilitates efficient candidate screening, identifies training needs, and predicts employee performance. As organizations strive for efficiency and effectiveness in their workforce management, the adoption of artificial intelligence (AI) and machine learning (ML) technologies has become increasingly prevalent. This study aims to investigate the utilization of intelligent systems in key HRM domains such as recruitment, employee engagement, and decision-making processes. The swift rise in digitization, coupled with the increasing adoption of Artificial Intelligence (AI), is transforming the business landscape. The integration of Information Technologies with Human Resource Management (HRM) has led to enhanced efficiency, influenced service delivery, established standardization, empowered managers, and revolutionized HR functions. The research methodology involves a comprehensive literature review and examining the implementation of intelligent systems in diverse organizational settings. The findings of this study aim to contribute valuable insights to companies, shedding light on the evolving landscape of HRM through the lens of intelligent systems. By understanding the challenges, benefits, and implications associated with the integration of AI in HRM, organizations can make informed decisions to enhance their human resource practices.