Behavioural Analytics: Machine Learning Approaches for Predictive Insights

The rapid advancement of Speech Recognition and Natural Language Processing (NLP) technologies offers transformative potential for academic development, aligning with the United Nations Sustainable Development Goal (SDG) 4: Quality Education. These technologies can revolutionize education by providing innovative tools that enhance inclusive and equitable quality education for all. Speech Recognition and NLP enable the automation of academic tasks, improve accessibility for diverse learners—including those with disabilities—and offer data-driven insights to improve student outcomes. The integration of these technologies into educational environments presents significant challenges like limited accessibility in underresourced educational settings, accuracy issues that affect their reliability, and ethical concerns regarding data privacy and algorithmic bias. This study aims to explore the current state of Automatic Speech Recognition (ASR) and NLP in academia, identify key challenges, and propose solutions to enhance their effectiveness in promoting equitable and inclusive education. The research employs a mixed-methods approach, combining quantitative and qualitative methods. Surveys and interviews with educators, students, and administrators are conducted to gather insights into the use and challenges of ASR and NLP technologies. Additionally, experimental studies are carried out to test the effectiveness of existing ASR systems in educational contexts. By offering workable solutions using usable frequencies from a student perspective to improve the integration of speech recognition and Natural Language Processing (NLP) technologies in education, the study's findings will help achieve SDG 4, guaranteeing that all students have access to high-quality, inclusive, and equitable educational opportunities.

Behavioural Analytics-driven Recommendation Systems (BARS) have become increasingly popular in conjunction with the growth of e-commerce. Their goal is to improve customer experiences by providing customised product recommendations. This research delves into how these recommendation systems affect consumer behaviour, particularly emphasising purchasing patterns, contentment, and brand loyalty. By examining diverse BARS, including content-based methods, collaborative filtering, and hybrid filtering, this study aims to comprehend how customised recommendations impact customers' interactions with online retailers. The principal objectives of this study are to analyse the influence of BARS on consumer purchasing patterns, examine the correlation between BARS and customer perception, and assess the algorithms that have the highest impact on customised recommendations from the standpoint of the consumer. The research employs an empirical approach, collecting data from 200 online consumers. The study found that behavioral analytics and recommendations have a moderate effect on purchases, with relevance being a key factor. Accuracy and trust-building are important because collaborative filtering lacks trust. While younger, more frequent shoppers respond best, suggesting targeted, customised marketing, content-based, or hybrid filtering approaches improve engagement in product discovery, with a strong positive correlation among them.

Managing human resources has undergone a trajectory of change from its evolution to the understanding of people analytics. The integration of People Analytics (PA) into Strategic Human Resource Management (SHRM) is reshaping the way organizations optimize workforce potential and achieve strategic goals. This paper explores the synergies between PA and SHRM, emphasizing the transformative role of data-driven decision-making in addressing contemporary human resource challenges. The theoretical underpinnings and methodological techniques for exploring the HR investments and performance linkages based on a technologically adaptive skill orientation are promising for bringing in a new perspective on micro-managing the challenges of the dynamic environment. Predictive analysis based on empirical estimates is where research is focusing and exploring, with a focus on longitudinal intra-firm research. This work aims to investigate the essential components that people analytics teams need in order to improve organizational performance through a narrative literature review. The findings and results of the study reveal that predictive analytics has the potential to significantly enhance strategic HR management by bolstering theoretical, contextual, and practical insights. Subsequent research can enhance academic understanding and guide effective implementation. Using datadriven decisions that require interdisciplinary collaboration among management, data science, information systems, and organizational psychology strengthens HR's position as a strategic partner.

This review provides a comprehensive evaluation of the role of Machine Learning in business, management, and related social sciences. Utilising a substantial sample size of 3682 articles from 53 years (1970-2023) from the Scopus database, this study meticulously traces the development of ML as a pivotal tool in the business sector. The analyses employ RStudios and VOSviewer software to identify prominent trends, influential contributors, and the thematic research landscape. One of the key findings is the significant uptick in ML research post-2020, reflecting the rapid adoption and diversification of ML techniques in response to global digital transformation. The study highlights “forecasting,” “learning systems”, and “learning algorithms” as the most preferred keywords, underscoring the emphasis on predictive analytics and the development of adaptive systems within the diverse ML field. The United States of America (USA), India, and China are the driving forces behind cutting-edge ML research in the business world. This study recommended a thorough examination of new research topics, questions, and keywords for the future that emphasize the convergence of machine learning, artificial intelligence, and other business domains. Ten major themes are identified by the analysis, demonstrating the wide range of possible business applications of machine learning and artificial intelligence. These themes include sentiment analysis, dynamic pricing, sustainable business practices, personalized marketing, and AI-driven human resource management. The study’s novelty lies in its extensive longitudinal analysis of the integration of ML across business disciplines. This groundbreaking research stands out for its extensive temporal scope and dedicated focus on business disciplines. This study traces the evolution of ML from a niche research interest in the 1970s to a widely used tool with diverse applications in 21st-century business processes and strategic management.

 Crime count prediction is essential for improving public safety and guiding law enforcement strategies. This chapter examines the effectiveness of various machine learning and time series models for crime prediction in India, focusing on the Autoregressive Integrated Moving Average (ARIMA) and Neural Network Autoregression (NNAR) models. We conduct a comparative analysis to evaluate the predictive performance of these models using metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Squared Error (MSE). Our findings provide key insights into the strengths and limitations of each model, highlighting their applicability for crime prediction tasks in the Indian context. The results indicate that the NNAR model's ME is marginally higher than that of the ARIMA model. The average magnitude of the discrepancies between the expected and actual values is measured by RMSE. It provides a clear picture of the prediction performance and accuracy of the model. By more successfully identifying underlying patterns in crime data with fewer errors and more precise performance metrics, the ARIMA model exhibits its overall superior predictive ability. Moreover, this study offers valuable recommendations for law enforcement agencies and policymakers, demonstrating how machine learning and time series modeling techniques can be leveraged to forecast crime rates and optimize resource allocation for crime prevention in India. 

 In today’s dynamic business landscape, organizations are increasingly leveraging data analytics to enhance employee engagement and foster a thriving organizational culture. It explores how data-driven insights can be utilized to boost workforce morale, productivity, and retention by identifying key engagement drivers and promoting a positive work environment. Advanced analytics tools, such as sentiment analysis, performance metrics, and feedback systems, enable organizations to monitor employee sentiments and behaviours continuously. The study examines data analytics to boost employee engagement, identifying drivers, best practices, cultural impacts, and future trends such as AI for proactive, tailored organizational strategies. By integrating predictive modelling and machine learning, organizations can gain deeper insights into engagement trends, allowing for tailored interventions at the individual, team, and organizational levels. Such strategies not only improve workforce satisfaction but also contribute to better alignment between employee goals and organizational objectives. The ability to identify disengagement early and implement corrective measures enhances retention and productivity, fostering sustainable growth. The use of employee data requires ethical considerations. This paper examines the ethical implications of data usage in employee engagement strategies and suggests best practices to uphold fairness and respect. Clear communication about data collection and its purposes is essential to maintain employee confidence. The findings underscore the transformative potential of data analytics in human resource management. Implemented responsibly, data-driven approaches can significantly improve employee well-being, drive organizational success, and build a culture that adapts to the evolving needs of the modern workforce.

Behavioral analytics helps organizations understand human behavior to guide decisions on products and services. Much of the data managed by businesses is unstructured text, such as customer reviews and social media posts, which contains valuable insights. Natural Language Processing (NLP) techniques, including sentiment analysis, topic modelling, and named entity recognition, transform this data into actionable business intelligence. This work explores how NLP enhances behavioral analytics across marketing, customer service, and Human Resources. It also addresses challenges like data quality, privacy, and domain specificity, offering solutions to integrate NLP into analytics frameworks. Case studies show how companies use NLP to understand customer behavior, forecast trends, and refine strategies. The findings reveal that NLP and behavioral analytics improve decision-making by extracting insights from unstructured text using tools like sentiment analysis, topic modelling, and NER. Sentiment analysis tracks customers’ emotions, with advanced models boosting accuracy. By combining behavioral analytics with NLP, organizations gain deeper insights into customer profiles, improving decision-making and operational efficiency. 

In recent times, banks and other sectors have implemented substantial innovations in modern electronic payment systems. The Reserve Bank's initiative to foster a less-cash society has led to widespread adoption of digital payment methods in the country. Amidst the surge in electronic payment systems, digital banking stands out as a significant milestone in banking history. In today's landscape, banks are exploring innovative ways to differentiate their services. Considering this evolution in banking practices, the introduction of digital currency by banks as a mainstream alternative to physical currency could have far-reaching implications. This paper delves into the potential consequences of integrating digital currency as a standard form of currency alongside physical cash in banking and various transactions. It explores the implications of incorporating digital currency into everyday e-payment practices within the framework of digital banking. Furthermore, it investigates the effects of digital currency on the broader economy, questioning whether its adoption would be advantageous or detrimental to society. Additionally, the study examines how this technological shift in monetary transactions would impact the general public. The survey shows high familiarity with e-payment options, with 91.1% using them, 7.3% avoiding them, and 1.6% relying on others. Awareness ratings were 26.6% excellent, 62.1% good, and 8.9% fair. While 71.8% were aware of digital currency, only 39.5% understood it fully, 36.3% partially, and 24.2% not at all. In terms of investment, 23.4% had invested, 46% had not, 26.6% planned future investment, and 4% were unaware. Advantages were acknowledged by 64.5%, while 35.5% disagreed. Acceptance stood at 38.7% outright, 16.9% in disagreement, and 44.4% conditional on centralized assurance. Regarding economic impact, 44.5% were positive, 15.3% were negative, and 41.2% were uncertain.

In today’s world, Artificial Intelligence has become extremely important, as it has the power to revolutionize many industries, including the educational sector. Student feedback on educational services is crucial since they are the ones who will ultimately benefit from it. Improved student accomplishment, attitudes towards learning, study motivation, and learning methodologies are some of the aspects that are focused on in this investigation of the link between AI and students' academic performance. It also aims to evaluate and investigate how Artificial Intelligence enhances students' ideas and creative thinking. Snowball sampling was used to select 104 respondents, based on preset parameters. The results imply that almost all identified variables are positively related, and that AI successfully meets the unique learning needs of students, resulting in more thorough and improved educational experiences. AI identifies students with academic difficulties and provides them with the support and treatment they need to improve. Furthermore, AI offers accurate assessments and enhancements for students' attitudes towards learning, leading to a more comprehensive comprehension of the learning process. Additionally, it boosts students' enthusiasm for learning and their study habits. AI's adaptive learning techniques provide students with insightful feedback and guidance for their learning. Moreover, the adaptive learning features of AI provide valuable feedback and guide students on their learning journeys.

Businesses may improve operations, tailor consumer experiences, and hone their strategy by analysing enormous volumes of data. But there are serious ethical questions raised by the growing use of behavioural analytics, especially regarding consent, privacy, and the potential for manipulation. This chapter explores the delicate balance between leveraging data for valuable business insights and maintaining ethical standards, with a focus on transparency. The chapter outlines strategies for transparency in data practices, emphasizing clear communication on data collection, usage purposes, and safeguards to protect consumer information. Additionally, it addresses the need for businesses to be accountable and to adopt practices that prioritize consumer welfare alongside business objectives. The chapter also discusses regulatory frameworks that govern data use, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), and their implications for businesses that employ behavioural analytics. The findings reveal that ethical behavioural analytics fosters trust, strengthens brand loyalty, and supports sustainable growth. Balancing data-driven insights with privacy protection encourages consumer partnership, enhances transparency, and aligns corporate values with societal expectations, ensuring innovation while safeguarding individual rights in an increasingly data-dependent business environment. It concludes that businesses should integrate ethics into their behavioural analytics strategies, balancing insights with ethical practices to achieve objectives while fostering trust-based, long-term customer relationships.

The Jagannath Temple in Puri, Odisha, is one of the holiest pilgrimage sites in India and a key destination in the sacred Char Dham circuit of Hindu culture, attracting millions of devotees annually. The Puri Jagannath Temple faces overcrowding, inadequate infrastructure, safety concerns, environmental issues, and post-pandemic travel hesitancy, affecting the spiritual experience. This study explores using digital technology to address these problems. A survey of 200 Generation Z respondents during the holy month of Kartik assessed the feasibility of a mobile app for managing temple visits. Strengths include personalized experiences, reduced physical contact, and streamlined access. Weaknesses include technological limitations, digital literacy challenges, and privacy concerns. Opportunities for crowd management and virtual booking are noted, while threats include technical glitches and resistance from traditional pilgrims. Results show that a Darshan Booking App can simplify booking, provide real-time information, and manage crowd density despite technical issues and privacy concerns. The findings highlight the need for targeted improvements for the app’s success and broader adoption. The novelty of this paper lies in its pioneering approach to integrating digital technology for managing religious tourism activities at the Puri Jagannath Temple. Unlike previous studies that primarily focus on traditional management methods, this research investigates the feasibility and effectiveness of a Darshan Booking App, offering a comprehensive solution to mitigate challenges like overcrowding, safety concerns, and accessibility issues.