Introduction: The advances in the digital era have necessitated the adoption of communication as the main channel for modern business. In the past, business negotiations, profiling, seminars, shopping, and agreements were in-person but today everything is almost digitalized. Objectives: The study aims to examine how the Internet of things (IoTs) connects text-object as part of NLP and AI responding to human needs. Also, how precipitated changes in the business environment and modern applications such as NLP and AI embedded with IoTs services have changed business settings. Problem statement: As communication takes lead in the business environment, companies have developed sophisticated applications of NLP that take human desires and fulfill them instantly with the help of text, phone calls, smart records, and chatbots. The ease of communication and interaction has shown a greater influence on customer choice, desires, and needs. Modern service providers now use email, text, phone calls, smart records, and virtual assistants as first contact points for almost all of their dealings, customer inquiries, and most preferred trading channels. Method: The study uses text content as part of NLP and AI to demonstrate how companies capture customers’ insight and how they use IoTs to influence customers’ reactions, responses, and engagement with enterprise management in industry 4.0. The “Behavior-oriented drive and influential function of IoTs on Customers in Industry 4.0” concept was used in this study to determine the influence of industry 4.0 on customers. Results: The result indicates the least score of 12 out of 15 grades for all the measurements on a behavior-oriented drive and influential function of IoTs on customers. Conclusion: The study concluded that NLP and AI are the preferred system for enterprise management in the era of industry 4.0 to understand customers’ demands and achieve customer satisfaction. Therefore, NLP and AI techniques are a necessity to attain business goals.

Purpose The study discusses the importance of workplace learning in the current era of work and how organisations are shifting their focus toward employee learning and development. It highlights the need for employees to continuously up-skill themselves to keep up with the demand for skills. The purpose of this study is to introduce a modern approach to evaluating workplace learning to promote and enhance better performance within the workplace. Design/methodology/approach It introduces a deep modern learning approach called “behavior-oriented drive and influential functions of formal and informal learning”. The study also develops the concept of the “Study, Plan, Do, Check, and Act” framework to simulate practise and theory within and outside of work to allow continuous improvement, learning new workplace tools, and bridging digital transformation challenges. The study highlights that workplace learning occurs in a variety of contexts and uses various tools, which poses challenges for the design and development of technology that supports and analyses workplace learning. Findings Based on behaviour-orientated drive and influential functions for formal and informal learning, a grade of 6.54% days was registered for formal learning tools and 4.89% days for informal learning tools. From the statistics in this study, This study concluded that informal learning tools contribute more to the development of the workplace than formal learning. In informal learning, employees act autonomously at their own will and pace to obtain the required knowledge. The time to acquire knowledge through informal learning tools is shorter than in formal learning. Future relevant research should review more learning tools for formal and informal learning. Practical implications Modern workplace learning is a key tool for organisations to gain a competitive advantage. Learning based on formal training and development programs, informal learning and knowledge sharing influence the development of human capital resources. Originality/value The study combines social science and engineering approaches to enable non-engineers to pioneer execution of tasks and examine their performance based on the approach detailed in the results, methodology and discussion sections. It contributes to the field of learning organisations and organisational learning by exploring the learning processes of modern professionals. By investigating the learning practices and experiences of knowledge workers, this study seeks to identify the factors that promote or learn and the impact of learning on the workplace.

FinTech is a digital innovation technology that aims to educate and enable the world on how to create utility values in every activity. Natural language processing is one of the umbrella systems that has unite other innovative technologies behind FinTech. Technological system drivers regarded as "ABCDE" of FinTech consist of Artificial intelligence, Blockchain, Cloud computing, big data, and the internet of things. As communication took the lead in the second half of the 21st century, most companies are moving remotely, leading to much-needed innovation in FinTech. The study presents how natural language processing enables different technologies and advances the technology behind FinTech. The study aims to identify areas of the modern world that can be transformed into a source of finance using the FinTechs drivers of "ABCDE”. The study observed FinTech as a digital Economics that integrates with different aspects of modern technology to create utility values. The study uses the 5 C's of credit as the source of finance for innovative ideas and the 5 P's of marketing as innovative network to reach ultimate FinTech utility values. Results based on the demand and supply analysis indicate that a combination of 5 C's and 5 P's is the bond behind FinTech with the support of the drivers of "ABCDE". Also that the increased demand for goods and services in every economy indicates a fall in the request for credit and vice versa. The study concluded that a well-structured 5 C's and 5 P's is the best route to FinTech technology which is financial freedom to the world.

INTRODUCTION: The study investigated how technological innovations and Embedded Systems, processes, and Communication tools influence human psychology. The IoTs have succeeded to connect humans with things, and it is now preoccupied with understanding how humans react to these things by influencing our actions, reactions, and behavior. OBJECTIVES: To identify items that IoTs use to influence human psychology in education, healthcare, and business and to find out how IoTs utilize AI, ML, Cloud computing, NLP, ICT tools, and big data technology to overpower human psychology. METHODS: The methods “Behavior-oriented drive and influential function method” and the qualitative-quantitative method were used. The qualitative-quantitative method makes use of 3 research questions while behavior-oriented drive and influential function focus on statistical data. RESULTS: The result indicates the least score of 12 out of 15 grades for all the measurements on a behavior-oriented drive and influential function and above 90% positive respondents for the qualitative-quantitative method. CONCLUSION: The study concluded that the high dependency of humans on IoTs services is the reason for the IoTs influence on our thoughts, actions, and behavior.

Environmental issues have remained one of the most challenging social-economic impacts on the world and most countries. Tackling these challenges has remained an underlying issue as a concise approach, method, and policy are yet to be globally made available. Machine learning (ML) with support from IoTs, big data, NLP, and cloud computing is radicalizing the development of a modern-day economy via human support systems. With technical devices, systems, and processes intricately oriented to human understanding. Little environmental needs have been developed to give humans a comfortable place. Even though sensors capture and satisfy human needs, global ecosystem barriers have weighed beyond. Following changes in the world today, automated restrictions and barriers have been seen limiting humans from enjoying opportunities offered by IoTs, big data, NLP, and cloud computing due to environmental impact. Machine learning with capabilities to help humans become more informed is insignificantly exploited on the environmental needs. To suggest an integrated system, method, and areas that IoTs, big data, NLP, and cloud computing should focus on to fight negative environmental impact as a major step to fight climate change. In the study, two research questions and a hypothesis were used. Daily data on emission accusations was collected and used to respond to research questions and hypotheses. In 30 minutes per day and within a month, 412 diesel cars emitted 54,384 g CO2/km, 636 petrol cars emitted 76,320 g CO2/km, and 157 LPG cars emitted 9,577 g CO2/km. Predictions and forecasts were determined based on the data collected. Data accusations reveal they worsen the future impact as both hypotheses and research questions positively support findings that integration of sensors with machine learning can predict future climate situations. Improved gardens are needed, limit artificial items and diesel cars, and improved afforestation is needed in this city.