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Effect of digital financial literacy on digital consumer protection: Mediating role of financial self-efficacy and financial confidence
The digital consumer is gaining importance in the current digital age and there is a need to adapt to the changing context. When discussing digital consumer protection, it's critical to gauge the degree of digital financial literacy. This study explores the mediating role of financial self-efficacy and financial confidence between digital financial literacy and digital consumer protection. The study was conducted in the Indian context, and the target population was users of digital platforms for financial activities in the age group of 20 to above 60. It was found that digital financial literacy significantly impacts digital consumer protection, financial self-efficacy, and financial confidence. However, only Financial self-efficacy and digital financial literacy impact digital consumer protection. It shows that financial self-efficacy mediates the relationship between digital financial literacy and digital consumer protection. This study will benefit the users of digital platforms and assist government/ non-government agencies in designing digital consumer protection programs. 2024, IGI Global. All rights reserved. -
Sentiment analysis with NLP: A catalyst for sales in analyzing the impact of social media ads and psychological factors online
This chapter explores the role of sentiment analysis, powered by NLP, in boosting sales amidst "Intersection of AI and Business Intelligence in Data-Driven DecisionMaking." It analyzes how social media ads and psychological factors shape online shopping behavior, demonstrating how sentiment analysis drives digital commerce sales. Sentiments from platforms like Twitter, Facebook, and Instagram are categorized into positive, negative, or neutral using advanced NLP algorithms. The chapter delves into psychological factors such as trust, credibility, brand perception, and emotional responses triggered by social media ads. Through sentiment analysis, patterns and correlations between sentiment expressions and consumer actions are revealed, illuminating the impact of social media advertising on online shopping behavior. This insight aids marketers in optimizing digital strategies, developing effective campaigns to enhance sales performance, and engaging customers in the online shopping domain. 2024, IGI Global. All rights reserved. -
Post-millennials: Psychosocial Characteristics, Determinants of Health and Well-Being, Preventive and Promotive Strategies
The post-millennial generation plays a significant role in the progress and development of every nation. The health and well-being of this generation needs critical focus. Post-millennial are unique, possesses an egalitarian worldview, global and open mind-set, commitment to the environment, society, and others. They are called digital natives due to their familiarity with social media and technology. They are also called snowflake generation due to their characteristics of being gentle and unique. Double income households and well-educated parents and their assistance make them a distinguished population. However, these characteristics are less explored while addressing the health and wellness concerns of this cohort. The present chapter discusses the psychosocial characteristics of the post-millennials and their implications exclusively in the mental health realm. It also presents the strength-based strategies to address the concerns of post-millennials and the significance of evidence-based practices in mental health and sensitizing mental health practitioners about the changing scenario. The Editor(s) (if applicable) and The Author(s), under exclusive license to Taylor and Francis Pte Ltd. 2022. -
Impact of COVID-19 on Gig Workers with Special Reference to Food Delivery Executives
The gig economy, popularly known as shared economy or collaborative work, has acquired a public and academic interest in the twenty-first century. The concept stresses entering into short-term contracts and does not promise any permanent job. The employees in the Gig economy with temporary work relationships are also referred to as gig workers. Any unexpected change in the pulse of the economy has an impact on the gig economy. The COVID-19 pandemic has affected the economy and the gig workers over the last few months. This book chapter provides a timely intervention into the gig workers, particularly the food delivery executives in India. Their remunerations, benefits, and difficulties from a pandemic perspective are included in the chapter. The authors try to bring in the employees views of the work they do and the differences, which have been brought in as an aftermath of the pandemic. The chapter also throws light on the recent government initiatives to benefit gig workers in the economy. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Exploring the facets of chatbots and automation in tourism with special reference to ChatGPT
This chapter examines the role of chatbots, specifically ChatGPT, in the tourism industry, focusing on their influence on employment opportunities and job roles. It evaluates both the positive and negative impacts of chatbots on various aspects of tourism job roles, emphasizing their potential to enhance efficiency and customer satisfaction while acknowledging concerns regarding job displacement and diminished human interaction. Additionally, the study explores the benefits and drawbacks of automation in the tourism sector, highlighting its potential to streamline operations and improve productivity while also addressing challenges such as technical issues and the need for workforce upskilling. Furthermore, the chapter discusses strategies for upskilling the tourist workforce to effectively navigate the increasing automation in the industry and mitigate potential job losses. Lastly, the ethical implications of using ChatGPT in the tourism sector are examined, emphasizing the importance of responsible implementation to ensure fairness and human-centric values in tourism experiences. 2024, IGI Global. All rights reserved. -
ChatGPT and Academia: Exploring the transformations and transitions
Since its launch in November 2022, this tool has brought massive transformations in almost every imaginable field. Among those fields, academia is perhaps the most discussed domain. However, much of what ChatGPT can do is still understudied. Therefore, this chapter aims to investigate the potential impact of ChatGPT in the domain of academia while exploring the possibilities for the future. The study emphasizes the theories that link ChatGPT's presence to its effects on academia and research. 2024, IGI Global. All rights reserved. -
Artificial intelligence towards smart green transportation: A path towards sustainability
Emerging technological advancements and sustainability concerns have initiated the integration of smart technologies into the transportation infrastructure at major cities and tourist hubs. The rising environmental concerns have called for a shift in focus from conventional methods to innovative green transport initiatives being formulated by DMOs and destination planners. The use of data analytics and artificial intelligence in transportation has been proven to be a reasonable method for sustainable transportation. This study focuses on assessing the value propositions of smart transportation systems in enriching the tourist experience by providing convenient travel solutions. The chapter focuses on understanding the value proposition of smart transport designs at destinations and the long-term prospects of installing such sustainable infrastructure at major tourist hubs. The study also aims to evaluate the tourist experience in using smart transportation services and the potential benefits and challenges involved in the practical implementation of such systems. 2023, IGI Global. All rights reserved. -
Applications of Machine Learning and Deep Learning Models in Brain Imaging Analysis
Brain imaging is an umbrella term including many non-invasive techniques that objectively monitor brain function. Such monitoring leads to understanding how the brain works by presenting selected stimuli. More importantly, brain function monitoring allows physicians to diagnose and predict brain disorders. In the last decade, several machine learning and deep learning models have been developed by researchers to process and analyse brain imaging data for the diagnosis, detection, and prediction of brain disorders, such as stroke, schizophrenia, autism, psychosis, and Alzheimers. This chapter reviews the various applications and properties of machine learning and deep learning models for brain image analysis. The chapter also highlights the deep learning models that have either understood the test of time or shown the promise to solve challenging problems involving brain imaging data. The review also discusses various open issues yet to have practical solutions or methodologies with the help of machine learning and deep learning. The research covers a wide range of imaging modalities, disorders and models to expose researchers and practitioners in neurological disorders and machine learning and deep learning to each others field, hopefully leading to fruitful collaborations and practical solutions for processing brain images. 2024 selection and editorial matter, Anitha S. Pillai and Bindu Menon; individual chapters, the contributors. -
Genome analysis for precision agriculture using artificial intelligence: a survey
Precision agriculture is a farm management technique which uses the help with the help of information technology to ensure that the crops and soil receive exactly what is required for optimum health and productivity. Genome analysis in plants helps to identify the plant structure and physiological traits. The identification of the right plant genome and the resulting traits help to optimize the cultivation of the plant for better productivity and adaptability. Genome analysis helps the biologist edit the plant genetic makeup structure to make the plant to adapt to the current conditions and thereby reducing the use of fertilizers. For precision agriculture, artificial intelligence techniques help to understand the relationships between plant genome and soil nutrient conditions that help in precision farming effectively reducing the usage of fertilizers by modifying the plants to adapt with the current soil characteristics. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Conversational Agents and Chatbots: Current Trends
Languages facilitate the communication and interaction process among people. Computers learn to communicate with humans intelligently with the help of conversational agents and chatbots based on Natural Language Processing (NLP). Conversational agents and chatbots are gaining popularity in various applications. The development of chatbots or conversational agents is tightly coupled with an organizations customer service requirement. However, the background procedures that power the bots brain are more or less dependent on Artificial Intelligence-based processes. NLP mechanisms powered by various Deep Learning techniques are often used in the training and development of such intelligent agents. These bots inevitably become more competent as they interact with more people. The interactions between a customer and the bot are usually used as data in further training iterations. Chatbots are likely to respond with faster and more precise suggestions leading to solutions for frequently asked questions. Therefore, the current trends indicate the need for a supplementary system rather than substituting human agents existing customer service. The customer experience and intelligence of the chatbots are improved with the help of data analysis and training with the use of Deep Learning techniques. The chapter covers the current trends of conversational agents and chatbots, how the various Artificial Intelligence techniques have transformed the development of multiple architectures of these intelligent systems, and it compares the different state-of-the-art NLP-based chatbot architectures. 2024 selection and editorial matter, Anitha S. Pillai and Roberto Tedesco. -
Workforce Forecasting Using Artificial Intelligence
Workforce forecasting predicts an organizations future demand and supply of the workforce. Each organization has its strategies to manage and track the appropriate workforce. The adequate forecasting technique for the workforce involves data analysis and pattern mining from various data points. Some of the critical attributes considered for the analysis and the forecasting of the workforce requirement include the data such as demographics, economic trends, and labor market conditions; these help in calculating informed predictions about future workforce requirements [1,2]. The primary aim of workforce forecasting is to ensure that an organization has suitable employees with the appropriate skills to meet its business needs by helping organizations make informed decisions about staffing levels, employee training, and other workforce management strategies. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
The role of incentives in fostering green behaviour among the emerging workforce
Green behaviour refers to those behaviours that are pro-environmental in nature. Green behaviour is essential in an organization to ensure that the organization is meeting its organizational sustainability. Employees at all levels must support and participate in green behaviour. One way of encouraging employees to participate in green behaviour is by inducing them through incentives. This chapter dwells on why it is important to introduce green incentives in the workplace to encourage green behaviour. A short survey was conducted among employees in the Indian subcontinent to understand whether incentives can act as a catalyst in motivating green behaviour in the organization. It explores the advantages and disadvantages of bringing green incentives in the organization and the guidelines a company must keep in mind while designing an attractive green incentive system to motivate employees to engage in green behaviour 2024, IGI Global. All rights reserved. -
An Integrated Approach Towards Sustainable Waste Management: Decentralized and Community-Based Practices
Waste management has always been a growing concern, since enormous quantities of waste are generated in vulnerable tourism regions, leading to mounting environmental concerns and hazardous health issues, which are faced by the majority of the local bodies and local communities. Vulnerable destinations are unable to handle such large quantities of solid waste due to financial and institutional debilities. This chapter will present a comprehensive view of solid-waste-management mechanisms, and most importantly, will highlight important issues, like segregation of waste, an integrated approach for the treatment of waste and scientific disposal methods. Critical directions are presented to reiterate the several policies and programmes so as to improve the current scenario, and thereby, support the cities and towns by devising integrated strategies towards community engagement in waste management and the role of regulators in overcoming the challenges of solid-waste management in our country. This chapter is built on a sustainable outlook by providing an integrated framework of decentralized and community-based practices. It will also explore important dimensions of sustainability that will require greater attention towards a preliminary framework of sustainable community-based waste management. 2024 CRC Press. -
Integration of technology initiatives with educational neuroscience and its impact on technology readiness to technology adoption by HSS Teachers, Kerala
The technology-enabled education process remoulded the modern education systems. The facelift of education 4.0 process harmonized the education systems with industrial demands and technology advancements. The education reforms of the State of Kerala with the tools of technology and neuroscience could achieve remarkable milestones in the education sector. This case study analyses the digital initiatives of KITE and its role on providing uninterrupted-effective education during the Covid-19 pandemic in Kerala. This study is affirmed with quantitative study on how these integrated technology initiatives impact on Technology Adoption of the HSS teachers with respect to their Technology Readiness. Responses of 857 teachers from six education districts of Kerala were used for this study. This study is relevant as it could connect the pre-Covid digital initiatives which could successfully empower the teachers to face the Covid-19 pandemic situation without interrupting the education process amidst the Covid-19 restrictions in Kerala. The study identified that the technology learning initiatives with tools of educational neurosciences have partially mediated teachers' Technology Readiness to Technology Adoption. The multiple learning initiatives integrated with the tools of technology and educational neuroscience could fully support the virtual learning throughout the State of Kerala during the Covid-19 pandemic situations. The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved. -
Technology-integrated after-school program: A case study of the DREAMS intervention
In the rapidly evolving context of the 21st century, education is experiencing a profound and transformative shift, driven by the emergence of digital technologies. This innovative paradigm seamlessly weaves technology into the very fabric of learning and administration, not only within the traditional school setting but also extending its transformative reach to affiliated initiatives like after-school programs. The aim of this chapter is to share the valuable insights and firsthand experiences gained from the integration of technology in the DREAMS program in both learning and project management. By sharing these learnings, the chapter aims to share the knowledge and best practices of adopting technology in any program. 2024, IGI Global. All rights reserved. -
New bounds of induced acyclic graphoidal decomposition number of a graph
An induced acyclic graphoidal decomposition (IAGD) of a graph G is a collection ? of nontrivial induced paths in G such that every edge of G lies in exactly one path of ? and no two paths in ? have a common internal vertex. The minimum cardinality of an IAGD of G is called the induced acyclic graphoidal decomposition number denoted by ? ia (G). In this paper we present bounds for ? ia (G) in terms of cut vertices and simplicial vertices of G. Springer Nature Switzerland AG 2019. -
Emerging Novel Functional Materials from Biomass for Environmental Remediation
The Earth faces complex environmental challenges caused by both human activities and natural processes, affecting all life forms and ecosystems. Biomass-derived materials, sourced from renewable resources, serve as effective adsorbents, catalysts, and ion exchangers, providing sustainable solutions to environmental issues like water and air pollution, soil contamination, and waste management. Their significance lies not only in their biodegradability and sustainability but also in standardized testing and scalability considerations. The field of functional materials from biomass has the potential to transform environmental remediation, leading to a cleaner and more sustainable world. Here, we aimed to portrait the key approaches and recent developments in emerging functional materials from biomass tailored for environmental remediation, delving into their fundamental theories and concepts, various applications, and potential to reshape the remediation landscape. It evaluates the sustainability and biodegradability aspects of these materials, addresses challenges, and peers into the dynamic and rapidly evolving future of this field. Collaborative efforts between researchers, industry, and policymakers are pivotal to establishing guidelines and regulations ensuring the safe and responsible use of these materials. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Recent advances in cancer nanotheranostics
The innovative synthetic approaches coupled with bioengineering aptitude created multiple functional materials in the nanoscale dimension aiming for a combination of therapeutic and diagnostic capacities, often referred to as nanotheranostics. The diverse role played by nanomaterials has been broadly examined in biomedicine, especially in the disciplines of imaging and drug delivery. In this view, cancer is an intimidating foe to the entire human species by adopting various survival skills. Conventional therapies remain to be a failure in meeting the anticipations of the entire medical community. Stepping to the emphasis on cancer nanotheranostics, which requires more advancement to amalgamate and fine-tune diagnosis and therapy, has already attracted significant research interest among researchers in chemistry, material science, life science, and clinicians. Monitoring the therapeutic response in a real-time manner with the intelligent fabrication of nanotheranostic agents could strike down the daunting claws of cancer by facilitating personalized treatment approaches. Here, we aimed to portrait the key approaches and recent developments in nanotheranostics with a focus on its clinical impact in oncology. 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Nanomaterials-Based Chemical Sensing
Nanotechnology is an achievement in the modern period because of its adaptable properties as per its size alterations. Nanomaterials with their size ranging from 1 to 100nm hold incredible novel properties and functionalities because of their molecular arrangements in nano-scale. Nanotechnologies add to pretty much every field of science, including material science, materials chemistry, physics, biology, software and computational engineering and so on. Lately, nanotechnology has been applied to different fields with promising outcomes, particularly in the field of detecting and remediation of toxicity levels, imperilling the ecological solidness just as it does to human wellbeing. One of the principal research interests using nanomaterials is detecting poisonous heavy metal ions. Carbon-based nanomaterials, which are remarkable in view of their toxic-free nature, high surface area and biocompatibility, are valuable for ecological treatments. Heavy metal pollution of water resources is a major issue that poses danger to health and wellbeing. Carbon-based nanomaterials have incredible potential for the detection as well as treatment of heavy metals from water sources in light of their large surface area, nano-scale and accessibility towards various functionalities as they are simpler to be chemically altered and hence reused. Apart from the conventional gas sensors based on SnO2, Fe2O3, In2O3 etc., gas sensors based on nanocarbons materials like carbon nanotubes (CNTs), nanosheets of graphene, carbon nano-fibres etc. exhibit high efficacy when it comes to gas-sensing strategy. Likewise, nanocarbon with hybrids of noble metals or semiconducting oxides can lead to a better performance considering gas-sensing applications. Here in this review, we describe the progress of carbon-based nanomaterials in toxicity detection and remediation. In addition to that, recent trends in nanomaterials-based sensing revealed the advancement of gas sensors based on nanocarbons. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Ocr system framework for modi scripts using data augmentation and convolutional neural network
Character recognition is one of the most active research areas in the field of pattern recognition and machine intelligence. It is a technique of recognizing either printed or handwritten text from document images and converting it to a machine-readable form. Even though there is much advancement in the field of character recognition using machine learning techniques, recognition of handwritten MODI script, which is an ancient Indian script, is still in its infancy. It is due to the complex nature of the script that includes similar shapes of character and the absence of demarcation between words. MODI was an official language used to write Marathi. Deep learning-based models are very efficient in character recognition tasks and in this work an ACNN model is proposed using the on-the-fly data augmentation method and convolution neural network. The augmentation of the data will add variability and generalization to the data set. CNN has special convolution and pooling layers which have helped in better feature extraction of the characters. The performance of the proposed method is compared with the most accurate MODI character recognition method reported so far and it is found that the proposed method outperforms the other method. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.