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This study aims to explore the potential of artificial intelligence with AI-powered humanoid Chatbots (AIPHC) as transformative tools to improve customer service quality in the insurance sector. The usability and efficiency of integrating advanced AI chatbots that can replicate human-like interactions in insurance services will be examined by taking into consideration customers’ technological readiness and chatbots’ anthropomorphism.

This empirical study analysed 688 customer responses collected through purposive sampling using structural equation modelling. With the help of SmartPLS 4.0, the study determines whether anthropomorphism, that is AIPHC system-specific and customer personality-specific dimensions, can influence the acceptance of AIPHC in the insurance sector.

The results show that the chatbot’s anthropomorphism positively influenced customers’ optimism and innovativeness but negatively impacted discomfort and security. Further optimism and innovativeness favourably impact AIPHC adoption. Insecurity had a significant negative impact, while discomfort was insignificant for AIPHC adoption.

The study determines how people will react to AI-powered information systems. The results could help us better understand how the technological readiness of customers can be used in emphasizing the significance of system-specific theories like anthropomorphism in sectors like insurance, where customer interactions and delivery of quality services are important.

The results highlight chatbots’ potential to raise the quality of service, simplify processes and enhance customers’ overall experiences in the insurance sector. This study contributes to the continuing discussion on using AI technologies in customer service by considering the interplay between technology readiness and anthropomorphism. It also provides insightful information for insurance professionals and technology developers.

Anthropomorphic humanoid chatbots can increase the availability, affordability and accessibility of essential services. They have the potential to increase users’ competence, autonomy and—possibly counterintuitively social relatedness.

This empirical research explores the link between anthropomorphism and technology readiness to enhance service quality provided by AI powered chatbots in the insurance sector.

This paper aims to present a study to measure “Consumer Preparedness” (CP) towards digital payment frauds by considering factors such as awareness regarding fraud risk (“Awareness”), implementation of measures (“Protection”) and actions to be taken in case of fraud (“Responsiveness”).

This study reviews existing literature to understand various typologies of digital payment fraud. The data of 372 consumers was collected using a structured questionnaire. The data was analyzed using analysis of variance (ANOVA) and Chi-square. CP score was calculated based on awareness score, protection score and responsiveness score.

The study shows that the score for the level of awareness was low, for the level of protection was moderate and for the level of responsiveness was high, leading to an overall moderate level of preparedness. Further, a moderate association was observed between demographic factors and the level of preparedness.

The authors recommend proactive and reactive measures for Central Banks regarding central fraud registry and intelligence exchange, consumer fraud vulnerability assessment model and mandating fraud risk management controls. Further, financial institutions are recommended to permit payment from registered devices only, implement strong customer authentication (including biometric authentication) and conduct periodic awareness sessions.

The existing body of knowledge does not have a model or scoring mechanism to assess the preparedness of consumers to tackle digital payment fraud. The research paper adds a classification of fraud typologies and an exploratory approach to measure the CP score.

This paper has used the well-known machine learning (ML) computational algorithm with Internet of Things (IoT) devices to predict the COVID-19 disease and to analyze the peak rate of the disease in the world. ML is the best tool to analyze and predict the object in reasonable time with great level of accuracy. The Purpose of this paper is to develop a model to predict the coronavirus by considering majorly related symptoms, attributes and also to predict and analyze the peak rate of the disease.

COVID-19 or coronavirus disease threatens the human lives in various ways, which leads to deaths in most of the cases. It affects the respiratory organs slowly and this penetration leads to multiple organ failure, which causes death in some cases having poor immunity system. In recent times, it has drawn the international attention because of the pandemic threat that is harder to control the spreading of infection around the world.

This proposed model is implemented by support vector machine classifier and Bayesian network algorithm, which yields high accuracy. The K-means algorithm has been applied for clustering the data set models. For data collection, IoT devices and related sensors were used in the identified hotspots. The data sets were collected from the selected hotspots, which are placed on the regions selected by the government agencies. The proposed COVID-19 prediction models improve the accuracy of the prediction and peak accuracy ratio. This model is also tested with best, worst and average cases of data set to achieve the better prediction rate.

From that hotspots, the IoT devices were fixed and accessed through wireless sensors (802.11) to transfer the data to the authors’ database, which is dedicated in data collection server. The data set and the proposed model yield good results and perform well with expected accuracy rate in the analysis and monitoring of the recovery rate of COVID-19.

This paper outlines how Design for Lean Six Sigma methods aided a medical device manufacturing company in developing a new strategic space management and approval process for its manufacturing site.

The project demonstrates the application of the Design for Lean Six Sigma and structured Define, Measure, Analyse, Design, and Verify methodology in designing and implementing a process that enables the case study manufacturing site to improve its space utilisation and free up space.

The project was validated in one manufacturing department, and the Design for Lean Six Sigma methodology resulted in creating 15% new space for that area, with opportunities identified to free up 44.7% of the total manufacturing floor space and realise over €2.2 million cost savings as well as start to manufacture new products launched.

The manuscript highlights for the first time how the Design for Lean Six Sigma methodology can be utilised for space utilisation and can be leveraged by other manufacturers. The current study's limitations are that it is a single-site case study application. Future longitudinal case studies on Design for Lean Six Sigma application in more manufacturing space utilisation projects would be useful. This study has implications for identifying best practices for Design for Lean Six Sigma methodology application in the device industry, thus improving the state of the art for introducing new manufacturing lines.

This is the first published work to utilise Design for Lean Six Sigma methodology for space utilisation in a medical device company. This review will provide medical devices and other manufacturing organisations with recommendations on utilising Design for Lean Six Sigma and design for improved space utilisation to reduce costs.

The present study was conducted to find what practices Pune city's management institutes are following for managing their online databases. Essentially, the purpose of the research was to study librarians' awareness about the life cycle of electronic resources and how to manage their life span, so that the data in use do not go obsolete.

This experimentative study was based on a structured questionnaire designed to discover whether librarians of Pune's management institute followed the recommended six-step process in managing the life cycle of institutes' electronic resources as proposed by Jill Emery and Graham Stone in their Techniques in Electronic Resource Management (TERMS).

Our study revealed that the librarians are not following any fixed protocol and are managing electronic resources in an ad hoc manner as per their own discretion. A majority are not even aware of the six steps the TERMS model prescribed for managing electronic databases.

The limitation of the study is to find the current practices of management of electronic resources at management institutes / colleges affiliated to Savitiribai Phule Pune University. Institutes which are not subscribing any database other than databases provided by UGC-INFONET thus got excluded from the study.

A comprehensive literature review revealed that although such studies have been carried out elsewhere in the world, there is nothing specific to the Indian scenario. This study aims to plug that information gap.