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This research is designed to meet two research objectives: firstly, to weigh up the criteria of Internet of Things (IoT) adoption in warehousing startups; secondly, to rank warehousing startups on the basis of benefits they derive from IoT adoption catering to an unorganized sector in the food supply chain.

A blend of analytic hierarchy process (AHP) and complex proportional assessment (COPRAS) methods of multi-criteria decision-making techniques were applied. AHP determined the weights of various criteria using pairwise comparison, and COPRAS technique ranked the 10 warehousing startups on account of performance indicators. The study has been conducted at the warehousing startups of Bangalore, a hub of food warehousing startups.

The critical findings of the study revealed that these food warehouse startups attain improved productivity in terms of enhancing efficiency when implemented with IoT adoption. When evaluated using both AHP and COPRAS techniques, the combined results show WH5 as the best performing and WH10 as the least performing warehouse startups.

Warehouses that are embarking on their business opportunity in food storage can strategize to leverage the benefits of IoT in terms of food safety and security, capacity planning, layout design, space utilization and resilience.

Despite the numerous research works on food supply chain, the research on IoT in warehousing startups is limited. The rankings for the 10 food warehousing startups integrated with IoT using AHP-COPRAS approaches are the novelty of this work.

This study aims to analyze the importance of disruptive technological innovations on qualitative service delivery and their impact on the investment banks’ employee performance.

The cluster sampling method has been used to collect the primary data from the 250 respondents from foreign investment banks. Variables used are employee performance, service delivery, technology, security, operations, strategy and quality through chi-square, linear stepwise multiple regression analysis and correlation.

Storage network, operating cost, client reporting, cloud system and money laundering are the highest and most significant predictors of employee performance. Employee performance multiplies every unit with a strategic solution owing to positive and robust correlation (0.944). Fusion technology-based banks offer quality service to their clients.

A combination of artificial intelligence and blockchain ensures increasing automation to improve efficiency and reduce the operating cost creating a seamless integration in fraud detection, customer support, risk management, security, digitization and automation process, algorithmic trading, wealth management, etc.

The study aim is to evaluate the contribution of Blockchain technology (Cryptobanking) using expected operating model (EOM) to address the pain points in reconciliation at middle and back-office operational levels in assessing the significance of this technology on return on investment.

A structured questionnaire was designed to collect primary data using a stratified sampling method from 120 respondents working in leading Investment banks operating in the geographical locality of urban Bangalore. Demographic variables, accounting variables, data reporting variables, approach variables, variables of EOM were considered to validate the hypothesis with the help of statistical tools, namely ANOVA, and Multiple Stepwise Regression Analysis.

The results obtained confirm that there is significant difference in reconciliation with implementation of an innovative business process. Financial analysis is the highest predictor of ROI when integrated with technology as the adapted Blockchain innovation in reconciliation is the most influencing factor in enhancing, improving ROI playing a pivotal role in the Investment banks.

Blockchain technology (Cryptobanking) facilitates in transforming the reconciliation process of these banks with improved operational efficiency. Blockchain and settlement platforms offer inter-organization solutions facilitating in the reconciliation of various transactions in real-time through a trust-based network in the form of digital settlements with better consortiums.

The current research study aims to examine the application feasibility and impact of artificial intelligence (AI) among higher educational institutions (HEIs) in talent acquisitions (TA).

A systematic sampling method was adopted to collect the responses from the 385 staff working across the various levels of management in HEIs in metropolitan cities in India. JAMOVI & SmartPLS 4 were applied to validate the hypothesis by performing the simple percentage analysis and structural equation modelling. The demographic and construct variables considered were adoption, actual usage, perceived usefulness, perceived ease of use and talent management.

The key indicators of perceived usefulness are productivity, perceived ease of use, adaptability, candidate experience with the adoption of AI, frequency in decision-making in its actual usage and career path of development in the HEIs. These are the most influential items impacting the application of AI in TA.

AI has the potential to revolutionize TA in HEIs in the form of enhanced efficiency, improved candidate experience, more objective hiring decisions, talent analytics and risk automation. However, they facilitate resume screening, candidate sourcing, applicant tracking, interviewing and predictive analytics for attrition.

The study aims to examine the impact of an artificial intelligent service agent (AISA) on customer services to the rural population provided by KAYA, Kotak Life's AI-enabled insurance chatbot avatar that offers quality insurance services.

Multi-stage cluster sampling method was adopted to collect the responses from the 707 customers across the rural population of southern states of India. SPSS V.2 and Smart PLS 4 were used to apply simple percentage analysis, multiple linear regression analysis, and structural equation modeling (SEM) to validate the hypothesis. The dependent variables are economic performance and market performance based on the independent variables: efficiency, security, availability, enjoyment and contact.

The study revealed that efficiency and security are the highest predictors and the most influencing variables in predicting the economic and market performance of the insurance companies in determining the quality of service when rendered through AISA among the customers. Efficiency, security, availability, contact and enjoyment are the critical dimensions of AISA. It has a more significant impact on quality service (claim processing) to the rural population. It improves the economic and market performance among the insurance companies and the rural population.

Customers need convenience when making claims. Even little challenges might lead to stress and unhappiness, depending on the situation. Restrictions on where customers can file claims may not be the most outstanding service insurance firms can offer, given rising travel and commuting costs and widening geographical borders. Customers value proactive communication from service providers about the status of their insurance claims.

The purpose of this study is to explore the financial sources and evaluate the credit facilities available to Tibetan entrepreneurs especially residing in the vicinity of the Karnataka district, India. The most significant problem is that lending rates are extremely high and there is a lack of professional skill to manage their operations. Availability of financial support is still a major barrier for established and potential Tibetan entrepreneurs in the growth of their enterprises.

A sample size of 115 respondents, belonging to the urban and rural districts of Karnataka were interviewed to collect the information as primary data. Correlation analysis, cluster analysis, one-way ANOVA and percent test have been applied for statistical analysis. The interest rate, bank loan, credit, savings, friends and relatives, corporate, retained profits and trade credit are the variables used for the research.

Personal savings, bank credit and bank loans are the most important variables reflecting the credit activities and are clustered having a total of 3.710. Corporate, trade credit and retained profits form minimal sources of credit having a total of 1.194. Hence, there is an important relationship between the variables and the credit facilities availed by the entrepreneurs.

The research emphasis on their credit facility, financial growth, availability of capital are some of the challenges encountered by the entrepreneurs hindering the growth of the new business. Hence the researcher has focused on understanding and exploring the various challenges faced by these entrepreneurs.

Digital transformation has made enormous changes in the banking domain, where FinTech is the salient driving force inside it. Especially in the banking industry, Artificial Intelligence (AI) and Blockchain technology act as the most innovative technologies. Similarly, Chatbots in commercial banking and Robo-advisors in investment banking has shifted banks into the entire virtual environment. In this context, the main objectives of this chapter are: to determine the current application of AI and blockchain in the banking industry, to identify the challenges faced by banks in adapting AI and blockchain technology, and to provide new insights on future banking in the industry 5.0 in this digital era. This chapter discusses the application of two robotic platforms widely used in banking, chatbots, and Robo-advisors. Chatbots are more like frontline employees of banks who are commonly engaged in customer relationship management, sales, and marketing. In contrast, Robo-advisors are a relatively advanced AI tool involved in investment and portfolio management. Blockchain will accelerate digital transformation where a decentralized digital ledger system is used for banking transactions. However, this is entirely the opposite of conventional centralized digital banking, and the adoption of these technologies is still in its infancy. Employment, performance, security, privacy and trust, cost, ethical and regulatory challenges are the most common challenges. To avoid the challenges, banks should concern with strategies like collaboration with robots, increasing the platforms’ performance, etc. Finally, the chapter provides insights on banking at industry 5.0. In the future, banking customers can experience completely virtual and customer-oriented banking services. In this regard, the fusion of all technologies and collaborative human effort is essential.

Purpose: This chapter aims to unveil the challenges of adopting and using banking chatbots in India and identify the challenges of Chat Generative Pre-trained Transformer (ChatGPT) for future banking.

Need for the study: Unveiling the challenges of chatbots and ChatGPT in the banking industry in India is crucial to understand the limitations and areas of improvement to enhance customer experience, ensure data security, and maintain regulatory compliance.

Methodology: The researchers conducted a narrative review systematically summarising and analysing existing literature on chatbots and ChatGPT, providing a comprehensive overview of the challenges faced in the industry.

Findings: The authors identify perceived risk, platform quality, connectivity and infrastructure, data privacy and security, user education and acceptance, existing legacy systems, and regulatory guidelines as the challenges of adopting chatbots. Additionally, the findings reveal that the challenges posed by ChatGPT in future banking include the potential reduction of traditional banking jobs, linguistic diversity, data privacy and security, ethical considerations and bias mitigation, explainability and accountability, integration with existing banking systems, and user trust and acceptance. However, implementing these new technologies also presents opportunities for individuals with unique human skills, such as critical thinking, empathy, and creativity, which are difficult to replace with technology.

Practical implications: By minimising the challenges of ChatGPT and chatbots, the banking industry could achieve improved customer service, cost efficiency, automation of routine tasks, and 24/7 availability, leading to enhanced customer satisfaction and operational efficiency in the banking industry. Additionally, these artificial intelligence (AI) tools enable data-driven insights, personalised experiences, scalability, and efficient handling of large customer volumes, contributing to better decision-making and enhanced customer engagement.

This paper expects to analyze the connection between occupational stigma and job meaningfulness among Chinese takeaway riders, the mediating role of occupational identity and relative deprivation, and the moderating effect of job stress based on resource conservation theory.

The sample was derived from 371 takeaway riders across China. PLS-SEM was mainly utilized for the data analysis.

The findings of the study indicated a significant negative correlation between occupational stigma and job meaningfulness. Furthermore, it is worth noting that relative deprivation and occupational identity served as mediators and masks, respectively, in the relationship between occupational stigma and job meaningfulness. Furthermore, job stress amplifies the association between occupational stigma and occupational identity. Additionally, job stress diminishes the connection between occupational stigma and relative deprivation.

This study proposes a positive correlation between occupational stigma and occupational identity in the Chinese context. It also enriches the empirical research based on resource conservation theory. Furthermore, it holds practical implications for takeaway riders in China, offering insights to bolster their job meaningfulness.