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Over the last decade, customer experience management has gradually emerged as the most important activity for organisations. Organisations have turned towards leveraging the ubiquitous and easy-to-use technology in enhancing and enabling experience for the time-crunched customers of today who are looking for greater convenience and choices. It is therefore not surprising that disruptive technologies such as smartphones, virtual and augmented reality, cloud computing, big data analytics, Internet of things, artificial intelligence and robotics have also found their way into the design of customer experience. This chapter aims to present an overview of the technologies that have transformed the customer experience landscape. This chapter contributes by showcasing two illustrative cases from very diverse domains, a private sector bank and a public sector transportation organisation, to elucidate how India, a rapidly developing economy, is embracing technology to enhance the customer experience.

Purpose: The purpose of the study is to determine the significant difference between the performance of the Indian banks in pre coronavirus disease (COVID 19) and post COVID 19 periods. Further, it explores the impact of COVID 19 on the profitability of the Indian banks by investigating variation between the non-performing assets (NPAs) and the net profit of the banks during pre and post COVID 19 periods.

Need of the study: The COVID 19 outbreak has affected various industries including Indian banks which reported an increase in NPAs, and demand for credit which in turn impacted profitability. This study was carried out to examine the impact of COVID 19 outbreak on Indian banking sector.

Methodology: This study uses different banks’ NPA and net profits performance to examine the effect of COVID 19 on banks’ overall performance. The data have been collected from secondary sources, commercial websites, and websites of Indian banks (private and public sectors). t-Test was used to analyse the data.

Findings: Among public sector banks, Canara Bank was found to have a significant difference in net profit in the pre and post COVID 19 periods. In private sector banks, HDFC Bank showed a significant difference in the net profit in pre and post COVID 19 periods. For NPAs, all private banks showed no significant difference in pre and post COVID 19 period results.

Implications: The study revealed that both private and public sector banks in India were mildly affected by pandemic and most of them are significantly reporting no difference in net profit and NPAs during pre and post COVID 19 periods.

The purpose of this paper is to critically examine sustainability reporting (SR) practices of top 10 Indian banks, on parameters derived from a Global Reporting Initiative (GRI)-G4-based persuasive communication framework.

SR metrics from GRI-G4 guidelines were mapped to persuasive communication parameters to develop a blended analytical framework. Content analysis (CA) technique was used to assess SR of top 10 banks on this framework.

The study has three key findings. First, most of the top 10 Indian banks are yet to adopt adequate disclosure and transparency practices in SR. Second, even though environmental and social goals are broadly reported, there are glaring omissions on metrics like “equal remuneration,” “occupational health and safety” and “customer privacy.” Third, stakeholder engagement focus is weak as reflected in low persuasive appeal of SR content of most banks.

The blended framework provides a theoretical and analytical pathway for operationalizing the sustainability context principle, which has been inadequately addressed even within the GRI framework implementation.

The paper provides a “health check” and identifies “red flags” in SR of top 10 Indian banks, enabling them to undertake a critical review of their sustainability metrics and reporting practices.

The paper establishes the significance of evaluating non-financial reporting practices addressing broader sustainability metrics in the banking sector, in an emerging economy context.

This paper develops a GRI-G4-based persuasive communication framework for SR assessment, and conducts an evaluation of top 10 Indian banks using CA technique.

Purpose: Worldwide economies have been shattered by the alarming increase in Non-Performing Assets (NPAs) in Banking Sector. In India, the rise in NPA levels gives a clear insight into the health of industry and state. This study aims to determine how NPAs in India impact the profitability of eight banks chosen from the public and private sectors; specifically: Punjab National Bank (PNB), Bank of India (BOI), UCO Bank, Punjab and Sind Bank (PSB), HDFC Bank, Axis Bank, ICICI Bank, and Yes Bank; during the period 2009/2010 to 2017/2018.

Design/methodology/approach: The study utilised IBM SPSS version 20 application to carry out our statistical analysis of measures of central location (mean and median), measures of dispersion (standard deviation), to carry out the Kolmogorov–Smirnov test to check the normality of data, the Mann–Whitney U test (for two groups) for median comparison between private and public sector banks and the Kruskal–Wallis test (for more than two groups) for median comparison for more than two banks. p ≤0.01 and p ≤0.05 were the two-tailed significance level used for determining the significance of all statistical tests.

Findings: Trend analysis and statistical tests show that the trend in public sector banks to have NPAs is higher compared to private sector banks, and losses arising from NPA impact the banks’ profitability.

Practical implications: It is apparent that NPAs are a large threat to banks in India as it reflects the state of the Indian economy. The growth of the economic cycle is predominantly dependent on the smooth and profitable functioning of private and public sector banks. This current study focusses on and compares the impact of NPAs on the profitability of public and private sector banks. NPAs have grown exponentially more in the case of public sector banks than private sector banks, which has affected the former banks’ financial health and performance. Increases in the level of NPAs adversely affect the working style and long-term stability of public and private sector banks in the economy.

Social Implications: NPAs have a negative influence on the profitability of the banks as well as on the economic growth of the country too. However, it is recommended that management in the banking sector, particularly the public banks, should use various preventive and recovery strategies to reduce the risk of failure and to keep track of NPAs to stay safe.

Originality/value: This study aims to determine how NPAs in India impact the profitability of eight banks chosen from the public and private sectors; specifically: PNB, BOI, UCO Bank, PSB, HDFC Bank, Axis Bank, ICICI Bank, and Yes Bank; during the period 2009/2010 to 2017/2018.

Purpose: This chapter discusses the role and use of chatbots adopted by the different categories of banks (private and public sector banks) in India. The chapter presents brief essential services offered by Indian chatbots regarding accuracy, technology providers and virtual assistance, ways to connect, etc. This chapter concluded that most of the questions answered by the Indian chatbots are already available on the banks’ websites, and there is a need for enhancement in the capabilities of Indian chatbots.

Need for the Study: The need for the study is based on the working of banking chatbots, customer query handling, and the efficiency of the chatbots in India. The chapter helps to analyze the services offered by various banks.

Methodology: This chapter is based on secondary data collected from banks’ websites and articles from various journals. The study is based on nine banks (both private and public sectors) those are having working chatbots (SBI, HDFC Bank, ICICI Bank, Yes Bank, IndusInd Bank, Kotak Mahindra Bank, Axis Bank, Andhra Bank, Bank of Baroda). The present study is focused on chatbots, their services, and software applications for various customer-handling capacities.

Findings: The research concluded that Indian banks are investing a small amount in using chatbots, yet Indian chatbots are deficient regarding far too provincial administrations as they are adequate just for standard and basic inquiries. Also, Indian customers are not properly aware of chatbots and virtual assistance.

Practical Implications: This study provides an overview of the working chatbots in India (for both public and private sector banks) and their functions, as well as the capacities of these chatbots. The previous conducted studies are based on the uses, importance, and working of chatbots/artificial intelligence (AI) in banking. In this study, after discussing the different services, it is found that Indian banks need to update their AI/Virtual assistance with more features.

The global demand for food is expected to increase by 60 percent by 2050 when the world’s population reaches 9.1 billion. To meet this challenge significant investment in the agricultural sector is required to embrace innovative financing mechanisms that can benefit sustainable agricultural development, food security and nutrition. The purpose of this paper is to analyze the agricultural value chain (AVC) financing approaches and tools in India. It presents a proper understanding of the different case studies of Indian AVC financing models and related instruments. It also offers some useful recommendations to improve their efficiency.

The authors employ the multiple case studies approach to research which allows for a purposive sample and the potential for generalizability of findings. This provides a more rigorous and inclusive approach than a single case study research due to the triangulation of evidence. Subsequently, the authors offer an explicit description of AVC financing models. In the next phase, a thorough assessment of these models is made. Finally, the authors formulate some useful policy recommendations based on the findings of the analysis.

There is a need to review the value chain models that exist in the context of – lead actors, business model and sustainability strategy. Determining actual and critical points of finance such as the current flows of funds and their sources of financing, what is needed and in what point in time is significant to enhance the effectiveness of the models. Further, there is a need to analyze and compare financing options such as their relative strengths, risks and costs of financing for each level of participant in the chain. The authors observe that rather than investing in one component of the chain, the financial institution can grow expertise in the chain, share this knowledge and provide financing to support services. This not only benefits clients, but also expands lending opportunities while lowering the risks.

The study primarily focusses on AVC financing approaches and tools in India and attempts to analyze the inadequacies in the value chain models. The case study approach is adopted as the accurate data on value chain financing are not available for the analysis.

The study has come out with the following policy recommendations: the governments (union government as well as state governments) – in partnership with the private sector need to spearhead and develop measures aimed at making the operation of the value chain efficient, fair, profitable and sustainable; governments have to focus on creating an enabling policy and regulatory environment and, providing the necessary support services in order to attract more investments. These will lower the transaction costs, facilitate the smooth flow of finance along the chain and ultimately increase value-added; financing for processing and marketing is particularly crucial for growth and expansion of the chain; bank finance should not be limited to short-term production loans, but also include big-ticket loans with longer maturities to finance investments in farming equipment and machinery, transportation, storage, mills and other processing/post-harvest facilities.

This study is the first of its kind as it is based on a multiple case studies approach in understanding and analyzing the efficiency and effectiveness of AVC financing models in India by evaluating eight of such models. Besides, it offers quite useful policy recommendations to improve their efficiency.

This study gauges the profitability and performance of Indian commercial banks under the technology advancements. In this study, the authors identified three domains that give advantage to banks due to technology incorporation, that is, increased sales revenue, reduced operating expenses, and increased employee productivity. The authors assess the effect of these domains on banks’ profitability and performance. This study is conducted for the period between the years 2003 and 2018 across 34 public and private banks for empirical analysis. The authors examined the impact of investment in technology on the profitability using panel data analysis and evaluated the long-term effect of technology investment using the vector error correction model. This study found that there is a mixed effect of technology spend on the profitability and performance of Indian banks, where private sector banks are more aggressive in technology investment as compared to the public sector banks. This study recommends an optimal technology-related strategy to gain improved productivity for the banking business, that is, planned technology reserves, customer awareness campaigns about technology-enabled products, and robust employee–customer motivation policy.

The purpose of this paper is to determine the factors that have an impact on customer satisfaction as regards the working of select Indian universal banks.

The study was conducted using the survey method. Data were collected through a well‐structured questionnaire from a sample of 456 respondents.

The major findings of the study show that customer satisfaction is influenced by seven factors: employee responsiveness, appearance of tangibles, social responsibility, services innovation, positive word‐of‐mouth, competence, and reliability. The results of multiple regression showed that three variables: social responsibility, positive word‐of‐mouth, and reliability, are statistically significant in the model at 5 percent significance level that have an impact on the overall satisfaction of the customer.

The study suffers from a regional bias since it covers only Punjab and Chandigarh. The results do not have general applications to different banks in the same sector and the same banks in different cities of India. Increased sample size and multi‐city sampling can be considered for future research for better generalization of the findings. Results would be more appropriate if the sample size could be large. These limitations offer an opportunity for further research.

The study is quite useful for understanding and comprehending the changes in customer banking behaviour, so to enable the policy makers to develop appropriate and adaptive strategies. The paper provides determinants of customer satisfaction. With an effective customer satisfaction research program, banks will be able to encash business, i.e. gain opportunities by putting themselves at a competitive advantage.

The study aims to evaluate the financial distress position of selected sample banks in India. The top 10 banks with the highest levels of gross non-performing assets (NPA) under both public and private sector ownerships have been chosen for the study. Application of the Altman Z-score model has been used to compare both ownership banks’ financial distress for five years from 2017 to 2021. Based on the study’s findings, it was found that private sector banks demonstrated better financial stability than their public sector counterparts. Specifically, the average Z-score of the selected sample banks was higher than the safe zone threshold of 2.9 during the study period.

This study develops a multidimensional framework using data envelopment analysis (DEA) as a benchmarking tool to assess the performance of the commercial banks in India. Using the DEA approach, this study compares the relative performance of 35 banks against one another with 8 variables as the benchmark parameters. This study finds that most of the banks are consistently performing well over a period from 2005 to 2009. The study also shows the areas in which inefficient banks are lagging behind and how they can improve their performance to bring them at par with the efficient commercial banks.