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Introduction: The Indian insurance sector has a large number of insurance companies. More than 20 companies are in the life insurance business, and nearly 35 are non-life insurers – only one public sector company among the life insurers (Life Insurance Corporation (LIC)). However, there are six public sector insurers in the property insurance division. The government policies have recently increased the foreign direct investment (FDI) share from 49% to 74%.

Purpose: The purpose of the study is based on the latest decision by the Government of India (GOI) to increase the FDI in the insurance sector, which was earlier 49% and now increased to 74%. The study will have objectives that impact change in FDI and its effect on customers’ decisions.

Methodology: This chapter is based on secondary information collected from the Insurance Regulatory and Development Authority and Articles from various journals for objectives 1 and 2. Qualitative analysis is done with the use of NVIVO software. There are primary two objectives taken under consideration in this chapter: objective 1: regulatory framework of insurance sector post-FDI change in limits by GOI and objective 2: customer awareness regarding changed limits of FDI in the insurance sector and its various factors. Fifty-four interviews were conducted, out of which a total of 40 responses have been considered for final analysis. An incomplete and unclear answer has been excluded from the study.

Findings: In the study’s findings, it was found that in accordance with the first objective, GOI changes policies according to time to time. Foreign Direct Investment (FDI) in the insurance sector recently increased by GOI Earlier, it increased in the year 2015 and recently this year, it increased by 49% again to 74%. In the second objective findings, the awareness about changes in FDI in the insurance sector respondent’s sentiments is positive and constructive. A maximum of respondents has said that they are aware of the insurance sector and the participation of various foreign international players in the insurance industry.

This chapter measures total factor productivity growth (TFPG) using Malmquist productivity index (MPI) and the growth of MPI of Indian Textile Industry employing nonparametric data envelopment analysis (DEA), during 1995–2016, exploring company (firm) level Center for Monitoring of Indian Economy (CMIE) Prowess data; examines whether TFPG has improved after the withdrawal of multifiber trade agreement (MFA) since 2005; decomposes TFPG into technical change (TC), technical efficiency change (TEC), and scale efficiency change (SEC); and explains the factors behind the movement of TFPG, considering the effect of R&D (RD), exports (EX), marketing expenditures (MKTs) advertisement expenditures (ADVs), imports (IMP), using second-stage panel regression. Empirical evidence supports fluctuating pattern of TFPG during 1995–2016, with a marginal declining tendency. TFPG has increased in 1999–2000, 2000–01, 2009–10, and 2012–13. After dismantling MFA, MPI level has significantly declined, with an increase in its growth rate, but the increase is not statistically significant. The effect of EX, RD, ADV are nonlinear, U-shaped, and IMP and MKT are inverted U-shaped, implying that the sign effect of any variable depends on its size. There are joint interaction effects of (a) RD and EX; RD and MKT which are positive, (b) ADV and MKT as represented by the ratio (ADV/MKT), having nonlinear inverted U-shaped relation. The joint interaction effect supports that the impact of one variable depends on the magnitude of other. The marginal effect of EX, IMP, and ADV are positive; increase in these variables promotes TFPG. The greater role of ADV over MKT is evident. The marginal effect of RD is negative; the average level of RD is too low to generate positive effects, and, thus, there is an urgency of increasing RD. The promising part of the decomposition analysis is that highest contribution to growth rate of TFPG is the growth rate of TEC followed by growth rate of TC, and thus by increasing TEC and TC, higher growth rate of TFPG is achieved and may be beneficial in the long run and may lead to absorption of economic shocks for an economy facing recession in its output growth. Some policy suggestions are made for boosting up TFPG.

This study is a response to the paucity of research into early internationalising firms based in India. We seek to explore the internationalisation of small and new Indian firms and the decision-making process of their entrepreneurs/managers.

The study uses original, primary data gathered from in-depth, semi-structured interviews conducted with the managers of six such firms to explore the factors that might facilitate, motivate, or impede the efforts undertaken by young Indian firms to embark upon a process of early internationalisation.

Our findings suggest that, in line with their counterparts from other countries, the early internationalisation of small firms from India is driven primarily by the search for more favourable demand conditions overseas and is facilitated by new technologies. However, we find no evidence suggesting that the emergence of early internationalising firms from India is driven by the search for more favourable production conditions or by the direct international experience and exposure of their founders. In line with prior scholarly work, our research suggests that government support is an important facilitator of early internationalisation of small firms.

The study provides insights into the internationalisation process of INVs from India and contributes to broadening our understanding of the behaviour of firms under a set of specific institutional conditions. Based on our findings, we develop a conceptual framework which can be useful for further empirical testing. Our study is also one of the few to be conducted on a sample of INVs from India.

One of the most hyped concepts in the manufacturing industry is ‘Industry 4.0’. The ‘Industry 4.0’ concept is grabbing the attention of every manufacturing industry across the globe because of its immense applications. This phenomenon is an advanced version of Industry 3.0, combining manufacturing processes and the latest Internet of Things (IoT) technologies. The main advantage of this paradigm shift is efficiency and efficacy in the manufacturing process with the help of advanced automated technologies. The concept of ‘Industry 4.0’ is contemporary, so it falls under exploratory study. Therefore, the research methodology is thematic narration grounded on secondary data (online) analysis. In this light, this chapter aims to explain ‘Industry 4.0’ in terms of concepts, theories and models based on the Web of Science (WoS) database. The data include research manuscripts, book chapters, blogs, white papers, news items and proceedings. The study details the latest technologies behind the ‘Industry 4.0’ phenomenon, different business intelligence technologies and their practical implications in some manufacturing industries. This chapter mainly elaborates on Industry 4.0 frameworks designed by (1) PwC (2) IBM (3) Frost & Sullivan.

Introduction: The insurance sector is playing a crucial role in the sustainable growth of the Indian economy. But in India, this sector loses crores of rupees every year due to the increasing fraud cases. With the increase in insurance customers, insurance companies need to efficiently equip themselves with a robust system to handle claims fraud. Detection of insurance fraud is a pretty challenging problem. Nowadays, machine learning (ML) and artificial intelligence (AI) are the strategic choices of many leading organisations that want to proceed in a new digital arena.

Purpose: This chapter’s main objective is to highlight the fundamental market forces driving the adoption of AI and ML and showcase the traditional and modern methods to predict insurance claims fraud intelligently.

Methodology: Various research papers have been reviewed, and ML methods have been discussed, which are all being used to predict insurance fraud claims. This chapter also highlights various driving forces influencing the adoption of ML.

Findings: This study highlights the introduction of blockchain technology in fraud detection and in combatting insurance fraud. Literature indicates that the quantity and quality of data significantly impact predictive accuracy. ML models are beneficial to identify the majority of fraudulent cases with reasonable precision. Insurance companies should explore the benefits of experienced resource persons from the same domain and develop unique business ideas/rules.