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The purpose of this paper is to analyse the relationship between foreign direct investment (FDI) inflows and economic growth with a special focus on the institutional environment at the state level. FDI-led economic growth and economic growth-led FDI have two dominant theoretical foundations, but empirical research supports contradictory findings. These perspectives largely ignore the institutional environment, assuming institutions to be background information.

To examine the causal relationship between FDI, the Granger causality method has been used. The impact of FDI inflows and other institutional factors on economic growth has been examined using the panel data regression method. The principal component analysis (PCA) method has also been used to develop indexes for some variables.

Results indicate a two-way Granger causality between FDI inflows and economic growth at the state level. Infrastructures, education expenses, labour availability and gross fixed-capital formation (GFCF) are positive and significant determinants, whilst corruption and FDI inflows are leaving negative impact on state-wise economic growth.

This study contributes to the body of the literature in four different ways: first, it empirically examines the trends and patterns of subnational FDI inflow and economic growth disparity in India; second, it examines the causality between FDI and economic growth. Third, with the institution-based paradigm in international business, it investigates how institutional variables affect the expansion of the economy. Fourth, it extends prior research by examining the link at the state level using a large panel data set made up of 29 states and 7 union territories (UTs) over the years 2000–2019.

The present paper makes an attempt to investigate the determinants that affect FDI inflows distribution among Indian states. Together with traditional determinants, the impact of institutional determinants on state-level FDI inflows distribution in India has been analysed.

The study uses panel data for a period of 20 years (2000–2019) for 17 groups of Indian states (29 states and 7 UTs). The empirical evidence is based on the panel data method and the findings support Dunning's OLI theory. As the data for some indicators for the institutional environment is not available at the state level, hence we used component analysis to arrive at the single component for the institutional factor. The study takes into account corruption, legal system, industrial disputes, man-days lost, labour availability, political risk, protection of IPR and agglomeration as potential macroeconomic and institutional determinants.

Results show that FDI inflows into Indian states is driven mainly by institutional environment. From our analysis, the author infers that the institutional variables such as legal system, IPR, corruption, political instability play an important role in determining the distribution of FDI inflows at the state level in India. Together with that GFCF and agglomeration are also important determinants of state-wise FDI inflows.

The major limitation of the study is that it doesn't include moderated impact of economic and institutional determinants of FDI inflows in Indian states, which can be an avenue for future research. Future research can also carried out taking district-level data to further examine the determinants at district level in India.

The contribution of the present paper is three-fold, first, the author constructs a measure of different institutional variables, after normalization of data for the period 2000–2019, and the author choose the highest explaining factor with the highest variance explained then we constructed the indices for select variable, which further has been used in the panel data analysis technique. The author has found that macroeconomic variables, as well as institutional variables, are significant to attract FDI at the state level in India. The paper shows that corruption, political risk, IPR and legal system are the major institutional determinants of FDI inflows in India at the state level. States with higher domestic investment attract more FDI inflows, moreover, agglomeration is a very important determinant as the investors are more confident in investing at the same location, the reason behind this may be that the investors want to avoid the registration procedure for new land, administrative formalities or they feel more secure at the same place and keen to invest at the same place again.

This chapter is focused on India's destination marketing strategies that promote religious tourism. It sheds light on the Government of India's initiatives to attract faithful tourists to sacred locations including holy temples and places of interest for spiritual pilgrims. The tourism business in India has gone through numerous phases of growth. This research reveals how the state government and central governments have stepped up their commitment to develop tourism, including religious tourism, on multiple fronts. It confirms that India can be rightly considered as the land of faith, as spirituality and religion are very prominent, as evidenced by its holy temples and landmarks, located in different regions of the subcontinent. In conclusion, it discusses about the challenges for the future, and elaborates on the opportunities related to promoting religious tourism to target faithful pilgrims and other visitors to “Incredible India.”

Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of agricultural products. Pesticides can be used to improve agricultural land products. This study aims to make the spraying of cherry trees more effective and efficient with the designed artificial intelligence (AI)-based agricultural unmanned aerial vehicle (UAV).

Two approaches have been adopted for the AI-based detection of cherry trees: In approach 1, YOLOv5, YOLOv7 and YOLOv8 models are trained with 70, 100 and 150 epochs. In Approach 2, a new method is proposed to improve the performance metrics obtained in Approach 1. Gaussian, wavelet transform (WT) and Histogram Equalization (HE) preprocessing techniques were applied to the generated data set in Approach 2. The best-performing models in Approach 1 and Approach 2 were used in the real-time test application with the developed agricultural UAV.

In Approach 1, the best F1 score was 98% in 100 epochs with the YOLOv5s model. In Approach 2, the best F1 score and mAP values were obtained as 98.6% and 98.9% in 150 epochs, with the YOLOv5m model with an improvement of 0.6% in the F1 score. In real-time tests, the AI-based spraying drone system detected and sprayed cherry trees with an accuracy of 66% in Approach 1 and 77% in Approach 2. It was revealed that the use of pesticides could be reduced by 53% and the energy consumption of the spraying system by 47%.

An original data set was created by designing an agricultural drone to detect and spray cherry trees using AI. YOLOv5, YOLOv7 and YOLOv8 models were used to detect and classify cherry trees. The results of the performance metrics of the models are compared. In Approach 2, a method including HE, Gaussian and WT is proposed, and the performance metrics are improved. The effect of the proposed method in a real-time experimental application is thoroughly analyzed.

The pandemic crisis has resulted in global chaos that had caused massive disruption to the supply chain. The pharmaceutical industry, in particular, has been working tirelessly to ensure that they can cater to the people who need them. With restrictions being imposed to prevent the spread of the COVID-19 virus, the movement of raw materials required has been affected, thus creating the need for the procurement function to be innovative. This study proposes the application of Industry 4.0 concepts into the procurement activities of an organization to make it more resilient and efficient.

To study the intensity of the challenges, Total Interpretive Structural Modelling is used alongside the “Matrice des Impacts Croises Multiplication Appliquee a un Classement” (MICMAC) technique.

Resilience can be achieved through the collaboration between the organization and its network of suppliers. This is however easier said than done. High and unclear investments have been identified as the challenge that is taking a toll on all technological investments in the pandemic era. The study also shows that organizational inertia which is present in established and structured firms are a deterrent as well.

This study is based on the application of procurement 4.0 to ensure that pharmaceutical supply chains stay least affected since they are essentials. This study using a multi-criteria decision-making approach to prioritize the challenges. This will help practitioners make decisions faster.