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This research attempted to establish the underlying dimensions of supply chain management practices, blockchain technology and supply chain performance in the Indian dairy industry. Additionally, the study proposes a conceptual model that shows the mediating effects of blockchain technology in the relationship between supply chain management practices and supply chain performance.

Structural equation modelling (SEM) is incorporated to examine the proposed model using SPSS and AMOS version 24. The study population includes 119 registered Indian dairy processing units operating in Uttar Pradesh and New Delhi (source: Dairy – India). Individual registered dairy processing unit's top four executives, that is Head of the Dairy Processing Plant, Supply Chain head and Marketing Head, and IT head are chosen as the respondents of the study, which renders the sample size of 476. Judgmental sampling based on the organisation's market position and plant production capacity (i.e. one lakh litre per day) has been set as the benchmark for selecting the dairy processing units. The executives are selected as respondents as they are well-versed in the phenomenon of supply chain management practices, blockchain technology and supply chain performance compared to other staff working in the dairy industry. The data was collected from December 2021 to March 2022 through judgmental sampling. The target sample size was 476, but only 286 questionnaires were received in a completed state and were further used for analysis.

Manufacturing practices, information sharing, distribution management, inventory management and blockchain technology have a significant and positive impact on supply chain performance in the Indian dairy industry. Furthermore, the research demonstrates that blockchain technology partially mediates the relationship between supply chain management practices and supply chain performance in the context of the Indian dairy industry.

This research is focused on the Indian dairy industry operating in only two states, namely New Delhi and Uttar Pradesh. More research is needed to determine whether SCM practices and the prospects for blockchain technology among channel members are universally applicable to merchants in non-dairy products. Similar investigations should be carried out on dairy industry operating in various formats and in numerous geographic locations. Further, case studies can be conducted by future researchers to learn how supply chain management methods are deployed, what precisely these practices entail and what costs and time demands are required by these practices in context of small independent retailers across different germane expanse.

While the available literature on the research area is spread out, the influence of blockchain technology in the Indian dairy industry has not yet been sufficiently analysed. Therefore, the research article focused on exploring underlying dimensions of the constructs of supply chain management practices, blockchain technology adoption and supply chain performance in the context of the Indian dairy industry.

Achieving sustainable development goals (SDGs) demands mobilising finance and aligning it with elements of sustainability. This study, thus, aims to investigate the impact of financial development of an economy on the achievement of SDGs.

The authors analyse a sample of 35 Asian countries based on their SDG trends and representative SDG indicators. An ordered probit model is employed for analysing the impact of financial development on the SDG trend. Subsequently, pairwise Granger causality test is employed for investigating the causality between the SDG and the financial development.

The findings indicate that financial development positively impacts the progress towards SDG achievement in the areas: (1) gender equality, (2) economic growth, (3) industry, innovation and infrastructure and (4) sustainable cities and communities; and adversely impacts the climate action. The causality test indicates a bidirectional causality for financial development and industry, infrastructure and innovation, financial development and sustainable cities and communities and financial development and climate action, and unidirectional causality from gender equality to financial development.

The findings have implications for the government of a nation as well as the private businesses. The goals allow businesses to implement well-articulated strategies which pay attention to the SDGs.

The novelty of the paper is that the authors provide evidence supporting the view that focusing on building a resilient and robust financial system is of importance for the achievement of SDGs.

This study aims to examine the value relevance of earnings in terms of predicting the value variables such as cash flow, capital investment (CI), dividend and stock return under the Indian institutional settings.

The study used panel Granger causality tests to examine causality relationships among variables and panel data regression models to check the statistical associations between earnings and value variables.

Based on a data set of 7,280 Bombay Stock Exchange-listed firm-years spanning over ten years from March 2009 to March 2018, the results show higher sensitivity of earnings toward cash flows, CI, divided and stock return and vice-versa. Further, the findings deduced from the empirical results demonstrate that earnings are positively related to value variables. Overall, the results established that earnings are value-relevant and have predictive ability to forecast the value variables that facilitate investors in portfolio valuation. The results are consistent with the predictive view of the value relevance of earnings. Several robustness checks confirm these results.

This study brings new empirical evidence from a distinct capital market, India, and provides a new facet to the value relevance debate in terms of its prediction view. The study is among earlier attempts that jointly measure the ability of earnings in forecasting different value variables by taking a uniform sample of firms at the same period. Hence, the study provides a comprehensive view of the predictive ability of reported earnings.

The purpose of this research is to conceptualize, define and measure resource orchestration capabilities of R&D teams pursuing advanced scientific research and technological innovation at public-funded R&D organizations in India.

A series of five mutually exclusive studies were designed over two years to develop and validate the ROCI scale within public research and development (R&D) organizations pursuing advanced scientific research and technological development in India. The first three studies address the refinement, reduction and rationalization of items for measuring the ROCI construct. The next study explores the factor structure underlying the ROCI construct whereas the subsequent one confirms the three-factor structure within empirical settings.

The resource orchestration capability towards innovation (ROCI) construct reflected through three sub-dimensions namely – adaptive structuring capability (ASC), synergistic leveraging capability (SLC) and decentralized decision-making capability (DDC), each loaded with their respective items can be used for capability measurement in public-funded R&D organizations.

R&D managers can use this ROCI scale to measure, monitor and improve the innovation-oriented resource orchestration capabilities of their R&D teams and help them improve their innovation performance.

This research contributes to the extant literature on resource orchestration for innovation management in three unique and original ways – theoretically-grounded conceptualization, empirical measurement and rigorous validation through multiple studies conducted in public-funded R&D organizations in India.

Purpose: With this study, the authors aim to highlight the application of machine learning in smart appliances used in our day-to-day activities. This chapter focuses on analysing intelligent devices used in our daily lives to examine various machine learning models that can be applied to make an appliance ‘intelligent’ and discuss the different pros and cons of the implementation.

Methodology: Most smart appliances need machine learning models to decrypt the meaning and functioning behind the sensor’s data to execute accurate predictions and come to appropriate conclusions.

Findings: The future holds endless possibilities for devices to be connected in different ways, and these devices will be in our homes, offices, industries and even vehicles that can connect each other. The massive number of connected devices could congest the network; hence there is necessary to incorporate intelligence on end devices using machine learning algorithms. The connected devices that allow automatic control appliance driven by the user’s preference would avail itself to use the Network to communicate with devices close to its proximity or use other channels to liaise with external utility systems. Data processing is facilitated through edge devices, and machine learning algorithms can be applied.

Significance: This chapter overviews smart appliances that use machine learning at the edge. It highlights the effects of using these appliances and how they raise the overall living standards when smarter cities are introduced by integrating such devices.