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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.

Robot selection is a critical decision-making task frequently experienced in almost every industries. It has become increasingly complex due to availability of large variety of robotic system in the present market with varying configuration, specification and flexibility. Improper selection may yield loss for the company in terms of potential profit as well as productivity. Hence, selection of an appropriate robot to suit a particular industrial application is definitely a challenging task. The paper aims to discuss these issues.

During robot selection, different criteria-attributes need to be taken under consideration. Criteria may be subjective or objective or a combination of both, depending on the situation. Criteria many be conflicting, in the sense that some criteria may require to be of higher value (higher-is-better), i.e. beneficial; while, others should correspond to lower values (lower-is-better), i.e. adverse or non-beneficial. Hence, the situation can be articulated as a multi-criteria decision-making problem. The specialty of Tomada de Decisión Inerativa Multicritero (TODIM) method is that it explores a global measurement of value calculable by the application of the paradigm of non-linear cumulative prospect theory. The method is based on a description, proved by empirical evidence, of how decision makers’ effectively make decisions in the face of risk.

Hence, the present work has aimed to explore the TODIM approach for industrial robot selection. Assuming all criteria have been quantitative in nature; the paper utilizes two different numeric data sets from available literature resource in perspectives of robot selection. Procedural hierarchy and application potential of the TODIM approach has been illustrated in detail in this reporting.

Variety of tools and techniques have already been documented in literature to solve different kinds of industrial decision-making problems; however, it seems that application of TODIM has got limited usage. Hence, application potential of TODIM has been demonstrated here in light of a robot selection problem.

The purpose of this paper is to deliberate the system reliability of a system in combination of three subsystems in a series configuration in which all three subsystems function under a k-out-of-n: G operational scheme. Based on computed results, it has been demonstrated that copula repair is better than general repair for system better performance. The supplementary variable approach with implications of copula distribution has been employed for assessing the system performance.

Probabilistic assessment of complex system consisting three subsystems, multi-failure threats and copula repair approach is used in this study. Abbas Jubrin Bin, V.V. Singh, D.K. Rawal, in this research paper, have analyzed a system consisting of three subsystems in a series configuration in which all three subsystems function under a k-out-of-n: G operational scheme. The supplementary variable approach with implications of copula distribution has been employed for assessing the system performance. Based on computed results, it has been demonstrated that copula repair is better than general repair for system better performance.

In this analysis, four different cases of availability are analysed for Gumbel–Hougaard family copula and also four cases for general repair with similar failure rates are studied. The authors found that when failure rates increase, the system availability decreases, and when the system follows copula repair distribution, the system availability is better than general repair.

This research may be implemented in various industrial systems where the subsystems are configured under k-out-of-n: G working policy. It is also advisable that copula repair is highly recommended for best performances from the system. On the basis of mean time to system failure (MTSF) computations, the failure rate which affects system failure more needs to be controlled by monitoring, servicing and replacing stratagem.

This research work has great implications in various industrial systems like power plant systems, nuclear power plant, electricity distributions system, etc. where the k-out-of-n-type of system operation scheme is validated for system operations with the multi-repair.

This work is a new work by authors. In the previously available technical analysis of the system, the researchers have analyzed the repairable system either supplementary variable approach, supplementary variable and system which have two subsystems in a series configuration. This research work analyzed a system with three subsystems with a multi-repair approach and supplementary variables.

This paper aims to examine the impact of investor attention due to the COVID-19 pandemic, Twitter-based sentiment towards uncertainty and public sentiment on the performance of cryptocurrencies.

The authors employ the simple linear regression, quantile regression (QR), the exponential generalised autoregressive conditional heteroskedasticity (EGARCH) model, and sentiment analysis to examine this phenomenon. The authors utilise the daily closing price of the 20 leading cryptocurrencies, the Google search volume index of the “Coronavirus” keyword, the Twitter-based economic uncertainty index, and textual data collected from the Reddit social media platform.

The results show that investor attention and Twitter uncertainty have a negative (positive) effect on cryptocurrency returns (volatility). The QR results indicate a heterogeneous effect of investor attention and Twitter economic uncertainty on cryptocurrency returns with a higher effect in the lower quantiles. The findings indicate that cryptocurrencies fail to act as a safe haven during this pandemic.

The study is amongst the very few studies that capture the impact of investor attention/sentiment due to COVID-19 on the performance of cryptocurrencies.

This paper aims to propose a framework based on the unbiased extreme value volatility estimator (namely, the AddRS estimator) to compute and predict the long position and the short position value-at-risk (VaR) and stressed expected shortfall (ES). The precise prediction of VaR and ES measures has important implications toward financial institutions, fund managers, portfolio managers, regulators and business practitioners.

The proposed framework is based on the Giot and Laurent (2004) approach and incorporates characteristics like long memory, fat tails and skewness. The authors evaluate its VaR and ES forecasting performance using various backtesting approaches for both long and short positions on four global indices (S&P 500, CAC 40, Indice BOVESPA [IBOVESPA] and S&P CNX Nifty) and compare the results with that of various alternative models.

The findings indicate that the proposed framework outperforms the alternative models in predicting the long and the short position VaR and stressed ES. The findings also indicate that the VaR forecasts based on the proposed framework provide the least total loss for various long and short position VaR, and this supports the superior properties of the proposed framework in forecasting VaR more accurately.

The study contributes by providing a framework to predict more accurate VaR and stressed ES measures based on the unbiased extreme value volatility estimator.

The purpose of this paper is to examine the growth and characterization of the two different compound semiconductors, namely, n-zinc oxide (ZnO) and p-gallium antimonide (GaSb). In this paper, fabrication and characterization of n-ZnO/p-GaSb heterojunction diode is analyzed.

Thermo vertical direction solidification (TVDS) method was used to synthesize undoped GaSb ingot from high purity Ga (5N) and Sb (4N) host materials. Thermal evaporation technique is used to prepare a film of GaSb on glass substrate from the pre-synthesized bulk material by TVDS method. Undoped ZnO film was grown on GaSb film by sol–gel method by using chemical wet and dry (CWD) technique to fabricate n-ZnO/p-GaSb heterojunction diode.

The formation of crystalline structure and surface morphological analysis of both the GaSb bulk and film have been carried out by x-ray diffraction (XRD) analysis and scanning electron microscopy analysis. From the XRD studies, the structural characterization and phase identification of ZnO/GaSb interface. The current–voltage characteristic of the n-ZnO/p-GaSb heterostructure is found to be rectifying in nature.

GaSb film growth on any substrate by thermal evaporation method taking a small piece of the sample from the pre-synthesized GaSb bulk ingot has not been reported yet. Semiconductor device with heterojunction diode by using two different semiconductors such as ZnO/GaSb was used by this group for the first time.

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.

Recently, in turbulent and highly competitive marketplace, organizational sustainability in the long run necessitates the adaptation of appropriate supply chain (SC) strategies. Hence, traditional SC philosophies are being restructured nowadays to fulfill different business goals. Articulation of lean, agile, green and resilient SC strategies could amply be found in the literature; however, integration of those in various modes may definitely improve overall SC’s performance. Past researchers have focused on the integration of lean, agile and green paradigms together to ensure an efficient SC construct. But the integration of green and resilient paradigm has been rarely reported in the literature. To deal with the unexpected situations/disturbances in the SC management along with embedded environmental consciousness, the purpose of this paper is to integrate the resilient SC and green SC philosophies; thereof to evaluate of an overall SC “g-resilient”/“ecosilient” index for a case automotive company.

A consolidated list consisting of supply chain practices (combining green and resilient performance indices) have been articulated in this study. A decision-making group has been assumed; where, the role of the decision makers is to provide individuals’ judgment (subjective opinion) toward determining the weight and the rating (performance extent) of various performance indices. The overall g-resilient SC performance has been determined by computing a unique ecosilient (g-resilient) index. The concepts of fuzzy performance importance index along with Degree of Similarity (DOS) adapted from fuzzy set theory (FST) have been applied to rank various performance indicators. In addition to that, the interrelationships amongst various g-resilient indices (performance indicators) have also been established through interpretive structural modeling.

By exploring the concept of fuzzy DOS, outlined in the trapezoidal fuzzy numbers set theory, various SC performance indicators have been classified into three distinct performance categories/levels (namely regretful, tolerable, and satisfactory). Such categorization has been found helpful in order to determine ill (poor) performing SC areas, which need future improvement toward boosting up the overall g-resilient index of the company’s SC.

The study bears significant managerial implications. The decision support framework suggested in this paper is found capable enough to determine a unique index known as “ecosilient (g-resilient) index” toward exploring “greenness” as well as “resiliency” for the case automotive company. Application potential of the proposed ecosilient (g-resilient) index evaluation system has been explored in this reporting. The recommended framework enables the managers to cope up with unexpected disruptions and found helpful in order to reduce the environmental impacts.

The purpose of this paper is to attempt supplier selection considering economic, environmental and social sustainability issues.

Subjective human judgment bears some kind of vagueness and ambiguity; fuzzy set theory has immense potential to overcome this. Owing to the advantage of intuitionistic fuzzy numbers set over classical fuzzy numbers set; three decision-making approaches have been applied here in intuitionistic fuzzy setting (namely, intuitionistic-TOPSIS, intuitionistic-MOORA and intuitionistic-GRA) to facilitate supplier selection in sustainable supply chain.

The stated objective of this research “to verify application potential of different decision support systems (in intuitionistic fuzzy setting) in the context of sustainable supplier selection” has been carried out successfully. A case empirical research has been conducted by applying three different decision-making approaches: intuitionistic fuzzy-TOPSIS, intuitionistic fuzzy-MOORA and intuitionistic fuzzy-GRA to an empirical data set of sustainable supplier selection problem. The ranking orders thus obtained through exploration of aforesaid three approaches have been explored and compared.

As compared to generalized fuzzy numbers, intuitionistic fuzzy numbers exhibit a membership degree, a non-membership degree and the extent of hesitation; a better way to capture inconsistency, incompleteness and imprecision of human judgment. Application potential of aforesaid three decision support approaches has been demonstrated in this reporting for a case sustainable supplier selection.