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Purpose: The global financial services business has been transformed by Blockchain technology, making it safer and more efficient. Keeping this fact in mind, the authors will study how Blockchain technology improves financial services, including the banking and insurance sectors. The risks and roadblocks in the path of Blockchain adoption in financial services will also be discussed.

Need of the Study: Blockchain operates without any central authority. Instead, it could be understood as a transaction-containing ledger shared among many users. The adoption of Blockchain is gaining traction in every field, but still, a sense of doubt about its reliability can be observed among ordinary people. Thus, an investigation of the operational intricacies and technicalities could assist in clarifying the confusion associated with this technology.

Methodology: To achieve the aims mentioned above, an exploratory research design involving a review of the secondary data linked with the implementation and impact of Blockchain technology in the domain of finance is conducted.

Findings: The mode of operation of Blockchain technology is thoroughly explained, along with the influence it has exercised in the financial domain in recent years.

Practical Implications: The findings of this study can mainly assist global investors and users worldwide by clarifying the concept and operations of Blockchain technology. Also, it could guide future studies assessing the role of Blockchain in the financial domain.

This study aims to examine the technical efficiency of the chemical-free farming system in India using a hybrid combination of data envelopment analysis (DEA) and machine learning (ML) approaches.

The study used a two-stage approach. In the first stage, the efficiency scores of decision-making units’ efficiency (DMUs) are obtained using an input-oriented DEA model under the assumption of a variable return to scale. Based on these scores, the DMUs are classified into efficient and inefficient categories. The 2nd stage of analysis involves the identification of the most important predictors of efficiency using a random forest model and a generalized logistic regression model.

The results show that by using their resources efficiently, growers can reduce their inputs by 34 percent without affecting the output. Orchard's size, the proportion of land, grower's age, orchard's age and family labor are the most important determinants of efficiency. Besides, growers' main occupation and footfall of intermediaries at the farm gate also demonstrate significant influence on efficiency.

The study used only one output and a limited set of input variables. Incorporating additional variables or dimensions like fertility of the land, climatic conditions, altitude of the land, output quality (size/taste/appearance) and per acre profitability could yield more robust results. Although pineapple is cultivated in all eight northeastern states, the data for the study has been collected from only two states. The production and marketing practices followed by the growers in the remaining six northeastern states and other parts of the country might be different. As the growers do not maintain farm records, their data might suffer from selective retrieval bias.

Given the rising demand for organic food, improving the efficiency of chemical-free growers will be a win-win situation for both growers and consumers. The results will aid policymakers in bringing necessary interventions to make chemical-free farming more remunerative for the growers. The business managers can act as a bridge to connect these remote growers with the market by sharing customer feedback and global best practices.

Although many developments have happened to the DEA technique, the present study used a traditional form of DEA. Therefore, future research should combine ML techniques with more advanced versions like bootstrap and fuzzy DEA. Upcoming research should include more input and output variables to predict the efficiency of the chemical-free farming system. For instance, environmental variables, like climatic conditions, degree of competition, government support and consumers' attitude towards chemical-free food, can be examined along with farm and grower-specific variables. Future studies should also incorporate chemical-free growers from a wider geographic area. Lastly, future studies can also undertake a longitudinal estimation of efficiency and its determinants for the chemical-free farming system.

No prior study has used a hybrid framework to examine the performance of a chemical-free farming system.

This study aimed to improve the financing credit evaluation for small and medium-sized real estate enterprises (SMREEs). A financing credit evaluation model was proposed, and a blockchain-driven financing credit evaluation framework was designed to improve the transparency, credibility and applicability of the financing credit evaluation process.

The design science research methodology was adopted to identify the main steps in constructing the financing credit model and blockchain-driven framework. The fuzzy analytic hierarchy process (FAHP)–entropy weighting method (EWM)–set pair analysis (SPA) method was used to design a financing credit evaluation model. Moreover, the proposed framework was validated using data acquired from actual cases.

The results indicate that: (1) the proposed blockchain-driven financing credit evaluation framework can effectively realize a transparent evaluation process compared to the traditional financing credit evaluation system. (2) The proposed model has high effectiveness and can achieve efficient credit ranking, reflect SMREEs' credit status and help improve credit rating.

This study proposes a financing credit evaluation model of SMREEs based on the FAHP–EWM–SPA method. All credit rating data and evaluation process data are immediately stored in the proposed blockchain framework, and the immutable and traceable nature of blockchain enhances trust between nodes, improving the reliability of the financing credit evaluation process and results. In addition, this study partially fulfills the lack of investigations on blockchain adoption for SMREEs' financing credit.

The study aims to shed light on the concepts most addressed in scientific research, which blockchain topics are of most interest, how relevant are these tools for academia, and how relevant are they?

The authors have developed a bibliometric study of scientific publications on blockchain made since 2016. For the analysis, the VOSViewer software version 1.6.19 has been used, which allows a statistical analysis of scientific publications on the subject.

The study manifest the relevance of Initial Coin Offering, growth of research interest in this field and the relevance of blockchain technology in the development of entrepreneurial projects.

This study provides a complete and updated picture of the scientific research on blockchain for the subsequent transfer of knowledge to the business world.

The study aims to determine the the optimal value of output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The performance parameters like brake thermal efficiency (BTE) and brake specific energy consumption (BSEC), whereas CO emission, HC emission, CO₂ emission, NO_x emission, exhaust gas temperature (EGT) and opacity are the emission parameters measured during the test. Tests are conducted for 2, 6 and 10 kg of load, 16.5 and 17.5 of CR.

In this investigation, the first engine was fueled with 100% diesel and 100% Calophyllum inophyllum oil in single-fuel mode. Then Calophyllum inophyllum oil with producer gas was fed to the engine. Calophyllum inophyllum oil offers lower BTE, CO and HC emissions, opacity and higher EGT, BSEC, CO₂ emission and NO_x emissions compared to diesel fuel in both fuel modes of operation observed. The performance optimization using the Taguchi approach is carried out to determine the optimal input parameters for maximum performance and minimum emissions for the test engine. The optimized value of the input parameters is then fed into the prediction techniques, such as the artificial neural network (ANN).

From multiple response optimization, the minimum emissions of 0.58% of CO, 42% of HC, 191 ppm NO_x and maximum BTE of 21.56% for 16.5 CR, 10 kg load and dual fuel mode of operation are determined. Based on generated errors, the ANN is also ranked for precision. The proposed ANN model provides better prediction with minimum experimental data sets. The values of the R² correlation coefficient are 1, 0.95552, 0.94367 and 0.97789 for training, validation, testing and all, respectively. The said biodiesel may be used as a substitute for conventional diesel fuel.

The blend of Calophyllum inophyllum oil-producer gas is used to run the diesel engine. Performance and emission analysis has been carried out, compared, optimized and validated.

Many educators are increasingly acquainting themselves and becoming adept with interactive technologies like augmented reality and virtual reality. Some of them are also looking forward to using Metaverse applications, as they want to benefit from its immersive three-dimensional capabilities. Therefore, the purpose of this study is to critically review the extant literature to investigate how, why, where and when the Metaverse can be used for educational purposes. This study also discusses opportunities, challenges and risks related to this disruptive technology.

A Preferred Reporting Items for Systematic Reviews and Meta-Analyses rigorous protocol is used to search, extract, scrutinize and synthesize content from high-impact articles focused on the use of the Metaverse technology in the realms of education. Afterwards, this study theorizes on the costs and benefits of using this interactive technology with students.

A number of researchers are already experimenting with virtual technologies that are very similar to the Metaverse, in different contexts. This research indicates that most students are lured by immersive multi-sensory three-dimensional environments as well as by virtual reality applications that could simulate real-life situations and provide engaging experiences with virtual representations of people, places and objects. On the other hand, this study reveals that educators ought to consider the potential pitfalls of the Metaverse, including privacy breaches and security risks, as well as possible addictions and the development of mental health issues, among others.

Students and educators can use the Metaverse to catapult themselves in a simulated digital universe that could reconfigure their sensory inputs, definitions of space, time and points of access to information. This research calls for the development of regulatory instruments, including sound principles, guidelines and procedures that are intended to safeguard and protect Metaverse users.

This contribution implies that there is scope for educators to continue developing the Metaverse’s virtual spaces to improve their students’ motivations, aptitudes and learning outcomes. This study clarifies that the use of the Metaverse in education can create infinite possibilities to enhance their knowledge, competences and abilities through its immersive applications. Yet this paper also raises awareness about possible challenges in the short term as well on other risks associated to the prolonged use of this captivating technology.

In recent years, fire accidents in engineering structures have often been reported worldwide, leading to a severe risk to life and property safety. The present study is carried out to evaluate the performance of Ground Granulated Blast Furnace Slag (GGBS) and fly ash–blended laterized mortars at elevated temperatures.

This test program includes the replacement of natural river sand with lateritic fine aggregates (lateritic FA) in terms of 0, 50 and 100%. Also, the ordinary Portland cement (OPC) was replaced with fly ash and GGBS in terms of 10, 20, 30% and 20, 40 and 60%, respectively, for producing blended mortars.

This paper presents results related to the determination of residual compressive strengths of lateritic fine aggregates-based cement mortars with part replacement of cement by fly ash and GGBS exposed to elevated temperatures. The effect of elevated temperatures on the physical and mechanical properties was evaluated with the help of microstructure studies and the quantification of hydration products.

A sustainable cement mortar was produced by replacing natural river sand with lateritic fine aggregates. The thermal strength deterioration features were assessed by exposing the control specimens and lateritic fine aggregates-based cement mortars to elevated temperatures. Changes in the mechanical properties were evaluated through a quantitative microstructure study using scanning electron microscopy (SEM) images. The phase change of hydration products after exposure to elevated temperatures was qualitatively analyzed by greyscale thresholding of SEM images using Image J software.

Disasters and pandemics pose challenges to health-care provision. Accordingly, the need for adopting innovative approach is required in providing care to patient. Therefore, the purpose of this study is to present telehealth as an innovative approach for providing care to patients and reducing spread of the infection and advocates for the adoption of telehealth for digitalized treatment of patients.

An integrative review methodology of existing evidence was conducted to provide implications for integration of telehealth for digitalized treatment of patients. This paper draws on Technology Organization Environment (TOE) framework to develop a model and propositions to investigate the factors that influence telehealth adoption from the perspective of the supply side and the demand side of medical services.

Findings from this study discuss applications adopted for telehealth and recommendations on how telehealth can be adopted for medical-care delivery. More importantly, the findings and propositions of this study can act as a roadmap to potential research opportunities within and beyond the pandemic. In addition, findings from this study help provide guidelines on how health practitioners can rapidly integrate telehealth into practice for public health emergencies.

This study identifies the social, technological and organizational factors that influence telehealth adoption, and opportunities of adopting telehealth during the public health emergencies. This study concludes that specific policy changes to improve integration of interoperable solutions; data security; better physical infrastructures; broadband access; better transition and workflow balance; availability of funding and remuneration; regulations and reimbursement; awareness; and training will improve telehealth adoption during public health emergencies.

The purpose of this study is to explore the factors influencing the happiness of customers of two global coffee food chains through qualitative research in the post pandemic era. Unlike existing studies, this study will compare and examine the differential points between two global coffee food chains so that others can improve their strategies to improve their competitiveness.

It is qualitative research employing sentiment analysis through “Sprinkler Software” to assess the sentiment of customers of Starbucks and Barista followed by focus interviews through the same customers who have visited both Starbucks and Barista.

The results showed that most important factors which motivate customers and make them happy to go for “Starbucks” or “Barista” are ambience, store location, quality of product offerings and service quality. Value for money, quality of products and service quality are the top three variables affecting the customers and have rated Starbucks better than Barista on these parameters. The happiness level of the same customers who have visited both the coffee chains is more with Starbucks compared to Barista.

This research contributes to better understanding the effects of different marketing strategies adopted by coffee chain stores and can provide direction to Barista and other coffee chains. The stimulus-organism-response (SOR) model in coffee chain store application is an additional contribution to existing knowledge.

• Most important factors which motivate customers and make them happy to go for “Starbucks” or “Barista” are ambience, store location, quality of product offerings and service quality.

• Value for money, quality of products and service quality are the top three variables affecting the customers and have rated Starbucks better than Barista on these parameters.

• The happiness level of the same customers who have visited both the coffee chains are more with Starbuck compared to that of Barista.

• This is significant and can give direction to Barista and other coffee chains through learning from this research.

• Using the extended SOR model, we explain the variation in response in the happiness level of customers of two coffee chains.

• In order to give an insight into the strategies adopted by Starbucks and Barista in emerging markets, a comparison of the happiness levels of clients of both coffee chains is presented.

• This original research can help coffee chains improve their return on investment.

• The SOR model in coffee chain store application is an additional contribution to existing knowledge.

Most important factors which motivate customers and make them happy to go for “Starbucks” or “Barista” are ambience, store location, quality of product offerings and service quality.

Value for money, quality of products and service quality are the top three variables affecting the customers and have rated Starbucks better than Barista on these parameters.

The happiness level of the same customers who have visited both the coffee chains are more with Starbuck compared to that of Barista.

This is significant and can give direction to Barista and other coffee chains through learning from this research.

Using the extended SOR model, we explain the variation in response in the happiness level of customers of two coffee chains.

In order to give an insight into the strategies adopted by Starbucks and Barista in emerging markets, a comparison of the happiness levels of clients of both coffee chains is presented.

This original research can help coffee chains improve their return on investment.

The SOR model in coffee chain store application is an additional contribution to existing knowledge.