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The authors propose a blockchain platform for managing clinical trial data to enhance data validity, integrity, trust and transparency in the pharmaceutical research process. The authors also provide an extensive review of how blockchain technology supports the business processes of clinical trials.

A systematic literature review was conducted to identify the existing applications of blockchain in pharmaceutical process management. A conceptual design for a blockchain infrastructure to address clinical trial challenges is developed by outlining the entire clinical trial value chain and identifying the coordination and communication among its stakeholders. A stakeholder analysis is conducted to ensure that the clinical trial processes satisfy the requirements and preferences of each stakeholder.

The proposed blockchain platform offers a promising solution for enhancing integrity, trust and transparency in the clinical trial process. Additionally, blockchain can help streamline communication and collaboration between stakeholders by enabling multiple parties to access and share data in real time, lowering the possibility of delays or errors in data analysis and reporting.

The proposed blockchain platform can benefit patients by empowering them to have better-controlled access to their data and by allowing researchers to maintain adherence to reporting requirements. Additionally, the platform can benefit granting agencies, researchers and decision-makers by ensuring the integrity of clinical trial data and streamlining communication and collaboration between stakeholders.

This study builds on existing blockchain applications in pharmaceutical process management by developing a blockchain framework that can address clinical trial concerns from an integrated perspective.

Owing to highly volatile and chaotic external events, predicting future movements of cryptocurrencies is a challenging task. This paper advances a granular hybrid predictive modeling framework for predicting the future figures of Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Stellar (XLM) and Tether (USDT) during normal and pandemic regimes.

Initially, the major temporal characteristics of the price series are examined. In the second stage, ensemble empirical mode decomposition (EEMD) and maximal overlap discrete wavelet transformation (MODWT) are used to decompose the original time series into two distinct sets of granular subseries. In the third stage, long- and short-term memory network (LSTM) and extreme gradient boosting (XGB) are applied to the decomposed subseries to estimate the initial forecasts. Lastly, sequential quadratic programming (SQP) is used to fetch the forecast by combining the initial forecasts.

Rigorous performance assessment and the outcome of the Diebold-Mariano’s pairwise statistical test demonstrate the efficacy of the suggested predictive framework. The framework yields commendable predictive performance during the COVID-19 pandemic timeline explicitly as well. Future trends of BTC and ETH are found to be relatively easier to predict, while USDT is relatively difficult to predict.

The robustness of the proposed framework can be leveraged for practical trading and managing investment in crypto market. Empirical properties of the temporal dynamics of chosen cryptocurrencies provide deeper insights.

In this study, the central objective is to foresee stock market signals with the use of a proper structure to achieve the highest accuracy possible. For this purpose, three hybrid models have been developed for the stock markets which are a combination of support vector machine (SVM) with meta-heuristic algorithms of particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).All the analyses are technical and are based on the Japanese candlestick model.

Further as per the results achieved, the most suitable algorithm is chosen to anticipate sell and buy signals. Moreover, the authors have compared the results of the designed model validations in this study with basic models in three articles conducted in the past years. Therefore, SVM is examined by PSO. It is used as a classification agent to search the problem-solving space precisely and at a faster pace. With regards to the second model, SVM and ICA are tested to stock market timing, in a way that ICA is used as an optimization agent for the SVM parameters. At last, in the third model, SVM and GA are studied, where GA acts as an optimizer and feature selection agent.

As per the results, it is observed that all new models can predict accurately for only 6 days; however, in comparison with the confusion matrix results, it is observed that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the data for stock market of the years 2013–2021 were analyzed; the long length of timeframe makes the input data analysis challenging as they must be moderated with respect to the conditions where they have been changed.

In this study, two methods have been developed in a candlestick model; they are raw-based and signal-based approaches in which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

Drawing on social identity, signalling and stakeholder theories, this paper aims to examine corporate reputation’s (CR) mediating role concerning corporate social responsibility (CSR) and customer loyalty (CL) among Generation Z customers of Nepali commercial banks.

The research applied a cross-sectional survey research design to collect data from 281 customers of Nepali commercial banks. The study used a purposive sampling method to reach the respondents and partial least squares structural equation model was used to test the hypotheses.

The results reveal that CSR significantly influences CR and CL. Likewise, CR positively influences CL. Moreover, CR partially mediates the relationship between CSR and CL. It implies that CSR and CR are critical variables for CL among Generation Z customers of Nepali commercial banks.

This study focuses on understanding the importance of CSR to Nepalese commercial bank managers to create a better customer base by focusing on the CSR dimensions, i.e. economic, environmental and social. It adds to the literature on the theoretical aspect of the study of CSR, particularly in the banking industry.

It has initially investigated CSR as a higher-order construct to explain the meditational mechanism of CR concerning CSR and CL. Moreover, the study examined the issue of endogeneity.

In this research, we seek to understand the effects of artificial intelligence (AI) and knowledge management (KM) processes in enhancing proactive green innovation (PGI) within oil and gas organizations. It also aims to investigate the moderator role of trust and sustainability in these relationships.

This paper employs a quantitative analysis. Surveys have been gathered from the middle-line managers of twenty-four oil and gas government organizations to evaluate the perceptions of the managers towards AI, KM processes, trust, sustainability measures and proactive measures toward green innovation. Analytical and statistical tools that were employed in this study, including structural equation modeling with SmartPLSv3.9, have been used to analyze the data and to examine the measurement and structural models of this study.

The study results reveal a significant and positive impact of AI utilization, KM processes and PGI within oil and gas organizations. Furthermore, trust and sustainability turn out to be viable moderators affecting, and influencing the strength and direction of AI, KM and PGI relationships. In particular, higher levels of trust and more substantial sustainability commitments enhance the positive impact of AI and KM on green innovation outcomes.

Understanding the impact of AI, KM, trust and sustainability offers valuable insights for organizational leaders and policymakers seeking to promote proactive green innovation within the oil and gas industry. Thus, organizations can increase the efficiency of sustainable product development, process improvement and environmental management by using robust AI technologies and effective KM systems. Furthermore, fostering trust among stakeholders and embedding sustainability principles into organizational culture can amplify the effectiveness of AI and KM initiatives in driving green innovation outcomes.

This study extends the current knowledge by assessing the effect of AI and KM on proactive green innovation while accounting for trust and sustainability as moderators. Utilizing quantitative methods offers a nuanced understanding of the complex interactions between these variables, thereby advancing theoretical knowledge in the fields of innovation management, sustainability and organizational behavior. Additionally, the identification of specific mechanisms and contextual factors enriches practical insights for organizational practitioners striving for a practical understanding of the dynamics of the complexities of sustainable innovation in an AI-driven era.

Need of circular economy (ce) practices for net-zero and sustainability in construction sector is well known, and thus the need for information flow between current and potential users about materials and processes. Material passports (MPs) are the tool for this information flow. This study aims to examine the research trend about digitalisation and MPs in construction, explore the application of digital technologies (DTs) for information management required for MPs and provide further research directions.

Systematic search and review of literature was conducted adopting both qualitative and quantitative approach for analysis. Firstly, quantitative bibliometric analysis of 201 papers was conducted to get the context from ongoing research around the area and qualitative content and thematic analysis of selected 14 papers were then done to further explore the literature.

Bibliometric analysis suggested building information modelling (BIM) as the most widely studied topic for digitalisation and MPs, which has been studied together with other DTs, whereas blockchain is niched within supply chain and waste management. Qualitative review observed BIM as the most prevalent technology, providing platform for information generation and management for MPs, and most other DTs are applicable mostly for information generation. Artificial intelligence (AI) is useful for information generation, but more suiting for information analysis. Blockchain, on the other hand, is for decentralised and reliable information management.

This study has tried to explore the digitalisation for circularity in construction with focus on information management for MPs. As the ce in construction boils down to information flow and MPs, this study provides the idea about possible applications of DTs for MPs and suggests further research directions for development and use of MPs for ce in construction.

The purpose of this study is to follow up on the structural and fatigue analysis of car wheel rims with carbon fibre composites in order to ensure the vehicular safety. The wheel is an essential element of the vehicle suspension system that supports the static and dynamic loads encountered during its motion. The rim provides a firm base to hold the tire and supports the wheel, and it is also one of the load-bearing elements in the entire automobile as the car's weight and occupants' weight act upon it. The wheel rim should be strong enough to withstand the load with such a background, ensuring vehicle safety, comfort and performance. The dimensions, shape, structure and material of the rim are crucial factors for studying vehicle handling characteristics that demand automobile designers' concern.

In the present study, solid models of three different wheel rims, namely, R-1, R-2 and R-3, designed for three different cars, are modelled in SOLIDWORKS. Different carbon composite materials of polyetheretherketone (PEEK), namely, PEEK 90 HMF 40, PEEK 450 CA 30, PEEK 450 GL 40 and carbon fibre reinforced polymer-unidirectional (CFRP-UD) are used as rim materials for conducting the structural and fatigue analysis using ANSYS Workbench.

The results thus obtained in the analyses are used to identify the better carbon fibre composite material for the wheel rim such that it gives better structural properties and less fatigue. The R-3 model rim has shown better structural properties and less fatigue with PEEK 90 HMF 40 material.

The carbon composite materials used in this study have shown promissory results that can be used as an alternative for aluminium, steel and other regular materials.

In the era of Industrial Revolution (IR) 4.0, the integration of digital technologies, automation and data-driven insights has generated a broad wave of transformation across all industries, including the insurance sector. The study focuses on determining how the adoption of these technologies (InsurTech) is changing the life insurance industry, ultimately enhancing the level of customer satisfaction.

The data analysis has been performed with 304 useable responses from the policyholders of life insurance in the north-west region of India. The methodology adopted for this study is partial least squares (PLS) structural equation modeling (SEM). To investigate the predictive relevance of customer satisfaction, the PLS predict technique has been used. Also, importance performance map analysis (IPMA) has been applied to assess the important and performing dimensions of customer satisfaction.

The outcomes show that the adoption of InsurTech has a positive impact on customer satisfaction. Customer service management and policy management are among the strongest predictors of customer satisfaction, and the predictive relevance is reported to be moderate. IPMA results have suggested that improvements in online distribution of insurance services and customer service management lead to higher customer satisfaction.

The conceptual model can be tested with the moderating effect of different demographic factors (age, gender etc.), and future research can be done to analyze the mediating role of customer satisfaction between InsurTech adoption and customer loyalty.

The study offers valuable contributions to the marketing literature, shedding light on the influence of InsurTech adoption on customer satisfaction within the Indian life insurance sector. The research offers a practical approach that could help marketing professionals and policymakers comprehend the utilization of online insurance services, and this understanding can help industry experts to develop customer-oriented products and services.

This research is the first of its kind to test the association between InsurTech adoption and customer satisfaction in the life insurance sector in the Indian context. Research also provides novel insights for policymakers to enhance the satisfaction of customers towards using online insurance services in the near future in developing countries like India.