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This study probes the psychological interplay between investor sentiment and the returns of cryptocurrencies Bitcoin and Ethereum. Employing the Granger causality test, the authors aim to gauge how extensively the Fear and Greed Index (FGI) can predict cryptocurrency return movements, exploring the intricate bond between investor emotions and market behavior.

The authors used the Granger causality test to achieve research objectives. Going beyond conventional linear analysis, the authors applied Smooth Quantile Regression, scrutinizing weekly data from July 2022 to June 2023 for Bitcoin and Ethereum. The study focus was to determine if the FGI, an indicator of investor sentiment, predicts shifts in cryptocurrency returns.

The study findings underscore the profound psychological sway within cryptocurrency markets. The FGI notably predicts the returns of Bitcoin and Ethereum, underscoring the lasting connection between investor emotions and market behavior. An intriguing feedback loop between the FGI and cryptocurrency returns was identified, accentuating emotions' persistent role in shaping market dynamics. While associations between sentiment and returns were observed at specific lag periods, the nonlinear Granger causality test didn't statistically support nonlinear causality. This suggests linear interactions predominantly govern variable relationships. Cointegration tests highlighted a stable, enduring link between the returns of Bitcoin, Ethereum and the FGI over the long term.

Despite valuable insights, it's crucial to acknowledge our nonlinear analysis's sensitivity to methodological choices. Specifics of time series data and the chosen time frame may have influenced outcomes. Additionally, direct exploration of macroeconomic and geopolitical factors was absent, signaling opportunities for future research.

This study enriches theoretical understanding by illuminating causal dynamics between investor sentiment and cryptocurrency returns. Its significance lies in spotlighting the pivotal role of investor sentiment in shaping cryptocurrency market behavior. It emphasizes the importance of considering this factor when navigating investment decisions in a highly volatile, dynamic market environment.

Quality 4.0 represents the integration of quality management principles with digital technologies to drive continuous improvement and innovation in organizations. The purpose of this paper is to explore the essential organizational variables (OVs) for the successful implementation of Quality 4.0 in the Indian furniture industry.

Through a broad literature review, data from the Indian furniture industry and experts’ judgments a list of nineteen OVs have been recognized and classified into four major categories of digitalization, design, continuous improvement and employee training and up-skilling. The analytic hierarchy process (AHP) has been used to give comparative importance and prioritize the identified nineteen OVs of Quality 4.0 in the context of the Indian furniture industry.

The results of this study reveal that the identified variables are very important for successful Quality 4.0 implementation and have been supported by empirical evidence from the Indian furniture industry. The variable “automation” under the digitalization-related category is a significant variable having a maximum weightage of 26.8% followed by Cloud computing (DI4) having a global weight of 12.8%.

In addition to offering valuable insights and practical recommendations, the study recognizes a few limitations, such as industry-specific and the limited sample size. To diminish these limitations, future research should believe in conducting similar studies in different industries and extend the scope of the study.

Quality 4.0 is a term that refers to the integration of advanced digital technologies and smart data analytics into quality management systems to implement it considering OVs.

The purpose of this paper is to scrutinize the safe haven benefits of 13 individual commodities for the USA and Chinese equity sectors during the financial turmoil period. Therefore, sectoral investors in the USA and China could invest in those specific commodities that provide stable returns during the health crisis and financial turmoil periods.

The daily data spans from February 1, 2015, to July 28, 2022. The present study applies several different approaches to analyzing the data set. The author apply the cross-quantilogram (C.Q) methodology to capture the lead-lag bivariate quantile interdependence between two stationary time series variables during the bearish, bullish and normal periods. Then the study used the hedging effectiveness (HE) and conditional diversification benefits (CDB) approaches to capture the hedging and diversification benefits of commodity classes and individual commodities.

The noteworthy findings of the quantilogram methodology reveal that livestock and agriculture commodities serve as better refuges as compared to the precious metals and energy index in both countries. On average, precious metals failed to serve as safe haven investments for the USA and Chinese equity market sectors. All energy commodities except soybean oil had strong comovements with China and the US equity sectors during bearish, bullish and normal periods. Lean hogs, fiddler cattle and live cattle are perfect hedging assets for both countries due to the presence of blue color at normal and bullish periods in all C.Q heat-maps. The HE table depicts that commodity indices and individual commodities failed to serve as hedging assets for the Chinese equity sectors. But commodities are semistrong hedging assets for the US equity sectors and the S&P 500 due to the average HE values being 0.7 and above. The CDB values depict that precious metals provide diversification benefits in both equity markets.

The present study results have important implications for equity sector investors of the USA and China in suggesting particular commodity during the financial turmoil period. During the bearish market condition, risk averse equity sector investors can invest in livestock commodities and agriculture commodities, due to their relatively stable returns. In addition, policymakers can use the analysis insights to formulate policy tools and monitoring mechanisms, effectively mitigating the unfavorable effects arising from asymmetric dependence between commodities and equity sectors during the upper tail, middle and lower tail. Policymakers can suggest equity investors to invest in which commodity during extreme conditions.

The current study has the following points of originality. First, to the best of the author’s knowledge, this is the first study to investigate the individual commodities’ roles as safe havens taken from all four major commodity classes. More importantly, it is also noticeable that the safe haven abilities of commodities are usually tested for the stock market, but the equity sectors are ignored. Therefore, the present study used both stock market and sectoral indices data.

Service organizations operate in an increasingly complex and uncertain context that makes decision-making challenging. Despite well-recognized changes in the operational context of government as service organization, service literature has given surprisingly limited attention to what these changes imply for organizational decision-making. This study aims to face with the lack of fit of decision-making theorizing with the reality, within which most service practitioners operate, in order to foster the relevance of decision-making in service research and properly approach the false assumptions and misguided instructions for action.

To rectify the situation, the purpose of this paper is to advance a more holistic understanding of decision-making in government as service organization. The authors do so by reviewing the sparse, though insightful, prior literature on decision-making in service research and identifying four foundational assumptions of decision-making in the service context, that radically differ from the traditional assumptions of decision-making within the wider management literature.

The authors contribute to service research by further advancing the emerging dynamic understanding of decision-making by developing eight systems thinking-informed research propositions and a connected research agenda. In doing so, the paper offers the essential ground work that can revitalize the field of service management and equip it for facing the challenges that government as service organization is encountering in the 21st century.

The formulated eight research propositions demonstrate that decision-making in a government as service organization occurs within complex adaptive systems composed of multiple subsystems and is characterized by a high degree of unpredictability. It is a process influenced by multiple actors part of the system and subsystems, through multiple feedback loops, where the implications of prior decisions inform the future decisions.

Experiential learning is crucial in education, as it offers hands-on, practical experiences that enable individuals to develop their skills and knowledge more engagingly and interactively. In recent years, experiential learning has become a significant aspect of education. To provide academic scholars with a thorough roadmap for further investigation, this study aims to provide useful insights into the bibliometric and content analysis of experiential learning, including keywords, well-known authors, publications, nations and topics.

This research does a rigorous bibliometric analysis to give a thorough and visually instructional assessment of the evolution and advancement of the literature on experiential learning. Its fast development between 1976 and 2022 is meticulously tracked in the research. By using VOSviewer and Biblioshiny tools, the present study presents a concise overview of 507 records retrieved from the Scopus database using the keyword “Experiential Learning”, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis protocol. Deeper text mining was done using Python libraries “Pandas” and “Natural Language Toolkit” and regular expressions.

The findings reveal a surge in the number of publications on experiential learning and provide insights, particularly using the theory, context, characteristics, methodology analysis, supporting researchers and practitioners to understand learning better and provide perspectives for future research. Descriptive bibliometric analysis showed that most contributions are from the USA, the UK and Canada. In-depth content analysis revealed five clusters: developments in learning, management education, engineering curricula, organisational learning and knowledge management and entrepreneurship education. The keyword co-occurrence analysis enabled linkages between relevant fields of study and significant research domains. The most commonly used theories were: experiential learning theory, social learning theory, relational coordination theory, empowerment theory, feedback learning theory, effectuation theory and human capital theory.

This study uses information from the Scopus database to do a bibliometric analysis of experiential learning from 1976 to 2022. This study serves as a valuable resource for researchers in the field, helping them to position their work more explicitly within the existing literature and highlighting potential areas for future research. It does this by thoroughly analysing the literature on experiential learning using bibliometrics.

This study aims to adopt a systematic review approach to examine the existing literature on law and LLMs.It involves analyzing and synthesizing relevant research papers, reports and scholarly articles that discuss the use of LLMs in the legal domain. The review encompasses various aspects, including an analysis of LLMs, legal natural language processing (NLP), model tuning techniques, data processing strategies and frameworks for addressing the challenges associated with legal question-and-answer (Q&A) systems. Additionally, the study explores potential applications and services that can benefit from the integration of LLMs in the field of intelligent justice.

This paper surveys the state-of-the-art research on law LLMs and their application in the field of intelligent justice. The study aims to identify the challenges associated with developing Q&A systems based on LLMs and explores potential directions for future research and development. The ultimate goal is to contribute to the advancement of intelligent justice by effectively leveraging LLMs.

To effectively apply a law LLM, systematic research on LLM, legal NLP and model adjustment technology is required.

This study contributes to the field of intelligent justice by providing a comprehensive review of the current state of research on law LLMs.