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This paper aims to investigate the effect of the economic policy uncertainty (EPU), geopolitical risk (GPR) and climate policy uncertainty (CPU) of USA on Bitcoin volatility from August 2010 to August 2022.

In this paper, the authors have adopted the empirical strategy of Yen and Cheng (2021), who modified volatility model of Wang and Yen (2019), and the authors use an OLS regression with Newey-West error term.

The results using OLS regression with Newey–West error term suggest that the cryptocurrency market could have hedge or safe-haven properties against EPU and geopolitical uncertainty. While the authors find that the CPU has a negative impact on the volatility of the bitcoin market. Hence, the authors expect climate and environmental changes, as well as indiscriminate energy consumption, to play a more important role in increasing Bitcoin price volatility, in the future.

This study has two implications. First, to the best of the authors’ knowledge, the study is the first to extend the discussion on the effect of dimensions of uncertainty on the volatility of Bitcoin. Second, in contrast to previous studies, this study can be considered as the first to examine the role of climate change in predicting the volatility of bitcoin. This paper contributes to the literature on volatility forecasting of cryptocurrency in two ways. First, the authors discuss volatility forecasting of Bitcoin using the effects of three dimensions of uncertainty of USA (EPU, GPR and CPU). Second, based on the empirical results, the authors show that cryptocurrency can be a good hedging tool against EPU and GPR risk. But the cryptocurrency cannot be a hedging tool against CPU risk, especially with the high risks and climatic changes that threaten the environment.

This study aims to quantify the concept of Industry 5.0, with a focus on human-centricity in the manufacturing sector.

The panel nonlinear autoregressive distributed lag method is applied to assess asymmetry and vulnerability in the food, textile, chemical manufacturing, high-tech and transportation sectors. The robustness of the results is tested using a panel Granger non-causality test and panel vector autoregressive models.

This study finds that financial unions, fair internal markets, gender and youth participation are significant factors for human centricity in the manufacturing sectors. The NARDL results suggest that both the chemical and high-tech industries human participation are insignificant in both the long run and short run. The results of the food industry are significant in both the sort run and the long run.

Manufacturing sectors need to create sustainable employment strategies that lead to stable, enduring and satisfying jobs in order to achieve human centricity. Involve skilled workers in important decision-making processes and empower them with technology.

This study differed from prior research in several ways. Firstly, it incorporates the social dimension as a control variable in the pursuit of I5.0 implementation across various manufacturing sectors. Secondly, it quantifies the human-centricity aspect of I5.0 within these sectors.

This paper aims to produce sustainable sport-hijab or veiling using cotton and bamboo as renewable and eco-material blending with polyester. Due to the unique characteristics of the knitting fabrics, the research focused on constructing the proposed samples using a circular knitting technique with a French terry structure, to achieve comfort, ease of care, good appearance and sustainability in different climatic conditions.

The researchers formed three different knitted samples using yarn count 30/1Ne for cotton and bamboo and 70 dens for polyester yarn, using the same blending ratio of 50:50% (cotton/polyester, bamboo/ polyester and cotton/ bamboo). They tested several mechanical and physical properties (weight, thickness, air permeability, water permeability, electrostatic charges, ultraviolet protection factor, stiffness, pilling resistance and bursting strength).

Using different tools, the researchers statistically analyzed the influence of variables on sample properties, including a Chart line, ANOVA test at p-value = 0.05 and the least significant differences values to identify the effect significantly as well as demonstrate the interaction among the samples at each tested property. Finally, radar chart areas to clarify the preferable sample performance.

The findings declared that blending materials used significantly affected most properties of the produced samples, except for the water permeability and an electrostatic charge. Furthermore, the findings pointed out that blending (cotton or bamboo/polyester) is more efficient and desirable than blending (cotton/ bamboo). Additionally, based on radar charts analysis, the cotton/polyester knitted outperforms other blended materials samples in producing sport-hijab or veiling fabric.

Transaction laundering has become an increasingly intricate and rampant form of financial misconduct in the age of digital commerce. This research paper conducts an exhaustive examination of this issue, categorizing the various techniques criminals use to highlight areas where existing risk management practices could be further refined. Amid escalating regulatory scrutiny of both financial and nonfinancial entities, the paper stresses the implications of not meeting regulatory standards. As a novel contribution, this study advocates for a shift in risk management strategies. It argues that entities under obligation should harness advanced technological methods to counter transaction laundering challenges effectively. The study serves as a relevant guide for online businesses aiming to strengthen their measures against transaction laundering. For future work, the potential effectiveness of technology-driven countermeasures deserves further scrutiny.

This study used a conceptual legal research method, using a library-based doctrinal legal research approach with a conceptual legal perspective, drawing from existing literature. This study reviewed primary and secondary legal sources, including case law and provisions of the Money Laundering (Prohibition) (Amended) Act, 2012, and the Terrorism (Prevention) Act 2013 (as amended). This study also assessed the provisions of the Economic and Financial Crimes Commission (Establishment) Act, Laws of the Federal Republic of Nigeria, 2004. This research further incorporated a blend of archival and secondary legal sources. This study conducted comparative analyses, examining the legal frameworks of Canada, the UK, Hong Kong and China alongside Nigeria to identify potential lessons for enhancing Nigeria’s legislation concerning money laundering and terrorism financing. This study also assessed problems and derived insights from the study’s findings. This research method was chosen to establish the credibility of the findings regarding the issues of money laundering and terrorist financing.

The analysis uses a comprehensive network dataset, encompassing ties among individuals and businesses in the Netherlands from 2005 to 2019. It integrates administrative data, including family ties, shared bank accounts and employment history, with corporate information and ownership relations from the Chamber of Commerce. Criminal data related to police interventions, legal convictions and suspicious money laundering transactions are linked to these networks. This unique approach overcomes the scarcity of large empirical datasets in criminological research, offering valuable insights into criminal network behavior and dynamics. Understanding how criminal networks adapt to anti-money laundering policies aids regulatory authorities in designing more effective and efficient measures while also enhancing the tools available to enforcement authorities for detection and investigation.

AML policies are often criticized for their high costs relative to the perceived benefits. This paper's method avoids dark number estimations and relies on high-quality administrative data. The theoretical contribution includes an examination of specialization, competition and collaboration within criminal networks. The empirical aspect uses a unique dataset and emerges as a methodology for evaluating the effects of AML policy measures using temporal cluster analysis.

eWOM is a very useful tool that is widely used nowadays to make a purchase decision. One of the sectors where this tool is very present is the gastronomic sector and Peru is no stranger to this gastronomic take-off. As a part of this take-off, Nikkei or Peruvian-Japanese cuisine have gained plenty of attention through digital social platforms, with a significant number of users relying on eWOM to determine which is their best purchase option; nevertheless, there is a lack of local investigations that addresses a correlation between eWOM and purchase intention in this type of restaurants. With the last statement, this study aims to determine the influence of eWOM on the purchase intention of consumers of Nikkei restaurants in Lima, Peru.

A quantitative descriptive and causal explanatory research was conducted, using the Information Acceptance Model (IACM) to test the purpose of the study. A total of 385 surveys were obtained and processed in SmartPLS.

The results show that consumers of Nikkei restaurants in Metropolitan Lima are mostly guided by the eWOM that is on social media to make a purchase decision. In addition, information usefulness is the eWOM dimension that most influences purchase intention. Finally, it was found that the credibility of the eWOM does not influence the information usefulness, which could be due to the distrust of the sources and the anonymity of the eWOM.

The present investigation identified that certain dimensions of the IACM does not influence their hypothesized counterparts, such as credibility and attitude towards information over information usefulness; circumstances that may conduct to other local studies based on the IACM applied to other type of restaurants to observe if there is some type of variation in the results obtained of both hypotheses.

From a practical point of view, the results obtained are valuable for social media marketers and generally digital marketing professionals, which by themselves provide a framework that assures a better understanding about the impact of eWOM in the purchase intention of Nikkei Restaurant consumers.

As the potential for this research to impact society in visible ways, it identifies how current individuals in a certain time and location, tend to act towards information obtained in social media or specialised forums; as well as how the characteristics of this information could affect their final purchase decision.

To the best of the authors’ knowledge, this is one of the first articles to study the influence of eWOM on purchase intention in the gastronomic sector in Peru, a country famous for its gastronomy. In this way, experts and professionals in this field will be able to generate knowledge of this tool, as well as generate strategies to improve sales of Nikkei restaurants and in general within the gastronomy industry in Peru.

Despite their centrality to organizing – acknowledged by several theorists – unplanned conversations are often marginalized in organizational theory. To remedy this oversight, we recenter attention on this understudied aspect of organizing. We draw on the affordances perspective to elaborate on the spatial and temporal factors influencing unplanned conversations.

This conceptual paper integrates multidisciplinary literature on unplanned conversations to identify a range of spatiotemporal factors influencing unplanned conversations. Our approach emphasizes how various situational factors afford or hinder opportunities for unplanned conversations among organizational members.

Unplanned conversations were precisely defined as opportunistic or spontaneous conversations, characterized by the absence of pre-planning, that can be work or non-work-related. Then, the characteristics of unplanned conversations (emergent, episodic and brief, interrelated, convenient, and improvisational) were outlined, indicating their distinct organizing and structuring capabilities. The spatial (i.e. spatial proximity, visibility, legitimacy, and psychological safety) and temporal (i.e. work time pressure, work history, work expertise, and work routineness) factors identified in the study both afford and constrain individuals’ unplanned conversations. The empirically testable propositions offered in the study have significant theoretical and practical implications.

This study enriches our understanding of unplanned conversations by offering a precise conceptual definition, outlining their essential characteristics, and underscoring their theoretical and practical significance in organizing. The study highlights the need for organizations to consider the spatiotemporal factors that influence unplanned conversations.

The new customer churn prediction (CCP) utilizing deep learning is developed in this work. Initially, the data are collected from the WSDM-KKBox’s churn prediction challenge dataset. Here, the time-varying data and the static data are aggregated, and then the statistic features and deep features with the aid of statistical measures and “Visual Geometry Group 16 (VGG16)”, accordingly, and the features are considered as feature 1 and feature 2. Further, both features are forwarded to the weighted feature fusion phase, where the modified exploration of driving training-based optimization (ME-DTBO) is used for attaining the fused features. It is then given to the optimized and ensemble-based dilated deep learning (OEDDL) model, which is “Temporal Context Networks (DTCN), Recurrent Neural Networks (RNN), and Long-Short Term Memory (LSTM)”, where the optimization is performed with the aid of ME-DTBO model. Finally, the predicted outcomes are attained and assimilated over other classical models.

The features are forwarded to the weighted feature fusion phase, where the ME-DTBO is used for attaining the fused features. It is then given to the OEDDL model, which is “DTCN, RNN, and LSTM”, where the optimization is performed with the aid of the ME-DTBO model.

The accuracy of the implemented CCP system was raised by 54.5% of RNN, 56.3% of deep neural network (DNN), 58.1% of LSTM and 60% of RNN + DTCN + LSTM correspondingly when the learning percentage is 55.

The proposed CCP framework using the proposed ME-DTBO and OEDDL is accurate and enhances the prediction performance.

This study aims to identify the challenges, promote respective solutions and construct strategies for the implementation of the halal supply chain in Indonesia’s small and medium-sized enterprises (SMEs).

This research used the analytic network process in nine respondents categorised into three groups of relevant stakeholders: government, academia and industry.

Seven essential criteria for the implementation of a halal supply chain in SMEs were identified, namely, the process, packaging, storage, transportation, fundamental aspects, supporting policy and technology. Notably, the high cost of adopting blockchain technology was the most crucial issue, particularly in SMEs.

Through the findings, several critical factors to consider in the implementation of halal supply chains for SMEs were identified. The investment in halal supply chain technology, which incurs a high cost carried by entrepreneurs, is a burden for SMEs. For this reason, further studies are required to formulate and develop cost-effective halal supply chain technology for SMEs.

To the best of the authors’ knowledge, this research is the first attempt of comprehensively mapping the barriers and developing strategic solutions to implement the halal supply chain by Indonesia’s SMEs.

The purpose of this study is to investigate the relationship between digital transformational leadership (DTL) and innovation capability (IIC) in multinational (MNC) firms’ settings. The current study emphasises the mediating-moderating impact of symmetrical internal communication (SIC) and trust in leadership (TIL) in further shaping this relationship.

The researchers adopted three-wave data consisting of responses from 323 cross-border team members working in MNC firms. A moderated-mediation model was tested using Hynes’ Process Macro and IBM Amos.

The empirical findings underscore a positive relationship between DTL and IIC and the mediation by SIC. Furthermore, the researchers also identified a moderated mediation relationship of TIL.

To the best of the authors’ knowledge, this is the first study to investigate the moderated mediation relationship among DTL, SIC, IIC and TIL using the complexity leadership theory perspective.

This paper aims to compare the mission statements of Indian and Singaporean firms in the healthcare sector, and define the main components of Indian and Singaporean mission statements.

The study was based on a network analytic approach and content analysis. The research was performed on 200 companies (100 Indian companies and 100 Singaporean companies). For each company, we searched for a mission statement published in the company website. Nonnegative Matrix Factorization (NMF) in Python programming language was utilized to obtain the differences in the components of mission statements between Indian and Singaporean firms.

The study results indicate a similarity and variation between Indian and Singaporean mission statements. Both countries are more concerned about patients, service, community, quality, and healthcare in their mission statements, but Indian mission statements emphasize quality, affordable price, and technology more than Singaporean firms. In contrast, Singaporean mission statements tend to highlight innovation and company value. This research will assist strategic managers in identifying the mission statement components and choosing the right strategy for the organization.

This study contributes to the literature and ethos theory by identifying and distinguishing the paramount differences between the Indian and Singaporean mission statement components in the healthcare sector.