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This study aims to assess the predictive performance of various factors on Bitcoin returns, used for the development of a robust forecasting support decision model using machine learning techniques, before and during the COVID-19 pandemic. More specifically, the authors investigate the impact of the investor's sentiment on forecasting the Bitcoin returns.

This method uses feature selection techniques to assess the predictive performance of the different factors on the Bitcoin returns. Subsequently, the authors developed a forecasting model for the Bitcoin returns by evaluating the accuracy of three machine learning models, namely the one-dimensional convolutional neural network (1D-CNN), the bidirectional deep learning long short-term memory (BLSTM) neural networks and the support vector machine model.

The findings shed light on the importance of the investor's sentiment in enhancing the accuracy of the return forecasts. Furthermore, the investor's sentiment, the economic policy uncertainty (EPU), gold and the financial stress index (FSI) are the top best determinants before the COVID-19 outbreak. However, there was a significant decrease in the importance of financial uncertainty (FSI and EPU) during the COVID-19 pandemic, proving that investors attach much more importance to the sentimental side than to the traditional uncertainty factors. Regarding the forecasting model accuracy, the authors found that the 1D-CNN model showed the lowest prediction error before and during the COVID-19 and outperformed the other models. Therefore, it represents the best-performing algorithm among its tested counterparts, while the BLSTM is the least accurate model.

Moreover, this study contributes to a better understanding relevant for investors and policymakers to better forecast the returns based on a forecasting model, which can be used as a decision-making support tool. Therefore, the obtained results can drive the investors to uncover potential determinants, which forecast the Bitcoin returns. It actually gives more weight to the sentiment rather than financial uncertainties factors during the pandemic crisis.

To the authors’ knowledge, this is the first study to have attempted to construct a novel crypto sentiment measure and use it to develop a Bitcoin forecasting model. In fact, the development of a robust forecasting model, using machine learning techniques, offers a practical value as a decision-making support tool for investment strategies and policy formulation.

The broad aim of this paper is to use Noddings' theory of ethical care to analyse mentors' caring experiences. More specifically, it aims to analyse how physical education (PE) mentors provide care, how they are cared for and how this impacts their role within the context of secondary PE initial teacher training (ITT).

Semi-structured interviews were used to generate data from 17 secondary PE mentors within the same university ITT partnership in the north-west of England. Questions focused on the mentors' experiences of care and the impact this had on their wellbeing and professional practice. A process of thematic analysis was used to identify, analyse and report patterns in the data.

The participants reflected established definitions of mentoring by prioritising the aim of developing the associate teachers' (ATs) teaching rather than explicitly providing support for their wellbeing. This aim could be challenging for mentors who face personal and professional difficulties while supporting the training of an AT. Mentors frequently referred to the support of their departmental colleagues in overcoming these difficulties and the importance of developing interdependent caring relationships. Receiving care did not impede mentors from providing support for others; it heightened awareness and increased their desire to develop caring habits.

Teacher wellbeing has drawn greater attention in recent years and is increasingly prioritised in public policy. These findings highlight the value of mentor wellbeing and how caring professional relationships can mitigate the pressures associated with performativity and managing a demanding workload.

The purpose of this paper is to examine the impact of social movements engendered by the Arab Spring crisis on the relationship between corporate social responsibility disclosure (CSRD) and corporate governance attributes, particularly board composition, considering the importance of governance after the Arab Spring event.

Content analysis was used to examine the extent and nature of CSRD in annual reports of Jordanian companies listed on the Amman Stock Exchange covering the period 2009–2016. A dynamic regression model using panel data is then undertaken for a sample of 114 listed companies over the period to analyse the potential impact of board composition on the level of CSRD.

The results reveal that there was a significant increase in the level of CSRD post-the Arab Spring crisis; and that governance appears to be a key driver. Specifically, board age, directors educated in business and/or accounting-related fields and foreign members are found to have a significant positive relationship with CSRD.

Looking at the Arab region pre- and after the Arab Spring helps to complete the global picture of how company governance can lead to improved CSR performance. Specifically, this region has been behind in developing rules and codes that include CSR. The results show that having a diverse board, with directors with expertise specific to the context, increases the effectiveness of stakeholder management through CSRD. The results, therefore, offer valuable insights for companies, policymakers and for the development of regulations.

This paper aims to add further evidence to adoption criteria for “revolutionary” business techniques.

Adoption criteria for business techniques with a high degree of novelty have been developed earlier. The case of exchange-traded funds supports the earlier findings. The methodology applied is explicative.

The analysis supports findings that an effective response to a problem, the availability of a controllable procedure, the means to apply the procedure easily and the hardware jointly explain adopting “revolutionary” business techniques.

The results of case studies, in general, do not permit induction. More research might identify additional adoption criteria or falsify the presently obtained results. Therefore, further research is invited.

Managers seeking or being introduced to new techniques in business administration might use the criteria outlined here for their evaluation.

The author believes this paper corroborates earlier findings on adopting “revolutionary” business techniques that draw on theoretically developed technologies.

This paper aims to study the impact of the adoption of eXtensible Business Reporting Language (XBRL) on corporate tax avoidance.

This paper used a quantitative method with panel data regression models using a sample of firms listed on the Indonesia Stock Exchange from 2011 to 2018.

The regression results demonstrate that XBRL implementation does not have any impact on corporate tax avoidance. The results indicate that tax avoidance is not reduced following XBRL adoption. This report shows unexpected and unfavourable outcomes of XBRL financial reporting in a developing country.

This study employs a sample of firms from one emerging country only.

The study proposes several implications for using XBRL in tax reporting, which may help the tax authorities reduce tax avoidance. Regulators need to develop adequate taxonomies with standardized extensions related to tax information in the XBRL format. They include tax tags from financial statements and tax tags from the disclosure section, to gain more comprehensive corporate tax information.

This study proposes and tests an explanation for the effect of XBRL adoption on corporate tax avoidance in the context of a developing country.

Based on the JD-R model and process-focused HRM perspective, this research paper aims to investigate the processes underlying the relationship between AI-enabled HR analytics and employee well-being outcomes (resilience) that received less attention in the AI-driven HRM literature. Specifically, this study aims to examine the indirect effect between AI-enabled HR analytics and employee resilience via job crafting, moderated by HRM system strength to highlight the contextual stimulus of AI-enabled HR analytics.

The authors adopted a time-lagged research design (one-month interval) to test the proposed hypotheses. The authors used two-wave surveys to collect data from 175 full-time hotel employees in China.

The findings indicated that employees' perceptions of AI-enabled HR analytics enhance their resilience. This study also found the mediation role of job crafting in the mentioned relationship. Moreover, the positive effects of AI-enabled HR analytics on employee resilience amplify in the presence of a strong HRM system.

Organizations that aim to utilize AI-enabled HR analytics to achieve organizational missions should also dedicate attention to its associated employee well-being outcomes.

This study enriched the literature with regard to AI-driven HRM in that it identifies the mediating role of job crafting and the moderating role of HRM system strength in the relationship between AI-enabled HR analytics and employee resilience.

Wine tourism is gaining increasing popularity among Chinese tourists, making it necessary to thoroughly examine tourist behavior. While online reviews posted by wine tourists have been extensively studied from the perspectives of destinations and wineries, the perspective of the tourists themselves has been overlooked. To address this gap, this study aims to identify significant attributes intrinsic to the tourism experiences of Chinese wine tourists by adopting a text-mining approach from a tourist-centric perspective.

The authors use topic modeling to extract these attributes, calculate topic intensity to understand tourists’ attention distribution across these attributes and conduct topical sentiment analysis to evaluate tourists’ satisfaction levels with each attribute. The authors perform importance-performance analyses (IPAs) using topic intensity and sentiment scores. Furthermore, the authors conduct semistructured in-depth interviews with Chinese wine tourists to gain insights into the underlying reasons behind the key findings.

The study identifies eleven attributes for domestic wine tourists and seven attributes for outbound wine tourists. From the reviews of both domestic and outbound tourists, three common attributes have been identified: “scenic view”, “wine tasting and purchase” and “wine knowledge”.

According to the results of the IPAs, there is a pressing need for enhancements in the wine tasting and purchasing experience at domestic wine attractions. Additionally, managers of domestic wine attractions should continue to prioritize the positive aspects of the family trip experience and scenic views. On the other hand, for outbound wine attractions, it is crucial for managers to maintain their efforts in providing opportunities for wine knowledge acquisition, ensuring scenic views and upholding the reputation of wine regions.

First, this study breaks new ground by adopting a tourist-centric perspective to extract significant attributes from real wine tourism reviews. Second, the authors conduct a comparative analysis between Chinese wine tourists who travel domestically and those who travel abroad. The third novel aspect of this study is the application of IPA based on textual review data in the context of wine tourism. Fourth, by integrating topic modeling with qualitative interviews, the authors use a mixed-method approach to gain deeper insights into the experiences of Chinese wine tourists.

Improving productivity and efficiency has always been crucial for industrial companies to remain competitive. In recent years, the topic of environmental impact has become increasingly important. Published research indicates that environmental and economic goals can enforce or rival each other. However, few papers have been published that address the interaction and integration of these two goals.

In this paper, we identify both, synergies and trade-offs based on a systematic review incorporating 66 publications issued between 1992 and 2021. We analyze, quantify and cluster examples of conjunctions of ecological and economic measures and thereby develop a framework for the combined improvement of performance and environmental compatibility.

Our findings indicate an increased significance of a combined consideration of these two dimensions of sustainability. We found that cases where enforcing synergies between economic and ecological effects were identified are by far more frequent than reports on trade-offs. For the individual categories, cost savings are uniformly considered as the most important economic aspect while, energy savings appear to be marginally more relevant than waste reduction in terms of environmental aspects.

No previous literature review provides a comparable graphical treatment of synergies and trade-offs between cost savings and ecological effects. For the first time, identified measures were classified in a 3 × 3 table considering type and principle.

The purpose of this paper is to give an overview of existing literature on post-traumatic growth (PTG), particularly in the ways that it relates to grief.

This narrative literature review brings together 125 sources and presents them in a readable way.

There is a great deal of evidence to suggest that PTG can come from grief. This is not always the case, however.

This review presents only a selection of the existing literature – the review is not systematic. However, this allows for a narrative to be crafted, to aid readability.

Suggestions for future research are made throughout, and potential therapeutic applications are mentioned.

This paper discusses stigma, in the form of “disenfranchised grief”. In this, social pressures and expectations affect how a person processes their grief psychologically. While movements to increase discourse and reduce stigma are on the rise, more is needed.

This review guides readers through existing literature, providing a wide overview of the topic of PTG in grief.

This study aims to conduct a complete bibliometric analysis of published research to develop a solid roadmap for future research into developing young adults’ mental health and resilience and to further the body of knowledge and scientific advancement in this area.

A literature search using VOSviewer, through Web of Science, was conducted to identify young adult mental health and resilience studies. A total of 226 studies were identified between 1993 and the end of June 2022, as the number of published studies increased. Extracted data included citation, co-citation, keyword and co-word analyses.

This study evaluates the current knowledge structure of young adult mental health and resilience through co-citation analysis and discovers the directions and trends of popular topics in young adult mental health and resilience through co-word analysis. It suggests that legislators and physicians should aim to provide social support via screening and interventions, especially for young adults struggling with disruption in their social lives.

Using a bibliometric approach, the findings provide a robust roadmap for further investigation into young adult mental health and resilience evolution. This method will further advance the knowledge and progress of this field, as this research is the first attempt at doing so – to the best of the authors’ knowledge.