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Micro-credentials, which have been widely used in re-skilling and skills development, provide advantages for improvement in the professional career. As a complementary approach to career and professional development in formal education, it can be said that micro-credentials are preferred for supporting professional development and/or to acquiring new skills to find a job in a new field (Fisher and Leder, 2022). This study aims to provide a detailed perspective on conceptual framework and the use of micro-credentials in higher education.

Many questions have arisen regarding micro-credentials, applicability, quality assurance, inclusion in formal educational settings and how to include them in traditional education systems, especially in higher education institutions. Oliver (2019) emphasizes that there is little research on micro-credentials or their derivatives in the literature. There is still uncertainty on micro-credential on behalf of students, employers or employees. In addition, although there are studies on micro-credentials in the USA, Europe and Australia, there is very little research around countries such as Turkey (Yilik, 2021). This study discusses the use of micro-credentials in the world and their reflections on higher education.

As it is shown in this article, micro-credentials have the potential to challenge or complement traditional ways of how learning is taking place, understood, recognized and certified. Research on micro-credentials is still in its infancy and could benefit a lot from more research. Especially research on employers' recognition and appreciation of digital credentials needs to be studied. Also, empirically confirming propositions of literature on micro-credentials could be beneficial.

This study is original in terms of discussing the use of micro-credential in the world and their reflections on higher education and presenting suggestions on this subject.

Radical changes in consumer habits induced by the coronavirus disease (COVID-19) pandemic suggest that the usual demand forecasting techniques based on historical series are questionable. This is particularly true for hospitality demand, which has been dramatically affected by the pandemic. Accordingly, we investigate the suitability of tourists’ activity on Twitter as a predictor of hospitality demand in the Way of Saint James – an important pilgrimage tourism destination.

This study compares the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) time-series model with that of the SARIMA with an exogenous variables (SARIMAX) model to forecast hotel tourism demand. For this, 110,456 tweets posted on Twitter between January 2018 and September 2022 are used as exogenous variables.

The results confirm that the predictions of traditional time-series models for tourist demand can be significantly improved by including tourist activity on Twitter. Twitter data could be an effective tool for improving the forecasting accuracy of tourism demand in real-time, which has relevant implications for tourism management. This study also provides a better understanding of tourists’ digital footprints in pilgrimage tourism.

This study contributes to the scarce literature on the digitalisation of pilgrimage tourism and forecasting hotel demand using a new methodological framework based on Twitter user-generated content. This can enable hospitality industry practitioners to convert social media data into relevant information for hospitality management.

2019冠狀病毒病引致消費者習慣有根本的改變; 這些改變顯示，根據歷史序列而運作的慣常需求預測技巧未必是正確的。這不確性尤以受到大流行極大影響的酒店服務需求為甚。因此，我們擬探討、若把在推特網站上的旅遊活動視為聖雅各之路 (一個重要的朝聖旅遊聖地) 酒店服務需求的預測器，這會否是合適的呢?

本研究比較 SARIMA 時間序列模型與附有外生變數 (SARIMAX)模型兩者在預測旅遊及酒店服務需求方面的表現。為此，研究人員收集在推特網站上發佈的資訊，作為外生變數進行研究。這個樣本涵蓋於2018年1月至2022年9月期間110,456個發佈資訊。

研究結果確認了傳統的時間序列模型，若涵蓋推特網站上的旅遊活動，則其對旅遊需求方面的預測會得到顯著的改善。推特網站的數據，就改善預測實時旅遊需求的準確度，或許可成為有效的工具; 而這發現對旅遊管理會有一定的意義。本研究亦讓我們進一步瞭解朝聖旅遊方面旅客的數碼足跡。

現存文獻甚少探討朝聖旅遊的數字化，而本研究不但在這方面充實了有關的文獻，還使用了一個根據推特網站上使用者原創內容嶄新的方法框架，進行分析和探討。這會幫助酒店從業人員把社交媒體數據轉變為可供酒店管理之用的合宜資訊。

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

The literature on ‘mixed’ families (in which members are socially viewed as ‘different’ due to their varying ethnicities and/or nationalities) identifies several stakes of mixedness. One of them arises from childbirth, after which parents need to give name(s) to their offspring. How does the parent–child dyad understand the giving of names in their mixed family? What does naming children unveil regarding interpersonal interactions and the value of children within this social unit? The chapter delves into these questions through a case study of forenaming children in Filipino-Belgian families in Belgium. Interview data analysis reveals two modes of forenaming in these families: individualisation through single forenames and reinforcement of collective affiliation through compound forenames. Through the analytical framework of social relatedness, this chapter uncovers the way the act of naming a child bridges families based on biological and social ties, generations, and parents' nations of belonging in their transnational spaces. The complex process of naming reflects the power dynamics not only within the parental couple but also within the wider set of social relations. Although the use of forename(s) in everyday life and in legal terms differ, the value of children in the mixed families studied lies in their symbolic role as social bridges linking generations and non-biological relationships, the then and now, and the here and there.

This paper aims to analyze the ethical responses of the fashion industry to the first wave of the COVID-19 pandemic when the entire world was shocked by the rapid spread of the virus. The authors describe lessons from emergency ethics of care in the fashion industry during the initial months of COVID-19, which can assist fashion managers in improving ethical decisions in future operations.

Rapid qualitative research methods were employed by conducting real-time, in-depth interviews with key informants from multinational fashion companies operating in Spain, a severely affected region. A content analysis of news articles published during the first months of 2020 was conducted.

Five critical disruptions in the fashion industry were identified: (1) changes in public needs, (2) transportation and distribution backlogs, (3) defective and counterfeit supplies, (4) stakeholder relationships at stake and (5) managers' coping challenges. Additionally, five business survival responses with a strong ethics of care component were identified, implemented by some fashion companies to mitigate the damage: (1) adapting production for public well-being, (2) enhancing the flexibility of logistic networks, (3) emphasizing quality and innovation, (4) reinventing stakeholder collaborations and (5) practicing responsible leadership.

Despite the well-documented controversies surrounding unethical practices within the fashion industry, even during COVID-19, our findings inform managers of the potential and capability of fashion companies to operate more responsibly. The lessons learned can guide fashion companies' operations in a post-pandemic society. Furthermore, they can address other grand challenges, such as natural disasters, geopolitical conflicts and climate change.