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Literature on choice has predominantly focused on selection decisions rather than rejection decisions. Research on rejection decisions has also only studied rejecting one option from two alternatives. This research aims to study the differences in decision confidence and satisfaction in rejection decisions between choice sets of small and large sizes.

The authors conducted three behavioral experiments in which they first tested the overall effect (Experiment 1) and then found out whether regulatory focus (Experiment 2) and the attractiveness of options (Experiment 3) moderated it.

The authors observed that decision satisfaction increased when rejecting larger (vs smaller) choice sets. Decision confidence mediated it (Experiment 1). The effect was strongest when participants had a prevention focus (Experiment 2) and when they were rejecting relatively unattractive options (Experiment 3).

This research expands the understanding of how individuals make rejection-based decisions and in particular how individuals make choices for one option out of many as in the selection-based choice overload literature.

The authors show how choice sets of varying sizes affect rejection decisions commonly faced by managers and consumers. This research provides implications for improving confidence and satisfaction, both of which are important elements of everyday decision-making, by suggesting that choice outcomes may differ depending on whether one is making a selection or a rejection decision and whether the choice set size is small or large.

This is the first study to examine rejection decisions with more than two alternatives. The findings complement the large body of work on the choice overload effect that focuses on selection decisions.

Smartphone multitasking behavior has become prevalent in our daily lives, yet factors influencing smartphone multitasking behavior have not been fully investigated. This study aimed to examine the roles of a set of demographic, personality and motivational factors on smartphone multitasking behavior, and how these factors were related to general and application-specific types of smartphone multitasking behavior.

A cross-sectional study was conducted in which 2,659 smartphone users were invited to complete an online survey on smartphone multitasking behavior. Hierarchical multiple regression analyses were used to examine the roles of demographic, personality and motivational factors on smartphone multitasking behavior.

The results showed that, in general, demographic factors, such as gender, age, occupation status, education and smartphone usage time significantly predicted smartphone multitasking behavior. People characterized by agreeableness, extraversion, neuroticism and openness to experience were more likely to multitask with smartphones. Information seeking, efficiency and habit motivations were identified as major motivational factors for smartphone multitasking behavior. The roles of demographic, personality and motivational factors differed much across varied types of application-specific smartphone multitasking behavior.

This study extends and advances the literature on media multitasking, smartphone multitasking in particular, by identifying a set of demographic, personality and motivational factors as antecedents of smartphone multitasking behavior. In addition, this study revealed the differentiated roles of the above-mentioned factors across varied types of smartphone application usages. The findings provide important implications for practitioners to tailor smartphone applications and services to different target smartphone users and use situations.

This paper aims to provide researchers and practitioners with an understanding of abusive supervision in the context of hospitality. It seeks to conduct a comprehensive review of the area and offer recommendations for future research by exploring the antecedents, consequences, mechanisms and designs of research on abusive supervision.

Content analysis was conducted to review and analyze studies on abusive supervision in the context of hospitality. Previous studies were searched in the EBSCO, Scopus, Web of Science and Google Scholar electronic databases.

In total, 36 referred articles related to abusive supervision in hospitality were reviewed across four key areas, namely, antecedents, consequences, mechanisms and research design. After reviewing the research on abusive supervision in the context of hospitality, this paper offers future research directions with respect to research focus and research design.

This paper only included English articles from peer-reviewed journals on abusive supervision. The number of reviewed articles was relatively small. This limitation may have arisen because abusive supervision is a new research field and is still a sensitive topic.

The results of this study may encourage managers to minimize or even halt abusive supervision. From an organizational perspective, formal policies may be developed to regularize supervisors’ behavior. In turn, employees could use this paper to learn further about abusive behavior and how to handle it effectively.

The review highlighted the negative consequences of abusive supervision. Managers should urgently realize the seriousness of abusive supervision and develop effective policies to minimize its negative effect.

This paper contributes to the emerging literature on abusive supervision in the context of hospitality by identifying key research trends and framing the outlines of empirical studies. It identifies research gaps, and as the first review of abusive supervision in hospitality, it may encourage researchers to explore the topic on the basis of the characteristics of the sector and offer suggestions for future research.

Electricity consumption prediction has been an important topic for its significant impact on electric policies. Due to various uncertain factors, the growth trends of electricity consumption in different cases are variable. However, the traditional grey model is based on a fixed structure which sometimes cannot match the trend of raw data. Consequently, the predictive accuracy is variable as cases change. To improve the model's adaptability and forecasting ability, a novel fractional discrete grey model with variable structure is proposed in this paper.

The novel model can be regarded as a homogenous or non-homogenous exponent predicting model by changing the structure. And it selects the appropriate structure depending on the characteristics of raw data. The introduction of fractional accumulation enhances the predicting ability of the novel model. And the relative fractional order r is calculated by the numerical iterative algorithm which is simple but effective.

Two cases of power load and electricity consumption in Jiangsu and Fujian are applied to assess the predicting accuracy of the novel grey model. Four widely-used grey models, three classical statistical models and the multi-layer artificial neural network model are taken into comparison. The results demonstrate that the novel grey model performs well in all cases, and is superior to the comparative eight models.

A fractional-order discrete grey model with an adaptable structure is proposed to solve the conflict between traditional grey models' fixed structures and variable development trends of raw data. In applications, the novel model has satisfied adaptability and predicting accuracy.

The purpose of this study is to deepen our understanding of the well-being of transient organizations/groups and to use this to develop a novel conceptual framework of gig worker well-being during times of crisis.

A qualitative approach was adopted combining in-depth semi-structured interviews and daily diaries. Twenty-two workers working in the sharing economy were recruited. Thematic analysis was conducted for the diary and interview data.

The findings illustrate a complex picture of sharing economy workers’ four dimensions of well-being, including physical, subjective, psychological and social well-being. A number of the COVID-19 pandemic contexts, such as more time, restriction, economic recession and uncertainty, were seen to influence these workers’ well-being in different ways including both positive and negative impacts. The precarious nature of gig work within the sharing economy was also found influential, which includes flexibility, uncertainty, temporality and diversity. Furthermore, the specific contexts of the hospitality, tourism and event industry (such as labor-intensive, low esteem, self-value and purpose in life) had also impacted gig workers physical and psychological well-being in various ways.

This study complements the gig workers’ view of the sharing economy by investigating their well-being during the COVID-19 pandemic. In addition, this study reveals the complex and various influences hospitality, tourism and events industry contexts made, amplified by the pandemic. Methodologically, the daily diary approach applied in this research has captured gig workers’ instant feelings and thoughts, which enriches the current understanding of gig workers’ well-being.

From the findings and the newly developed conceptual framework, practical implications are proposed focusing on how the tourism, hospitality and event industries should look after their gig workers’ well-being in the COVID-ized environment. From the physical well-being perspective, businesses should consider partnering with gym operators to provide corporate packages or discounted membership to their gig workers. From psychological well-being perspective, a recognition system integrating gig workers would be useful to strengthen gig workers’ perception of value in their jobs. In addition, technology can be used to introduce more resources to their gig workers, particularly when distancing.

A conceptual framework is developed, which captures the influence of both “internal” and “external” determinants of gig worker well-being during times of crisis. This research contributes to theory by developing a framework of well-being in the context of the sharing economy, as well as explicitly addressing how the uncertainty and precariousness of sharing economy work and the hospitality, tourism and event industry contexts relate to well-being. This model is likely to have applicability beyond COVID-19 as the pandemic made clear many existing challenges – rather than just simply creating new ones.

Special education in China has lagged behind regular education for many years, however, the past few decades, the government has made considerable efforts to develop and improve the special education system. While the citizens of China have had a generic moral interest in disability since ancient times, the development of special education schools did not occur until American and European missionaries started schools for the visually and hearing impaired in the 19th century. The next major influence in the development of the special education system occurred with China’s Cultural Revolution in 1978. Interestingly, there is not any exclusive legislation on special education but in the 1980s, the government started Learning in Regular Classrooms (LRC), which is China’s version of inclusion. LRC has progressed rapidly the past two decades; however, the quality of instruction is low due to a lack of specialists, a shortage of personnel, inadequate funding, and limited technology as well as other barriers that are delineated in the chapter. The chapter emphasizes the government’s recent efforts in in-service teacher training, the preparation of preservice teachers, working with families, developing community rehabilitation training programs, and implementing evidence-based practices. Special education in China today is at a good place but it has quite a way from the ideal situation.

This paper aims to amplify the retrieval and utilization of historical newspapers through the application of semantic organization, all from the vantage point of a fine-grained knowledge element perspective. This endeavor seeks to unlock the latent value embedded within newspaper contents while simultaneously furnishing invaluable guidance within methodological paradigms for research in the humanities domain.

According to the semantic organization process and knowledge element concept, this study proposes a holistic framework, including four pivotal stages: knowledge element description, extraction, association and application. Initially, a semantic description model dedicated to knowledge elements is devised. Subsequently, harnessing the advanced deep learning techniques, the study delves into the realm of entity recognition and relationship extraction. These techniques are instrumental in identifying entities within the historical newspaper contents and capturing the interdependencies that exist among them. Finally, an online platform based on Flask is developed to enable the recognition of entities and relationships within historical newspapers.

This article utilized the Shengjing Times·Changchun Compilation as the datasets for describing, extracting, associating and applying newspapers contents. Regarding knowledge element extraction, the BERT + BS consistently outperforms Bi-LSTM, CRF++ and even BERT in terms of Recall and F1 scores, making it a favorable choice for entity recognition in this context. Particularly noteworthy is the Bi-LSTM-Pro model, which stands out with the highest scores across all metrics, notably achieving an exceptional F1 score in knowledge element relationship recognition.

Historical newspapers transcend their status as mere artifacts, evolving into invaluable reservoirs safeguarding the societal and historical memory. Through semantic organization from a fine-grained knowledge element perspective, it can facilitate semantic retrieval, semantic association, information visualization and knowledge discovery services for historical newspapers. In practice, it can empower researchers to unearth profound insights within the historical and cultural context, broadening the landscape of digital humanities research and practical applications.

This paper analyzes the connectedness with network among the major cryptocurrencies, the G7 stock indexes and the gold price over the coronavirus disease 2019 (COVID-19) pandemic period, in 2020.

This study used a multivariate approach proposed by Diebold and Yilmaz (2009, 2012 and 2014).

For a stock index portfolio, the results of static connectedness showed a higher independence between the stock markets during the COVID-19 crisis. It is worth noting that in general, cryptocurrencies are diversifiers for a stock index portfolio, which enable to reduce volatility especially in the crisis period. Dynamic connectedness results do not significantly differ from those of the static connectedness, the authors just mention that the Bitcoin Gold becomes a net receiver. The scope of connectedness was maintained after the shock for most of the cryptocurrencies, except for the Dash and the Bitcoin Gold, which joined a previous level. In fact, the Bitcoin has always been the biggest net transmitter of volatility connectedness or spillovers during the crisis period. Maker is the biggest net-receiver of volatility from the global system. As for gold, the authors notice that it has remained a net receiver with a significant increase in the network reception during the crisis period, which confirms its safe haven.

Overall, the authors conclude that connectedness is shown to be conditional on the extent of economic and financial uncertainties marked by the propagation of the coronavirus while the Bitcoin Gold and Litecoin are the least receivers, leading to the conclusion that they can be diversifiers.

本文分析於2020年2019冠狀病毒病肆虐期間、主要的加密貨幣、七國集團 (G7) 股價指數與黃金價格三者之間在網絡上的連通性。

分析使用迪博爾德和耶爾馬茲 (Diebold and Yilmaz (2009, 2012, 2014)) 提出的多變量分析法。

就一個股票指數投資組合而言,靜態連結的結果顯示、在2019冠狀病毒病肆虐期間,股票市場之間有更高的獨立性。值得我們注意的是:一般來說,加密貨幣在股票指數投資組合起著多元化投資作用,這可減低不穩定性,尤其是在危機時期。動態連結的結果與靜態連結的結果沒有顯著的分別。我們剛提到、比特幣黃金已成為純接收者。除了處於先前水平的達世幣和比特幣黃金外,就大部分的加密貨幣而言,連通的範圍在衝擊後都得以維持。事實上,在這危機時期,比特幣一直是波動性連結或溢出的最大淨傳播者。掛單者 (Maker) 是從全球系統中出現的最大波動淨接收者。至於黃金,我們注意到在危機時期、它仍然是在網絡接收方面擁有顯著增長的淨接收者,這確認其為安全的避難所。

總的來說,我們的結論是:連通性被確認為取決於標誌著受廣泛傳播的冠狀病毒影響下的經濟和金融欠缺穩定的程度,而比特幣黃金和萊特幣則是最小的接收者,這帶出一個結論、就是:比特幣黃金和萊特幣、可以成為多元化投資項目。

China's urbanization is a traditional development way of high energy consumption, low efficiency and extensive type. The resulting ecological environment damage and other problems seriously restrict the healthy and sustainable development of the city. Based on this, aiming at the complexity of the system, the multi-objective classification matrix method of fuzzy decision was selected, and the life cycle assessment model of the urban innovation ecosystem under the background of new urbanization in China was constructed. The research on the innovative ecological system planning of a heavy industrial city was carried out, and the wall was greening in the top space of the building. In the town planning, wind energy, solar power system, green corridor and ground floor were designed. Practice has proved that introducing innovative ecosystem into urbanization has played an important role in purifying urban air, increasing urban greening, improving energy structure and air pollution.

This paper aims to explore various tensions related to the diffusion and reception of the New Qing History (NQH) in China, and more specifically, it aims at underlying a recurrent tension within the core of this debate, between a Global and a Nationalist historical narrative.

The author’s focus is to analyze various texts published in China between 2006 and 2018.

The author argues that the intensity of the current debate is partly related on the one hand, to the fact that NQH undermines various aspects of China’s Nationalist teleology and territorial claims and, on the other hand, to the basic difficulty of accepting the coexistence of various historical representations that are risking to weaken contemporary’s justifications of its rising schemes.

The text presents an original reading of some important issues raised by the NQH debate.