Chunlan Li, Xinwu Xu, Hongyu Du, Debin Du, Walter Leal Filho, Jun Wang, Gang Bao, Xiaowen Ji, Shan Yin, Yuhai Bao and Hossein Azadi
The paper aims to investigate the possible changes in mean temperature in the Mongolian Plateau associated with the 1.5 and 2°C global warming targets and how snow changes in the…
Abstract
Purpose
The paper aims to investigate the possible changes in mean temperature in the Mongolian Plateau associated with the 1.5 and 2°C global warming targets and how snow changes in the Mongolian Plateau when the mean global warming is well below 2°C or limited to 1.5°C.
Design/methodology/approach
In total, 30 model simulations of consecutive temperature and precipitation days from Coupled Model Inter-comparison Project Phase 5 (CMIP5) are assessed in comparison with the 111 meteorological monitoring stations from 1961–2005. Multi-model ensemble and model relative error were used to evaluate the performance of CMIP5 models. Slope and the Mann–Kendall test were used to analyze the magnitude of the trends and evaluate the significance of trends of snow depth (SD) from 1981 to 2014 in the Mongolian Plateau.
Findings
Some models perform well, even better than the majority (80%) of the models over the Mongolian Plateau, particularly HadGEM2-CC, CMCC-CM, BNU-ESM and GFDL-ESM2M, which simulate best in consecutive dry days (CDD), consecutive wet days (CWD), cold spell duration indicator (CSDI) and warm spell duration indicator (WSDI), respectively. Emphasis zones of WSDI on SD were deeply analysed in the 1.5 and 2 °C global warming period above pre-industrial conditions, because it alone has a significant negative relation with SD among the four indices. It is warmer than before in the Mongolian Plateau, particularly in the southern part of the Mongolian Plateau, indicating less SD.
Originality/value
Providing climate extremes and SD data sets with different spatial-temporal scales over the Mongolian Plateau. Zoning SD potential risk areas and proposing adaptations to promote regional sustainable development.
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Peide Liu, Xiaoxiao Liu and Hongyu Yang
Accurately judging the quality of marine economic development is the premise of grasping the level and status of marine economic development. In order to scientifically evaluate…
Abstract
Purpose
Accurately judging the quality of marine economic development is the premise of grasping the level and status of marine economic development. In order to scientifically evaluate the development quality of regional marine economy, the purpose of this paper is to select the marine area of Qingdao as the research object, and construct a marine economic development quality evaluation index system with 16 indicators.
Design/methodology/approach
The raw data is normalized by the range conversion method, and the weight of the index is determined by the information entropy model. Further, the grey relational analysis (GRA) method is used to evaluate the quality of marine economic development of Qingdao from 2012 to 2017.
Findings
The results show that the marine economic development capacity of Qingdao is with the generally increasing trend, the total marine economy is with on the rising trend, the marine storage and transportation capacity, and marine ecological environment are first decreased, and then increased. The utilization of marine resources is generally decreasing, and the comprehensive management of oceans varies with the changes of environment and economy. Therefore, in view of the development capacity of marine economy, the coordinated development of economy and environment should be carried out.
Originality/value
This paper uses the GRA to evaluate the quality of marine economic development and provides a reference for the development of marine economy in Qingdao.
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Talat Islam, Saleha Sharif, Hafiz Fawad Ali and Saqib Jamil
Nurses' turnover intention has become a major issue in developing countries with high power distance cultures. Therefore, the authors attempt to investigate how turnover intention…
Abstract
Purpose
Nurses' turnover intention has become a major issue in developing countries with high power distance cultures. Therefore, the authors attempt to investigate how turnover intention among nurses' can be reduced through paternalistic leadership (PL). The authors further investigate the mediating role of job satisfaction between the associations of benevolent, moral and authoritarian dimensions of PL with turnover intention. Finally, the authors examined perceived organizational support (POS) as a conditional variable between job satisfaction and turnover intention.
Design/methodology/approach
The authors collected data from 374 nurses working in public and private hospitals of high power distance culture using a questionnaire-based survey on convenience basis.
Findings
Structural equation modeling confirms that benevolent and moral dimensions of PL positively affect nurses' job satisfaction which helps them reduce their turnover intention. While the authoritarian dimension of PL negatively affects job satisfaction to further enhance their turnover intention. In addition, the authors noted POS as a conditional variable to trigger the negative effect of job satisfaction on turnover intention.
Research limitations/implications
The authors used a cross-sectional design to collect responses and ensured the absence of common method variance through Harman's Single factor test.
Originality/value
This study identified the mechanism (job satisfaction and POS) through which benevolent, moral and authoritative dimensions of PL predict turnover intention among nurses working in high power distance culture.
研究目的
護士有離職意向,在擁有高權力距離文化的發展中國家,已成為一個重大的問題。因此,我們擬探討如何可以透過採用家長式領導、把護士離職的意欲減低,繼而研究工作滿足感,在離職意向與家長式領導中仁慈、道德和獨裁這三個層面的關係中所起的中介作用。最後,我們就組織支持感,作為是工作滿足感與離職意向之間的一個條件變數,進行了研究。
研究設計/方法/理念
本研究透過採用在便利的基礎上進行的問卷調查,從374名在高權力距離文化的公營和私營醫院內工作的護士取得數據,進行分析。
研究結果
結構方程模型證實了家長式領導中的仁慈和道德這兩個層面,會對可減低護士離職意欲的工作滿足感,產生積極的影響。家長式領導中的獨裁層面、則會對護士的工作滿足程度產生負面的影響,繼而增強其離職意欲。而且,我們確認了組織支持感是一個會增強工作滿足感與離職意向之間負相聯的條件變數。
研究的局限/啟示
我們以橫斷面的設計法來收集回應,並透過採用哈曼 (Harman) 的單因素檢定法,來確保共同方法變異不會存在。
研究的原創性/價值
本研究確定了一個 (工作滿足感與組織支持感) 機制,透過這機制,家長式領導中的仁慈、道德和獨裁這三個層面可預測於高權力距離文化工作的護士的離職意向。
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This study investigated the factors that influence customer satisfaction with AI-driven services by focusing on chatbot agents. The conceptual model included psychological and…
Abstract
Purpose
This study investigated the factors that influence customer satisfaction with AI-driven services by focusing on chatbot agents. The conceptual model included psychological and social factors, such as trust, perceived social presence, competence perception, social-oriented communication style, warmth perception, subjective norms and attachment anxiety.
Design/methodology/approach
A quantitative methodology was employed utilising a survey conducted among 525 consumers who interacted with chatbot services. The data were analysed using structural equation modelling (Smart-PLS 4.0) to test the proposed hypotheses.
Findings
The study revealed that social-oriented communication, perceptions of competence and warmth, trust and subjective norms significantly enhanced customer satisfaction with chatbots. Trust was critical in fostering satisfaction, whereas perceived social presence and attachment anxiety had minimal impact. The findings suggest that despite the emphasis on social presence, its influence on satisfaction may depend on contextual factors that were not captured in this study.
Originality/value
This study extended the Technology Acceptance Model and Stereotype Content Model by integrating factors such as perceived social presence, trust, competence perception, social-oriented communication style, warmth perception, subjective norm and attachment anxiety. Challenging conventional assumptions on the role of social presence and attachment anxiety, the study provides new insights into the complex dynamics of human–chatbot interactions, offering practical implications for improving chatbot design and enhancing user experience that emphasise the importance of trust, competence and social-oriented communication in customer satisfaction.
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Spam emails classification using data mining and machine learning approaches has enticed the researchers' attention duo to its obvious positive impact in protecting internet…
Abstract
Spam emails classification using data mining and machine learning approaches has enticed the researchers' attention duo to its obvious positive impact in protecting internet users. Several features can be used for creating data mining and machine learning based spam classification models. Yet, spammers know that the longer they will use the same set of features for tricking email users the more probably the anti-spam parties might develop tools for combating this kind of annoying email messages. Spammers, so, adapt by continuously reforming the group of features utilized for composing spam emails. For that reason, even though traditional classification methods possess sound classification results, they were ineffective for lifelong classification of spam emails duo to the fact that they might be prone to the so-called “Concept Drift”. In the current study, an enhanced model is proposed for ensuring lifelong spam classification model. For the evaluation purposes, the overall performance of the suggested model is contrasted against various other stream mining classification techniques. The results proved the success of the suggested model as a lifelong spam emails classification method.