Yixuan Zhao, Guangyuan He, Danxia Wei and Shuming Zhao
The purpose of this study is to explore the mechanism of digitalized transformation in organizations’ human resource management (HRM). This study summarizes three basic factors…
Abstract
Purpose
The purpose of this study is to explore the mechanism of digitalized transformation in organizations’ human resource management (HRM). This study summarizes three basic factors driving the digital transformation process in China: level of perception, level of application and speed of transformation.
Design/methodology/approach
This study analyzes the strategic transformation process of HRM in Haier, Hisense and Chambroad to explore the human resource digital transformation mechanism in Chinese enterprises.
Findings
The results of this study show that three HR value chain models can be constructed based on how well HRM deals with business: the efficiency-oriented HRM value chain, quasi-business-oriented HRM value chain and business-oriented HRM value chain. The basic factors – level of perception, level of application and speed of transformation – are observed in the entire HRM digital transformation process.
Originality/value
This study provides theoretical and empirical insights for enterprises to explore the value of digital technology in HRM and facilitate the digital transformation of HRM.
Details
Keywords
Dexin Chen, Hongyuan He, Zhixin Kang and Wei Li
This study aims to review the current one-step electrodeposition of superhydrophobic coatings on metal surfaces.
Abstract
Purpose
This study aims to review the current one-step electrodeposition of superhydrophobic coatings on metal surfaces.
Design/methodology/approach
One-step electrodeposition is a versatile and simple technology to prepare superhydrophobic coatings on metal surfaces.
Findings
Preparing superhydrophobic coatings by one-step electrodeposition is an efficient method to protect metal surfaces.
Originality/value
Even though there are several technologies, one-step electrodeposition still plays a significant role in producing superhydrophobic coatings.
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Fengjun Tian, Yang Yang, Zhenxing Mao and Wenyue Tang
This paper aims to compare the forecasting performance of different models with and without big data predictors from search engines and social media.
Abstract
Purpose
This paper aims to compare the forecasting performance of different models with and without big data predictors from search engines and social media.
Design/methodology/approach
Using daily tourist arrival data to Mount Longhu, China in 2018 and 2019, the authors estimated ARMA, ARMAX, Markov-switching auto-regression (MSAR), lasso model, elastic net model and post-lasso and post-elastic net models to conduct one- to seven-days-ahead forecasting. Search engine data and social media data from WeChat, Douyin and Weibo were incorporated to improve forecasting accuracy.
Findings
Results show that search engine data can substantially reduce forecasting error, whereas social media data has very limited value. Compared to the ARMAX/MSAR model without big data predictors, the corresponding post-lasso model reduced forecasting error by 39.29% based on mean square percentage error, 33.95% based on root mean square percentage error, 46.96% based on root mean squared error and 45.67% based on mean absolute scaled error.
Practical implications
Results highlight the importance of incorporating big data predictors into daily demand forecasting for tourism attractions.
Originality/value
This study represents a pioneering attempt to apply the regularized regression (e.g. lasso model and elastic net) in tourism forecasting and to explore various daily big data indicators across platforms as predictors.
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Li Ding and Caifen Jiang
This study aims to explore the impact of tourists’ perceptions of two rural destination attractiveness dimensions on tourists’ environmentally responsible behavioral intentions…
Abstract
Purpose
This study aims to explore the impact of tourists’ perceptions of two rural destination attractiveness dimensions on tourists’ environmentally responsible behavioral intentions (ERBI). Further, the mediating effects of tourists’ green self-identity on the relationship between the perception of rural destination attractiveness and ERBI are investigated.
Design/methodology/approach
This study collected survey data from 188 tourists who had visiting experiences in rural attractions located in the Guangdong Province of China. Partial least squares structural equation modeling (PLS-SEM) was used to test the proposed hypotheses.
Findings
The results found that rural destination specialty fresh food attractiveness perceived by tourists was positively associated with their ERBI. Moreover, tourists’ green self-identity positively mediated the perception of rural destination attractiveness and ERBI.
Originality/value
This study explains how the tourists’ perceptions of two rural destination attractiveness dimensions influence their ERBI. By exploring the mediating role of tourists’ green self-identity, this study also emphasizes the transforming mechanism from tourists’ perceived experience to their ERBI. The study provides insights into nature-based tourism destination management and sustainability practices.