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The study aims to give a descriptive account of university students’ engagement with non-learning-related activities during class time and explore the relationship between off-task multitasking and learning. The predictive factors for off-task multitasking from individual, social and class-related dimensions are also examined.

Contextualized in a comprehensive university in Hong Kong, the study adopts a survey design and involves 79 samples.

The data show that Hong Kong university students are avid users of mobile phones and heavily engage with digital devices. Off-task multitasking with mobile phones is a common phenomenon, yet not related to learning performance. Among the various media and apps on mobile phones, instant messenger stands out as the most frequently used app on a daily basis and inside the classroom. The individual device-use habit and classroom engagement are significant predictors for off-task multitasking during class time.

This paper will allow teachers and students to be more aware of the causes and effects of off-task multitasking behaviors during class time and derive practical guidance and strategies to pay heed to and resist the disruptive influence of technologies on learning.

The existing scholarly work show a mixed and incomplete picture regarding the effects and determining factors of students’ multitasking. This study includes three variables from individual, social and teaching/learning dimensions and seeks to evaluate their predictive strengths. The results of the study will deepen our understanding of the patterns of off-task multitasking.

The purpose of this paper is to compare the use of WebCT in support of online and blended learning in the Faculty of Architecture of a Hong Kong university.

Paper questionnaires are used to collect students' experiences, perceptions and attitudes towards the online course they have undertaken. Focus group interviews are conducted with two groups of students at the end of the semester. In addition, both formal and informal interviews are conducted with the instructor to understand his perceptions and perspectives.

In general, students prefer to have course management systems (CMS) as a supplement to face‐to‐face lectures rather than as its replacement. They perceive WebCT as mainly a platform for downloading materials and submitting assignments rather than a platform for teaching and learning. Learning fully online is perceived as self‐learning. The traditional lecture is considered more effective and efficient by many students to grasp concepts and principles.

This is an exploratory study at a Hong Kong university. However, it provides initial evidence to shed light on issues concerning the use of CMS in support of online and blended learning.

In order to ensure a more engaging and rewarding online learning experience, teacher's online presence should be strengthened. Other than teacher presence, the cognitive presence could be fostered through engineering the student‐to‐student interaction in co‐located settings.

The paper usefully demonstrates how the blended mode of learning that combines face‐to‐face and online learning has become increasingly popular, while the courses conducted entirely online are comparatively rare at universities in Hong Kong.

The course management system (CMS), as an evolving tool and innovation, is increasingly used to promote the quality, efficiency and flexibility of teaching and learning in higher education. This paper aims to examine students' experiences of CMSs across faculties at a comprehensive university in Hong Kong.

This is an exploratory study. With questionnaires as the means of data collection, the exploration focuses on: perceived usefulness of technologies for study, usage pattern of CMSs, students' perceptions of CMSs, user support preference, and self‐reported experiences.

The results show significant differences between academic levels of students in their uses and perceptions, and shed light on issues concerning technology, pedagogy, and implementation strategies of CMSs in higher education.

This is a small‐scale study and results will not produce generalizations that can be applied directly in other institutions. However, empirical studies in CMS implementation are relatively rare, and results of this study provide initial evidence to shed light on a number of issues concerning the implementation of CMSs.

On the whole, this study centering on university students' experiences and perceptions could inform the decision‐making process concerning successful implementation of CMSs.

Many studies concerning e‐learning have focused on practitioner perspective or course design and overlooked students' voices. In this study, students' experiences are central. In particular, the paper probes the possible differences between academic levels of students.

The purpose of this work is to construct a grey entropy comprehensive evaluation model to measure the regional green innovation vitality (GIV) of 31 provinces in China.

The traditional grey relational proximity and grey relational similarity degree are integrated into the novel comprehensive grey evaluation framework. The evaluation system of regional green innovation vitality is constructed from three dimensions: economic development vitality, innovative transformation power and environmental protection efficacy. The weights of each indicator are obtained by the entropy weight method. The GIV of 31 provinces in China is measured based on provincial panel data from 2016 to 2020. The ward clustering and K-nearest-neighbor (KNN) algorithms are utilized to explore the regional green innovation discrepancies and promotion paths.

The novel grey evaluation method exhibits stronger ability to capture intrinsic patterns compared with two separate traditional grey relational models. Green innovation vitality shows obvious regional discrepancies. The Matthew effect of China's regional GIV is obvious, showing a basic trend of strong in the eastern but weak in the western areas. The comprehensive innovation vitality of economically developed provinces exhibits steady increasing trend year by year, while the innovation vitality of less developed regions shows an overall steady state of no fluctuation.

The grey entropy comprehensive relational model in this study is applied for the measurement and evaluation of regional GIV, which improves the one-sidedness of traditional grey relational analysis on the proximity or similarity among sequences. In addition, a three-dimensional evaluation system of regional GIV is constructed, which provides the practical guidance for the research of regional development strategic planning as well as promotion paths.

A comprehensive grey entropy relational model based on traditional grey incidence analysis (GIA) in terms of proximity and similarity is proposed. The three-dimensional evaluation system of China's regional GIV is constructed, which provides a new research perspective for regional innovation evaluation and expands the application scope of grey system theory.

Given the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to construct a grey system forecasting model with intelligent parameters for predicting provincial electricity consumption in China.

First, parameter optimization and structural expansion are simultaneously integrated into a unified grey system prediction framework, enhancing its adaptive capabilities. Second, by setting the minimum simulation percentage error as the optimization goal, the authors apply the particle swarm optimization (PSO) algorithm to search for the optimal grey generation order and background value coefficient. Third, to assess the performance across diverse power consumption systems, the authors use two electricity consumption cases and select eight other benchmark models to analyze the simulation and prediction errors. Further, the authors conduct simulations and trend predictions using data from all 31 provinces in China, analyzing and predicting the development trends in electricity consumption for each province from 2021 to 2026.

The study identifies significant heterogeneity in the development trends of electricity consumption systems among diverse provinces in China. The grey prediction model, optimized with multiple intelligent parameters, demonstrates superior adaptability and dynamic adjustment capabilities compared to traditional fixed-parameter models. Outperforming benchmark models across various evaluation indicators such as root mean square error (RMSE), average percentage error and Theil’s index, the new model establishes its robustness in predicting electricity system behavior.

Acknowledging the limitations of traditional grey prediction models in capturing diverse growth patterns under fixed-generation orders, single structures and unadjustable background values, this study proposes a fractional grey intelligent prediction model with multiple parameter optimization. By incorporating multiple parameter optimizations and structure expansion, it substantiates the model’s superiority in forecasting provincial electricity consumption.

As the cost of completing a transaction, the green merger and acquisition (M&A) premium paid on mergers can influence whether the acquisition creates value or not. However, studies linking M&A premiums to firm value have had mixed results, even fewer studies have examined the effect of green M&A premiums on bidders’ firm value. The purpose of this paper is to investigate whether and how green M&A premiums affect firm value in the context of China’s heavy polluters.

Using 323 deals between 2008 and 2019 among China’s heavy polluters, this paper estimates with correlation analysis and multiple regression analysis.

Green M&A premiums are negatively associated with firm value. The results are more significant when firms adopt symbolic rather than substantive corporate social responsibility (CSR) strategies. Robustness and endogeneity tests corroborate the findings. The negative relation is stronger when acquiring firms have low governmental subsidy and environmental regulation, when firms have overconfident management, when firms are state-owned and when green M&A occurs locally or among provinces in the same region. This study also analyzes agency cost as an intermediary in the relationship between green M&A premium and firm value, which lends support to the agency-view hypothesis.

This study provides systemic evidence that green M&A premiums damage firm value through agency cost channel and the choice of CSR strategies from the perspective of acquirers. These findings enrich the literature on both the economic consequences of green M&A premiums and the determinants of firm value and provide a plausible explanation for mixed findings on the relationship between green M&A premiums and firm value.

The purpose of this paper is to realize dynamical grey incidence order of influencing factors of grain production in Henan province using grey systems theory.

Starting from choosing influence factors on grain production and dividing the 30 years (from 1979 to 2009 year) of grain production in Henan province into three periods, the authors calculate grey incidence degree between grain yield and every influencing factor by grey incidence analysis method, respectively, then obtain the grey incidence order of influencing factors in every period. Also based on the three grey incidence orders from different periods, the authors find a changeable tendency of influencing factors on grain production and key influencing factors on grain production in different periods. Finally, to keep Henan province grain production stable and sustainable, several policy suggestions are given.

The results are convincing: it is effective and powerful to analyze dynamically influencing factors of grain production using grey systems theory, and it is urgent to strengthen agricultural science and technology input, and pay close attention to the influence of dosage of pesticide and fertilizers on grain production.

Grey incidence analysis and findings exposed in the paper can be used by agricultural firms to optimize grain production plans, and by government to formulate reasonable agricultural production policies.

The paper succeeds in getting dynamical grey incidence order of influencing factors of grain production in Henan province using grey systems theory.

The purpose of this paper is to explore the effects of main meteorological factors on the mortality of urban residents and provide empirical evidence for the prevention of effects of climate changes.

Grey relational analysis (GRA) was used to analyse the interrelationships between meteorological factors and mortality among residents in Chaoyang District, Beijing, during the period between 1998 and 2008.

The changes of annual average mortality had a strong grey relation with temperature and relative humidity. The monthly average mortality (MAM) showed a strong grey relation with air pressure and the MAM in Summer season had a strong grey relation with air pressure, relative humidity and wind speed.

Meteorological factors including temperature, relative humidity, air pressure and wind speed are all related with mortality changes. GRA can well reveal the trend of the curve approximation between meteorological factors and mortality and can quantify the different approximation.

Legislation plays a vital role in solving carbon emissions reduction and climate change issues. China began to implement a low-carbon economic policy in 2010, but the effect of the policy needs to be evaluated. Accordingly, this paper aims to discuss China’s low-carbon policy through exploring the following two questions, namely, whether the policy effect reaches the expected goal and whether the policy effects will balance economic development and emission reduction. Then, the paper puts forward suggestions for the improvement of China’s low-carbon policy.

This paper is organized around three distinct aspects of policy effect evaluation. This paper uses the synthetic control method to construct a policy effect evaluation model and conducts a quasi-natural experiment. The paper selects annual panel data from 2003 to 2015, which is selected from 33 provinces. A comparative analysis of carbon dioxide emissions, energy consumption and economic development between Hubei Province and Liaoning Province.

The results reveal that the implementation of the low-carbon pilot province policy in 2010 has a significant impact on the emission reduction effect of Liaoning Province, but the impact on the emission reduction effect of Hubei Province is not significant. The carbon emission trading system implemented in 2012 has reduced the emission reductions in Hubei Province and Liaoning Province has achieved better emission reduction effects after the implementation of this policy. After the implementation of the policy, the economic development of Hubei Province has been improved, but it has not brought help to the economic development of Liaoning Province. These findings provide new insights into the use of an emissions trading system for improving economic development and ultimately facilitate the attainment of the broader goal of sustainability.

This paper proposes an innovative policy effect evaluation method by considering the status of unit gross domestic product, fixed asset investment in the energy industry, energy consumption, emission reduction technology innovation and other evaluation indicators. This paper contributes to broadening current methods of policy effect evaluation in China.

This paper attempts to extend classic absolute degree of grey incidence so that the extended model can be used for grey number sequences.

The classic absolute degree of grey incidence was extended according to basic principles of grey incidence analysis. First, modelling methods and theories of the classic grey incidences were summarized. Then, the zeroing starting operator in grey incidence was extended for grey sequence. Third, the parameters in classic incidence degree were redefined, and an absolute degree of grey incidence for grey number sequences was proposed. The degree can not only be applied to grey number sequence, but also contains the uncertain information of analysis result. Fourth, two non‐linear programming models were constructed to estimate the grey coverage interval of absolute degree of incidences. Finally, the comparison method of grey numbers was introduced for sorting the different absolute degrees of incidences.

A theoretically feasible absolute degree of grey incidence was constructed for grey sequence. A case study showed that the proposed incidence degree was an effective method for grey sequence incidence analysis.

The method exposed in the paper can be used for grey sequences clustering, grey decision making, multi‐attribute decision making theory, uncertain target recognition and other related fields.

The paper succeeded in establishing an incidence analysis model for grey sequences which was still a research gap in grey system theory.