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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.

This study aims to integrate conservation of resources theory discus the antecedents and consequences of cyberloafing as well as the boundary effect between cyberloafing and mental health.

The authors collected 431 valid questionnaires in four months. Each questionnaire was divided into two parts that had to be distributed. The interval between the first distribution and the second distribution was 15 days.

The research study revealed that employees’ Zhong-Yong thinking and cyberloafing promote mental health, and cyberloafing has a mediating effect. Mindfulness weakens the positive impact of cyberloafing on mental health.

The research study’s results break the stereotype that cyberloafing is not good for organizations. When managers allow employees to engage in cyberloafing at work, this is conducive to employees’ mental health, which can ensure the company’s sustainable development.

The booming social media attracts construction professionals (CPs) to express emotions caused by work pressure (WP) through online behaviors. Previous works focus on the analysis of WP and emotions but do not adequately consider how WP can be reflected through online emotions. Thus, this study aims to attempt to explore the quantitative relationship between online emotional intensity and WP.

This study developed a linguistic-sticker (LS) model to quantitatively evaluate the sentiment intensity of posts published on social media. Moreover, the authors designed two econometric models of ordinary least squares regression and negative binomial regression to test the hypothesis.

The research found that posts with stronger negative sentiment (or positive sentiment) indicate that CPs face higher (or lower) WP. Besides, there is a negative bias between the sentiment intensity of posts and the comment quantity.

The positive correlation between sentiment intensity of posts and WP has been confirmed, which indicates that construction managers should pay more attention to CPs' behavior on social media, and take a more direct way to analyze work-related online behavior (e.g. posting, commenting). The dynamic monitoring of emotion-related posts also provides a direct basis for the management team to learn about CP's pressure status and propose measures to reduce their negative emotions. Furthermore, the emotional posts published by CPs on social media provide a direct basis for team managers to obtain their psychological state.

The research contributes to incorporating CPs' emotions into the LS model and to providing information systems artifacts and new findings on the analysis of WP and online emotions.

This study was based on situated learning, by combining mobile learning and augmented reality, so that students could not only access information content in a real environment but also obtain such information via augmented reality, to support mobile learning.

The research included development of an augmented reality system combined with situational learning, used by students to learn about campus plants as part of the college life technology curriculum. Students took part in mobile learning, and an investigation was conducted into the computer learning behaviour of notebook users. College students were used as the experimental subjects. Data were collected using questionnaire surveys and were evaluated in order to identify the behavioural intentions of learners in outdoor learning activities.

The questionnaire survey covered environmental interactivity, system quality and textbook content. It was found that learners who used mobile learning augmented reality (MLAR) generally managed to browse all the contents of the textbook at each learning location, without spending too much time looking for information, and learners could quickly integrate this into the learning situation. Learners who used MLAR had a strong motivation to study plants at the learning site because they wanted to use the augmented reality technology to observe virtual plant models. Learners who used MLAR in their field learning liked using augmented reality for further learning, for example, using a magic wand to interact with the technology.

This study adopted a new approach to deliver elements of the life technology curriculum, integrating augmented reality into mobile learning. All participating students gave positive reviews of six aspects of the proposed system: their behavioural intentions, cognitive usefulness, cognitive ease of use, environmental interactivity, system quality and textbook content.

This research explores the appropriateness of using a digital learning system based on a multiplayer online animated game in firefighting education and training. Use of the game improved learning was effective. It can increase learners' understanding of the taught content and their willingness to learn.

The research applies digital learning based on a multiplayer online animated game coupled with 3D and virtual reality (VR) technology. To verify the effectiveness of this approach, participants were divided into two groups, an experimental group (using the game) and a control group (not using the game). T-tests were used to compare the learning of the two groups.

Firefighting knowledge was significantly better among the experimental group than the control group. Learning was explored in three different domains: basic firefighting knowledge, fire extinguisher operation and fire source identification; the latter two showed statistically significant differences. After learners acquired knowledge from multimedia, the addition of a VR exercise effectively helped them to remember what they learned and increased their understanding of the taught content.

The approach proposed in this study applies digital learning via a multiplayer online animated game to explore the appropriateness of such games in firefighting education and training. Use of the game improved learning and was effective. It can not only increase learners' understanding of the taught content, but also their willingness to learn.

The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.

We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.

Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.

This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.

This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.

Given the growing preference for internet celebrity restaurants, it is crucial to explore how internet celebrity restaurants can maintain customer loyalty. Therefore, this study aims to examine the connections between brand cognition [emotion value, brand symbol (BS) and brand experience (BE)], brand resonance (BR) and revisit intention.

In this paper, the authors use a theoretical model to test the relationship between cognition and intention. A total of 366 volunteers were recruited to participate in this research. Hypothesis testing and a moderated mediation model were used to measure the results.

BR acts as a mediator in the interaction between emotion value, BS, experience and repurchase intention (RI). Surprisingly, the authors also discover that electronic word-of-mouth (e-WOM) acceptance negatively modifies the relationship between brand cognition and BR. Internet exposure (IE) helps consumers perceive BE and BSs more favorably.

Managers should be aware of how internet celebrity BR is built. Specifically, they can use cultural or emotional elements to maintain relationships with consumers. Furthermore, to lessen the negative consequences of e-WOM, managers should work to maintain positive WOM consistency.

The research advances our knowledge of RI in internet celebrity restaurants settings. This study pioneers an investigation of how brand cognition is related to RI through BR’s mediating effect. It enriches this research perspective of the emerging restaurant literature. By analyzing the boundary impact of internet transmission on resonance, it also advances the literature.

From a psychosocial perspective, this study aims to understand the impact of psychosocial safety climate (PSC) on the intent to stay of construction workers and provides practical recommendations for construction enterprises to retain construction workers.

This study proposes the conceptual framework explained by the conservation of resources (COR) theory and develops a mediation model of “PSC – job satisfaction – intent to stay” within the framework supported by the stimulus–organism–response (SOR) model. Then, a questionnaire survey of 489 construction workers in Guangzhou was conducted and structural equation modeling (SEM) analysis was performed on the data collected.

Results show that PSC has a significant and positive effect on job satisfaction and intent to stay. In addition, job satisfaction partially mediates the effect of PSC on intent to stay. Hence, the theoretical model of “PSC – job satisfaction – intent to stay” has been empirically tested and supported.

This study is the first to investigate the effect of PSC on intent to stay and enriches the research on the retention of construction workers. The COR theory explains well the mechanism of PSC influence on intent to stay, thus expanding its application to the construction field. Moreover, this study provides practical recommendations for construction enterprises to retain workers so as to build a stable and productive workforce.

Existing control circuits for piezoelectric energy harvesting (PEH) suffers from long startup time or high power consumption. This paper aims to design an ultra-low power control circuit that can harvest weak ambient vibrational energy on the order of several microwatts to power heavy loads such as wireless sensors.

A self-powered control circuit is proposed, functioning for very brief periods at the maximum power point, resulting in a low duty cycle. The circuit can start to function at low input power thresholds and can promptly achieve optimal operating conditions when cold-starting. The circuit is designed to be able to operate without stable DC power supply and powered by the piezoelectric transducers.

When using the series-synchronized switch harvesting on inductor circuit with a large 1 mF energy storage capacitor, the proposed circuit can perform 322% better than the standard energy harvesting circuit in terms of energy harvested. This control circuit can also achieve an ultra-low consumption of 0.3 µW, as well as capable of cold-starting with input power as low as 5.78 µW.

The intermittent control strategy proposed in this paper can drastically reduce power consumption of the control circuit. Without dedicated cold-start modules and DC auxiliary supply, the circuit can achieve optimal efficiency within one input cycle, if the input signal is larger than voltage threshold. The proposed control strategy is especially favorable for harvesting energy from natural vibrations and can be a promising solution for other PEH circuits as well.