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A time-varying grey Fourier model (TVGFM(1,1,N)) is proposed for the simulation of variable amplitude seasonal fluctuation time series, as the performance of traditional grey models can't catch the time-varying trend well.

The proposed model couples Fourier series and linear time-varying terms as the grey action, to describe the characteristics of variable amplitude and seasonality. The truncated Fourier order N is preselected from the alternative order set by Nyquist-Shannon sampling theorem and the principle of simplicity, then the optimal Fourier order is determined by hold-out method to improve the robustness of the proposed model. Initial value correction and the multiple transformation are also studied to improve the precision.

The new model has a broader applicability range as a result of the new grey action, attaining higher fitting and forecasting accuracy. The numerical experiment of a generated monthly time series indicates the proposed model can accurately fit the variable amplitude seasonal sequence, in which the mean absolute percentage error (MAPE) is only 0.01%, and the complex simulations based on Monte-Carlo method testify the validity of the proposed model. The results of monthly electricity consumption in China's primary industry, demonstrate the proposed model catches the time-varying trend and has good performances, where MAPE_F and MAPE_T are below 5%. Moreover, the proposed TVGFM(1,1,N) model is superior to the benchmark models, grey polynomial model (GMP(1,1,N)), grey Fourier model (GFM(1,1,N)), seasonal grey model (SGM(1,1)), seasonal ARIMA model seasonal autoregressive integrated moving average model (SARIMA) and support vector regression (SVR).

The parameter estimates and forecasting of the new proposed TVGFM are studied, and the good fitting and forecasting accuracy of time-varying amplitude seasonal fluctuation series are testified by numerical simulations and a case study.

This study aims to investigate the engagement gap between Metaverse and in-person travel, the influence of Metaverse tourism on tourists and the industry and the challenges and responses associated with Metaverse technology. The study presents practical cases and highlights the implications of this research for practice, society and future research.

This study uses a literature review to explore concerns about Metaverse technology in tourism. It analyzes the difference between in-person travel and Metaverse tourism, the impact on tourists and the industry and challenges and responses to Metaverse. The review shows a rising trend in Metaverse tourism research.

These findings suggest differences between Metaverse tourism and in-person travel. By providing personalized travel options, social interaction, immersive experiences and soliciting visitor feedback, it is possible to enhance the tourist experience. Additionally, the study highlights the opportunities and challenges that Metaverse tourism presents to the tourism industry. The study provides practical cases in the tourism industry and implications for practice, society and future research.

The study’s implications for Metaverse tourism are practical, societal and future research-related. Metaverse technology can enhance the tourist experience through personalized options, social interaction, immersive experiences and feedback. This inclusivity can promote social equity and cultural exchange. Further research is needed to explore the social effects of Metaverse tourism and its long-term impacts on local communities, economies and the environment.

This study contributes by exploring the impact of Metaverse tourism, supporting academic research and practice. It fills a knowledge gap by analyzing the application of Metaverse technology in tourism, providing insights for researchers and practitioners. It offers practical guidance by identifying opportunities and challenges in Metaverse tourism, fostering industry innovation. Additionally, it informs policymakers about the impact of Metaverse tourism on development.

This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish effective boards of directors and strengthen their corporate governance mechanisms.

This paper uses machine learning methods to investigate the predictive ability of the board of directors' characteristics on firm performance based on the data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges in China during 2008–2021. This study further analyzes board characteristics with relatively strong predictive ability and their predictive models on firm performance.

The results show that nonlinear machine learning methods are more effective than traditional linear models in analyzing the impact of board characteristics on Chinese firm performance. Among the series characteristics of the board of directors, the contribution ratio in prediction from directors compensation, director shareholding ratio, the average age of directors and directors' educational level are significant, and these characteristics have a roughly nonlinear correlation to the prediction of firm performance; the improvement of the predictive ability of board characteristics on firm performance in state-owned enterprises in China performs better than that in private enterprises.

The findings of this study provide valuable suggestions for enriching the theory of board governance, strengthening board construction and optimizing the effectiveness of board governance. Furthermore, these impacts can serve as a valuable reference for board construction and selection, aiding in the rational selection of boards to establish an efficient and high-performing board of directors.

The study findings unequivocally demonstrate the superiority of nonlinear machine learning approaches over traditional linear models in examining the relationship between board characteristics and firm performance in China. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. The study reveals that the predictive performance of board attributes is generally more robust for state-owned enterprises in China in comparison to their counterparts in the private sector.

The damping accumulated discrete MGM(1, m) power model is proposed for the problem of forecasting the share of agricultural output value and the share of employment in China.

In this study, the damping accumulated discrete MGM(1, m) power model was developed based on the idea of discrete modelling by introducing a damping accumulated generating operator and power index. The new model can better identify the non-linear characteristics existing between different factors in the multivariate system and can accurately describe and forecast the trend of changes between data series and each of them.

The validity and rationality of the new model are verified through numerical experiment. It is forecasted that in 2023, the share of agricultural output value in China will be 7.14% and the share of agricultural employment will be 21.98%, with an overall decreasing trend.

The simultaneous decline in the share of agricultural output value and the share of employment is a common feature of countries that have achieved agricultural modernisation. Accurate forecasts of the share of agricultural output value and the share of employment can provide an important scientific basis for formulating appropriate agricultural development targets and policies in China.

The new model proposed in this study fully considers the importance of new information and has higher stability. The differential evolutionary algorithm was used to optimise the model parameters.

The main aim of this study is to examine the effect of virtual store atmospheric factors on impulsive purchasing in the metaverse context.

Grounded in purposive sampling, 451 individuals with previous metaverse experience were recruited to accomplish the objectives of this research. Next, to identify both linear and nonlinear relationships, the data were analyzed using partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) approaches.

The findings underscore the significance of the virtual store environment and online trust in shaping impulsive buying behaviors within the metaverse retailing setting. Theoretically, this study elucidates the impact of virtual store atmosphere and trust on impulsive buying within a metaverse retail setting.

From the findings of the study, because of the importance of virtual shop content, practitioners must address its role in impulse purchases via affective online trust. The study’s findings are likely to help retailers strategize and improve their virtual store presentations in the metaverse.

The discovery adds to the understanding of consumer behavior in the metaverse by probing the roles of virtual store atmosphere, online trust and impulsive buying.

The aim of our study is to explore the nexus between the gender composition of board and firm financial performance. We use the data of 114 listed banks from 10 Asian emerging economies. Data were extracted from the DataStream for the year 2012–2021. We apply fixed effect model to analyze the data. In addition, we use generalized method of moments (GMM) to verify our main findings. We find that both proxies of board gender composition which are the proportion of female board members and the percentage of female executives on the board have a significant impact on banks' financial performance. Findings suggest that female representation on board provides more insights of monitoring and optimal advisory capabilities and, therefore, gender-diversified board enhances firm performance. Females are more active in business matters and take more interests to fulfill their responsibilities. The results of our study provide useful signals for corporate and regulatory policymakers. Board gender disparities between enterprises should be better understood by all stakeholders to have the optimal combination of board members that ultimately lead to better performance of the firm.

The purpose of this paper is to develop a comprehensive overview of green innovation (GI) in China, which is carried out by reviewing the evolution of GI from 2000 to 2019, and the main type of technology, actors and localizations. When appropriate, GI is compared to non-GI.

The study uses patent data from the European Patent Office database (PATSTAT); these data are processed to map trends and identify the main contributors to GI and the location of such innovation. The findings are then discussed and complemented with academic literature.

Key findings reveal an increasing divergence between GI and nongreen innovation after the 2008 crisis. It is also observed that solar energy appears to be the main component of GI in China, with a shift from photovoltaic thermal energy to solar photovoltaic energy after 2008. Other areas, such as waste management, greenhouse gases capture and climate change adaptation, are less innovative. Companies play an essential role in the development of all types of innovation. In terms of location, green patents are mainly filed in China’s three main megacities. The study also highlights the significant role of the Chinese state, which led policies shaping the trajectories and forms of GI.

This study expands knowledge on GI in China, highlighting its main specificities and the role of key actors. It provides to the reader a comprehensive picture of China’s green policies and innovation realities. The results can therefore be used to improve the understanding of GI evolution in China and facilitate the formulation of new research questions.

This study aims to investigate the relationship between dynamic capabilities and sustainable business performance in Vietnamese small- and medium-sized enterprises (SMEs), focusing on the mediating role of digitalization strategies. Specifically, the authors seek to explore whether and how the three critical characteristics of dynamic capabilities (DCs) – sensing, seizing and transforming capabilities – are linked to business model innovation (BMI) or sustained performance and what dimensions contribute to their development and adoption in digitalization strategies.

The authors analyse a sample of 596 Vietnamese SMEs using a validated measurement framework to explore the three clusters of DCs activities and their contributions to digitalization strategies, BMI and sustainable business performance across economic, social and environmental dimensions.

The study highlights the pivotal role of sensing, seizing and transforming capabilities in the adoption of digitalization strategies, BMI, as well as in promoting sustainable business performance. Firstly, sensing capability profoundly influences product digitalization strategy, whereas seizing capability has the greatest impact on process digitalization strategy. Secondly, sensing and transforming capabilities significantly contribute to BMI. Thirdly, both process and product digitalization strategies exert a significant positive influence on sustainable business performance, especially the environmental dimension. Finally, the study exhibits the indirect impacts of seizing and sensing capabilities on sustainable business performance through product and process digitization strategies.

This study extends recent research by investigating the DCs underlying a firm’s digitalization strategies and contribute to ongoing calls for further investigation in the DCs literature. This research design, which draws from a validated measurement framework, responds to recent calls to broaden the toolkit used in DCs research. The practical implications of this study can benefit SMEs in Vietnam and beyond as they seek to enhance their digitalization strategies and achieve sustainable competitive advantage.

Building on the upper echelons and natural resource-based view (NRBV) theory, this study aims to examine the role of green innovation in mediating the relationship between female directors and firm value.

This study uses panel data for 2016–2020 of 108 manufacturing firms listed on the Indonesia Stock Exchange with 518 observations. This study collects data from the firm’s annual and sustainability reports and the Osiris database. This study uses feasible generalized least squares in controlling heteroscedasticity and correlation to validate the relationship.

The results show that green innovation mediates the relationship between female directors and firm value. The results support the upper echelons theory, which views that the impact of the female directors’ policy has a positive effect on green innovation. The results also support the NRBV theory, which views green innovation as an environmentally friendly resource capable of increasing firm value.

In examining the indirect effect of female directors on firm value, this study is one of the early works that discuss the mediation relationship using green innovation in the relationship of female directors to firm value drawn from upper echelons and NRBV theory.

This paper aims to investigate how user expectations affect value-added service (VAS) investment and pricing decisions of two-sided platforms. It draws on the information asymmetry theory and offers suggestions on how platform operators can manage user expectations.

According to the game theory, this study considers three user expectations (responsive, passive and wary). By framing the Hotelling duopoly model and comparing the VAS investment, price and platform profits, the optimal platform decision is analyzed and discussed.

The conclusions demonstrate that the monopolistic two-sided platform obtains more profits from the informed users with responsive expectations than uninformed users with passive or wary expectations. The marginal investment cost and cross-network externalities are two key factors that determine the platform's VAS investment and pricing strategies of passive or wary users. Furthermore, considering the expectation preferences, i.e. the uniformed users hold wary expectations with more information and hold passive expectations with less or no information, the results suggest that the proportion of wary users to all uninformed users increases the platform's VAS investment, profits and the price of informed users, and increase (decrease) the price of uninformed users when the cross-network externalities of informed users are relatively small (larger).

These results can provide insightful enlightenment into how platform operators utilize bilateral users' expectations and information level to guide their VAS investment and pricing decisions.

This paper is one of the first to explore the impact of three user expectations and the heterogeneity of preferences in informing users' passive or wary expectations, based on different levels of information on the decision-making of two-sided platforms regarding VAS.