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Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions.

Based on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed.

The results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability.

By analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.

The purpose of this paper is to construct an interval grey number NGM(1,1) direct prediction model (abbreviated as IGNGM(1,1)), which need not transform interval grey numbers sequences into real number sequences, and the Markov model is used to optimize residual sequences of IGNGM(1,1) model.

A definition equation of IGNGM(1,1) model is proposed in this paper, and its time response function is solved by recursive iteration method. Next, the optimal weight of development coefficients of two boundaries is obtained by genetic algorithm, which is designed by minimizing the average relative error based on time weighted. In addition to that, the Markov model is used to modify residual sequences.

The interval grey numbers’ sequences can be predicted directly by IGNGM(1,1) model and its residual sequences can be amended by Markov model. A case study shows that the proposed model has higher accuracy in prediction.

Uncertainty and volatility information is widespread in practical applications, and the information can be characterized by interval grey numbers. In this paper, an interval grey numbers direct prediction model is proposed, which provides a method for predicting the uncertainty information in the real world.

The main contribution of this paper is to propose an IGNGM(1,1) model which can realize interval grey numbers prediction without transforming them into real number and solve the optimal weight of integral development coefficient by genetic algorithm so as to avoid the distortion of prediction results. Moreover, the Markov model is used to modify residual sequences to further improve the modeling accuracy.

The purpose of this paper is to construct an unbiased interval grey number prediction model with new information priority for dealing with the jumping errors from difference equation to the differential equation in the prediction model of interval grey number.

First, this study obtains a set of linear equations about the model parameters by taking the minimum error sum of squares between the accumulative sequence and its simulation values as criterion, and solves them on the basis of the Crammer rule. Then, according to the new information priority principle, it selects the last number of the accumulated generation sequence as the initial value and gives the expression of the time response function by the recursive iteration method to establish the interval grey number prediction model.

This paper provides an unbiased interval grey number prediction model with new information priority, and the example analysis shows that the method proposed in this paper has higher prediction precision and practicality.

If there is a better method to whiten the interval grey number, so as to fully tap the grey information contained in it, the accuracy of the model will be higher.

The model proposed in this paper can avoid the error caused by jumping from difference equation to differential equation and make full use of new information. It can be better used in a problem where new information has a great influence on prediction results.

This paper selects the last number of the accumulated generation sequence as the initial value and gives the expression of the time response function by the recursive iteration method. Then, it constructs an unbiased interval grey number prediction model with new information priority.

The purpose of this paper is to construct a prediction model of three-parameter interval grey number based on kernel and double information domains to expand the modeling object of grey prediction model from interval grey number to three-parameter interval grey number.

First, the study decomposes the grey valued interval into upper and lower cells with the “center of gravity” as the dividing point and defines the upper and lower information domains of the three-parameter interval grey number. Second, it calculates the kernel, the upper and lower information domains of the three-parameter interval grey number. Then, it constructs the prediction model for kernel sequence and upper and lower information domain sequences, respectively. By deducing the time response expressions of “center of gravity”, lower and upper limits of three-parameter interval grey number, a prediction model of three-parameter interval grey number based on kernel and double information domains is obtained.

This paper provides a prediction model of three-parameter interval grey number based on kernel and double information domains, and the example analysis shows that the method proposed in this paper has higher prediction accuracy and practicality.

In this paper, the modeling object of grey prediction model is extended to the three-parameter interval grey number, so it can be used for the prediction of uncertainty problems, such as stock changing trend, temperature and so on.

By decomposing the grey valued interval into upper and lower cells with the “center of gravity” as the dividing point, gives the definition of upper and lower information domains and then obtains a new method for whitening the three-parameter interval grey number.

This study aims to investigate the relationships between service quality, perceived value and hotel guest satisfaction, drawing upon data from TripAdvisor – an online travel agent (OTA) platform. The study also investigates the mediating role of perceived value on the relationship between service quality and satisfaction, as well as the moderating role of hotel star ratings on all direct and indirect relationships.

Data for this study were collected via Web scraping from August–October 2018. Data were collected from 192 three- to five star-rated hotels in Kuala Lumpur, Malaysia. Partial least squares – structural equation modeling was used for data analysis. Furthermore, importance-performance map analysis (IPMA) was performed to identify the most important items of service quality and perceived value in improving customer satisfaction.

The findings of this study provide support for all direct and indirect relationships for three-star and four- and five-star hotels. Moreover, the results indicate that perceived value mediates the relationship between service quality and customer satisfaction. These results support the moderating role of hotel star ratings for the relationship between service quality and perceived value. The results also show that after perceived value, three-star hotels looking to improve customer satisfaction should prioritize improving the quality of their services, sleep quality, cleanliness and rooms. Four- and five-star hotels, on the other hand, should prioritize service, cleanliness, room and sleep quality.

OTA platforms collect a wealth of data pertaining to large number of hotels; nevertheless, few studies to date have drawn on this data to examine a pre-determined conceptual framework developed based on the literature. As such, this study makes a valuable methodological contribution to the tourism and hospitality literature. In terms of theoretical contributions, this study examines the mediating role of perceived value between service quality and satisfaction using OTA data. In addition, this study assesses the moderating role of hotel star ratings for the direct and indirect effects of service quality on satisfaction. Using IPMA, this study compares the importance and performance of service quality indicators to generate satisfaction between three-star and four- and five-star hotels.

本论文检测了服务质量、价值感知、和酒店顾客满意度之间的关系, 使用TripAdvisor的数据—OTA。本论文还检测了价值感知对服务质量和满意度之间的中介作用, 以及酒店星级评价对其中直接和间接关系的调节作用。.

本论文采样通过网络爬虫技术, 截取了2018年八月至十月之间的数据。研究样本为192家马来西亚Kuala Lumpur地区的三星-五星酒店。样本分析方法为PLS-SEM。此外, 本论文采样IPMA分析法来找出提高顾客满意度中的服务质量和价值感知中最重要的因子。.

研究结果指出了三星、四星、五星酒店的直接和间接关系。此外, 研究还显示了服务质量和顾客满意度关系的价值感知中介作用。研究结果还指出了酒店星级评价对服务质量和价值感知关系的调节作用。此外, 研究还指出, 除了价值感知, 如果三星酒店想提高顾客满意度, 那么他们应该优先提高其服务质量、睡眠质量、清洁度、和房间。另一方面, 四星和五星酒店应该优先提高其服务质量、清洁度、房间、和睡眠质量。.

OTA平台搜集大量酒店数据, 但是很少作品研究这些数据, 以检测根据文献提出的理论模型。因此, 本论文在方法论上对旅游酒店文献做出宝贵贡献。理论贡献而言, 本论文使用OTA数据检测了价值感知对服务质量和满意度关系之间的中介作用。此外, 本论文检测了酒店星级评价对服务质量和满意度之间直接和间接关系的调节作用。本论文使用IPMA方法, 比较各种服务质量指标的重要性对在三星、四星、五星酒店的提高满意度的不同作用。.

This study aims to enhance the prediction accuracy of hydroelectricity consumption in China, with a focus on addressing the challenges posed by complex and nonlinear characteristics of the data. A novel grey multivariate prediction model with structural optimization is proposed to overcome the limitations of existing grey forecasting methods.

This paper innovatively introduces fractional order and nonlinear parameter terms to develop a novel fractional multivariate grey prediction model based on the NSGM(1, N) model. The Particle Swarm Optimization algorithm is then utilized to compute the model’s hyperparameters. Subsequently, the proposed model is applied to forecast China’s hydroelectricity consumption and is compared with other models for analysis.

Theoretical derivation results demonstrate that the new model has good compatibility. Empirical results indicate that the FMGM(1, N, a) model outperforms other models in predicting the hydroelectricity consumption of China. This demonstrates the model’s effectiveness in handling complex and nonlinear data, emphasizing its practical applicability.

This paper introduces a scientific and efficient method for forecasting hydroelectricity consumption in China, particularly when confronted with complexity and nonlinearity. The predicted results can provide a solid support for China’s hydroelectricity resource development scheduling and planning.

The primary contribution of this paper is to propose a novel fractional multivariate grey prediction model that can handle nonlinear and complex series more effectively.

In this essay, a new NDAGM(1,N,α) power model is recommended to resolve the hassle of the distinction between old and new information, and the complicated nonlinear traits between sequences in real behavior systems.

Firstly, the correlation aspect sequence is screened via a grey integrated correlation degree, and the damped cumulative generating operator and power index are introduced to define the new model. Then the non-structural parameters are optimized through the genetic algorithm. Finally, the pattern is utilized for the prediction of China’s natural gas consumption, and in contrast with other models.

By altering the unknown parameters of the model, theoretical deduction has been carried out on the newly constructed model. It has been discovered that the new model can be interchanged with the traditional grey model, indicating that the model proposed in this article possesses strong compatibility. In the case study, the NDAGM(1,N,α) power model demonstrates superior integrated performance compared to the benchmark models, which indirectly reflects the model’s heightened sensitivity to disparities between new and old information, as well as its ability to handle complex linear issues.

This paper provides a scientifically valid forecast model for predicting natural gas consumption. The forecast results can offer a theoretical foundation for the formulation of national strategies and related policies regarding natural gas import and export.

The primary contribution of this article is the proposition of a grey multivariate prediction model, which accommodates both new and historical information and is applicable to complex nonlinear scenarios. In addition, the predictive performance of the model has been enhanced by employing a genetic algorithm to search for the optimal power exponent.

The minimum wage has been regarded as an important element of public policy for reducing poverty and inequality. Increasing the minimum wage is supposed to raise earnings for millions of low-wage workers and therefore lower earnings inequality. However, there is no consensus in the existing literature from industrialized countries regarding whether increasing the minimum wage has helped lower earnings inequality. China has recently exhibited rapid economic growth and widening earnings inequality. Since China promulgated new minimum wage regulations in 2004, the magnitude and frequency of changes in the minimum wage have been substantial, both over time and across jurisdictions. The growing importance of research on the relationship between the minimum wage and earnings inequality and its controversial nature have sparked heated debate in China, highlighting the importance of rigorous research to inform evidence-based policy making. We investigate the contribution of the minimum wage to the well-documented rise in earnings inequality in China from 2004 to 2009 by using city-level minimum wage panel data and a representative Chinese household survey, and we find that increasing the minimum wage reduces inequality – by decreasing the earnings gap between the median and the bottom decile – over the analysis period.

Purpose: The purpose of this chapter is to elaborate on the major conceptual and practical considerations of the use of robots, artificial intelligence and service automation (RAISA) in travel, tourism, and hospitality companies (TTH).

Design/methodology/approach: The chapter develops a conceptual framework of the major issues related to the use of RAISA in the travel, tourism and hospitality context.

Findings: The findings indicate that while there is a creeping incursion of RAISA into TTH, there are major concerns that the TTH industry has to consider in regard to automating TTH services.

Practical implications: In a practical sense, the chapter identifies the decisions that TTH industry professionals need to take when dealing with RAISA technologies. Furthermore, the chapter elaborates on the impacts RAISA have on business operations, marketing management, human resources and financial management of TTH companies. The TTH industry has to adjust its practices and communicate with its workforce in ways as not to increase Luddite tendencies and resistance among employees.

Social implications: The analysis shows that there is an upcoming era in which automation of services will be so advanced that wealthy countries may not need to import labour to make up with its own aging workforce, suggesting that RAISA and its further development has the potential for disrupting society and international relations.

Originality/value: This chapter provides a comprehensive review of the issues related to the use of RAISA in the TTH industry, including the drivers of RAISA adoption in tourism, advantages and disadvantages of RAISA technologies compared to human employees, decisions that managers need to take, and the impacts of RAISA on business processes. It shows how macroenvironmental pressures shape the microeconomic decisions to use RAISA in a TTH context.

Based on the work–home resources model regarding the work domain and the home domain as a whole resource exchange system with directional resource flows, this study proposed that perceived overqualification could lead to personal resources drain, especially for employees with high work–family centrality (i.e. valuing work more than family). Furthermore, the drained personal resources of the focal employees brought in more spouse undermining and less spouse support at home.

A quantitative approach in which Study 1 involving 259 pairs and Study 2 involving 260 pairs of employees and their spouses from China provided support to the first-stage moderated mediation model.

Results revealed that when employees' work–family centrality is high, perceived overqualification could elicit personal resources drain and induce more spouse undermining and less spouse support. On the contrary, when employees' work–family centrality is low, perceived overqualification could reduce personal resources drain and render less spouse undermining and more spouse support. The two studies consistently provided support for most of the hypotheses.

The research results suggest that organizations could take some feasible measures to help overqualified employees articulate the value of work–family centrality to manage overqualified employees' work–family resources further, bringing appropriate sequential behaviors at home.

Research on perceived overqualification has primarily focused on its consequences in the work domain, paying scant attention to whether it can influence the home domain outside work. This research contributes to this line of literature by investigating how and when perceived overqualification leads to family outcomes.