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This study aims to identify Chinese municipal agencies’ and cadres’ drivers to implement government websites’ accessibility upgrades, and to explain how these drivers are interrelated to shape the implementation outcome.

The authors conducted a single case study using qualitative interviews, online follow-up conversations, fieldwork observations and policy documents in the capital municipality MD of J Province, East China. The authors analyzed the case from the theoretical perspectives of institutional pressures, organizational capacity and individual intentions.

Coercive pressure through policy mandate and benchmark incentivized the responsible agency and cadres in MD to initiate the implementation of the accessibility upgrades and “meet the set targets.” The responsible agency’s enhanced organizational capacity and local cadres’ engagement allowed them to “outperform” as their eventual way of achieving the mandate requirements. The implementation outcome resulted from the interplay of all levels of incentives. Coercive pressure predominantly drove the launch of the upgrade project, meanwhile significantly influencing the organizational- and individual-level incentives that additionally explained the outperformance.

This study provides a nuanced, in-depth understanding of how sedimented factors and especially their interrelationships drive the implementation of e-government initiatives and shape the implementation outcome in Chinese municipal agencies.

This study attempts to explore major attributes of food delivery applications in consideration of their impacts upon perceived service quality, satisfaction and usage intention. A multi-group analysis is performed to examine the hypothesized relationships in the structural models for the millennials and non-millennials.

The data was collected from 311 food delivery application users. The structural equation model (SEM) was designed to examine the interrelationships among variables.

The findings indicate that five salient dimensions that include ease of use, facility aesthetics, trustworthiness, value for money and product portfolio have a significant impact on overall service quality. Results further demonstrate that differences in generational difference partially moderate the relationship between each attribute of the food delivery application service quality as well as overall service quality.

The rapid evolution of the Internet and mobile communication has resulted in the proliferation of food delivery applications in China. However, to the best of our knowledge, only a few studies have focused on measuring the key dimension of service quality of food delivery applications.

Drawing upon Hofstede's cultural dimensions theory, a comprehensive model exploring the relationships among four distinct culture values and their influence on employees' turnover intention through the mediating roles of organizational commitment and organizational citizenship behavior was developed and tested.

This study covers 585 migrant workers from nine countries who work in the food and beverage industry in Guangzhou, China. Structural equation modeling (SEM) was used to analyze the data collected from these migrant workers.

The results reveal that (1) among the four culture values, only uncertainty avoidance, collectivism orientation and masculinity orientation significantly affect organizational commitment and organizational citizenship behavior, (2) organizational commitment is significantly positively related to organizational citizenship behavior and (3) both organizational commitment and organizational citizenship behavior have a negative direct effect on turnover intention, while organizational citizenship behavior has a stronger impact on migrants' turnover intention than organizational commitment.

The issue of cultural diversity, particularly among relevant migrant workers in the hospitality industry in developing countries, such as China, needs more attention than ever.

The purpose of this paper is to establish a combined forecasting model to predict regional logistics demand, which is an important procedure on decision making of regional logistics planning.

There are several kinds of mathematical models often used in forecasting regional logistics demand. Trend extrapolation method extrapolates the future development trends bases on the hypothesis that the regional logistics will develop steadily. Grey system method predicts the change of logistics demand by the generation and development of original data sequence and excavation of inherent rules of the original data. Regression method obtains the change rules through the analysis between explained variable and explanatory variables. Each method has unique characteristics. In order to improve the accuracy of the prediction, combined methods are established. Genetic algorithm is used to determine the weights of different single models.

The results show that the combined forecasting model optimised by genetic algorithm can improve the accuracy.

Combined forecasting model can integrate the advantages of different single forecasting models. The key of improving the accuracy is to determine the weights of single forecasting models. Genetic algorithm can do well in finding suitable weights of each single forecasting model.

The paper succeeds in providing a combined forecasting model using genetic algorithm to determine the weights of each single prediction model, which helps to the decision making of regional logistics demand.

The present study is intended to develop an effective approach to the real-time modeling of general dynamic nonlinear systems based on the multidimensional Taylor network (MTN).

The authors present a detailed explanation for modeling the general discrete nonlinear dynamic system by the MTN. The weight coefficients of the network can be obtained by sampling data learning. Specifically, the least square (LS) method is adopted herein due to its desirable real-time performance and robustness.

Compared with the existing mainstream nonlinear time series analysis methods, the least square method-based multidimensional Taylor network (LSMTN) features its more desirable prediction accuracy and real-time performance. Model metric results confirm the satisfaction of modeling and identification for the generalized nonlinear system. In addition, the MTN is of simpler structure and lower computational complexity than neural networks.

Once models of general nonlinear dynamical systems are formulated based on MTNs and their weight coefficients are identified using the data from the systems of ecosystems, society, organizations, businesses or human behavior, the forecasting, optimizing and controlling of the systems can be further studied by means of the MTN analytical models.

MTNs can be used as controllers, identifiers, filters, predictors, compensators and equation solvers (solving nonlinear differential equations or approximating nonlinear functions) of the systems of ecosystems, society, organizations, businesses or human behavior.

The operating efficiency and benefits of social systems can be prominently enhanced, and their operating costs can be significantly reduced.

Nonlinear systems are typically impacted by a variety of factors, which makes it a challenge to build correct mathematical models for various tasks. As a result, existing modeling approaches necessitate a large number of limitations as preconditions, severely limiting their applicability. The proposed MTN methodology is believed to contribute much to the data-based modeling and identification of the general nonlinear dynamical system with no need for its prior knowledge.

The purpose of constructing a degradation index (DI) is to better characterize the degradation degree of mechanical transmission compared with relying solely on spectral oil data, which leads to an accurate estimation of the failure time when the transmission no longer fulfills its function.

The DI is modeled using a weighted average function with two desirable properties: maximizing the monotonic trend and minimizing the variance of failure threshold between different transmissions. The method includes concentration modification, data selection and data fusion steps that lead to a reasonable mechanical transmission degradation model. The proposed methodology was verified through a case study involving multispectral oil data sampled from several power-shift steering transmissions.

The results show that the DI outperforms all spectral oil data. Compared with the existing spectral oil data-based degradation modeling approach for mechanical transmissions, the present methodology provides an accurate RUL prediction.

There are several important directions for future research: First, more degradation data (i.e. ferrography) that are tailored to the degradation modeling of mechanical transmission need to be involved. Second, more effective degradation data selection methodologies that are applicable for multiple data types need to be developed. Third, kernel methods that can fuse the nonlinear degradation data need to be investigated.

The novelty of this methodology lies in integrating the multiple degradation data in a unified DI. And the main contribution of this paper is to establish a new direction in degradation modeling and RUL prediction of mechanical transmission.

The accurate prediction of driving torque demand is essential for the development of motion controllers for mobile robots on complex terrains. This paper aims to propose a hybrid model of torque prediction, adaptive EC-GPR, for mobile robots to address the problem of estimating the required driving torque with unknown terrain disturbances.

An error compensation (EC) framework is used, and the preliminary prediction driving torque value is achieved using Gaussian process regression (GPR). The error is predicted using a continuous hidden Markov model to generate compensation for the prediction residual caused by terrain disturbances and uncertainties. As the final step, a gain coefficient is used to adaptively tune the significance of the compensation term through parameter resetting. The proposed model is verified on a sample set, including the driving torque of a mobile robot on three different sandy terrains with two driving modes.

The results show that the adaptive EC-GPR yields the highest prediction accuracy when compared with existing methods.

It is demonstrated that the proposed model can predict the driving torque accurately for mobile robots in an unconstructed environment without terrain identification.

This paper aims to explore the mediating role of organizational entrepreneurial capability in the link between entrepreneurs’ effectuation and new venture performance, and whether entrepreneurs’ passion positively moderates this relationship in the Chinese emerging economy.

This study collected survey data from 140 Chinese new ventures. Following an empirical design, hierarchical regression analysis and bootstrapping analysis were applied to examine six hypotheses.

Results reveal that organizational entrepreneurial capability plays a positively mediating role in the association between entrepreneurs’ effectuation and new venture performance. Moreover, the whole mediation model is positively moderated by entrepreneurs’ passion, not only the association but also between entrepreneurs’ effectuation and organizational entrepreneurial capability.

The study is limited to the static relationships between key variables using the data obtained at one point in an emerging economy, which cannot investigate the dynamic evolution between variables. More longitudinal designs or cases to track the dynamic association should be considered.

The findings provide useful suggestions for entrepreneurs to enhance their effectual logic and entrepreneurs’ passion to better perceive and exploit opportunities and further improve new venture performance. The results also provide guidance for other groups, such as angel investors and policymakers, regarding how to use effectuation logic as an evaluation criterion to judge whether a new venture or program has investment potential.

These findings enrich the effectuation theory by providing the empirical evidence of the effect of entrepreneurs’ effectuation on new venture performance in an emerging economy. They also provide deeper insights into opportunity research by uncovering the mediating role of organizational entrepreneurial capability in the relationship between entrepreneurs’ effectuation and new venture performance.

The purpose of this paper is to investigate the relationship between customer’s electronic word-of-mouth (eWOM) regarding their direct service experiences with firms and these firms’ company value. The authors drew on the marketing-finance interface research approach to demonstrate how interactive social media adopted by individual customer relate to important firms’ financial performances.

The authors used seven American airline companies’ customers’ tweets collected during a 52-week observation period and paired with their corresponding financial data using stock returns and volatilities. Sentiment analysis algorithm and a vector autoregressive (VAR) model quantified the strong association between customer’s eWOM and these firms’ stock returns and volatilities.

The results show that customer’s eWOM regarding a firm positively associate with the firm’s stock return but negatively associate with its stock volatility; as negative valence of customer’s eWOM increases, the positive effect of eWOM on firm’s stock return decreases; the negative eWOM impacts on the stock market more profoundly compared with when both positive and negative sensitivities are considered; and eWOM’s wear-out effect is much shorter than that of traditional WOM.

The authors address a literature gap where little is known for how customer’s eWOM, that is evaluating firm services, can ultimately impact on firms’ long-term financial performances. The authors discuss how findings from this study offer implications for marketing management as well as strategic insights for practitioners and investment analysts alike.

Academic and industrial researches on nanoscale flows and heat transfers are an area of increasing global interest, where fascinating phenomena are always observed, e.g. admirable water or air permeation and remarkable thermal conductivity. The purpose of this paper is to reveal the phenomena by the fractional calculus.

This paper begins with the continuum assumption in conventional theories, and then the fractional Gauss’ divergence theorems are used to derive fractional differential equations in fractal media. Fractional derivatives are introduced heuristically by the variational iteration method, and fractal derivatives are explained geometrically. Some effective analytical approaches to fractional differential equations, e.g. the variational iteration method, the homotopy perturbation method and the fractional complex transform, are outlined and the main solution processes are given.

Heat conduction in silk cocoon and ground water flow are modeled by the local fractional calculus, the solutions can explain well experimental observations.

Particular attention is paid throughout the paper to giving an intuitive grasp for fractional calculus. Most cited references are within last five years, catching the most frontier of the research. Some ideas on this review paper are first appeared.