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The extensive distribution of fake news on the internet (FNI) has significantly affected many lives. Although numerous studies have recently been conducted on this topic, few have helped us to systematically understand the antecedents and consequences of FNI. This study contributes to the understanding of FNI and guides future research.

Drawing on the input–process–output framework, this study reviews 202 relevant articles to examine the extent to which the antecedents and consequences of FNI have been investigated. It proposes a conceptual framework and poses future research questions.

First, it examines the “what”, “why”, “who”, “when”, “where” and “how” of creating FNI. Second, it analyses the spread features of FNI and the factors that affect the spread of FNI. Third, it investigates the consequences of FNI in the political, social, scientific, health, business, media and journalism fields.

The extant reviews on FNI mainly focus on the interventions or detection of FNI, and a few analyse the antecedents and consequences of FNI in specific fields. This study helps readers to synthetically understand the antecedents and consequences of FNI in all fields. This study is among the first to summarise the conceptual framework for FNI research, including the basic relevant theoretical foundations, research methodologies and public datasets.

The objective of this study is to investigate the interaction effect between incentive type (financial and compassionate incentives) and the ethicality of merchant strategy on consumer willingness to post positive reviews, while also examining potential variations in consumer responses based on consumption experience, shopping frequency and social class.

Building upon construal level theory, we hypothesized the moderating influence of the ethicality of merchant strategy and examined the three-way interaction among consumers’ demographic characteristics (i.e. consumption experience, shopping frequency and social class), incentive type and the ethicality of merchant strategy. To empirically test our hypotheses, we conducted four experiments and employed ANOVA for data analysis.

The ethicality of merchant strategies moderates the association between incentive type and consumer willingness to post positive reviews, with compassionate incentives eliciting more pronounced moral judgments toward merchant strategies compared to financial incentives. The moderating effect of the ethicality of merchant strategy on the relationship between incentive type and consumer willingness to post positive reviews is particularly strong among consumers who have favorable consumption experiences, engage in frequent shopping and belong to lower social classes.

This study contributes to the existing literature on online reviews by examining the impact of compassionate incentives on consumer review behaviors, analyzing the ethicality of merchant strategies within the realm of online reviews and investigating variations in consumer responses to merchant strategies regarding consumption experience, shopping frequency and social class.

An effective machine vision-based method for micro-crack detection of solar cell can economically improve the qualified rate of solar cells. However, how to extract features which have strong generalization and data representation ability at the same time is still an open problem for machine vision-based methods.

A micro-crack detection method based on adaptive deep features and visual saliency is proposed in this paper. The proposed method can adaptively extract deep features from the input image without any supervised training. Furthermore, considering the fact that micro-cracks can obviously attract visual attention when people look at the solar cell’s surface, the visual saliency is also introduced for the micro-crack detection.

Comprehensive evaluations are implemented on two existing data sets, where subjective experimental results show that most of the micro-cracks can be detected, and the objective experimental results show that the method proposed in this study has better performance in detecting precision.

First, an adaptive deep features extraction scheme without any supervised training is proposed for micro-crack detection. Second, the visual saliency is introduced for micro-crack detection.

This paper aims to improve the generalization capability of feature extraction scheme by introducing a micro-cracks detection method based on self-learning features. Micro-cracks detection of multicrystalline solar cell surface based on machine vision is fast, economical, intelligent and easier for on-line detection. However, the generalization capability of feature extraction scheme adopted by existed methods is limited, which has become an obstacle for further improving the detection accuracy.

A novel micro-cracks detection method based on self-learning features and low-rank matrix recovery is proposed in this paper. First, the input image is preprocessed to suppress the noises and remove the busbars and fingers. Second, a self-learning feature extraction scheme in which the feature extraction templates are changed along with the input image is introduced. Third, the low-rank matrix recovery is applied to the decomposition of self-learning feature matrix for obtaining the preliminary detection result. Fourth, the preliminary detection result is optimized by incorporating the superpixel segmentation. Finally, the optimized result is further fine-tuned by morphological postprocessing.

Comprehensive evaluations are implemented on a data set which includes 120 testing images and corresponding human-annotated ground truth. Specifically, subjective evaluations show that the shape of detected micro-cracks is similar to the ground truth, and objective evaluations demonstrate that the proposed method has a high detection accuracy.

First, a self-learning feature extraction method which has good generalization capability is proposed. Second, the low-rank matrix recovery is combined with superpixel segmentation for locating the defective regions.

The purpose of this paper is to explore the role of middle managers in the corporate entrepreneurship process that drives new capability development. Middle managers are highlighted as key entrepreneurial agents because of their special position in an organization.

The paper draws on existing capability development and corporate entrepreneurship literature and develops a conceptual model and research propositions that are illustrated through three examples from a Chinese private firm.

This paper contends the dual role of middle managers, both as change implementers to follow pre-set rules of an existing corporate entrepreneurship system and as change initiators to bring new rules to improve the existing system.

The paper is conceptual in nature, advancing the understanding of middle managers’ role in corporate entrepreneurship. The paper provides directions for future empirical research.

The interactions between middle managers and other organizational agents are discussed in the propositions. This paper suggests the importance of empowering middle managers to facilitate changes in complex internal environments.

The paper provides a unique theoretical contribution by introducing the interface-based, multi-level conceptual model of corporate entrepreneurship toward new capability development.

The purpose of this paper is to use the wavelet neural network and genetic algorithm to study the effects of polyalphaolefin, TMP108 and OCP0016 on the kinematic viscosity, viscosity index and pour point of lubricating oil.

Wavelet neural network is used to train the known samples, test the unknown samples and compare the obtained results with those obtained with a traditional empirical formula.

It is found that the wavelet neural network prediction value is closer to the experimental value than the traditional empirical formula calculation value.

The results show that the wavelet neural network can be used to study the physical and chemical indexes of lubricating oil.

The purpose of this paper is to study a fractional grey model FAGM(1,1,t^α) based on the GM(1,1,t^α) model and the fractional accumulated generating operation, and then predict the national health expenditure, the government health expenditure and the out-of-pocket health expenditure of China.

The presented univariate grey model is systematically studied by using the grey modelling technique, the fractional accumulated generating operation and the trapezoid approximation formula of definite integral. The optimal system parameters r and α are evaluated by the particle swarm optimisation algorithm.

The expressions of the time response function and the restored values of this model are derived. The GM(1,1), NGM(1,1,k,c) and GM(1,1,t^α) models are particular cases of the FAGM(1,1,t^α) model with deterministic r and α. Compared with other forecasting models, the results of the FAGM(1,1,t^α) model have higher precision.

The superiority of the new model has high potential to be used in the medicine and health fields and others. Results can provide a guideline for government decision making.

The univariate fractional grey model FAGM (1,1,t^α) successfully studies the China’s health expenditure.

This study aims to investigate the antecedents of design crowdsourcing decision-making, the impact of design crowdsourcing on new product performance and the moderating effect of network connectivity.

The sample (n = 104) was collected from a leading social product development website; the fuzzy-set qualitative comparative analysis and two-stage least square methods were used in the investigation.

Three design attribute feature configurations (rational, emotional and kinesthetic value configurations) are conducive to firms’ adoption of design crowdsourcing and there are two configurations in which firms do not adopt design crowdsourcing. Design crowdsourcing influences new product performance positively. Network connectivity has an inverted U-shaped effect on the relationship between design crowdsourcing and new product performance.

These findings not only enrich crowdsourcing and social network studies but also guide crowdsourcing firms to better manage their processes and community members.

Integrating artificial intelligence and quantitative investment has given birth to various agricultural futures price prediction models suitable for nonlinear and non-stationary data. However, traditional models have limitations in testing the spatial transmission relationship in time series, and the actual prediction effect is restricted by the inability to obtain the prices of other variable factors in the future.

To explore the impact of spatiotemporal factors on agricultural prices and achieve the best prediction effect, the authors innovatively propose a price prediction method for China's soybean and palm oil futures prices. First, an improved Granger Causality Test was adopted to explore the spatial transmission relationship in the data; second, the Seasonal and Trend decomposition using Loess model (STL) was employed to decompose the price; then, the Apriori algorithm was applied to test the time spillover effect between data, and CRITIC was used to extract essential features; finally, the N-Beats model was selected as the prediction model for futures prices.

Using the Apriori and STL algorithms, the authors found a spillover effect in agricultural prices, and past trends and seasonal data will impact future prices. Using the improved Granger causality test method to analyze the unidirectional causality relationship between the prices, the authors obtained a spatial effect among the agricultural product prices. By comparison, the N-Beats model based on the spatiotemporal factors shows excellent prediction effects on different prices.

This paper addressed the problem that traditional models can only predict the current prices of different agricultural products on the same date, and traditional spatial models cannot test the characteristics of time series. This result is beneficial to the sustainable development of agriculture and provides necessary numerical and technical support to ensure national agricultural security.

Multinational small-to-medium-sized enterprises (SMEs) are increasingly participating in cross-border digital platforms – especially amid the COVID-19 pandemic. Accordingly, knowledge integration (KI) has become more and more important. In fact, it has been deemed by many as the key to organizational resilience. Given this burgeoning phenomenon, this study aims to explore a path for improving the resilience of multinational SMEs. Through this process, this study also finds a relationship between the KI processes associated with adopting global digital platforms and the resiliency of local–global businesses. Hence, in part, this paper also explores the effectiveness of all these mechanisms.

This study used the stepwise regression method in Stata 16.0 to analyze the direct effects of both horizontal and vertical KI processes on the resilience of local–global businesses. Additionally, t-tests were also used to compare the differences in coefficients between the mechanisms. The sample analyzed comprised data on multinational manufacturing SMEs in the Yangtze River Delta region of China who are using global digital platforms.

The KI processes of these firms, both horizontal and vertical, positively correlate to resilience. Horizontal KI processes more efficiently increase the resilience of global businesses, whereas vertical processes more efficiently increase the resilience of local businesses.

First, this study provides insights into how multinational SMEs can improve their resilience in a crisis. In addition to adding to the knowledge of KI processes, this expands the KM literature on pandemics. Second, by creating two KI processes based on global digital platforms and discussing their influence on resilience, this research deepens the understanding of affordance in the KM literature. Third, focusing on the KI research stream, the results shed light on how KI processes might occur and how firms develop their KI processes.