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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 importance of online reviews on online hotel booking has been widely acknowledged. However, not all online reviews affect consumers equally. Compared with common online reviews, key online reviews (KORs) have a greater influence on consumers' decisions and online hotel booking. This study takes the first step to investigate the factors affecting the identification of KORs and the role of KORs in online hotel booking.

To test the research hypotheses, this study develops a crawler to obtain 551,600 online reviews of 650 hotels in ten representative large cities in China. This study first uses a binary logistic regression to identify KORs by combining review content quality and reviewer characteristics and then uses a log-regression model to investigate the role of KORs in online hotel booking.

This study mined the factors affecting the identification of KORs by analyzing review contents and reviewer characteristics. Our results revealed that KORs play a mediating role in the effects of review content and reviewer characteristics on online hotel booking.

This study focuses on KORs, which have received limited attention in research but are important to practitioners. Specifically, this study investigates the antecedents and consequences of KORs. Our results enable hotel managers to manage online reviews effectively, particularly KORs.

This paper aims to propose a bed and breakfast (B&B) recommendation method that takes into account review timeliness and user preferences to help consumers choose the most satisfactory B&B.

This paper proposes a B&B ranking method based on improved intuitionistic fuzzy sets. First, text mining and cluster analysis are combined to identify the concerns of consumers and construct an attribute set. Second, an attribute-level-based text sentiment analysis is established. The authors propose an improved intuitionistic fuzzy set, which is more in line with the actual situation of sentiment analysis of online reviews. Next, subjective-objective combinatorial assignments are applied, considering the consumers’ preferences. Finally, the vlsekriterijumska optimizacija i kompromisno resenje (VIKOR) algorithm, based on the improved score function, is advised to evaluate B&Bs.

A case study is presented to illustrate the use of the proposed method. Comparative analysis with other multi-attribute decision-making (MADM) methods proves the effectiveness and superiority of the VIKOR algorithm based on the improved intuitionistic fuzzy sets proposed in this paper.

Proposing a B&B recommendation method that takes into account review timeliness and user customization is the innovation of this paper. In this approach, the authors propose improved intuitionistic fuzzy sets. Compared with the traditional intuitionistic fuzzy set, the improved intuitionistic fuzzy set increases the abstention membership, which is more in line with the actual situation of attribute-level sentiment analysis of online reviews.

The purpose of this paper is to discuss the role of reference effect on newsvendor’s decision behavior in a market with strategic customers and work out the newsvendor’s optimal pricing policy and ordering quantity.

This study utilizes the prospect theory and strategic customer framework to analyze the decision-making behavior on the newsvendor’s optimal pricing policy and ordering quantity. The paper further presents an extension of newsvendor model and provides the model’s properties. The paper finally analyzes the results with various parameters on the model and reports on the insights generated by the model.

The paper indicates that the ordering quantity is not altered with the changing proportion of strategic customers and myopic customers, but the ordering quantity and the pricing strategy are influenced in terms of newsvendor’s reference effect, loss aversion, product cost, and salvage price.

The research findings have important implications for decision makers. Previous researches have studied the incomplete rationality newsvendor’s decision-making behavior mainly by analyzing the vendor’s risk preferences or loss aversion, but the effect of reference point also plays an important role in analyzing the decision-maker’s behavior. The paper provides the optimal pricing policy and ordering quantity with the reference effect considering the strategic customers behavior. This model is also a valid complementarity to behavioral operations management research area.

The paper examines the role of reference effect in newsvendor problem with the strategic customers and analyzes the impact of parameters such as loss aversion on the newsvendor’s decision behavior.

While the Chinese securities market is booming, the phenomenon of listed companies falling into financial distress is also emerging, which affects the operation and development of enterprises and also jeopardizes the interests of investors. Therefore, it is important to understand how to accurately and reasonably predict the financial distress of enterprises.

In the present study, ensemble feature selection (EFS) and improved stacking were used for financial distress prediction (FDP). Mutual information, analysis of variance (ANOVA), random forest (RF), genetic algorithms, and recursive feature elimination (RFE) were chosen for EFS to select features. Since there may be missing information when feeding the results of the base learner directly into the meta-learner, the features with high importance were fed into the meta-learner together. A screening layer was added to select the meta-learner with better performance. Finally, Optima hyperparameters were used for parameter tuning by the learners.

An empirical study was conducted with a sample of A-share listed companies in China. The F1-score of the model constructed using the features screened by EFS reached 84.55%, representing an improvement of 4.37% compared to the original features. To verify the effectiveness of improved stacking, benchmark model comparison experiments were conducted. Compared to the original stacking model, the accuracy of the improved stacking model was improved by 0.44%, and the F1-score was improved by 0.51%. In addition, the improved stacking model had the highest area under the curve (AUC) value (0.905) among all the compared models.

Compared to previous models, the proposed FDP model has better performance, thus bridging the research gap of feature selection. The present study provides new ideas for stacking improvement research and a reference for subsequent research in this field.

Because the dynamic characteristics of the stock market are nonlinear, it is unclear whether stock prices can be predicted. This paper aims to explore the predictability of the stock price index from a long-memory perspective. The authors propose hybrid models to predict the next-day closing price index and explore the policy effects behind stock prices. The paper aims to discuss the aforementioned ideas.

The authors found a long memory in the stock price index series using modified R/S and GPH tests, and propose an improved bi-directional gated recurrent units (BiGRU) hybrid network framework to predict the next-day stock price index. The proposed framework integrates (1) A de-noising module—Singular Spectrum Analysis (SSA) algorithm, (2) a predictive module—BiGRU model, and (3) an optimization module—Grid Search Cross-validation (GSCV) algorithm.

Three critical findings are long memory, fit effectiveness and model optimization. There is long memory (predictability) in the stock price index series. The proposed framework yields predictions of optimum fit. Data de-noising and parameter optimization can improve the model fit.

The empirical data are obtained from the financial data of listed companies in the Wind Financial Terminal. The model can accurately predict stock price index series, guide investors to make reasonable investment decisions, and provide a basis for establishing individual industry stock investment strategies.

If the index series in the stock market exhibits long-memory characteristics, the policy implication is that fractal markets, even in the nonlinear case, allow for a corresponding distribution pattern in the value of portfolio assets. The risk of stock price volatility in various sectors has expanded due to the effects of the COVID-19 pandemic and the R-U conflict on the stock market. Predicting future trends by forecasting stock prices is critical for minimizing financial risk. The ability to mitigate the epidemic’s impact and stop losses promptly is relevant to market regulators, companies and other relevant stakeholders.

Although long memory exists, the stock price index series can be predicted. However, price fluctuations are unstable and chaotic, and traditional mathematical and statistical methods cannot provide precise predictions. The network framework proposed in this paper has robust horizontal connections between units, strong memory capability and stronger generalization ability than traditional network structures. The authors demonstrate significant performance improvements of SSA-BiGRU-GSCV over comparison models on Chinese stocks.

As a typical creative behavior, creative process engagement (CPE) has received increased attention in recent years. Leadership behaviors such as leader–member exchange (LMX) and leader creativity expectations (LCE) have been found as two key predictive factors of CPE. However, the mechanism underlying this relationship is not well understood. This study aims to clarify how LMX influences follower CPE by considering the interplay among LCE, decision autonomy and task interdependence from an interactionist perspective.

Using a sample of 371 leader–employee dyads from eight enterprises in mainland China, this study conducts a hierarchical regression analysis to test the hypotheses for the proposed model.

Results reveal that the significant two- and three-way interactions where LCE, decision autonomy and task interdependence moderate the relationship between LMX and follower CPE. The relationship between LMX and follower CPE is not significant as expected, but the moderating role of LCE is positive and significant, and the relationship is strongest when conducted with either low task interdependence or high decision autonomy.

Different from previous research that only investigated one certain leadership factor’ effect on employees' innovative behaviors, this study comprehensively considered the combined influence of two related but significantly different connotation leadership factors on follower CPE and found the contingency effect of LCE on the relationship between LMX and follower CPE. Furthermore, the authors found the regional effectiveness of the leadership factor. The effect of leadership factors on follower CPE varies under the influence of different job characteristics, and is conducive to enrich the interactionist view on follower CPE.

Long-term contract is an important developing direction of China's coal industry coordination. This paper aims to discuss how to use contract for difference (CFD) to avoid risk and effectively increase the benefit of both coal and thermal power plants in the coal-electricity supply chain.

Based on prospect theory, this paper takes the risks and benefits of the coal and coal-fired power plants in the coal supply chain under CFD into balanced consideration to construct the contract coordination mechanism. In this mechanism, the coal demand in the coal supply chain equilibrium under centralized decision-making is regarded as the total annual volume of transactions needed to design the contract coordination mechanism and solve double marginalization. Then, based on prospect theory, in the construction of CFD, this paper takes the income of power and coal enterprises when they are in equilibrium under Stackelberg non-cooperative game as the reference point. In addition, considering that coal demand is a random variable, the CFD with a one-year trading session can be designed.

The research derives the coal price of the contract for difference, contract trading volume and its proportion of the total trading volume. A numerical example shows that the model above can be used to effectively avoid the risk of both coal and electricity sides.

To solve the conflict between coal enterprises and thermal power plants, let the coal-electricity supply chain be converted from non-cooperative game to cooperative game. Based on the prospect theory, this paper takes the income of the non-cooperative game of coal and thermal power plants as a reference point and considers how to design the coordination mechanism, the contract for difference, so as to make the two parties cooperate to solve the double marginal utility of the non-cooperative game in a chain supply. The main innovation of the work lies in the following: first, the coal demand when the coal-electrical supply chain is in balance under centralized decision-making is taken as the total annual trading volume needed to design the contract coordination mechanism and solve double marginalization. Second, based on prospect theory, in the construction of CFD, the benefits of coal-fired power plants and coal enterprises when both sides are in equilibrium under the Stackelberg non-cooperative game are taken as the reference points, and coal demand is taken as a random variable to design the CFD with a one-year transaction period. The price of coal that is not traded through CFD is calculated according to the daily market price. Third, this paper proposes the prospect M-V criterion of the risk-benefit equilibrium of both power and coal enterprises, which means that the risk-benefit equilibrium of both sides is the prospect variance effect of both sides relative to the reference point benefit divided by the prospect expectation effect.

The purpose of this paper is to rank products by combining sentiment analysis (SA) and multiple attribute decision-making (MADM).

This research constructs intuitionistic fuzzy (IF)-based sentiment word framework and corresponding computation rules, which aim to calculate the sentiment score of each sentiment phase. Based on intuitionistic fuzzy weighted averaging operator, the authors aggregate the overall performance of each feature for different products. Then, the MADM method can be used, TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) in this paper, to rank product through online reviews.

The results of the research show the superiority and applicability of proposed method in ranking products with online reviews.

This paper proposes IF-based sentiment word framework and corresponding computation rules, which is a novel idea to express both the sentiment orientations (positive, negative and neutral) and emotional intensities. In addition, this research makes full use of knowledge from both experts and online reviewers. Further, attention degree of each feature is considered in the process of calculating weight of different features, which is rarely seen in current studies. This paper makes full use of SA, fuzzy control theory and MADM theory to handle vague information (online comments) and rank alternative products, which can promote future perspectives and developments.

Coal and power generation are related upstream and downstream industries. Coal price marketization and electricity price regulation have caused the price of coal to be sensitive to the benefits of generators. The paper aims to discuss these issues.

As a financial tool, contracts for differences can both help balance interests and reduce risks caused by spot price fluctuation. This thesis regards coal demand as a triangular fuzzy stochastic variable while directing a levelling consideration towards risk returns for coal and power enterprises that are involved in coal generation contracts for differences. Risk and benefit measurement models were established between coal suppliers and power generators, and risk and benefit balance optimization models for contract negotiation were constructed.

A numerical example showed that the above models can be effectively used to avoid the risks of coal-electricity parties.

This thesis regards coal demand as a triangular fuzzy random variable while directing a levelling consideration towards the risk return to coal and power enterprises that are involved with coal generation contracts for differences. The features of this thesis are the following: demand information is regarded as a fuzzy random variable instead of a random variable. With historical data, sales experience and increasingly clear macro-economic conditions, coal and power enterprises are able to make a fuzzy decision – to a certain extent – when the transaction approaches. Accurate market information enables the supply chain system to satisfy the clients’ needs better, improve the profit level or avoid severe financial damages; by developing a feasible set of contracts for different parameters, it is possible to estimate whether the price difference enables supply chain coordination, requires changes or gives accounts to all involved parties of the supply chain; and without the assumption that the traditional M-V rule is unfavourable to decision makers, this thesis proposes the prospect M-V rule, which involves decision makers’ projections of future coal generation prices and enables wide applicability of the response method to contracts for differences.