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Companies are increasingly designing pro-environmental games to motivate users to implement pro-environmental behaviors (PEBs). However, how different types of virtual interactions affect PEBs in pro-environmental games is not clear. Thus, the authors propose that two types of virtual interaction, interactions with game objects and interactions with other users, can induce platform intimacy and love for nature and that platform intimacy has a direct effect on love for nature. Simultaneously, the authors examine the moderating effect of network externality on the relationship between the two types of virtual interaction and platform intimacy.

The authors, respectively, employed data from 92 students and 574 Chinese mobile users to empirically investigate the research framework.

The findings indicate that participants in interactions with game objects and interactions with other users reported stronger feelings regarding platform intimacy and love for nature, which, in turn, positively influences PEBs. Consumers with stronger perceptions of network externalities were more likely to be affected by the initiation effect of the interaction with game objects.

The authors introduce the notion of love for nature to the pro-environmental behaviors field and discuss the priming effect of two types of interactions on platform intimacy and love for nature. In addition, the authors focus on the important effect of network externality on users' emotions.

This study aims to investigate the predictive role of team personality composition in facilitating shared leadership through team member exchange (TMX), while also to examine the moderating effect of organizational culture.

The authors conducted a two-stage online survey and selected the customer service teams, claims teams and financial teams of 26 Chinese insurance companies as the research samples. The authors finally obtained validated questionnaires from 107 teams with 457 members. The hypothesized relationships were tested using SPSS 25.0 and Mplus.

The results indicate that both team relationship-oriented and task-oriented personality composition have significant positive effects on shared leadership with team-member exchange serving as a full mediator for both paths. As a boundary condition, organizational culture (i.e. including internal integration values and external adaptation values) has a moderating effect on the influence of TMX on shared leadership.

The study investigates the predictive role of team personality composition on shared leadership, which complements the empirical studies of shared leadership antecedents in the literature. Drawing on social exchange perspective, the authors find out that TMX serves as a mediator between team personality composition and shared leadership. The authors also identify the moderating effect of organizational culture on the emergence of shared leadership. The research emphasizes the contextual boundary condition in this process.

Fake news in cyberspace has greatly interfered with national governance, economic development and cultural communication, which has greatly increased the demand for fake news detection and intervention. At present, the recognition methods based on news content all lose part of the information to varying degrees. This paper proposes a lightweight content-based detection method to achieve early identification of false information with low computation costs.

The authors' research proposes a lightweight fake news detection framework for English text, including a new textual feature extraction method, specifically mapping English text and symbols to 0–255 using American Standard Code for Information Interchange (ASCII) codes, treating the completed sequence of numbers as the values of picture pixel points and using a computer vision model to detect them. The authors also compare the authors' framework with traditional word2vec, Glove, bidirectional encoder representations from transformers (BERT) and other methods.

The authors conduct experiments on the lightweight neural networks Ghostnet and Shufflenet, and the experimental results show that the authors' proposed framework outperforms the baseline in accuracy on both lightweight networks.

The authors' method does not rely on additional information from text data and can efficiently perform the fake news detection task with less computational resource consumption. In addition, the feature extraction method of this framework is relatively new and enlightening for text content-based classification detection, which can detect fake news in time at the early stage of fake news propagation.

This study aims to examine how business executives' political connections are associated with government subsidies and strategic change, and how they, in turn, influence firm performance, measured by return on assets (ROA) and market share.

Hypotheses were tested using the large firm-level dataset provided by the National Bureau of Statistics (NBS) of China for the period 2003–2013. This is one of the most comprehensive datasets of Chinese manufacturing companies and includes 321,722 firms on average per year, which spans over 37 industries.

The authors found that political connections, measured by senior executives' membership in the National People's Congress of China (NPC), were positively associated with government subsidies but were not associated with strategic change. Also, government subsidies, as the underlying mechanism, mediated the relationships between NPC membership and firm performance but strategic change did not.

By examining the possible mediators between corporate political strategies and firm performance, the authors confirmed the thought that the impact of political connections on firm performance is a complex phenomenon and goes beyond a simple direct effect. However, future research could explore other mediators in this relationship.

While the direct relationship between political connections and firm performance has been examined in management literature, the results are mixed. For the first time, the authors addressed the gap and opened the “black box” – the underlying mechanisms of this relationship. This study's findings contribute to the literature on corporate political activity, strategic change, and their influences on firm performance.

Fall accidents can cause casualties and economic losses in the construction industry. Fall portents, such as loss of balance (LOB) and sudden sways, can result in fatal, nonfatal or attempted fall accidents. All of them are worthy of studying to take measures to prevent future accidents. Detecting fall portents can proactively and comprehensively help managers assess the risk to workers as well as in the construction environment and further prevent fall accidents.

This study focused on the postures of workers and aimed to directly detect fall portents using a computer vision (CV)-based noncontact approach. Firstly, a joint coordinate matrix generated from a three-dimensional pose estimation model is employed, and then the matrix is preprocessed by principal component analysis, K-means and pre-experiments. Finally, a modified fusion K-nearest neighbor-based machine learning model is built to fuse information from the x, y and z axes and output the worker's pose status into three stages.

The proposed model can output the worker's pose status into three stages (steady–unsteady–fallen) and provide corresponding confidence probabilities for each category. Experiments conducted to evaluate the approach show that the model accuracy reaches 85.02% with threshold-based postprocessing. The proposed fall-portent detection approach can extract the fall risk of workers in the both pre- and post-event phases based on noncontact approach.

First, three-dimensional (3D) pose estimation needs sufficient information, which means it may not perform well when applied in complicated environments or when the shooting distance is extremely large. Second, solely focusing on fall-related factors may not be comprehensive enough. Future studies can incorporate the results of this research as an indicator into the risk assessment system to achieve a more comprehensive and accurate evaluation of worker and site risk.

The proposed machine learning model determines whether the worker is in a status of steady, unsteady or fallen using a CV-based approach. From the perspective of construction management, when detecting fall-related actions on construction sites, the noncontact approach based on CV has irreplaceable advantages of no interruption to workers and low cost. It can make use of the surveillance cameras on construction sites to recognize both preceding events and happened accidents. The detection of fall portents can help worker risk assessment and safety management.

Existing studies using sensor-based approaches are high-cost and invasive for construction workers, and others using CV-based approaches either oversimplify by binary classification of the non-entire fall process or indirectly achieve fall-portent detection. Instead, this study aims to detect fall portents directly by worker's posture and divide the entire fall process into three stages using a CV-based noncontact approach. It can help managers carry out more comprehensive risk assessment and develop preventive measures.

This paper aims to develop a novel grey Bernoulli model with memory characteristics, which is designed to dynamically choose the optimal memory kernel function and the length of memory dependence period, ultimately enhancing the model's predictive accuracy.

This paper enhances the traditional grey Bernoulli model by introducing memory-dependent derivatives, resulting in a novel memory-dependent derivative grey model. Additionally, fractional-order accumulation is employed for preprocessing the original data. The length of the memory dependence period for memory-dependent derivatives is determined through grey correlation analysis. Furthermore, the whale optimization algorithm is utilized to optimize the cumulative order, power index and memory kernel function index of the model, enabling adaptability to diverse scenarios.

The selection of appropriate memory kernel functions and memory dependency lengths will improve model prediction performance. The model can adaptively select the memory kernel function and memory dependence length, and the performance of the model is better than other comparison models.

The model presented in this article has some limitations. The grey model is itself suitable for small sample data, and memory-dependent derivatives mainly consider the memory effect on a fixed length. Therefore, this model is mainly applicable to data prediction with short-term memory effect and has certain limitations on time series of long-term memory.

In practical systems, memory effects typically exhibit a decaying pattern, which is effectively characterized by the memory kernel function. The model in this study skillfully determines the appropriate kernel functions and memory dependency lengths to capture these memory effects, enhancing its alignment with real-world scenarios.

Based on the memory-dependent derivative method, a memory-dependent derivative grey Bernoulli model that more accurately reflects the actual memory effect is constructed and applied to power generation forecasting in China, South Korea and India.

Online retailers are pioneering the incentivization of customers to generate more product reviews by rewarding them. However, little is known about the impact of reward types on customers' review behavior, including review frequency and sentiment. To address this gap, we investigated the effects of different reward types on customers' review behavior and how these rewards influence customers' review behavior.

We collected secondary data and empirically tested the hypothesis by analyzing the change in reward policy. Regression and two-stage Heckman models were applied to investigate the effects, with the latter used to control potential selection issues.

The results revealed that monetary rewards can stimulate customers to generate more positive product reviews. Furthermore, the reward amount has a negative moderating effect on the aforementioned relationship. Additionally, customer tenure negatively moderates the relationship between monetary rewards and review behavior.

This study contributes to the understanding of user-generated content motivation and provides managerial implications for reward programs.

The purpose of this study is to examine supplier–customer capabilities in solution co-creation and how they are matched from a relational process perspective.

Using a qualitative approach, the authors identified 20 sets of supplier–customer capability matches by conducting in-depth interviews with 34 matched informants and retrieving suppliers’ archival data (project documents and success stories).

The authors identified 20 capability matching sets (21 supplier and 23 customer capabilities) and developed a process-based model of bilateral capabilities that match at the organizational level in solution co-creation. The authors reveal their match forms (complementarity and compatibility) and offer suggestions for future research.

This paper is qualitative; quantitative studies are required for testing and extending the initial conclusions.

This study guides the supplier and customer to cultivate different capabilities at different stages of solution co-creation and alerts them to the importance of capability complementarity and compatibility.

To the best of the authors’ knowledge, this study is the first to introduce the bilateral perspective into dynamic capability research in the context of solution co-creation. The authors discuss the abilities the supplier and customer must possess at different stages and how they match dynamically. The analysis extends the research on solution-specific capabilities and dynamic matching, offering useful implications for solution co-creation in practice.

This study is conducted to determine the factors that affect profitability in Chinese listed companies (by using financial ratios). Four independent variables liquidity, intangible assets, working capital and company leverage were empirically tested for their relationships with profitability besides two control variables which are firm size and company efficiency.

This study used secondary data extracted manually from the annual reports of non-financial Chinese listed companies on the Shanghai stock exchange (http://www.szse.cn/); the data set covers 100 companies during the period of 2017–2019, and a random selection method was used in order to achieve credibility and fairness as much as possible.

The findings show firm size, working capital and intangible assets have positive and significant relationships with profitability [return on assets (ROA) and earnings per share (EPS)]. Positive working capital is important to lower the cost of capital and improve companies' profitability. Intangible assets are also an essential source to improve profitability due to their low costs. In addition, the findings display a negative and strong relationship between liquidity and profitability, meaning that companies suffer low profit due to inefficient use of liquid items. Interestingly, leverage, which is measured by debt ratio and leverage ratio, shows mixed results; debt ratio shows a positive and strong association with ROA but not with EPS; while leverage ratio displays a strong but negative association with ROA but not with EPS. These results confirm the inverted U-shape relationship between leverage and profitability, which depends on the balance between benefit and cost of debt.

Profitability is also important for employees and society where business organization provides sustainability and stability for both of them. Employees can then significantly contribute to achieve higher firm's profitability by efficiently using firm's resources.

This study differs than previous studies in number of aspects: First, this study focuses on financial ratios to explain profitability in Chinese companies. This study provides empirical results about the factors connected to profitability and help stakeholders to make their right decisions. Second, it examines the impact of four independent factors and two control variables that some of them are new in Chinese context such as intangible assets. Third previous studies focus on financial industry such as banks; however, this study focuses on non-financial industry.

Inefficiency dilemmas in project governance are caused by various risks arising from the characteristic of construction supply chain projects, such as poor project performance, conflicts between stakeholders and cost overrun. This research aims to establish a fuzzy synthetic evaluation (FSE) model to analyze construction supply chain risk factors. Corresponding risk mitigation strategies are provided to facilitate the improvement performance of ongoing construction supply chain projects.

A literature review is utilized to reveal the deficiencies of construction supply chain risk management. Thus, a total of five hundred (500) questionnaires are distributed to construction professionals, and four hundred and thirty-five (435) questionnaires are recovered to obtain the evaluation data of construction professionals on critical risk factors. Additionally, the FSE is used to analyze and rank the significance of critical risk factors. Finally, this research discusses nine critical risk factors with high weight in the model, and explains the reason for the significance of critical risk factors in the construction supply chain.

The questionnaire results show that the thirty-one (31) identified critical risk factors are verified by related practitioners (government departments, universities and research institutions, owners, construction units, financial institutions, design units, consulting firms). Thirty-one (31) identified critical risk factors are divided into common risks, risks from contractors and risks from owners. The most significant factors in the three categories, respectively, are “political risks,” “owner's unprofessional” approach and “cash flow.” Managing these risks can facilitate the development of the construction supply chain.

This paper expands the research perspective of construction supply chain risk management and complements the risks in the construction supply chain. For practitioners, the research result provides some corresponding measures to deal with these risks. For researchers, the research result provides the direction of construction supply chain risk treatment.