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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.

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.

Explore how the degree of humanization affects user misconduct, and provide effective misconduct prevention measures for the wide application of artificial intelligence in the future.

Based on the “Uncanny Valley theory”, three experiments were conducted to explore the relationship between the degree of humanization of service machines and user misbehavior, and to analyze the mediating role of cognitive resistance and the moderating role of social class.

There is a U-shaped relationship between the degree of humanization of service machines and user misbehavior; Social class not only regulates the main effect of anthropomorphism on misbehavior, but also regulates the intermediary effect of anthropomorphism on cognitive resistance, thus affecting misbehavior.

The design of the service robot can be from the user’s point of view, combined with the user’s social class, match different user types, and provide the same preferences as the user’s humanoid service robot.

This study is an important reference value for enterprises and governments to provide intelligent services in public places. It can prevent the robot from being vandalized and also provide users with a comfortable human-computer interaction experience, expanding the positive effects of providing smart services by government and enterprises.

This study avoids and reduces users' misbehavior towards intelligent service robots, improves users' satisfaction in using service robots, and avoids service robots being damaged, resulting in waste of government, enterprise and social resources.

From the perspective of product factors to identify the inducing factors of improper behavior, from the perspective of social class of users to analyze the moderating effect of humanization degree and user improper behavior.

The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

This study aims to review the status quo, current state of research, research hot themes and research gaps in sustainable facilities management (SFM) in the built environment (BE) through an extant literature review.

To map and analyze knowledge paths in the context of SFM research, a sequential explanatory mixed-method review involving bibliometric and content analysis was used to help identify current research trends, research hot themes and knowledge gaps. The Scopus search engine was used to find 169 relevant articles. For a better understanding of the literature accumulated, a bibliometric analysis was carried out by using VOSviewer to reveal current research themes, the status quo and current state of research as well as research gaps.

Through the literature review and content analysis, the current research themes on SFM revealed from the study include green building technologies, assessment methods of SFM, smart buildings and building information modeling. The research hot themes in SFM include smart buildings and green building technologies, green buildings (GB), architectural and building designs in the university sector, assessment methods in buildings and decision-making and the adoption of asset and facility management in the university sector. Indoor air pollution, intelligent buildings, climate change, maintenance, environmental management, facilities, historic preservation, environmental performance, energy management, etc. are the research gaps identified from the study, and these serve as potential areas for future research studies under SFM. It was recognized that facilities managers are increasingly involved with sustainability policies within their organizations and are developing sustainability agendas to keep up with the changing nature of the facilities management (FM) profession.

The findings of this study hold relevance to the FM practice, as the integration of SFM by facilities managers can lead to waste reduction, decreased operating expenses and reduced energy consumption. In addition, occupants of sustainable buildings experience improved conditions that contribute to better health and productivity, thus boosting their overall well-being. Consistent with the themes of smart buildings and green technologies, revealed to be the hot themes in the SFM research scope, properties with sustainable features can command higher rental rates and property values, appealing to a broader range of stakeholders. SFM practices in universities can aid in saving money from reduced facility operational costs and improve the image of institutions while creating better indoor environments for students and staff. The analyses of countries involved in research can open doors for the establishment of research groups and the development of collaboration between universities in different countries researching similar topics of interest.

The geographical scope of this study is not limited and, therefore, encourages broad applicability of the findings to the global sustainable BE.