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This paper aims to investigate the adverse effects of addiction to social media usage on expatriates' cultural identity change in cross-cultural settings.

A questionnaire survey was conducted in two public universities in China. Among the questionnaires distributed, 333 useful responses were obtained from international students for data analysis.

Regression results show addiction to social media usage exerts adverse effects by negatively moderating the relationship between associations with locals and the three dimensions of cultural intelligence. Addiction to social media usage impairs expatriates from developing cultural intelligence from associations with locals, which in turn affects their cultural identity change.

Research findings suggest that expatriates, administrators and educators should be highly aware of the adverse effects of addiction to social media usage in complex cross-cultural settings wherein expatriates are more dependent on information technology. The important role of cultural intelligence should also be highlighted for its bridging role in managing cultural identity change for acculturation purpose. No causal relationships between variables can be established considering the cross-sectional design of the research. Longitudinal or experimental design could be a promising methodology for future efforts.

The current research contributes to the knowledge on information management applied to cross-cultural settings. The present study combines an IT contingent view with cross-cultural study to explore the adverse effects of addiction to social media usage on the development of expatriates' cultural intelligence from associations with locals, thereby influencing cultural identity change. The research provides new perspectives to expand the nomological framework of cross-cultural studies by combining the enabling roles of information technology.

Firms often encounter complicated external relationships and conflicts in inbound and outbound open innovation (OI). Conflict management significantly affects innovation results. Guided by resource dependence theory (RDT), this study aims to examine the moderating effects of conflict management styles in the relationship between OI and organizational performance (OP).

This study focuses on manufacturing and service firms in China, with the respondents composed of senior managers. Using hierarchical regression analysis, data from 270 firm samples are used to empirically test the hypotheses.

Inbound and outbound OI openness positively affects OP. Cooperative conflict management positively moderates the relationship between inbound OI openness and OP, whereas it negatively moderates the impact of outbound OI openness on OP. By contrast, competitive conflict management positively moderates the relationship between outbound OI openness on OP.

Guided by RDT, this study explores the relationship between OI and OP and the moderating role of conflict management styles. However, it does not measure the level of resource dependence, which is among the future research directions for further validating the results of this study.

This study is among the first to investigate the impact of OI on OP in different conflict management styles. Findings suggest that choosing a suitable conflict management style may strengthen the positive effects of OI on OP.

Achieving sustainability and sustainable performance has emerged as a critical area of focus for both academic research and practice. However, this pursuit faces challenges, particularly concerning the inadequacy of supply chain information. To address this issue, our study employs the organizational information processing theory to explore how adopting blockchain technology enables firms to learn from and collaborate with their supply chain partners, ultimately facilitating their sustainable performance even in the presence of organizational inertia.

Underpinned by the organizational information processing theory and drawing data from 220 manufacturing firms in China, we use structural equation modeling to test our conceptual model.

Our results demonstrate that blockchain technology adoption can significantly enhance sustainable performance. Furthermore, supply chain learning acts as a mediator between blockchain technology adoption and sustainable performance, while organizational inertia plays a negative moderating role between blockchain technology adoption and supply chain learning.

These findings extend the existing literature on blockchain technology adoption and supply chain management, offering novel insights into the pivotal role of blockchain in fostering supply chain learning and achieving sustainable performance. Our study provides valuable practical implications for managers seeking to leverage blockchain technology to enhance sustainability and facilitate organizational learning.

Supply chain resilience and knowledge management (KM) processes have received increasing attention from researchers and practitioners. Nevertheless, previous studies often treat the two streams of literature independently. Drawing on the knowledge-based theory, this study aims to reconcile these two different streams of literature and examine how and when KM processes influence supply chain resilience.

This research develops a conceptual model to test a sample of data from 203 Chinese manufacturing firms using a structural equation modeling method. Specifically, the current study empirically examines how KM processes affect different forms of supply chain resilience (supply chain readiness, responsiveness and recovery) and examines the moderating effect of blockchain technology adaptation and organizational inertia on the relationship between KM processes and supply chain resilience.

The findings show that KM processes positively affect three dimensions of supply chain resilience, i.e., supply chain readiness, responsiveness and recovery. Besides, the study reveals that blockchain technology adoption positively moderates the relationships between KM processes and supply chain resilience, whereas organizational inertia negatively moderates these above relationships.

This research linked the two research areas of supply chain resilience and KM processes, further bridging the gap in the research exploration of KM in the supply chain field. Next, this study contributes to supply chain resilience research by investigating how KM systems positively impact supply chain readiness, responsiveness and recovery. In addition, this study found a moderating effect of blockchain technology adaption and organizational inertia on the relationship between KM processes and supply chain resilience. These findings provide a reference for Chinese manufacturing firms to strengthen supply chain resilience, achieve secure supply chain operations and gain a competitive advantage in the supply chain. This studys’findings advance the understanding of supply chain resilience and provide practical implications for supply chain managers.

Building supply chain resilience is increasingly recognized as an effective strategy to deal with supply chain challenges, risks and disruptions. Nevertheless, it remains unclear how to build supply chain resilience and whether supply chain resilience could achieve a competitive advantage.

By analyzing the data collected from 216 firms in China, the current study empirically examines how information technology (IT) capability and supply chain collaboration affect different forms of supply chain resilience (external resilience and internal resilience) and examines the performance implications of these two forms of supply chain resilience.

Results show that IT capability is positively related to external resilience, whereas supply chain collaboration is positively related to internal resilience. The combination of IT capability and supply chain collaboration is positively related to external resilience. In addition, internal resilience is positively related to firm performance.

This study used only cross-sectional data from China for hypothesis testing. Future studies could utilise longitudinal data and research other countries/regions.

The findings systematically assess how IT capability and supply chain collaboration contribute to supply chain resilience and firm performance. The results provide a benchmark of supply chain resilience improvement that can be expected from IT capability and supply chain collaboration.

The study findings advance the understanding of supply chain resilience and provide practical implications for supply chain managers.

In recent years, personalized recommendations have facilitated easy access to users' personal information and historical interactions, thereby improving recommendation effectiveness. However, due to privacy risk concerns, it is essential to balance the accuracy of personalized recommendations with privacy protection. Accordingly, this paper aims to propose a neural graph collaborative filtering personalized recommendation framework based on federated transfer learning (FTL-NGCF), which achieves high-quality personalized recommendations with privacy protection.

FTL-NGCF uses a third-party server to coordinate local users to train the graph neural networks (GNN) model. Each user client integrates user–item interactions into the embedding and uploads the model parameters to a server. To prevent attacks during communication and thus promote privacy preservation, the authors introduce homomorphic encryption to ensure secure model aggregation between clients and the server.

Experiments on three real data sets (Gowalla, Yelp2018, Amazon-Book) show that FTL-NGCF improves the recommendation performance in terms of recall and NDCG, based on the increased consideration of privacy protection relative to original federated learning methods.

To the best of the authors’ knowledge, no previous research has considered federated transfer learning framework for GNN-based recommendation. It can be extended to other recommended applications while maintaining privacy protection.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.

In 2005, China carried out a major reform that allows the previously untradeable shares controlled by large shareholders to become tradable in the secondary market. This reform and subsequent dramatic change of behavior of controlling shareholders, offer researchers a unique opportunity to study the behavior of controlling shareholders and its implication for corporate governance. Asset injection, by which controlling shareholders sell their high quality assets to the listed companies they controlled, became instantly popular after the reform. The purpose of this paper is to provide strong evidence that such asset injection improves both the Tobin's Q and the composite financial performance score of the injected firm.

Due to the availability of sample data, this paper focuses on two major types of assets injection: the listed companies purchase the large shareholders' physical assets or equity assets (their shares of other companies) in cash; and the listed companies purchase the large shareholders' physical assets or equity assets through private stock offering, often increasing the share proportion of large shareholders.

The research findings suggest that this full listing reform aligned the interest of controlling shareholders with the company and that controlling shareholders change their behavior from tunneling to propping.

The contributions of this paper are threefold: First, the paper provides strong evidence of large shareholders' propping behavior. Second, the authors use long‐term corporate financial performance measures to study the impact of asset injection. Third, the authors investigate what types of injections will have a bigger impact on financial performance of the injected firms.

The purpose of this paper is to examine the impact of intellectual capital (IC) and its components (human, structural and relational capitals) on the performance of manufacturing listed companies in China. This paper also investigates the impacts of company ownership, industry attributes and region on the IC-performance relationship.

The study uses the data of 953 manufacturing companies listed on the Shanghai and Shenzhen Stock Exchanges over the period 2012–2016. The modified value-added intellectual coefficient (MVAIC) model is applied to measure IC efficiency. Finally, multiple regression analysis is employed to test the research hypotheses.

This study reveals that IC can enhance firm performance in China's manufacturing sector. Overall, earnings are affected by physical capital, human capital (HC) and structural capital (SC), and profitability and productivity are influenced by physical capital, HC, SC and relational capital. Physical capital is the most influential contributor to firm performance. In addition, state-owned enterprises have a greater impact of IC on firm performance than private-owned enterprises; high-tech manufacturing companies have higher IC performance than non-high-tech manufacturing companies; manufacturing companies in China's eastern region have higher IC performance than the counterparts in central and western regions.

The findings may help managers, stakeholders and policymakers in developing countries to effectively and efficiently manage their IC resources.

This is the first study to evaluate IC and its relationship with firm performance among Chinese manufacturing listed companies using the MVAIC model.