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With the widespread collection and utilization of user data, privacy security has become a crucial factor influencing online engagement. In response to the growing concern about privacy security issues on social media, this research aims to examine the key causes of social media users' privacy calculus and how the balance between perceived privacy risks and benefits affects users' privacy concerns and their subsequent willingness to disclose personal information.

The characteristics of the privacy calculus were extracted through partially structured interviews. A research model derived from privacy calculus theory was constructed, and latent variable modeling was employed to validate the proposed hypotheses.

Information sensitivity, experiences of privacy violations, social influence and the effectiveness of privacy policies influence users' privacy calculus. Privacy risk positively influences privacy concerns. Personal information disclosure willingness is positively influenced by privacy benefits and negatively influenced by privacy concerns, with both paths moderated by social media identification.

This study explores the key antecedents of users' privacy calculus and how these factors influence privacy concerns and subsequent willingness to disclose information on social media. It offers new insights into the privacy paradox observed within social media by validating the moderating role of social media identification on users' information disclosure willingness.

With the recent advances in mobile networking, context‐aware computing, and sensor‐based computing, researchers and game designers are able to explore the potential of combining these new technologies to develop mobile, networked, context‐aware, augmented reality multiplayer games. As part of new research collaboration between Lancaster University, Cisco Systems, Microsoft Research and Orange – MIPv6 Systems Research Lab, such a mobile context‐aware multiplayer game is proposed and explored. The proposed game, Real Tournament, gathers real‐time contextual information, e.g. physical location and orientation, from the players and injects them into the game engine to generate game events.

This study is to reconfigure a hierarchical supply chain model utilizing databases and text files to understand future pathways due to COVID-19 pandemic has had a bullwhip effect, disrupting the global supply chain, and a mechanism is needed to address this disruptive event under pandemic uncertainties.

To address this mechanism, this study employs bibliometric analysis and text mining to reconfigure a hierarchical supply chain model under pandemic conditions and associates it with social media to conduct an intuitive visual analysis.

The current academic concerns are related to an overconcentration on risk management and a data-driven approach, generating an enormous gap between the concerns of academics and those of the public. The evidence shows that for both countries with outstanding performance and those that need improvement, the efficiency in terms of preventing the spread of the pandemic should be promoted.

This study contributes to (1) reconfiguring a hierarchical supply chain model under pandemic uncertainties and (2) bridging theory and practice by offering comparable interrelated attributes to guide post-COVID-19 strategies in the supply chain. The findings are that the supply management approach and big data are attributes that involve the concerns of world public and academics under pandemic uncertainties.

Drawing on and extending the push-pull-mooring (PPM) framework, this paper aims to empirically explore the influencing mechanism of traveler switching intention from the hotel reservation platforms to the sharing accommodation platforms (SAPs).

This study adopts structural equation modeling to analyze the 543 responses collected among hotel reservation platforms and SAPs travelers.

The results support the positive effect of both push factors (e.g. dissatisfaction with product, service and information quality of hotels on the hotel reservation platform) and pull factors (e.g. price value, authenticity, interaction, home benefits and novelty of SAPs) on traveler switching intention. Except for the negative effect of switching cost, other mooring factors including prior switching experience and social influence positively affect traveler switching intention. The authors also found the switching cost negatively and prior switching experience positively moderated the push effects on traveler switching intention, while the social influence positively moderated the pull effects on traveler switching intention.

Recommendations of future SAP participation research to consider the competing platforms, the unique experiences of SAPs and mooring factors. Examining the factors of different sources is also useful for practitioners to better understand travelers’ demands and to improve the overall welfare of travelers.

This paper embraces an extended PPM framework to explore traveler switching intention in online travel platforms. Moreover, this paper provides unique insights into the switching behavior from the hotel reservation platforms to the SAPs.

This study aims to detect consumer sarcasm through inconsistencies in sentiment features between text and images of hotel reviews.

This paper proposes a model for sarcasm detection based on multimodal deep learning using reviews of three hotel brands collected from two travel platforms, which can identify emotional inconsistencies within a modality and across modalities. Text-image interaction information is explored using graph neural networks (GNN) to detect essential clues in sarcasm sentiment.

The research results show that the multimodal deep learning model outperforms other baseline models, which can help to understand hotel service evaluation and provide hotel managers with decision-making opinions.

This research can help hoteliers in two ways: detecting service quality and formulating strategies. By selecting reference hotel brands, hoteliers can better assess their level of service quality (optimal resource allocation ensues); therefore, sarcasm detection research is not only beneficial for hotel managers seeking to improve service quality. The multimodal deep learning method introduced in the present study can be replicated in other industries to help travel platforms optimize their products and services.

本研究通过分析酒店评论文本和图像之间情感特征的不一致性来检测消费者的讽刺。

本文提出了一种基于多模态深度学习的讽刺检测模型, 使用从两个旅行平台收集的三个酒店品牌的评论, 该模型能够识别模态内部和模态之间的情感不一致性。利用图神经网络（GNN）探索文本-图像交互信息, 以检测讽刺情感中的关键线索。

研究结果显示, 多模态深度学习模型优于其他基线模型, 这有助于理解酒店服务评估, 并为酒店经理提供决策建议。

该研究可以在两方面帮助酒店业者：检测服务质量和制定策略。通过选择参考酒店品牌, 酒店业者可以更好地评估其服务质量水平（随之而来的是最佳资源分配）, 因此, 讽刺检测研究不仅有助于寻求提高服务质量的酒店经理。本研究介绍的多模态深度学习方法可以在其他行业复制, 帮助旅行平台优化其产品和服务。

Based on the literature of IT strategic alignment and e-collaboration, the purpose of this paper is to specify how e-business strategic alignment (e-alignment) influences e-collaboration capabilities and improves firm performance, and whether the time-lag effect existed in this relationship.

The authors tested the research hypotheses using a field survey of 145 Chinese corporations. The research model was validated using SmartPLS 2.0 with both subjective and objective data collected from the survey and Oriana database.

The results support the notion of a positive and significant link between e-alignment and e-collaboration capabilities and between e-collaboration capabilities and firm performance. The authors also show that the effect of e-alignment on performance is fully mediated by e-collaboration capabilities and that e-collaboration with suppliers has a one-year time-lag effect on firm performance.

This research extends and integrates the literature on IT strategic alignment and e-collaboration, and explains why and how e-alignment generates firm performance.

This paper includes two implications for managers. First, when formulating e-business strategies, managers should focus on establishing e-collaboration capabilities with partners. Second, the downstream process is the direct sources of business value. Managers should take the establishment of e-selling process as a critical business strategy.

By focussed on intermediate factors and time-lag effects, this study provides significant implications for IT strategic alignment and e-collaboration literature.

The e-business platform has become a major driver for economic growth and development. The economic success of an e-marketplace greatly depends on the extent to which buyers and sellers are attracted to enter and actively participate in the e-business platform. Existing literature lacks empirical examination of factors influencing e-business platform attractiveness (EBPA) from a seller's perspective and understudies the interplay between technical and managerial considerations.

Based on the literature on network economy, modular systems theory, control theory and social exchange theory, the paper proposed that platform flexibility and platform control (PC) would affect EBPA through both direct and interaction effects. From a survey of platform sellers, the paper explored the influencing mechanisms of EBPA using hierarchical regression and fuzzy-set qualitative comparative analysis (fsQCA) to understand the statistical associations and the set relations of the conjunctions and conditions.

The paper found that platform flexibility (PF), process control and clan control (CC) positively affected EBPA. In addition, the interaction between PF and process control demonstrated a substitution effect on EBPA, and the interaction between PF and CC demonstrated a complementary effect on EBPA. Also, the authors found that the interaction between process control and CC demonstrated a complementary effect on EBPA. fsQCA provided the configurations of causal recipes associated with EBPA.

From a seller's perspective, the paper presents both theoretical explanation and empirical evidence for how design factors (e.g. PF) and governance factors (e.g. process control and CC) may interplay to influence EBPA.

This research aims at explaining the phenomenon of the “black children” (heihaizi), a very little-known generation who lived with concealment under the one-child policy in China. The one-child policy was officially introduced to nationwide at the end of 1979 by permitting per couple to have one child only, later modified to a second child allowed if the first was a girl in rural China in 1984. It was officially replaced by a nation-wide two-child policy and most existing research focused on the parents’ sufferings and policy changes. The term “black children” has been mainly used to describe their absence from their family hukou registration and education. However, this research aims at expanding the meaning of being “black” to explain the children who were concealed more than at the level of family formal registration, but also physical freedom and emotional bond. What we do not yet know are the details of their lived experiences from a day-to-day base: where did they live? How were they raised up? Who were involved? Who benefited from it and who did not? In this way, this research challenges the existing scholarship on the one-child policy and repositions the “black children” as primary victims, and reveals the family as a key figure in co-producing their diminished status with the support of state power. It is very important to understand these children’s loss of citizenship and human freedom from the inside of the family because they were concealed in so many ways away from public view and interventions. This research focuses on illustrating how their lack of access to continued, stabilized, and reciprocally recognized family interactions framed their very idea of self-worth and identity.

With technological innovation elements and the competitive market environment becoming increasingly complex, numerous firms utilize network embeddedness to achieve and sustain innovation. However, empirical research has not conclusively established which form of network embeddedness more effectively facilitates corporate innovation. Drawing on the heterogeneous network resources perspective, this study explores the impact of market network embeddedness, technology network embeddedness and their synergy on the green innovation performance of manufacturing small and medium-sized enterprises (SMEs). Furthermore, it investigates the moderating role of resource orchestration capability in these relationships.

Through an online questionnaire survey of Chinese manufacturing SMEs, 293 sample data were collected, and the hierarchical regression analysis was conducted to test the hypothesis.

The results indicate that market and technology network embeddedness significantly enhance green innovation performance, with the former exerting a more significant impact. Furthermore, the synergy between market and technology network embeddedness positively influences green innovation performance. Additionally, resource orchestration capability strengthens the positive effects of both market and technology network embeddedness on green innovation performance, while the moderating effect of resource orchestration capability on the relationship between the synergy of the two and green innovation performance was insignificant.

The study faced many limitations, such as collecting primary data, which relied on a questionnaire only, using cross-sectional data and examining only manufacturing SMEs.

Based on the heterogeneous network resources perspective and integrating social network theory and resource orchestration theory, this study explores the impact of network embeddedness on the green innovation performance of manufacturing SMEs, which sheds new light on the network embeddedness research framework and also enriches the antecedents of green innovation. In addition, this study provides implications on how manufacturing SMEs effectively utilize network embeddedness and resource orchestration capability to enhance green innovation performance.

A series of policies have been formulated to promote off-site construction (OSC) development. However, the effectiveness of these policies has yet to be fully explored. This study aims to explore the practical policy configurational conditions for achieving a high level of OSC development.

Data on policy configurational conditions and the OSC development level were collected from 28 provinces/municipalities in China using text mining. A mixed-method approach was used to conduct data analysis involving text analysis and qualitative comparative analysis (QCA). Text analysis was used to identify current policies with frequencies as independent variables and obtain the proportion of OSC projects in new construction projects as the dependent variable. QCA is employed to analyze the necessary and sufficient conditions of policy conditions for the high-level development of OSC by exploring the complex causal relationships between policy configurational conditions and the OSC development level.

Nine policies are obtained from the current policy framework for promoting OSC development using text analysis: industry supports, land policies, presale policies, reputation rewards, regulation controls, tax incentives, floor area rewards, financial subsidies, and loan subsidies. Four policy configurational conditions are identified that can achieve a high development level of OSC: three incentive-driven policy conditions with industry supports, presale policies, tax incentives, and floor area rewards as the core conditions, and one compulsory-driven policy conditions with land policies, financial supports, and loan supports as the core conditions using fuzzy set QCA. Moreover, our study found that a single policy cannot achieve a high level of OSC development as a necessary condition, and the policy configurational conditions that lead to high and nonhigh levels of OSC development are asymmetrical.

The study did not test the policy’s dynamic effects on the OSC development. A longitudinal study will be conducted to further identify the impact of policy on OSC implementation.

It provides policymakers with four practical policy configurational conditions to promote OSC development and guides policymakers to optimize effective policies considering the interaction effects of effective policies such as industry supports, presale policies, tax incentives, and floor area rewards instead of their net effects.

It contributes to the existing literature concerning the effects of policy on emerging technology (i.e. OSC) promotion by elucidating configurative effects rather than net effects. Meanwhile, it enriches the research on the antecedent conditions that affect the implementation of other emerging technologies in the architecture engineering and construction industry.