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As an unhealthy dependence on social media platforms, social media addiction (SMA) has become increasingly commonplace in the digital era. The purpose of this paper is to provide a general overview of SMA research and develop a theoretical model that explains how different types of factors contribute to SMA.

Considering the nascent nature of this research area, this study conducted a systematic review to synthesize the burgeoning literature examining influencing factors of SMA. Based on a comprehensive literature search and screening process, 84 articles were included in the final sample.

Analyses showed that antecedents of SMA can be classified into three conceptual levels: individual, environmental and platform. The authors further proposed a theoretical framework to explain the underlying mechanisms behind the relationships amongst different types of variables.

The contributions of this review are two-fold. First, it used a systematic and rigorous approach to summarize the empirical landscape of SMA research, providing theoretical insights and future research directions in this area. Second, the findings could help social media service providers and health professionals propose relevant intervention strategies to mitigate SMA.

Government employees must comply with policies on information security regulations, online security practices, social networking usage, internet addiction, online cyberthreats and other related habits. These activities are considered cybersecurity behaviors. Government social media (GSM) accounts are increasingly used to educate employees about cybersecurity risks. To support the effectiveness of cybersecurity practices in government organizations, the purpose of this study is to investigate the impacts of GSM and organizational policy compliance on employees’ cybersecurity awareness, motivation and behaviors.

Data were obtained by administering a questionnaire survey to public personnel in Vietnam. A total of 330 valid responses were obtained, and the research hypotheses were tested using partial least squares–structural equation modeling.

First, cybersecurity awareness enhances information protection motivation and employee protective behavior. Second, GSM has positive impacts on cybersecurity knowledge and information protection motivation. Third, there is a strong positive association between information protection motivation and employee protective behavior. Finally, while organizational compliance significantly increases cybersecurity awareness, its impact on employee protective behavior is ind irect.

This research enhances the literature on the behavioral dimension of cybersecurity. The primary objective of this study is to assess the influence of cybersecurity awareness on protective behaviors rather than intents and attitudes alone. Furthermore, this research integrates protection motivation theory and cultivation theory to provide a more thorough assessment of cybersecurity awareness and protective behavior. By investigating the impact of GSM on the level of cybersecurity awareness among employees within government organizations, this study provides valuable insights into the efficacy of recent governmental initiatives aimed at fostering cybersecurity.

The global market for cloud gaming is growing rapidly. How gamers evaluate the service quality of this emerging form of cloud service has become a critical issue for both researchers and practitioners. Building on the literature on service quality and software as a service, this study develops and validates a gamer-centric measurement instrument for cloud gaming service quality.

A three-step measurement instrument development process, including item generation, scale development and instrument testing, was adopted to conceptualize and operationalize cloud gaming service quality.

Cloud gaming service quality consists of two second-order constructs of support service quality and technical service quality with seven first-order dimensions, namely rapport, responsiveness, reliability, compatibility, ubiquity, smoothness and comprehensiveness. The instrument exhibits desirable psychometric properties.

Practitioners can use this new measurement instrument to evaluate gamers' perceptions toward their service and to identify areas for improvement.

This study contributes to the service quality literature by utilizing qualitative and quantitative approaches to develop and validate a new measurement instrument of service quality in the context of cloud gaming and by identifying new dimensions (compatibility, ubiquity, smoothness and comprehensiveness) specific to it.

The aging and deterioration of engineering building structures present significant risks to both life and property. Fiber Bragg grating (FBG) sensors, acclaimed for their outstanding reusability, compact form factor, lightweight construction, heightened sensitivity, immunity to electromagnetic interference and exceptional precision, are increasingly being adopted for structural health monitoring in engineering buildings. This research paper aims to evaluate the current challenges faced by FBG sensors in the engineering building industry. It also anticipates future advancements and trends in their development within this field.

This study centers on five pivotal sectors within the field of structural engineering: bridges, tunnels, pipelines, highways and housing construction. The research delves into the challenges encountered and synthesizes the prospective advancements in each of these areas.

The exceptional performance of FBG sensors provides an ideal solution for comprehensive monitoring of potential structural damages, deformations and settlements in engineering buildings. However, FBG sensors are challenged by issues such as limited monitoring accuracy, underdeveloped packaging techniques, intricate and time-intensive embedding processes, low survival rates and an indeterminate lifespan.

This introduces an entirely novel perspective. Addressing the current limitations of FBG sensors, this paper envisions their future evolution. FBG sensors are anticipated to advance into sophisticated multi-layer fiber optic sensing networks, each layer encompassing numerous channels. Data integration technologies will consolidate the acquired information, while big data analytics will identify intricate correlations within the datasets. Concurrently, the combination of finite element modeling and neural networks will enable a comprehensive simulation of the adaptability and longevity of FBG sensors in their operational environments.

The main objective of this article is to visualize the structure and trends of pro-environmental behavior (PEB) between 1999 and 2023 through mapping and in-depth analysis. The aim is to analyze PEB, which has received considerable academic attention in recent years due to its key role in the conservation of the environment and the protection of local communities in tourist destinations. This paper provides an important summary of the recent research that has explored the role that tourists have in protecting the environment through PEB.

This study presents a visual analysis of 2005 scholarly articles between the years 1999 and 2023 related to PEB. Using the knowledge mapping based on VOSviewer it presents the current status of research, which includes the analysis of citation analysis, co-citation analysis, co-citation network and longitudinal analysis.

PEB is an emerging topic due to its relevance to protecting the environment in the context of travel. The citation and co-citation analysis show the relevance of the behavior of tourists with regard to protecting the environment. The co-word analysis highlights the current significance of research concerning green hotels and the destination image of environmentally responsible destinations.

This study sheds light on the current research progress of PEB in the context of tourism through a comprehensive analysis (citation, co-citation and co-word). In addition, we provide theories and factors that have been previously used to study PEB in the context of tourism. The findings contribute to a broad and diverse understanding of the concept of PEB, which can provide important insights for policymakers in formulating management strategies and policies aimed at reducing environmental impacts in destinations.

With the development of data mining technology, diverse and broader domain knowledge can be extracted automatically. However, the research on applying knowledge mapping and data visualization techniques to genealogical data is limited. This paper aims to fill this research gap by providing a systematic framework and process guidance for practitioners seeking to uncover hidden knowledge from genealogy.

Based on a literature review of genealogy's current knowledge reasoning research, the authors constructed an integrated framework for knowledge inference and visualization application using a knowledge graph. Additionally, the authors applied this framework in a case study using “Manchu Clan Genealogy” as the data source.

The case study shows that the proposed framework can effectively decompose and reconstruct genealogy. It demonstrates the reasoning, discovery, and web visualization application process of implicit information in genealogy. It enhances the effective utilization of Manchu genealogy resources by highlighting the intricate relationships among people, places, and time entities.

This study proposed a framework for genealogy knowledge reasoning and visual analysis utilizing a knowledge graph, including five dimensions: the target layer, the resource layer, the data layer, the inference layer, and the application layer. It helps to gather the scattered genealogy information and establish a data network with semantic correlations while establishing reasoning rules to enable inference discovery and visualization of hidden relationships.

Influencer transgressions can disappoint their followers. However, there is a lack of clarity about the effects of a false allegation on an influencer–follower relationship. Drawing from cognitive dissonance and moral reasoning theory, the current study aims to examine how this relationship is shaped across three time periods (before the allegation is leveled, after the allegation is leveled, and when the allegation is found to be baseless).

We study comments posted by followers of two falsely alleged social media influencers (SMI) on their YouTube and Instagram channels. Latent Dirichlet allocation (LDA) followed by netnography is used for thematic analysis. LDA is a social media topic modeling method that processes a statistically representative set of words to explain the tone and tenor of qualitative conversations. A sentiment analysis of the comments is done using SentiStrength.

When an allegation is leveled initially, the response from followers is overwhelmingly negative toward the influencer owing to moral coupling. However, when the allegations are proven to be false, the followers return to a positive opinion of the influencer, owing to feelings of dissonance and guilt.

The study contributes to the fields of influencer marketing, cognitive dissonance and moral reasoning. It highlights how endorsers can take advantage of the positive sentiment that arises once an accused SMI’s transgression is proven false.

This study introduces the concept of “Sentiment Reversal,” which is exhibited in the social media space. In this phenomenon, sentiments move from negative to positive toward the falsely accused SMI as they are vindicated of the previous charge.

This paper deconstructs the boundary-spanning technology innovation (BSTI) of manufacturing enterprises based on dual-meta and dual-degree perspective. We aim to explore the impact of differentiated internal reconfigurations on networking capabilities and, thus, different BSTIs and then reveal the optimal transformation paths of different BSTIs.

We use fuzzy-set qualitative comparative analysis (fsQCA) to conduct an empirical study on 128 manufacturing enterprises in China to reveal the heterogeneous combinatorial path of internal reconfigurations on BSTI through the networking capability, and the case analysis in specific technology fields of Haier, Gree, Midea and TBEA is used to verify our results. The transfer entropy (TE) method is used to reveal the best transformation paths of different BSTIs.

The results show that the manufacturing enterprises follow the “I,” “T,” “⊥” and “|” reconfiguration logic to effectively realize multiple boundaries breakthrough (MBB), tick boundaries breakthrough (TBB), multiple boundaries reproducing (MBR) and tick boundaries reproducing (TBR) BSTI, respectively. The BSTI has two adjacent transformations named “dual-meta transformations” and “dual-degree transformations.” Nonadjacent transformations follow the “clockwise” transformation law. In “quality transformation,” “degree” transforms first and then “meta” follows, while in “feature transformation,” “meta” transforms first and then degree follows.

Firstly, the scientific classification of BSTI is carried out to guide enterprises to carry out accurate BSTI. Secondly, the “internal reconfiguration-networking capability-BSTI” paths of manufacturing enterprises are explored. Finally, the different laws of different BSTIs’ transformations are revealed.

This article explores the heterogenous social mechanisms that drive responsible environmental behaviours by investigating differences in the mean effect of the psychosocial determinants of the intention to buy organic foods.

Using data for a representative sample of the Spanish population, we estimated the mean effect of the constructs represented in the responsible environmental behaviour (REB) theory that affect sustainable food consumption, and examined the social mechanisms that may explain heterogeneity in the mean effect of those constructs. Confirmatory factor analysis, linear regression, and latent class regression were used in the analysis.

We found that the effect of REB’s psychosocial constructs varied significantly, demonstrating social heterogeneity in the estimated average effect. We identified different social mechanisms that explain variations in organic food purchase intentions: environmental attitudes and social norms shape these intentions among socioeconomically privileged consumers, whereas personal norms shape these intentions among less socially advantaged consumers.

Our research contributes to the literature by highlighting the existence of differing social mechanisms explaining organic food purchase intentions. The uncovering of three social mechanisms explaining differences in the mean effect of factors driving those intentions provides valuable insights with regard to both further developing a holistic framework for responsible environmental behaviours and developing new public policies and marketing strategies aimed at improving sustainable food consumption.

Point-of-interest (POI) recommendation techniques play a crucial role in mitigating information overload and delivering tailored services. To address limitations in conventional POI recommendation systems, constrained by sparse user-POI interactions and incomplete consideration of temporal dynamics, POI recommendation based on the spatial-temporal graph (STG-POI) is proposed.

Spatial-temporal sequence graphs from geographical locations and user interaction history data are constructed, which are used to mine spatial-temporal sequence information. Using the data filtered by the band-pass filter, graph neural networks with distance-awareness and sequence-awareness are applied to capture high-order spatial-temporal connections within diverse graph topologies. The model leverages contrastive learning for self-supervised disentanglement of graph representations, providing self-supervised signals for sequential and geographical intent perception, thereby achieving more precise POI personalization.

Compared to the baseline model GSTN, experiments on the Foursquare and Gowalla data sets reveal that STG-POI improves testing AUC by 2.0%, 2.1%, 2.0% and decreases logloss by 1.9%, 3.3%, 0.3%, respectively. These results indicate the model’s effectiveness in capturing spatial-temporal information, surpassing mainstream POI recommendation baseline models.

This approach constructs a dual graph from user interaction data, harnessing sequential and geographical information as self-supervised signals. It yields decoupled representations of these influences, offering a comprehensive insight into user behaviors and preferences within location-based social networks, thus enhancing recommendation accuracy and interpretability. This approach addresses the challenge in graph convolutional network where only rough and smooth features are conducive to recommendation by using band-pass filters to significantly reduce computational complexity, thereby enhancing recommendation speed by filtering out noise data that does not contribute to recommendation performance. Experimental results indicate that this model surpasses current mainstream approaches in POI recommendation tasks, effectively integrating both geographical and temporal features.