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Technological innovations provide huge opportunities to expand and revolutionize the scope of products and services offered. This is particularly true for tourism, which is undergoing significant changes due to the development of new technologies. The level of technology diffusion depends on several factors like the exchange of information among peers, and the attitude and shared perception among the contributors. The aim of the study is to explore the diffusion of technology in tourism with a specific focus on the social media discourse around new technologies. Thus, the paper investigates the level of interest in these new technologies analysing the information exchange occurring between individuals on Twitter in order to explore the influence of reciprocal networking.

To capture the attitudes expressed in the industry, the study analyses the ongoing discourse on Twitter as a proxy for the participants “interest in new technologies. Through a social network analysis of the tweets and retweets conducted over a period of nine months, the research maps the level of information exchange about the diffusion of new technologies. Moreover, the sentiment analysis provides an interesting overview of the individuals” attitudes towards the awareness or the adoption of new technologies.

Our analysis has provided several insights: (1) the information network on blockchain in tourism consists of participants who change very quickly over time (high turnover of accounts); (2) some contributors have an extremely important role in influencing the flow of information in the system (information centralization), they can have a generalist (discussing several topics) or a specialist (focusing on a specific topic) behaviour and this strategic choice influences their network's structure; (3) these central nodes also have an impact on the definition of positive and negative sentiment towards a topic (sentiment influencer).

The paper contributes to the literature on technology diffusion, by focusing on one of the preconditions of diffusion that is the shared positive attitude towards technological innovation. More specifically, we adopt a network-based approach, which is useful to explain the level of information exchange and the public discourse that can impact the shared perception and attitude towards technological innovation. The study also highlights the role of knowledge brokers in influencing this public discourse. Future studies can deepen the association between positive perception, higher levels of information exchange and increasing usage of specific technologies. Our results also suggest further exploring the opportunity to combine social media data and other sources of information to shed more light on the technological innovation diffusion processes.

This paper shows how practitioners can benefit from the analysis of information exchange about new technologies in tourism adopting a network perspective with the aim of understanding the level of influence among contributors. Moreover, the increasing interest in blockchain technology and the potential combination between social media data and other sources of information can offer promising insights.

The present study explores the level of technology diffusion through the analysis of information exchange on social media (Twitter). Furthermore, the dynamics of individual user behaviour offers a better understanding about media effects.

While previous research is focused on the users' perception towards the development of new technologies in tourism, the aim of this study is to investigate the dynamics behind the level of diffusion of information and awareness about these new technologies, which still represents an unexplored area of research.

This study aims to use geographical information on social media for public opinion risk identification during a crisis.

This study constructs a double-layer network that associates the online public opinion with geographical information. In the double-layer network, Gaussian process regression is used to train the prediction model for geographical locations. Second, cross-space information flow is described using local government data availability and regional internet development indicators. Finally, the structural characteristics and information flow of the double-layer network are explored to capture public opinion risks in a fine-grained manner. This study used the early stages of the COVID-19 outbreak for validation analyses, and it collected more than 90,000 pieces of public opinion data from microblogs.

In the early stages of the COVID-19 outbreak, the double-layer network exhibited a radiating state, and the information dissemination was more dependent on the nodes with higher in-degree. Moreover, the double-layer network structure showed geographical differences. The risk contagion was more significant in areas where information flow was prominent, but the influence of nodes was reduced.

Public opinion risk identification that incorporates geographical scenarios contributes to enhanced situational awareness. This study not only effectively extends geographical information on social media, but also provides valuable insights for accurately responding to public opinion.

In light of the stimulus-organism-response (SOR) theoretical paradigm, this paper explores how information relevance and media richness affect social network exhaustion and, moreover, how social network exhaustion ultimately leads to health anxiety and COVID-19-related stress.

The conceptual model is explicitly analyzed and estimated by using data from 309 individuals of different ages in mainland China. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were utilized to validate the proposed hypotheses through the use of online data.

The findings suggest that information relevance is negatively associated with social network exhaustion. In addition, social network exhaustion is a significant predictor of health anxiety and stress. Furthermore, information relevance and media richness can indirectly influence health anxiety and stress through the mediating effect of social network exhaustion.

Theoretically, this paper verifies the causes and consequences of social network exhaustion during COVID-19, thus making a significant contribution to the theoretical construction and refinement of this emerging research area. Practically, the conceptual research model in this paper may provide inspiration for more investigators and scholars who are inclined to further explore the different dimensions of social network exhaustion by utilizing other variables.

Although social network exhaustion and its adverse consequences have become prevalent, relatively few empirical studies have addressed the deleterious effects of social network exhaustion on mobile social media users’ psychosocial well-being and mental health during the prolonged COVID-19. These findings have important theoretical and practical implications for the rational development and construction of mobile social technologies to cultivate proper health awareness and mindset during the ongoing worldwide COVID-19 epidemic.

The privacy calculus based on a single stakeholder failed to explain users' co-owned information disclosure owing to the uniqueness of co-owned information. Drawing on collective privacy calculus theory and impression management theory, this study attempts to explore the co-owned information disclosure of social network platform users from a collective perspective rather than an individual perspective.

Drawing on collective privacy calculus theory and impression management theory, this study explores the co-owned information disclosure of social network platform users from a collective perspective rather than an individual perspective based on a survey of 740 respondents.

This study finds that self-presentation and others presentation directly positively affect users' co-owned information disclosure. Also, self-presentation, others presentation and relationship presentation indirectly positively affect users' co-owned information disclosure via relationship support. Furthermore, personal privacy concern, others' privacy concern and relationship privacy concern indirectly negatively affect users' co-owned information disclosure via relationship risk.

The findings develop the theory of collective privacy calculus and impression management, which offer insights into the design of the collective privacy protection function of social network platform service providers.

The present study aimed to investigate the impact of social networks on the use of academic libraries by university students.

The method used in the present study was a survey. The statistical population included 461 university students. The data collection tool was a questionnaire. The result of the Cronbach test was equal to 0.726 indicating the acceptable reliability of the questionnaire. For data analysis, descriptive statistical methods and inferential statistical methods using SPSS 21 software were employed.

The findings showed that 243 of the participants used social networks for 4–6 h a day, 192 students never used university libraries and 229 used the university library only once in a month. Communication with friends was also reported to be one of the main goals in using social networks. The results of regression analysis also indicated that four predictor variables including information retrieval, social influence, trust and attractiveness of social networking environment were statistically able to explain the variance of reluctance to use university libraries.

The present study is one of the few studies that has examined the negative impact of social networks on visiting university libraries.

A successful process of design concept evaluation has positive influence on subsequent processes. This study aims to consider the evaluation information at multiple stages and the interaction among evaluators and improve the credibility of evaluation results.

This paper proposes a multi-stage approach for design concept evaluation based on complex network and bounded confidence. First, a network is constructed according to the evaluation data. Depending on the consensus degree of evaluation opinions, the number of evaluation rounds is determined. Then, bounded confidence rules are applied for the modification of preference information. Last, a planning function is constructed to calculate the weight of each stage and aggregate information at multiple evaluation stages.

The results indicate that the opinions of the evaluators tend to be consistent after multiple stages of interactive adjustment, and the ordering of design concept alternatives tends to be stable with the progress of the evaluation.

Updating preferences according to the bounded confidence rules, only the opinions within the trust threshold are considered. The attribute information of the node itself is inadequately considered.

This method addresses the need for considering the evaluation information at each stage and minimizes the impact of disagreements within the evaluation group on the evaluation results.

Firms embedded in networks of relations are theorized through Gnyawali and Madhavan’s (2001) (G&M) structural embeddedness model to gain competitive advantage from topological characteristics. Empirical studies to support their theory have never been executed in full. Our study provided a full empirical test of their model in a digital trading network to achieve a higher degree of certainty that those network structural characteristics can have a major impact on the degree to which certain firms lead to competitiveness in a digital trading network environment.

To examine how firms respond in competitive situations, we chose the hyper-active digital trading network, eBay as our empirical context. We used eBay auction data to analyze how the network characteristics of eBay resellers impact their competitive behaviors.

Our study found strong support for the G&M model of competitiveness. We offer explanations for where support was not as strong as the Gynawali and Madavan theory proposes.

Our research is limited by our chosen context and findings in support of part of G&M model. Future studies in other digital contexts are needed to enhance the modeling of network topologies and further study the impacts of network density and structural autonomy on competitive action.

Our study suggests that managers proceed cautiously in forming partnerships, weighing circumstances where the firm can find itself with increased information power and avoiding, to the greatest extent possible, situations where the playing field is roughly equal.

Theory-making in this domain has begun as well as initial empirical testing. Much more needs to be accomplished, though, before embeddedness modeling can be thought of as being well established.

The G& M Model of competitiveness is an SNA explanation of why some competitive units succeed and others do not. Our study is the first, full blown empirical analysis of the theory.

The study investigates the information-related challenges as well as the practices adopted by early-career researchers during transitions between roles and institutions. Its primary goal is to delve into how information behaviors serve as scaffolding during significant life shifts. Moreover, the research aims to provide actionable insights based on this scaffolding concept for individuals navigating transitions.

This preliminary and exploratory study took a phenomenological approach to examine the role of information seeking and personal information management (PIM) behaviors during life transitions. In-depth semi-structured interviews were held with 15 early-career researchers from various disciplines, who were about to finish their PhDs or had recently graduated.

By employing information seeking and PIM practices, participants were able to address three main information challenges that arose during the transition process: the timing of information behavior, the nature of information and the social components of the transition. The use of networked and independent information seeking/validation practices enabled to establish a sustainable network of transition-related information, reducing uncertainty. PIM practices helped planning the transition, maintaining information over the long-term and gaining control over personal information.

This study underscores the significance of information behaviors, encompassing both information seeking and PIM, as scaffolding mechanisms during crucial life transitions. It offers essential insights that can guide the creation of impactful interventions and resources. Additionally, the research illuminates the pressing demand for more in-depth exploration in this domain.

The present research is trying to construct the network of relationships between different department of an organization during the design and development of car engine. Analyzing the structure of the network, finding the patterns of collaborations, and determining the important departments are the main purposes of the present research.

Improving relationships during a project life is an effective way to enhance employee performance in project-oriented organizations. This paper examines the collaborative relationships between internal project stakeholders through social network analysis (SNA) in a project for the design and development of car engine. In the first step of the research, the network of internal stakeholders was studied based on collaboration in the common activities performed by the resources. Then, the network of correspondences between internal stakeholders was studied. Finally, the two networks were integrated into a single network.

In the integrated network, the “fuel and combustion department” had the largest degree centrality (i.e. highest collaboration with others). The “integration department” was found to have the highest closeness centrality (i.e. more rapid access to other nodes). Furthermore, the “procurement department” had the highest betweenness centrality (i.e. the most strategic department). Our results revealed the potential capabilities of SNA method for the project management in the vehicle industry.

The obtained results of the present research show us the value of applying SNA methods and concepts to analyze the inter-organizational network of the Project Stakeholders relationship.

This study aims to explore how communication visibility affects employees’ work engagement from the negative perspective of employees’ perceived overload in the context of enterprise social media (ESM) and the role of ESM policies in the relationship between communication visibility and perceived overload.

This study examines how communication visibility (i.e. message transparency and network translucence) affects employees’ perceived overload (i.e. information overload and social overload), which in turn affects employees’ work engagement, and how ESM policies moderate the relationship between communication visibility and perceived overload. Partial least squares (PLS) analysis was conducted on data gathered from 224 ESM users in workplaces.

Communication visibility has significant positive impacts on perceived overload, perceived overload has significant negative impacts on work engagement and ESM policies negatively moderate the relationships between communication visibility and perceived overload, except for the relationship between message transparency and social overload.

The findings provide new insights for organizational managers to formulate ESM policies to mitigate perceived overload and guidance for ESM developers to improve ESM functions to alleviate perceived overload.

This study provides empirical evidence to explain the role of communication visibility and perceived overload in employees’ work engagement, which contributes to the existing literature on the negative impacts of communication visibility.