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An organization can be understood as a knowledge network in which teams send and receive knowledge. Many studies have explored knowledge sharing across teams but did not consider the direction of knowledge flows (KF), specifically how the knowledge inflow (KIF) and knowledge outflow (KOF) can be induced and influence team activities differently. To fill this gap, this paper distinguishes between KIF and KOF, examines their antecedents and consequences and considers how KIF and KOF within a team moderate the relationship between antecedents and KF of a team.

This study used structural equation model analysis of a sample of 341 individuals within 73 teams from four companies.

The results suggest that IT support is essential because it influences both KIF and KOF. However, only KOF has a significant effect on team performance suggesting that ambidexterity is not always necessary. In promoting KOF, increasing task interdependency is also effective. The effect of IT support varies with the level of KIF diversity.

The findings emphasize the importance of distinguishing KOF from KIF in a team’s knowledge network under the theoretical lens of ambidexterity. Identifying how IT support influences KF and how these flows separately affect team performance can provide useful insights into managing and facilitating KF in an organization.

As teams are built around specialized and different knowledge, they need to regulate their knowledge boundaries to exchange their specialized knowledge with other teams and to protect the value of such specialized knowledge. However, prior studies focus primarily on boundary spanning and imply that boundaries are obstacles to sharing knowledge. To fill this research gap, this study aims to indicate the importance of knowledge protection regulation, an activity that sets an adequate boundary for protecting knowledge, and investigate the factors that facilitate knowledge protection regulation and its consequences.

This study collected empirical data from 196 teams in seven organizations. Through a validation of the measurement model, data from 138 teams are used for further analysis. The hypotheses effects are assessed using a structural equation model.

The analysis results indicate that both task uncertainty and task interdependency enhance knowledge protection regulation in teams, and that information technology support moderates the relationship between task uncertainty and knowledge protection regulation. The results also indicate that knowledge protection regulation improves inter-team coordination and team performance.

This study focuses on knowledge protection regulation by adopting communication privacy management theory at the team level. The findings imply that boundary management is the process of communication and depends on the role the teams play in accomplishing their tasks. The findings also provide a new way to understand knowledge flow of the teams as well as the entire organization.

Various individual and environmental factors influencing employees’ online knowledge sharing have been identified, but the understanding regarding these has been mostly limited because of their independent and direct effects our understanding has been mostly limited to their independent and direct effects. This study aims to propose that the fit between employees and their environments (PE fit) matters. A model explaining how PE fit and misfit affect employees’ knowledge sharing behavior through influencing their affective commitment is developed and assessed.

The proposed model was assessed with data collected in a survey of 218 employees.

Results indicate that PE fit in the norm of collaboration, innovativeness and skill variety leads to the development of stronger affective commitment and, therefore, more knowledge sharing behavior than when they are in shortfall or excess in the environment (i.e. PE misfit).

The findings indicate a new direction for knowledge sharing research that focuses on PE fit and suggest that knowledge sharing can be improved more proactively in practice by assessing PE fit during recruitment.

Rather than viewing social network technology (SNT) as a mere tool to access a networked audience, we emphasize its role as both a means and a social actor to help verify people’s self-images in an online social context.

Drawing upon self-verification theory, this study investigates a mechanism of how users are willing to use SNTs continuously through the cognitive and affective reactions on two different SNTs. Structural equation modeling was used via data collected from 320 Facebook and 313 Twitter users.

Our results demonstrated that Facebook users regard it only as a useful tool for presenting self-images, while Twitter users are likely to feel an emotional attachment to technology as a social actor when ideal self-verification is gained, and that different types of SNTs create differential contexts for self-verification.

This study suggests a new lens to understand SNT’s role as a social actor in the self-verification process, further identifying the SNT context in which SNT takes different roles.

In a certain SNT usage context, users are attached to SNTs, suggesting SNT providers consider features that enable SNT users to fulfill their own self-verification motives.

This study explores the roles of SNTs from a self-verification perspective. Our conceptualization of technology as a self-verifying social actor can further extend existing discussions on the role of SNT in response to self-verifying needs, while also promoting the continued use of SNTs in the future.

The purpose of this paper is to investigate the effects of dedication- and constraint-based mechanisms on users’ post-adoption behavior in the social networking site (SNS) context.

The proposed framework uses user satisfaction and trust belief to capture the dedication-based mechanism and perceived switching costs and social norms to capture the constraint-based mechanism. Hypotheses were tested by applying partial least squares to data from 250 experienced Facebook users. A structural equation modeling was used to test the validity of the proposed research models.

The analysis results show that SNS users’ continuance intention is jointly affected by two distinctive mechanisms: a dedication-based one and a constraint-based one, the former playing a more critical role. The findings indicate that both perceived relative benefits and perceived enjoyment significantly influence user satisfaction. Learning and network size were found to be the key predictors of perceived switching costs.

This study applies the dedication- and constraint-based models by incorporating numerous sets of antecedents. The framework provides a theoretical lens of how two distinctive mechanisms influence SNS users’ post-adoption behaviors.

The analysis results provide several insights that can aid SNS providers understand SNS users post-adoption behaviors. Moreover, the findings will help SNS providers effectively facilitate dedication- and constraint-based mechanisms by enhancing the key antecedents of two distinctive mechanisms.

SNSs have become an important component of individuals lives. However, few systematic works investigate the fundamental mechanisms leading to SNS users’ continued usage. In an attempt to extend the horizons of SNS research, this study incorporates a set of antecedents to dedication- and constraint-based models.

This paper aims to clarify how organizations manage their participation in networks to share and jointly create knowledge but also risk unwanted knowledge spillovers at the same time. As formal governance, trust and observation are less applicable in informal networks, the authors need to understand how members address the need to protect knowledge by informal practices. The study aims to investigate how the application of knowledge protection practices affects knowledge sharing in networks. The insights are relevant for organizational and network management to control knowledge risks but harvest the benefits of network engagement.

The authors opted for an exploratory study based on 60 semi-structured interviews with members of 10 networks. In two rounds, network managers, representatives and members of the networks were interviewed. The second round of interviews was used to validate the intermediate findings. The data were complemented by documentary analysis, including network descriptions.

Through analyzing and building on the theory of psychological contracts, two informal practices of knowledge protection were found in networks of organizations: exclude crucial topics and share on selected topics and exclude details and share a selected level of detail. The authors explored how these two practices are enacted in networks of organizations with psychological contracts.

Counter to intuition that the protection of knowledge can be strengthened only at the expense of knowledge sharing and vice versa, networks benefitted from more focused and increased knowledge sharing while reducing the risk of losing competitive knowledge by performing these knowledge protection practices.

Although the notable and significant role of building information modeling (BIM) technologies in construction industries has gained user attention, only few studies have been examined on the user adoption of the technologies. The purpose of this paper is to introduce an acceptance model for BIM technologies and investigate how external factors which were extracted by in-depth interviews promote the adoption of such technologies.

An on-line survey was conducted by two South Korean survey agencies to test the acceptance model for BIM technologies. Then, the structural equation modeling (SEM) and confirmatory factor analysis (CFA) methods were used.

The results of the SEM and CFA methods from on-site construction employees (n=818) in Korea collected by the online survey indicate that compatibility and organizational support play a core role in positively and significantly affecting both perceived ease of use and usefulness, and that the connections introduced by the origin technology acceptance model are mainly confirmed.

Using the findings of the results, both implications and notable limitations are presented. Moreover, practical developers, as well as academic researchers can employ the results when they attempt to conduct future research.

Understanding customers' revisiting behavior is highlighted in the field of service industry and the emergence of online communities has enabled customers to express their prior experience. Thus, purpose of this study is to investigate customers' reviews on an online hotel reservation platform, and explores their postbehaviors from their reviews.

The authors employ two different approaches and compare the accuracy of predicting customers' post behavior: (1) using several machine learning classifiers based on sentimental dimensions of customers' reviews and (2) conducting the experiment consisted of two subsections. In the experiment, the first subsection is designed for participants to predict whether customers who wrote reviews would visit the hotel again (referred to as Prediction), while the second subsection examines whether participants want to visit one of the particular hotels when they read other customers' reviews (dubbed as Decision).

The accuracy of the machine learning approaches (73.23%) is higher than that of the experimental approach (Prediction: 58.96% and Decision: 64.79%). The key reasons of users' predictions and decisions are identified through qualitative analyses.

The findings reveal that using machine learning approaches show the higher accuracy of predicting customers' repeat visits only based on employed sentimental features. With the novel approach of integrating customers' decision processes and machine learning classifiers, the authors provide valuable insights for researchers and providers of hospitality services.

Localization is one of the critical issues in Ocean Internet of Things (OITs). The existing research results of localization in OITs are very limited. It poses many challenges due to the difficulty of deploy beacon accurately, the difficulty of transmission distance estimation in harsh ocean environment and the underwater node mobility. This paper aims to provide a novel localization algorithm to solve these problems.

This paper takes the ship with accurate position as a beacon, analyzes the relationship between underwater energy attenuation and node distance and takes them into OITs localization algorithm design. Then, it studies the movement regulation of underwater nodes in the action of ocean current, and designs an Energy-aware Localization Algorithm (ELA) for OITs.

Proposing an ELA. ELA takes the ship with accurate position information as a beacon to solve the problem of beacon deployment. ELA does not need to calculate the information transmission distance which solves the problem of distance estimation. It takes underwater node movement regulation into computation to solve the problem of node mobility.

This paper provides an ELA based on the relationship between propagation energy attenuation and node distance for OITs. It solves the problem of localization in dynamic underwater networks.

The purpose of this paper is to investigate the process by which the level of immersion in virtual reality-based behavioral simulation (VR-BS) impacts on the non-cognitive and cognitive outcomes. The cognitive outcome is measured using the increase in the level of Sharia financial literacy, while the noncognitive outcome is measured using the behavioral intention to use VR-BS.

The method consists of two parts: First, the development of VR-BS, in the context of sharia financial literacy, using the waterfall model. Second, testing the effectiveness of VR-BS using the theory of interactive media effects framework. The participants were 142 students from three secondary schools (two Islamic religious schools and one public school) in Yogyakarta and Central Java, Indonesia. Partial least squares structural equation modeling was used for testing the hypotheses.

VR-BS creates a perceived coolness and vividness, which in turn has an impact on increasing the participants’ engagement. Also, the use of VR has an impact on natural mapping, which increases a user’s engagement through its perceived ease of use. As predicted, the user’s engagement affects VR’s behavior, mediated by the user’s attitude toward VR media. VR’s interactivity, however, does not impact on the cognitive aspect.

The participants were not randomly selected, as the data were collected during the COVID-19 pandemic. As a result, the majority of the participants had never tried VR before this study. The participants, however, were digital natives.

It is implied from the findings that Islamic financial business actors and the relevant government agencies (e.g. the Indonesian Financial Services Authority [OJK], the Ministry of Education, Culture, Research and Technology and the Ministry of Religious Affairs) should collaborate to best prepare the future generation of ummah by using VR-BS in their joint promotion and education programs. The results of the current study reveal that the use of VR-BS may attract people to engage in Islamic financial activities. By engaging in such activities, or at least engaging in real-life simulations/classes/workshops, people may gradually acquire more knowledge about Islamic finance.

As predicted, the user’s engagement has an impact on behavior toward VR-BS, which is mediated by attitude toward VR-BS.