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The authors empirically examined social capital factors affecting patients' social support acquisition with the aim of providing guidance to patients seeking social support online.

The authors used social network analysis to extract data about social capital factors from online health communities and text mining to identify forms of informational support and emotional support grounded in online, text-based communication. Moreover, the authors employed a random coefficient model to understand the dynamic influence of social capital factors on both informational and emotional support.

The results from the empirical analyses show that structural connections have a lasting impact on the acquisition of both types of support; that is, social connections developed in the past will have an effect on the future. For relational capital, strong ties were less important; the quantity of connections mattered more than the quality when acquiring informational support. The use of health-related language increased the amount of informational support acquired. Over time, patients gained increasing social support, which primarily came from the patients' historical threads, likely via searches from peers facilitated by accumulated social capital.

The authors' research adds to the literature on social capital and social support in online health communities by exploring how the three dimensions of social capital affect social support acquisition. The authors' research also contributes to the online health care literature by examining social support from a dynamic perspective. Practically, the authors' findings provide guidance for patients on what decisions to make to acquire more social support.

This study investigates drivers and dynamics of preferences for brand prominence among the Chinese little emperors (LEs) residing in the US, a unique but powerful consumer group with dual-cultural characteristics.

Using an online survey, the proposed model was tested with a convenience sample of the Chinese LE generation residing in the US

Susceptibility to normative influence was a significant cultural driver of conspicuous, social, and unique value perceptions of luxury consumption among the Chinese LE generation residing in the US Perceived conspicuous and social values of luxury consumption were the primary drivers of this group's brand prominence preference for luxury fashion bags. However, perceived unique value of luxury consumption did not necessarily lead these consumers to prefer prominent logos or marks on a luxury bag. Furthermore, sociodemographic factors (gender, age, and time lived in the US) significantly affected perceptions and preferences related to luxury consumption among this consumer group.

This study advances the luxury literature by examining the drivers and dynamics of brand prominence preference among the Chinese LE generation residing in the US By testing the role of different sociodemographic factors, we demonstrate heterogeneity within this group and the evolving nature of their perceptions and preferences related to luxury consumption as they are acculturated to Western culture. We used a convenient sample and focused on luxury fashion bags for measuring preference for brand prominence, limiting the generalizability of the findings.

Luxury brands should effectively convey conspicuous and social values in product designs, advertising and promotions as these values play integral roles in determining the Chinese LE generation's preference for brand prominence. Our findings also highlight the importance of fine-tuned approaches to different segments within the LE generation cohort.

This study fills several gaps in the luxury literature by empirically investigating various factors affecting preference for brand prominence among the Chinese LE generation residing in the US, an important but under-researched luxury segment.

Social loafing in knowledge contribution (namely, knowledge contribution loafing [KCL]) usually happens in group context, especially in the mobile collaboration tasks. KCL shows dynamic features over time. However, most previous studies are based on static assumption, that is, KCL will not change over time. This paper aims to reveal the dynamics of KCL in mobile collaboration and analyze how network centrality influences KCL states considering the current loafing state.

This study is based on empirical study design. Real mobile collaboration behavioral data related to knowledge contribution were collected to investigate the dynamic relationship between network centrality and KCL. In total, 4,127 chat contents were collected through Slack (a mobile collaboration APP). The text data were first analyzed using the text analysis method and then analyzed by a machine learning method called hidden Markov model.

First, the results reveal the inner structure of KCL, showing that it has three states (low, medium and high). Second, it is found that network centrality positively influences individuals involved in medium and high loafing state, while it has a negative influence on individuals with low loafing state.

The limitations are related to the single machine learning method and no subdivision of social network. First, this paper only uses one kind of text classification model (TF-IDF) to divide chat contents, which may not be superior to other classification models. This paper considers the eigenvector centrality, and not further divides the social network into advice network and expressive network.

This study helps companies infer tendency of different KCL and dynamically re-organize a mobile collaborative team for better knowledge contribution.

First, previous studies based on static assumptions regarding KCL as static and the relationship between loafing reducing mechanisms and team members KCL does not change over time. This study relaxes static assumptions and allows KCL to change during the process of collaboration. Second, this study allows the impact of network centrality to be different when members are in different KCL states.

In an intelligent transportation system (for short, ITS) environment, a vehicle’s motion is affected by the information in a large scale. The purpose of this paper is to study the integration effect of multiple vehicles’ delayed velocities on traffic flow.

This paper constructed a new car-following model to study the integration effect of multiple vehicles’ delayed velocities on traffic flow. The new model is analyzed by linear and nonlinear perturbation method theoretically and also verified by simulation.

It is found out that the integration of preceding vehicles’ delayed velocities affect the stability of traffic flow importantly, and three preceding vehicles’ delayed velocities information should be considered in real traffic.

The new car-following model by considering the integration effect of multiple vehicles’ delayed velocities is firstly proposed in this paper. The research result shows that three preceding vehicles’ delayed velocities information is the best choice to stabilizing traffic flow.

The purpose of this paper is to develop a theoretical model of a jump diffusion-mean reversion constant proportion portfolio insurance strategy under the presence of transaction cost and stochastic floor as opposed to the deterministic floor used in the previous literatures.

The paper adopts Merton’s jump diffusion (JD) model to simulate the price path followed by risky assets and the CIR mean reversion model to simulate the path followed by the short-term interest rate. The floor of the CPPI strategy is linked to the stochastic process driving the value of a fixed income instrument whose yield follows the CIR mean reversion model. The developed model is benchmarked against CNX-NIFTY 50 and is back tested during the extreme regimes in the Indian market using the scenario-based Monte Carlo simulation technique.

Back testing the algorithm using Monte Carlo simulation across the crisis and recovery phases of the 2008 recession regime revealed that the portfolio performs better than the risky markets during the crisis by hedging the downside risk effectively and performs better than the fixed income instruments during the growth phase by leveraging on the upside potential. This makes it a value-enhancing proposition for the risk-averse investors.

The study modifies the CPPI algorithm by re-defining the floor of the algorithm to be a stochastic mean reverting process which is guided by the movement of the short-term interest rate in the economy. This development is more relevant for two reasons: first, the short-term interest rate changes with time, and hence the constant yield during each rebalancing steps is not practically feasible; second, the historical literatures have revealed that the short-term interest rate tends to move opposite to that of the equity market. Thereby, during the bear run the floor will increase at a higher rate, whereas the growth of the floor will stagnate during the bull phase which aids the model to capitalize on the upward potential during the growth phase and to cut down on the exposure during the crisis phase.

The purpose of paper is twofold. First, it provides a consolidated overview of the existing literature on “big data” and second, it presents the current trends and opens up various future directions for researchers who wish to explore and contribute in this rapidly evolving field.

To achieve the objective of this study, the bibliographic and network techniques of citation and co-citation analysis was adopted. This analysis involved an assessment of 57 articles published over a period of five years (2011-2015) in ten selected journals.

The findings reveal that the number of articles devoted to the study of “big data” has increased rapidly in recent years. Moreover, the study identifies some of the most influential articles of this area. Finally, the paper highlights the new trends and discusses the challenges associated with big data.

This study focusses only on big data concepts, trends, and challenges and excludes research on its analytics. Thus, researchers may explore and extend this area of research.

To the knowledge of the authors, this is the first study to review the literature on big data by using citation and co-citation analysis.

Despite the documented benefits of voice behavior for projects, little is known about antecedents of voice behavior in the project context, especially construction projects. Against this background, adopting a multi-team system perspective, this study attempts to investigate antecedents of team voice behavior from a contextual view.

This study identifies and examines six factors that influence team voice behavior. Specifically, project urgency, project temporality, and project complexity are identified from the project nature perspective. Satisfaction, trust, and commitment are generated from the relationship quality approach. Then, data from completed construction projects in China was collected to verify the effectiveness of these factors. Besides, the partial least squares structural equation modeling (PLS-SEM) technique was used in this study.

All six factors are found to be significant predictors of promotive team voice behavior. For prohibitive team voice behavior, only project complexity and project commitment make significant effects. Further, the differential effects of these factors on two types of voice behavior are revealed.

This study contributes to the literature on voice behavior in the project context, especially construction projects consisting of multiple teams. Also, this research enriches our knowledge on antecedents of team voice behavior in construction projects and thus affords practical implications to foster voice behavior.

A lack of reliable and effective communication tools poses major barriers impeding the performance of construction projects consisting of diverse participants. Although some construction project teams (CPTs) begin to apply social media (SM) as an available approach for project management the entire mechanism of SM adoption in this specific context remains understudied. Therefore, this study aims to adopt a CPT's lens to investigate the critical antecedents and associated effects underlying SM adoption in the construction industry.

Based on the technology–organization–environment (TOE) theory, a conceptual model was proposed and tested by empirical data collected from 159 CPTs in China. Structural equation modeling technique was employed for data analysis.

The results demonstrate that all the five extracted TOE-based antecedents including two technological factors (i.e. compatibility and expected cost), one organizational factor (i.e. top management support) and two environmental factors (i.e. project partner collaboration and project fit) are crucial to the adoption of SM in CPTs. Besides, SM acceptance is found to mediate the relationships between organizational and environmental factors and SM use. Moreover, SM use significantly predicts the communication effectiveness of CPTs.

A questionnaire study based on cross-sectional data from China may only unveil the logic of SM adoption in the context of Chinese construction industry within a shorter time interval. It is recommended that future research could develop longitudinal studies among various construction practitioners in different countries to further specify and generalize the current findings.

This paper provides a comprehensive understanding of SM adoption in the construction industry by exploring the preadoption antecedents and postadoption effects from the perspective of project teams. The empirical findings advance the current web-based project management literature and afford new insights for construction practitioners into better managing SM application to reap its full capabilities in projects.

Although social media has been increasingly applied and valued in the construction industry, there has been little evidence revealing the influence mechanism of social media use in the construction context. In this way, this paper aims to explore how different purposes of social media use affect project performance from a project manager's perspective.

Drawing on the mechanism–outcome–performance framework, this paper developed a research model to figure out the mechanism through which work-oriented and socialization-oriented social media use influences construction project performance. The empirical data were collected from a survey of 249 construction project managers, and the structural equation modeling technique was applied to test the proposed model.

Results indicate that both work-oriented and socialization-oriented social media use promote knowledge acquisition and project social capital, which both further positively impact the project performance. Additionally, the negative moderating role of information overload is identified on the relationship between social media use and knowledge acquisition.

This study fulfills the need for an in-depth investigation of social media use on construction project performance, contributing to the project management and social media literature. Furthermore, this study provides recommendations for project managers to advance social media applications in the construction domain.

Although social media use at work has made great impact on employee work performance, little is known about the effect of social media use at work on construction employees, especially construction managers. In this way, the purpose of this study aims to investigate the impact of social media use at work on construction managers' work performance based on the enabler-process-intermediate outcome-performance framework.

This study adopts the knowledge seeker's perspective to empirically investigate the mechanism through which social media use at work impacts construction managers' work performance. Questionnaire survey was conducted with 210 construction managers to test the research model proposed in this study. A component-based structural equation modeling technique was employed to analyze the data.

Results show that social media use at work positively influences knowledge acquisition both internally and externally, and knowledge acquisition promotes task self-efficacy and creativity, which in turn improve construction managers' work performance. In addition, the interaction of task self-efficacy and creativity is found to negatively influence work performance.

These findings contribute to a comprehensive understanding about the impact of social media use at work on construction managers' work performance. This research also provides informative insights for practitioners on how to improve work performance.