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This study examines whether relational capital influences self-disclosure behavior through the mechanism of needs-based motivation in virtual communities.

This study adopts hierarchical linear model (HLM) to differentiate between the relationships at different levels, with 378 online questionnaires recovered from 42 virtual communities.

The results show that group-level relational capital is positively related to self-disclosure and affects it through the partially mediating mechanism of motivation. Relational capital also strengthens the positive influence of the need to be on trend on individual self-disclosure behavior.

This study makes four research contributions. Firstly, we identify the means by which relational capital established within a virtual community influences user disclosure behavior. This focus differs from those of previous studies, which have emphasized privacy and security of information systems, cost–benefit considerations, and/or adopted personality traits as the research basis. Secondly, this study examines and verifies the mediating mechanism of motivation, establishing an alternative perspective for theoretical studies, and providing future studies with a reference for investigating the self-disclosure behavior of members. Thirdly, this research introduces and verifies the moderating effects of relational capital based on member relationships, thus making further theoretical and empirical contributions. Finally, we adopt HLM to conduct our analyses, thereby ensuring higher precision regarding the explanatory power of group-level explanatory variables for individual-level dependent variables.

Given the maturation of the internet and virtual communities, an important emerging issue in the humanities and social sciences is how to accurately analyze the vast quantity of documents on public and social network websites. Therefore, this chapter integrates political blogs and news articles to develop a public mood dynamic prediction model for the stock market, while referencing the behavioral finance perspective and online political community characteristics. The goal of this chapter is to apply a big data and opinion mining approach to a sentiment analysis for the relationship between political status and economic development in Taiwan. The proposed model is verified using experimental datasets collected from ChinaTimes.com, cnYES.com, Yahoo stock market news, and Google stock market news, covering the period from January 1, 2016 to June 30, 2017. The empirical results indicate the accuracy rate with which the proposed model forecasts stock prices.

The purpose of this paper is to review and analyze the characteristics of the literature related to financial innovation, because financial technology (fintech) has been appropriately applied in academic circles as well as in the policy-making arena. The authors further estimate the implications of financial innovations for bank performance and liquidity risk.

The authors use a sample of commercial banks operating in Taiwan over the period 2010–2017 and utilize three proxies for financial innovation including R&D expenditures, financial patents (i.e. innovation applications) and financial news such as that concerning fintech (i.e. innovation intentions).

The effects of financial innovation on bank performance are mixed, with too much of R&D expenditures having the worst bank performance, whereas innovation intentions benefit their performance. The paper concludes that financial innovation does increase banks’ liquidity risk, thus supporting the innovation-fragility hypothesis.

It is an important issue in academic circles as well as in the policy-making arena to ensure that financial innovation has been appropriately applied.

Based on laser-range-finder (LRF) sensing, the control design of location and orientation stabilization for the mobile robot is investigated. However, the practical limitation of the LRF sensing is usually ignored in the control design, which leads to incorrect localization and unexpected control results. The purpose of this study is to design the fuzzy controller subject to the practical limitation on the LRF-based localization for a differentially driven wheeled mobile robot.

First, the Takagi–Sugeno (T-S) fuzzy model is derived from the polar kinematic model of a differentially driven mobile robot. Then, the fuzzy controller is designed to the derived T-S fuzzy kinematic model in accordance with the Lyapunov stabilization theorem. The derived Lyapunov stabilization conditions for the fuzzy control design are expressed as the linear matrix inequality (LMI) form and effectively solved by LMI tools. The practical limitation on the LRF-based localization is also expressed as the LMI form and simultaneously solved with the control design.

The location and posture stabilization experiments are carried out on a mobile robot with LRF-based localization to prove the effectiveness of the proposed T-S fuzzy model-based control design. Furthermore, the ground truth experiment evaluates the accuracy of LRF-based localization.

The contribution of this study is to develop the fuzzy control law for a differentially driven wheeled mobile robot under the practical limitation on LRF-based localization. The proposed control design can be applied to other robots with practical limitations on the sensors.

The authors investigate the association of the constructed corporate social responsibility (CSR) measures with the banks’ profitability, social contributions and CSR spending as well as the market reaction to CSR spending.

Using textual analysis of the CSR reports of banks listed on the Chinese market, the authors construct CSR measures in six domains: business, environment, human rights, corporate governance, charity and social capital. Our textual-based CSR measures contain substantial and valuable information beyond what Rankins CSR ratings offer.

The findings suggest that banks with stronger engagements and interests in the business-related CSR domain experience higher profitability, while those that are more committed to the corporate governance and charity-related domains create larger social contributions. Banks tend to incur higher CSR spending when they are more active in corporate governance. Although the stock market reacts positively to CSR expenditures, the reaction is less favorable for banks with CSR expenditures above the industry norm.

This study offers insights to policymakers of the regulatory bodies and the banks in China. To enhance the financial safety and soundness of the banking system, the regulatory bodies should encourage banks to strategically allocate corporate resources to achieve higher CSR ratings and engage more business-related CSR activities. To create larger social values, bank management should invest more in philanthropic CSR initiatives such as corporate governance and charity activities. To pursue higher corporate profits, they should engage more in self-centered business-related CSR activities. However, according to the reaction of the market, they should not over-invest in CSR activities.

While the use of textual analysis to evaluate CSR disclosure has recently emerged in the literature, few studies focus on banks in China. Using the term frequency–inverse data frequency (TF-IDF) method, the authors constructed a score for each of the six CSR domains: business (BUS), environment (ENV), human rights (HR), corporate governance (GOV), charity (CHY) and social capital (SCAP). To the best of our knowledge, no studies have adopted the textual approach to evaluate social reporting quality and CSR activities in the context of the banking industry in China.

The purpose of this study is to develop resin composite materials composed of polycaprolactone (PCL) acrylates and hydroxyapatite (HA) nanoparticles for ultraviolet digital light projection (DLP) three-dimensional (3D) printing technique.

Two PCL-based triacrylates, namely, glycerol-3 caprolactone-triacrylate (Gly-3CL-TA) and glycerol-6 caprolactone-triacrylate (Gly-6CL-TA) were synthesized from ring-opening polymerization of ε-caprolacton monomer in the presence of glycerol and then acrylation was performed using acryloyl chloride. 3D printing resins made of Gly-3CL-TA or Gly-6CL-TA, 5% HA and 3% of photoinitiator 2,4,6-Trimethylbenzoyl-diphenyl-phosphineoxide were then formulated. The surface topography, surface element composition, flexural strength, flexural modulus, cytotoxicity and degradation of the PCL-based scaffolds were then characterized.

Resin composite composed of Gly-3CL-TA or Gly-6CL-TA and 5% (w/w) of HA can be printed by 405 nm DLP 3D printers. The former has lower viscosity and thus can form a more uniform layer-by-layer structure, while the latter exhibited a higher flexural strength and modulus after being printed. Both composite materials are non-cytotoxic and degradable.

This study provides a direction of the formulation of environment-friendly resin composite for DLP 3D printing. Both resin composites have huge potential in tissue engineering applications.

The purpose of this study is to examine the knowledge gap hypothesis in the context of smartphone use for news to understand whether mobile news consumption could bridge or widen the knowledge gap between people of higher and lower socioeconomic status (SES).

The authors examine how smartphone news consumption is associated with the knowledge gap hypothesis by analyzing a survey dataset from Hong Kong. This study focuses specifically on a moderated mediation model in which the indirect effect of mobile news consumption on political knowledge via discussion network heterogeneity is contingent on level of education.

Smartphone use for news/information was positively associated with level of discussion network heterogeneity. The indirect effect of smartphone news use on political knowledge via discussion network heterogeneity was stronger for those with lower levels of education.

This study advances the understanding of the role of smartphone use in contributing to the functioning of deliberative democracy as this use enhances discussion network heterogeneity and general levels of political knowledge. Moreover, our study contributes to the literature on the knowledge gap by not only examining the relationship between smartphone use, discussion heterogeneity, and political knowledge but also taking into consideration individual levels of education.

Interdependency among industries is vital for understanding economic structures and managing industrial portfolios. However, it is hard to precisely model the interconnecting structure among industries. One of the reasons is that the interdependencies show a different pattern in tail events. This paper aims to investigate industry interdependency with the tail events.

General predictive model of Rapach et al. (2016) is extended to an interdependency model via least absolute shrinkage and selection operator quantile regression and network analysis. A dynamic network approach was applied on the Fama–French industry portfolios to study the time-varying interdependencies.

A denser network with heterogeneous central industries is found in tail cases. Significant interdependency varieties across time are shown under dynamic network analysis. Market volatility is identified as an influential factor of industry connectedness as well as clustering tendency under both normal and tail cases. Moreover, combining dynamic network with prediction direction information into out-of-sample industry return forecasting, a lower tail case is obtained, which gives the most accurate prediction of one-month forward returns. Finally, the Sharpe ratio criterion prefers high-centrality portfolios when tail risks are considered.

This study examines the industry portfolio interactions under the framework of network analysis and also takes into consideration tail risks. The combination of economic interpretation and statistical methodology helps in having a clear investigation of industry interdependency. Moreover, a new trading strategy based on network centrality seems profitable in our data sample.