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This paper aims to study the mass loss of three-dimensional (3D) printed materials at high temperatures. A preconcentration and analysis technique, static headspace gas chromatography-mass spectrometry (SHS-GC-MS), is demonstrated for the analysis of volatile compounds liberated from fused deposition modeling (FDM) and stereolithography (SLA) 3D printed models under elevated temperatures.

A total of seven commercial 3D printing materials were tested using the SHS-GC-MS approach. The printed model mass and mass loss were examined as a function of FDM printing parameters including printcore temperature, model size and printing speed, and the use of SLA postprocessing procedures. A high temperature resin was used to demonstrate that thermal degradation products can be identified when the model is incubated under high temperatures.

At higher printing temperatures and larger model sizes, the initial printed model mass increased and showed more significant mass loss after thermal incubation for FDM models. For models produced by SLA, the implementation of a postprocessing procedure reduced the mass loss at elevated temperatures. All FDM models showed severe structural deformation when exposed to high temperatures, while SLA models remained structurally intact. Mass spectra and chromatographic retention times acquired from the high temperature resin facilitated identification of eight compounds (monomers, crosslinkers and several photoinitiators) liberated from the resin.

The study exploits the high sensitivity of SHS-GC-MS to identify thermal degradation products emitted from 3D printed models under elevated temperatures. The results will aid in choosing appropriate filament/resin materials and printing mechanisms for applications that require elevated temperatures.

Smart city services (SCS) in contrast with other technology-based services, demand significant interaction and collaboration between the users and the service providers. This study examines the SCS delivery-channel characteristics and the users' personal (behavioral and demographic) characteristics that influence their satisfaction or dissatisfaction with the services, as well as their intention to adopt (i.e. continue using) the SCS-delivery channels.

A quantitative study using a structured questionnaire was conducted for this paper. The data-collection method was administered by emailing the survey to a list of 2,350 city/urban residents who are members of the two largest universities in the greater Dubai metropolis. A total of 600 completed responses (26 percent) were received back, while 580 useable responses (25 percent) were analyzed for this paper.

Our initial findings suggest that contrary to popular belief, it is not only SCS channel factors that influence user satisfaction and continuance intention. SCS users' personal characteristics (such as their user innovativeness and control-seeking behavior) are also pivotal in determining their satisfaction and intention to continue or not continue using the SCS-delivery channels.

The paper argues that both SCS channel factors and SCS users' personal characteristics jointly influence the users' experience of the services and therefore jointly determine their satisfaction with the service as well as their SCS usage continuance intention. The result of our research gives important insights into users' behaviors toward the emerging SCS channels in general, and it will be of great value to architects and designers of Smart City technologies around the world.

The paper argues that both SCS channel factors and SCS users' personal (behavioral and demographic) characteristics jointly influence the users' trials of the services, and therefore jointly determine their satisfaction with the service as well as their SCS usage continuance intention. The result of our research gives important insights into users' behavioral intentions toward the emerging SCS channels in general; and it will be of great value to architects and designers of Smart City technologies around the world.

This paper is one of the first few studies focused on investigating the antecedents of SCS usage behaviors in the Middle Eastern region.

This study aims to investigate the association between board diversity and systematic risk. The theoretical framework used in this study is based on agency and resource dependency theories.

Using a panel data set of 788 firms listed in the Morgan Stanley Capital International (MSCI) Emerging Markets index from 2015 to 2020, the authors apply Panel-Corrected Standard Error estimation method to test the three proposed hypotheses and the two-stage least squares method is adopted for the endogenous test.

The results suggest that board-specific skills diversity (BSSD) and board independence (BIND) have a negative impact on systematic risk. On the other hand, board gender diversity does not affect systematic risk. The findings reinforce the relevance of board diversity for reducing systematic risk and offer valuable insights for policymakers and investors, suggesting that the presence of directors with specific skills and independent directors could reduce firms’ systematic risk.

The study extends the scope of agency and resource dependency theories by suggesting that the BSSD and BIND reduce agency costs and bring critical resources to the firm’s survival.

The findings support policymakers and managers in reducing systematic risk. In addition, the results demonstrate the importance of policies that encourage board diversity and BIND.

The study demonstrates how companies can reduce systematic risk through board diversity and BIND.

To the best of our knowledge, this is the first study to investigate the association between board diversity and systematic risk only in emerging markets.

This study intends to investigate the determinants of learners' continuance intention to use massive open online courses (MOOCs) for personal or professional development.

This study employed quantitative research design. The respondents were individual learners from six selected universities in China who used MOOCs for continuous learning. A purposive sampling technique was employed to obtain 270 valid samples. Data were analyzed and analytical outputs were produced using the techniques of Partial Least Squares Structural Equation Modeling and Importance-Performance Matrix.

Expectation confirmation was found to have a positive relationship with perceived usefulness, flow experience, learning self-efficacy and satisfaction with MOOCs. Perceived usefulness, flow experience and leaning self-efficacy were also found to have a positive relationship with MOOC satisfaction. In addition, perceived usefulness, flow experience, learning self-efficacy and MOOC satisfaction had a positive impact on continuance usage intention.

The outcomes of the study can serve as a practical reference for MOOC providers and decision-makers to develop relevant strategies to increase the course completion rates.