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The purpose of this paper is to investigate the factors that influence college students’ smartphone use for academic purposes by identifying the task-technology fit (TTF) of smartphones. A research model is proposed to explain how TTF of smartphones affects college students’ perceived academic performance and smartphone use.

Online surveys were administered to college students at a South Korean university that has offered online academic services for more than five years, and 1,923 valid responses were analyzed. The study used partial least squares path modeling to evaluate the measurement model, and the bootstrapping technique to test the significance of the hypotheses.

The findings highlight that the TTF of smartphones has a direct influence on students’ perceptions of performance impact and an indirect influence on smartphone use through a precursor of utilization, such as attitude toward smartphone use, social norms and facilitating conditions.

Despite a reasonably large sample, a single cross-sectional survey has a likelihood of selection bias in the sample.

This study applies the TTF model to smartphone use among college students and suggests an effective way to motivate them to use mobile technologies for their academic activities.

The present study develops an empirical model to assess the adoption of smartphones and its effect on college students’ academic performance. Above all, the study identifies a causal relationship among TTF, precursor of utilization, smartphone use and a perceived impact on academic performance based on the development and validation of the TTF constructs of smartphones.

The purpose of this paper is to understand consumers’ preferences for answers about sexually transmitted diseases on social question and answer (Q&A) sites by employing message features and information sources as conceptual frameworks.

The study compared best answers selected by questioners with their randomly drawn counterpart non-best answers on Yahoo! Answers as a paired sample (n=180).

The findings indicate that questioners on social Q&A sites were more likely to prefer answers including message features such as numeric information, social norms, optimistic information, and loss-framing, as well as information sources that featured expertise, references, and links to other websites. Pessimistic information was negatively associated with questioners’ preference for answers.

The study extended the discussion of consumers’ selection of best answers to message features and information sources as additional criteria.

The findings suggest that answerers on social Q&A sites communicate more effectively with their audiences by utilizing persuasive communication.

There is a quality issue on social Q&A sites. The findings will be helpful for health professionals to develop answers that are more likely to be selected as best answers, which will enhance overall quality of health information on social Q&A sites.

Consumers’ preference criteria for health information have been investigated using many different approaches. However, no study has used a persuasion framework to examine how consumers appraise answer quality. The present study confirmed consumers’ preference criteria as found in previous social Q&A studies and extended the discussion of consumers’ perceptions of answer quality by applying the frameworks of message features and information sources.

This study aims to examine the extent to which anti‐smoking websites use intervention strategies that have been informed by four prominent theories of health‐related behavior change: the health belief model, the theory of reasoned action/theory of planned behavior, the transtheoretical model, and social cognitive theory.

Content analysis was applied to 67 unique and independent anti‐smoking websites to determine their use of 20 intervention strategies based on the four theories.

The findings reveal that anti‐smoking websites used the health belief model the most and social cognitive theory the least. In addition, websites devoted to smoking cessation used these theories more extensively than websites devoted to smoking prevention.

The sample size is somewhat small, which may result in lack of sufficient statistical power. Also, the analysis may have overlooked some important intervention strategies that are particularly effective for smoking intervention programs.

Anti‐smoking website designers should take more advantage of the internet as a health promotion medium and use more intervention strategies that have been informed by scientifically tested theories of behavior change, particularly with respect to affective and behavioral strategies.

This study contributes to current knowledge about which kinds of anti‐smoking messages are available online and how extensively they employ theory‐based intervention strategies.

This paper investigates a retrospective on the Journal of Derivatives and Quantitative Studies (JDQS) on its 30th anniversary based on bibliometric. JDQSs yearly publications, citations, impact factors, and centrality indices grew up in early 2010s, and diminished in 2020. Keyword network analysis reveals the JDQS's main keywords including behavioral finance, implied volatility, information asymmetry, price discovery, KOSPI200 futures, volatility, and KOSPI200 options. Citations of JDQS articles are mainly driven by article age, demeaned age squared, conference, nonacademic authors and language. In comparison between number of views and downloads for JDQS articles, we find that recent changes in publisher and editorial and publishing policies have increased visibility of JDQS.

The purpose of the present work is to develop the combination of the radial point interpolation method (RPIM) with a bi-directional evolutionary structural optimization (BESO) algorithm and extend it to the analysis of benchmark examples and automotive industry applications.

A BESO algorithm capable of detecting variations in the stress level of the structure, and thus respond to those changes by reinforcing the solid material, is developed. A meshless method, the RPIM, is used to iteratively obtain the stress field. The obtained optimal topologies are then recreated and numerically analyzed to validate its proficiency.

The proposed algorithm is capable to achieve accurate benchmark material distributions. Implementation of the BESO algorithm combined with the RPIM allows developing innovative lightweight automotive structures with increased performance.

Computational cost of the topology optimization analysis is constrained by the nodal density discretizing the problem domain. Topology optimization solutions are usually complex, whereby they must be fabricated by additive manufacturing techniques and experimentally validated.

In automotive industry, fuel consumption, carbon emissions and vehicle performance is influenced by structure weight. Therefore, implementation of accurate topology optimization algorithms to design lightweight (cost-efficient) components will be an asset in industry.

Meshless methods applications in topology optimization are not as widespread as the finite element method (FEM). Therefore, this work enhances the state-of-the-art of meshless methods and demonstrates the suitability of the RPIM to solve topology optimization problems. Innovative lightweight automotive structures are developed using the proposed methodology.