670 research outputs found
Determinants of purchasing online courses through education platform in China
Online courses have become the main source of income for online education platforms. It
is of great value to explore the factors affecting users’ intention to buy online courses. Based
on the technology acceptance model, perceived value theory and perceived risk theory, this
study constructs the structural equation model of the purchase intention of online courses by
taking users on online education platforms as the research object, and resorts PLS-SEM for
analysis. This research explores factors that impact the intention to buy online courses and
levels of these impact. The research questionnaire of this study was developed through literature
review, and 669 valid questionnaires were collected. Data analysis was performed using IBM
SPSS V24.0 and SmartPLS 3.2.8.
Before the multi-group analysis, it is found that perceived value, time and space autonomy,
course free trail exert positive impact on purchase intention, while perceived risk has negative
impact on it. Perceived cost does not significantly affect purchase intention. Time and space
autonomy, perceived usefulness, and perceived ease of use are positively affecting perceived
value, while perceived cost and perceived risk negatively affect perceived value. Course free
trail does not significantly affect perceived value. Perceived value completely mediates
perceived cost, and partially mediates the impact of time and space autonomy and perceived
risk on purchase intention. Word-of-mouth positively moderates the impact of perceived value
on purchase intention. Age has negative impact on purchase intention, while education
positively affects purchase intention. Perceived profit (perceived ease of use, perceived
usefulness, time and space autonomy) significantly and positively affects perceived value, and
perceived loss (perceived cost, perceived risk) significantly and negatively affects perceived
value. Perceived value mediates the impact of perceived gain and perceived loss on the users’
purchase intention.
PLS multi-group analysis is conducted according to the users’ experience in buying a
course. Users with different purchasing experience have different significance in terms of Online courses have become the main source of income for online education platforms. It
is of great value to explore the factors affecting users’ intention to buy online courses. Based
on the technology acceptance model, perceived value theory and perceived risk theory, this
study constructs the structural equation model of the purchase intention of online courses by
taking users on online education platforms as the research object, and resorts PLS-SEM for
analysis. This research explores factors that impact the intention to buy online courses and
levels of these impact. The research questionnaire of this study was developed through literature
review, and 669 valid questionnaires were collected. Data analysis was performed using IBM
SPSS V24.0 and SmartPLS 3.2.8.
Before the multi-group analysis, it is found that perceived value, time and space autonomy,
course free trail exert positive impact on purchase intention, while perceived risk has negative
impact on it. Perceived cost does not significantly affect purchase intention. Time and space
autonomy, perceived usefulness, and perceived ease of use are positively affecting perceived
value, while perceived cost and perceived risk negatively affect perceived value. Course free
trail does not significantly affect perceived value. Perceived value completely mediates
perceived cost, and partially mediates the impact of time and space autonomy and perceived
risk on purchase intention. Word-of-mouth positively moderates the impact of perceived value
on purchase intention. Age has negative impact on purchase intention, while education
positively affects purchase intention. Perceived profit (perceived ease of use, perceived
usefulness, time and space autonomy) significantly and positively affects perceived value, and
perceived loss (perceived cost, perceived risk) significantly and negatively affects perceived
value. Perceived value mediates the impact of perceived gain and perceived loss on the users’
purchase intention.
PLS multi-group analysis is conducted according to the users’ experience in buying a
course. Users with different purchasing experience have different significance in terms of course free trail, perceived risk, perceived ease of use, time and space autonomy, and education.
Based on the data analysis, this study also discussed the corresponding recommendations,
such as guaranteeing course quality, providing word-of-mouth, grouping users according to
their purchasing experience, using artificial intelligence to recommend online courses for users;
optimizing functions of time and space autonomy, improving user experience with 5G, virtual
reality, and augmented reality; reducing course price with cloud services and big data
technologies, and providing more course free trial in an attempt to reduce users’ perceived risk.Os cursos à distância tornaram-se a principal fonte de rendimento para as plataformas de
educação online. O estudo dos fatores que afetam a intenção dos utilizadores de compra de
cursos online tem uma grande valia. Com base no modelo de aceitação de tecnologia, na teoria
do valor percebido e na teoria do risco percebido, este estudo constrói o modelo de equações
estruturais da intenção de compra de curso online ao considerar os utilizadores de plataformas
de educação online como objeto de investigação, com base na metodologia PLS-SEM (mÃnimos
quadrados parciais – modelação com equações estruturais). Esta pesquisa explora fatores que
influenciam a intenção de compra de cursos e os seus nÃveis de impacto. O questionário de
investigação deste estudo foi desenvolvido através da revisão da literatura e foram recolhidos
669 questionários válidos. A análise de dados foi realizada utilizando o IBM SPSS V24.0 e o
SmartPLS 3.2.8.
Antes da análise multi grupos, verificou-se que as variáveis valor percebido, autonomia de
espaço e tempo e teste gratuito de curso exercem um impacto positivo na intenção de compra,
enquanto o risco percebido tem um impacto negativo sobre o mesmo. O custo percebido não
afeta significativamente a intenção de compra. Autonomia de espaço e tempo, utilidade
percebida e perceção de facilidade de utilização afetando positivamente o valor percebido,
enquanto custo percebido e risco percebido afetam negativamente o valor percebido. O teste
gratuito do curso não afeta negativamente o valor percebido. O valor percebido é um mediador
integral do custo percebido, e mediador parcial do impacto da autonomia de espaço e tempo,
risco percebido na intenção de compra. Palavra-de-boca (word of mouth) modera positivamente
o impacto do valor percebido na intenção de compra. A idade tem um impacto negativo na
intenção de compra, enquanto a educação afeta-a positivamente. O lucro percebido (facilidade
percebida de utilização, utilidade percebida, autonomia de espaço e tempo) afeta significativa
e positivamente o valor percebido, e a perda percebida (custo percebido, risco percebido) afeta significativa e negativamente o valor percebido. O valor percebido é mediador do impacto
do lucro percebido e da perda percebida na intenção de compra dos utilizadores.
A análise de vários grupos da análise PLS foi realizada de acordo com a experiência dos
utilizadores na compra de um curso. Os utilizadores com diferentes experiências de compra têm
uma significância diferente em termos de experimentação gratuita dos cursos, risco percebido,
facilidade percebida de utilização, autonomia de espaço e tempo e educação.
Com base na análise de dados, este estudo também analisou as recomendações
correspondentes, como garantir a qualidade do curso, fornecer informações de word of mouth,
agrupar os utilizadores de acordo com a sua experiência de compra, utilizando a inteligência
artificial para recomendar cursos online para os utilizadores; otimizar funções da autonomia de
espaço e tempo, melhorar a experiência do utilizador com 5G, realidade virtual e realidade
aumentada; reduzir o preço do curso com serviços na nuvem e tecnologias de grandes bases de
dados, e fornecer mais cursos experimentais gratuitos para tentar reduzir o risco percebido dos
utilizadores
A design view of capability
In order to optimise resource deployment in a rapid changing operational environment, capability has received increasing concerns in terms of maximising the utilisation of resources. As a result of such extant research, different domains were seen to endow different meanings to capability, indicating a lack of common understanding of the true nature of capability. This paper presents a design view of capability from design artefact knowledge perspective. Capability is defined as an intrinsic quality of an entity closely related to artefact behavioural and structural knowledge. Design artefact knowledge was categorised across expected, instantiated, and interpreted artefact knowledge spaces (ES, IsS, and ItS). Accordingly, it suggests that three types of capability exist in the three spaces, which can be used in employing resources. Moreover, Network Enabled Capability (NEC), the capability of a set of linked resources within a specific environment is discussed, with an example of how network resources are deployed in a Virtual Integration Platform (VIP)
A device-level characterization approach to quantify the impacts of different random variation sources in FinFET technology
A simple device-level characterization approach to quantitatively evaluate the impacts of different random variation sources in FinFETs is proposed. The impacts of random dopant fluctuation are negligible for FinFETs with lightly doped channel, leaving metal gate granularity and line-edge roughness as the two major random variation sources. The variations of Vth induced by these two major categories are theoretically decomposed based on the distinction in physical mechanisms and their influences on different electrical characteristics. The effectiveness of the proposed method is confirmed through both TCAD simulations and experimental results. This letter can provide helpful guidelines for variation-aware technology development
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