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The Relationship Between Cloud Computing Factors and Cloud Computing Behavioral Intention of First-Year University Students /

Record Type:	Electronic resources : Monograph/item
Title/Author:	The Relationship Between Cloud Computing Factors and Cloud Computing Behavioral Intention of First-Year University Students /Gabriel Albino.
Author:	Albino, Gabriel,
Description:	1 electronic resource (214 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 87-07, Section: B.
Notes:	Advisors: Anthony, Kimberly Committee members: Camarota, Anton.
Contained By:	Dissertations Abstracts International87-07B.
Subject:	Information technology.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32403881
ISBN:	9798273306202

The Relationship Between Cloud Computing Factors and Cloud Computing Behavioral Intention of First-Year University Students /
Albino, Gabriel,

The Relationship Between Cloud Computing Factors and Cloud Computing Behavioral Intention of First-Year University Students /Gabriel Albino. - 1 electronic resource (214 pages)

Source: Dissertations Abstracts International, Volume: 87-07, Section: B.

In an era where vast amounts of data are generated daily, the ability to manage information through cloud computing has become critical; however, asymmetries in using this technology persist, particularly in developing countries. Academic managers often lack information about the factors influencing cloud computing usage. The purpose of this quantitative correlational cross-sectional study was to examine the relationship between cloud computing factors, including effort expectancy (EE), performance expectancy (PE), social influence (SI), and facilitating conditions (FC), and the first-year university students' cloud computing behavioral intention (CCBI) in the developing country of Angola. A structured questionnaire was administered to a random sample of 195 first-year university students. The unified theory of acceptance and use of technology (UTAUT) was used to create a research model, incorporating EE, PE, SI, and FC as predictor variables of CCBI. The results of the multiple linear regression model were statistically significant, F(4, 190) = 24.05, p < .001, explaining 33.6% of the variance in CCBI. Individually, EE, PE, and SI emerged as significant positive predictors of CCBI, whereas FC demonstrated a weaker, more indirect influence. Understanding this relationship could help academic managers design targeted policies and frameworks to enhance cloud computing usage in educational settings where the technology is still emerging in the current digitized world. The implications for positive social change include the potential for university leaders and policymakers to reinforce the UTAUT's relevance in guiding their technology implementation strategies in diverse educational environments, thereby improving the quality of life for individuals and communities.

English

ISBN: 9798273306202Subjects--Topical Terms:

184390
Information technology.
Subjects--Index Terms:

Behavioral intention

The Relationship Between Cloud Computing Factors and Cloud Computing Behavioral Intention of First-Year University Students /
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520 $a In an era where vast amounts of data are generated daily, the ability to manage information through cloud computing has become critical; however, asymmetries in using this technology persist, particularly in developing countries. Academic managers often lack information about the factors influencing cloud computing usage. The purpose of this quantitative correlational cross-sectional study was to examine the relationship between cloud computing factors, including effort expectancy (EE), performance expectancy (PE), social influence (SI), and facilitating conditions (FC), and the first-year university students' cloud computing behavioral intention (CCBI) in the developing country of Angola. A structured questionnaire was administered to a random sample of 195 first-year university students. The unified theory of acceptance and use of technology (UTAUT) was used to create a research model, incorporating EE, PE, SI, and FC as predictor variables of CCBI. The results of the multiple linear regression model were statistically significant, F(4, 190) = 24.05, p < .001, explaining 33.6% of the variance in CCBI. Individually, EE, PE, and SI emerged as significant positive predictors of CCBI, whereas FC demonstrated a weaker, more indirect influence. Understanding this relationship could help academic managers design targeted policies and frameworks to enhance cloud computing usage in educational settings where the technology is still emerging in the current digitized world. The implications for positive social change include the potential for university leaders and policymakers to reinforce the UTAUT's relevance in guiding their technology implementation strategies in diverse educational environments, thereby improving the quality of life for individuals and communities.
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