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Generation Z use of artificial intelligence products and its impact on environmental sustainability: A cross-cultural comparison

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dc.citedby49
dc.contributor.authorAl-Sharafi M.A.en_US
dc.contributor.authorAl-Emran M.en_US
dc.contributor.authorArpaci I.en_US
dc.contributor.authorIahad N.A.en_US
dc.contributor.authorAlQudah A.A.en_US
dc.contributor.authorIranmanesh M.en_US
dc.contributor.authorAl-Qaysi N.en_US
dc.contributor.authorid57196477711en_US
dc.contributor.authorid56593108000en_US
dc.contributor.authorid35728204400en_US
dc.contributor.authorid57201450886en_US
dc.contributor.authorid57222525211en_US
dc.contributor.authorid55226710300en_US
dc.contributor.authorid57205206257en_US
dc.date.accessioned2024-10-14T03:18:10Z
dc.date.available2024-10-14T03:18:10Z
dc.date.issued2023
dc.description.abstractArtificial intelligence (AI) products play a significant role in achieving environmental sustainability. These products can save various resources (e.g., energy, water), achieve cost savings, and manage waste better. However, understanding the determinants affecting the use of AI products and their impact on environmental sustainability is relatively low, specifically in developing countries. To fill this gap in the literature, this study develops a theoretical model by integrating two well-known theories, UTAUT and PMT, to explain the determinants influencing Generation Z use of AI products and their impact on environmental sustainability. The developed model was then evaluated using the PLS-SEM approach based on data collected from 562 respondents in Malaysia and Turkey. Although effort expectancy, performance expectancy, social influence, perceived severity, response efficacy, and response costs are significant drivers of green behavior among Malaysian individuals, effort expectancy, facilitating conditions, perceived severity, response efficacy, and response costs are essential determinants among Turkish individuals. Interestingly, there is no significant difference between the importance of coping appraisals (i.e., self-efficacy, response efficacy, and response costs) among these two populations. The outcomes provide several contributions to the literature on AI and environmental sustainability and offer valuable insights for the practitioners, policymakers, and AI product developers. � 2023 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo107708
dc.identifier.doi10.1016/j.chb.2023.107708
dc.identifier.scopus2-s2.0-85149061015
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85149061015&doi=10.1016%2fj.chb.2023.107708&partnerID=40&md5=790f6faa171ad3d4853df94b1a991bdc
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34150
dc.identifier.volume143
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleComputers in Human Behavior
dc.subjectArtificial intelligence
dc.subjectCross-cultural comparison
dc.subjectEnvironmental sustainability
dc.subjectGeneration Z
dc.subjectProducts
dc.subjectBehavioral research
dc.subjectDeveloping countries
dc.subjectEnvironmental impact
dc.subjectSustainable development
dc.subjectCost saving
dc.subjectCross-cultural comparisons
dc.subjectDeveloped model
dc.subjectEnergy
dc.subjectEnvironmental sustainability
dc.subjectGeneration Z
dc.subjectIntelligence products
dc.subjectMalaysia
dc.subjectProduct
dc.subjectTheoretical modeling
dc.subjectadult
dc.subjectarticle
dc.subjectartificial intelligence
dc.subjectcontrolled study
dc.subjectcultural factor
dc.subjectdrug efficacy
dc.subjectenvironmental sustainability
dc.subjectexpectancy
dc.subjectfemale
dc.subjecthuman
dc.subjecthuman experiment
dc.subjectmajor clinical study
dc.subjectMalaysia
dc.subjectMalaysian
dc.subjectmale
dc.subjectphysician
dc.subjectself concept
dc.subjectTurk (people)
dc.subjectTurkey (republic)
dc.subjectArtificial intelligence
dc.titleGeneration Z use of artificial intelligence products and its impact on environmental sustainability: A cross-cultural comparisonen_US
dc.typeArticleen_US
dspace.entity.typePublication
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