Utilize este identificador para referenciar este registo: http://hdl.handle.net/10362/190176
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dc.contributor.advisorNeves, Maria de Fátima dos Santos Trindade-
dc.contributor.authorGălbinaşu, Georgiana-Ioana-
dc.date.accessioned2025-11-06T09:52:34Z-
dc.date.issued2025-10-27-
dc.identifier.urihttp://hdl.handle.net/10362/190176-
dc.descriptionDissertation presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Digital Marketing and Analyticspt_PT
dc.description.abstractRecycling is a critical component of urban sustainability, yet international residents often encounter structural and informational barriers that limit their engagement with local waste management systems. This study examines the behavioral, informational, digital, and financial (BIDF) factors that influence recycling practices among newcomers in Lisbon—an increasingly diverse and rapidly expanding urban community. Adopting a mixed-methods design under the broader umbrella of Action Research (AR), the study integrates quantitative modeling— grounded in the Theory of Planned Behavior (TPB), elements of the Unified Theory of Acceptance and Use of Technology (UTAUT), the information construct from the InformationMotivation-Behavior (IMB) model, and economic incentive models such as Pay-As-You-Throw (PAYT) and Save-As-You-Throw (SAYT)—with qualitative stakeholder engagement. Stakeholder involvement spanned from initial consultations on the current recycling landscape in Lisbon through interpretive interviews concerning survey insights, culminating in a co-creative participatory Design Thinking workshop where both stakeholders and international newcomers collaborated to generate solutions for enhanced recycling participation. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to analyze survey data from 209 participants representing 50 nationalities, highlighting the study’s international scope. Key findings revealed that attitude, perceived behavioral control, and information quality served as direct and indirect predictors of recycling behavior, while perceived digital usefulness significantly influenced intention to use digital tools for recycling. The participatory Design Thinking workshop validated survey findings and generated actionable concepts, most notably a gamified, multilingual digital platform with smart UX to help newcomers navigate daily life and integrate recycling education and circularity nudges. Features include a “Re-swap” marketplace for exchanging reusable items, a rental service for occasional-use products, and trading opportunities for upcycled goods. Additionally, a structured onboarding process was prioritized, positioning landlords as key facilitators for early engagement in recycling. While this AR cycle focused on solution design rather than implementation, it lays a strong foundation for future phases dedicated to developing and testing urban recycling interventions. Methodologically, the study demonstrates the value of combining behavioral modeling with stakeholder and community co-creation approaches.pt_PT
dc.language.isoengpt_PT
dc.rightsembargoedAccesspt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectRecycling Behaviorpt_PT
dc.subjectInternational Residentspt_PT
dc.subjectUrban Sustainabilitypt_PT
dc.subjectAction Researchpt_PT
dc.subjectDigital Engagementpt_PT
dc.subjectPLS-SEMpt_PT
dc.subjectDesign Thinkingpt_PT
dc.subjectSDG 10 - Reduced inequalities-
dc.subjectSDG 11 - Sustainable cities and communities-
dc.subjectSDG 12 - Responsible production and consumption-
dc.subjectSDG 13 - Climate action-
dc.subjectSDG 17 - Partnerships for the goals-
dc.titleConsumer Behavior and Recycling: Digital Approaches for Newcomers in Lisbonpt_PT
dc.typemasterThesispt_PT
thesis.degree.nameMestrado em Marketing Analítico, especialização em Digital Marketing and Analyticspt_PT
dc.date.embargo2027-10-27-
dc.subject.fosDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informaçãopt_PT
Aparece nas colecções:NIMS - Dissertações de Mestrado em Marketing Analítico (Data-Driven Marketing)

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