Skip to main content

ETHNOS_APP

Home • Search • Journals • List 0

Büşra Özdenizci

Biographic Data

ID6586451
NAMEBüşra Özdenizci
GIVEN NAMESBüşra
FAMILY NAMEÖzdenizci
SIGNATUREÖZDENIZCI B
AFFILIATIONSDepartment of Management Information Systems, Faculty of Business Administration Gebze Technical University Gebze Kocaeli Türkiye
ORCID0000-0002-8414-5252
VERIFIEDYes
TOTAL WORKS4
TOTAL CITATIONS0
WORKS AS AUTHOR4
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2024
LATEST PUBLICATION YEAR2026
H-INDEX0
  • How SMEs Make Sense of Green Technology Adoption for Sustainability Performance

    Open Access•Erman Gokmen, Büşra Özdenizci et al.•ARTICLE•Sustainable Development•2026

    Green technology adoption is increasingly recognized as a critical pathway through which Small and Medium sized Enterprises (SMEs) can improve sustainability performance. However, SMEs operating in emerging economies frequently encounter structural, organizational, and institutional constraints that limit the effective integration of green technologies into their business practices. This study explores how SMEs in Türkiye, as an emerging economy …

  • Pathways to Sustainability in SMEs

    Open Access•Erman Gokmen, Büşra Özdenizci et al.•ARTICLE•Sustainable Development•2026•References: 9

    Climate change, resource scarcity, and rising societal expectations have made sustainability a strategic imperative for firms worldwide. To address these global challenges, organizations are increasingly embedding environmental principles into their operations and adopting green technologies as a pathway to balance economic growth with ecological responsibility. This study examines the factors influencing green technology adoption among small‐ an…

  • A unified theoretical model for understanding mobile application usage behavior supported with real usage data

    Open Access•Nurullah TAŞ, Farid Huseynov et al.•ARTICLE•Information Development•2024

    Technological advancements have added numerous features to smartphones, enhancing our daily lives through various apps. But with so many apps out there, picking the right one can be tough. Plus, developing an app requires a lot of money and time, risking losses if it doesn't attract users. This study looks into how users are influenced by others’ opinions and experiences when choosing and sticking with an app. Three theories–Social Impact Theory …

  • Using machine learning algorithms to predict individuals’ tendency to be victim of social engineering attacks

    Open Access•Farid Huseynov, Büşra Özdenizci•ARTICLE•Information Development•2024

    In information security context, social engineering is defined as malicious activities caused by cybercriminals by means of human interactions. It is mainly a psychological manipulation technique which gets benefit of human error to reach private information. This study used machine learning algorithms to predict individuals’ susceptibility to be tricked by social engineering attacks. Simulated scenarios were presented to study participants, and …

No prominent works on this page.

  • A unified theoretical model for understanding mobile application usage behavior supported with real usage data

    Open Access•Nurullah TAŞ, Farid Huseynov et al.•ARTICLE•Information Development•2024

    Technological advancements have added numerous features to smartphones, enhancing our daily lives through various apps. But with so many apps out there, picking the right one can be tough. Plus, developing an app requires a lot of money and time, risking losses if it doesn't attract users. This study looks into how users are influenced by others’ opinions and experiences when choosing and sticking with an app. Three theories–Social Impact Theory …

  • Using machine learning algorithms to predict individuals’ tendency to be victim of social engineering attacks

    Open Access•Farid Huseynov, Büşra Özdenizci•ARTICLE•Information Development•2024

    In information security context, social engineering is defined as malicious activities caused by cybercriminals by means of human interactions. It is mainly a psychological manipulation technique which gets benefit of human error to reach private information. This study used machine learning algorithms to predict individuals’ susceptibility to be tricked by social engineering attacks. Simulated scenarios were presented to study participants, and …

  • How SMEs Make Sense of Green Technology Adoption for Sustainability Performance

    Open Access•Erman Gokmen, Büşra Özdenizci et al.•ARTICLE•Sustainable Development•2026

    Green technology adoption is increasingly recognized as a critical pathway through which Small and Medium sized Enterprises (SMEs) can improve sustainability performance. However, SMEs operating in emerging economies frequently encounter structural, organizational, and institutional constraints that limit the effective integration of green technologies into their business practices. This study explores how SMEs in Türkiye, as an emerging economy …

  • Pathways to Sustainability in SMEs

    Open Access•Erman Gokmen, Büşra Özdenizci et al.•ARTICLE•Sustainable Development•2026•References: 9

    Climate change, resource scarcity, and rising societal expectations have made sustainability a strategic imperative for firms worldwide. To address these global challenges, organizations are increasingly embedding environmental principles into their operations and adopting green technologies as a pathway to balance economic growth with ecological responsibility. This study examines the factors influencing green technology adoption among small‐ an…

Computer Science (2 works) · Emerging markets (2 works) · Environmental Sustainability in Business (2 works) · Green computing (2 works) · Psychology (2 works) · Social Psychology (2 works) · Sustainability (2 works) · Sustainable Business (2 works) · Sustainable Supply Chain Management (2 works) · World Wide Web (2 works)

Ethnos_APP • Open Source Project • MIT License • Frontend v2.0.0 • Privacy and Cookies • API Documentation: api.ethnos.app/docs • API Source Code: GitHub • DOI: 10.5281/zenodo.17049435 • Frontend Source Code: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae