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A machine learning ensemble approach to predicting factors affecting the intention and usage behavior towards online groceries applications in the Philippines
16 references·2 citing·40 second hop
A machine learning ensemble approach to predicting factors affecting the intention and usage behavior towards online groceries applications in the Philippines (2023)A machine learning ensemble approach to p…Grocery Shopping Preferences during the COVID-19 Pandemic (2020)Grocery Shopping Preferences …COVID ‐19 and the Demand for Online Food Shopping Services: Empirical Evidence from Taiwan (2020)COVID ‐19 and the Demand for …Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse (2019)Mobile food ordering apps: An…Determinants of Continuous Intention on Food Delivery Apps: Extending UTAUT2 with Information Quality (2019)Determinants of Continuous In…The non-monetary benefits of mobile commerce: Extending UTAUT2 with perceived value (2018)The non-monetary benefits of …Mobile shopping apps adoption and perceived risks: A cross-country perspective utilizing the Unified Theory of Acceptance and Use of Technology (2018)Mobile shopping apps adoption…Research Design and Methods: A Systematic Review of Research Paradigms, Sampling Issues and Instruments Development (2017)Research Design and Methods: …Predicting process behaviour using deep learning (2017)Predicting process behaviour …Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology (2016)Mobile payment: Understanding…Applications of structural equation modeling (SEM) in ecological studies: an updated review (2016)Applications of structural eq…The Impact of the Multi-channel Retail Mix on Online Store Choice: Does Online Experience Matter? (2015)The Impact of the Multi-chann…Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology1 (2012)Consumer Acceptance and Use o…Model of Adoption of Technology in Households: A Baseline Model Test and Extension Incorporating Household Life Cycle1 (2005)Model of Adoption of Technolo…User Acceptance of Information Technology: Toward A Unified View1 (2003)User Acceptance of Informatio…Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology (1989)Perceived Usefulness, Perceiv…The Health Belief Model: A Decade Later (1984)The Health Belief Model: A De…Exploring Factors Influencing E-Bike Adoption Among Filipino Commuters: An Integrated Diffusion of Innovation and Technology Acceptance Model (2025)Exploring Factors Influencing…Determining Factors Influencing Indonesian Higher Education Students' Intention to Adopt Artificial Intelligence Tools for Self-Directed Learning Management (2025)Determining Factors Influenci…Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research (1975)Self-efficacy mechanism in human agency. 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(2003)Determining factors in the adoption and recommendation of mobile wallet services in India: Analysis of the effect of innovativeness, stress to use and social influence (2019)The extended Unified Theory of Acceptance and Use of Technology (UTAUT2): A systematic literature review and theory evaluation (2020)Understanding consumer adoption of mobile payment in India: Extending Meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal (2020)A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies (2000)Why Don’t Men Ever Stop to Ask for Directions? 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References

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  • 2017Predicting process behaviour using deep learning430 cited
  • 2020Grocery Shopping Preferences during the COVID-19 Pandemic391 cited

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  • 2019Determinants of Continuous Intention on Food Delivery Apps: Extending UTAUT2 with Information Quality350 cited
  • 2020COVID ‐19 and the Demand for Online Food Shopping Services: Empirical Evidence from Taiwan322 cited
  • 2015The Impact of the Multi-channel Retail Mix on Online Store Choice: Does Online Experience Matter?316 cited
  • Cited by

    • 2025Determining Factors Influencing Indonesian Higher Education Students' Intention to Adopt Artificial Intelligence Tools for Self-Directed Learning Management14 cited
    • 2025Exploring Factors Influencing E-Bike Adoption Among Filipino Commuters: An Integrated Diffusion of Innovation and Technology Acceptance Model13 cited