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Predicting the Intention to Adopt Innovation in Supply Chain Finance
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References

  • 1981Evaluating Structural Equation Models with Unobservable Variables and Measurement Error108,884 cited
  • 2014A new criterion for assessing discriminant validity in variance-based structural equation modeling38,283 cited
  • 2019Assessing measurement model quality in PLS-SEM using confirmatory composite analysis5,439 cited
  • 2009How to Write Up and Report PLS Analyses5,274 cited
  • 2003Scaling Procedures2,135 cited
  • 2015Green R&D for eco-innovation and its impact on carbon emissions and firm performance1,027 cited
  • 2017Covariance-Based Structural Equation Modeling in the Journal of Advertising : Review and Recommendations782 cited
  • 2021Adoption and use of AI tools: a research agenda grounded in UTAUT684 cited
  • 2022Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis358 cited
  • 2006Where To Go From Here? Thoughts on Future Directions for Research on Individual‐Level Technology Adoption with a Focus on Decision Making*283 cited
  • 2022A decision-making framework for Industry 4.0 technology implementation: The case of FinTech and sustainable supply chain finance for SMEs283 cited
  • 2019Predicting the daily return direction of the stock market using hybrid machine learning algorithms276 cited
  • 2020COVID-19 and repeat visitation: Assessing the role of destination social responsibility, destination reputation, holidaymakers’ trust and fear arousal

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265 cited
  • 2021The impact of gamification adoption intention on brand awareness and loyalty in tourism: The mediating effect of customer engagement206 cited
  • 2021The Impact of Artificial Intelligence on Branding144 cited
  • 2023Predicting Fintech Innovation Adoption: the Mediator Role of Social Norms and Attitudes134 cited
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