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<p>Consumers’ initial trust perceptions are critical for the survival of start-ups developing Digital Innovations (DIs). The high rate of failures among these start-ups is attributed to their challenges in gaining consumers’ initial trust. Developing an effective business model to address the initial trust perception is among the most challenging aspects of start-ups adopting DIs. In this study, using the Extra Tree Regression (ETR) algorithm, the key factors influencing the consumers’ initial trust perception were investigated in start-ups adopting DIs. The study is anchored in signalling theory, which examines how start-ups can project trust to prospective customers to overcome the hurdle of low initial trust. Signalling theory provides a framework for understanding the dynamics between parties when there is an information imbalance. A comprehensive survey targeting potential Spruce &amp; Snow application users was conducted to gather data on trust perceptions. This survey incorporated a mix of quantitative and qualitative queries to gauge general impressions, trust levels, and the effects of customer feedback, marketing efforts, and business models on trust. A sensitivity analysis was also carried out to determine the influence of these elements on trust perceptions. The study utilized the ETR model, derived from the Random Forest algorithm, to forecast crucial trust metrics, including the degree of trust from in-store details (X2), the significance of user reviews and ratings (X11), trust based on marketing (X23), and confidence in various business models (X38). The K-fold cross-validation method with (K = 3) was used to train and evaluate the machine learning algorithm. Findings indicate that specific indicators, especially user feedback and clear business models, significantly improve initial trust, thus fostering the adoption of digital innovations. The research provides practical recommendations for start-ups to tailor their digital strategies and communication methods to build trust with consumers, equipping them with actionable strategies to navigate the complex landscape of consumer trust in digital innovation.</p>

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Assessment of Initial Trust Perceptions in Digital Innovation Adoptions of Start-up Companies Using Extra Tree Regression Machine Learning

  • 期刊论文

  • 发表日期：

    2026年6月17日

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  • Amir Shiri,
  • Hamed Azimi,
  • Carlos Bazan

摘要

Consumers’ initial trust perceptions are critical for the survival of start-ups developing Digital Innovations (DIs). The high rate of failures among these start-ups is attributed to their challenges in gaining consumers’ initial trust. Developing an effective business model to address the initial trust perception is among the most challenging aspects of start-ups adopting DIs. In this study, using the Extra Tree Regression (ETR) algorithm, the key factors influencing the consumers’ initial trust perception were investigated in start-ups adopting DIs. The study is anchored in signalling theory, which examines how start-ups can project trust to prospective customers to overcome the hurdle of low initial trust. Signalling theory provides a framework for understanding the dynamics between parties when there is an information imbalance. A comprehensive survey targeting potential Spruce & Snow application users was conducted to gather data on trust perceptions. This survey incorporated a mix of quantitative and qualitative queries to gauge general impressions, trust levels, and the effects of customer feedback, marketing efforts, and business models on trust. A sensitivity analysis was also carried out to determine the influence of these elements on trust perceptions. The study utilized the ETR model, derived from the Random Forest algorithm, to forecast crucial trust metrics, including the degree of trust from in-store details (X2), the significance of user reviews and ratings (X11), trust based on marketing (X23), and confidence in various business models (X38). The K-fold cross-validation method with (K = 3) was used to train and evaluate the machine learning algorithm. Findings indicate that specific indicators, especially user feedback and clear business models, significantly improve initial trust, thus fostering the adoption of digital innovations. The research provides practical recommendations for start-ups to tailor their digital strategies and communication methods to build trust with consumers, equipping them with actionable strategies to navigate the complex landscape of consumer trust in digital innovation.