Journal of Theoretical and Applied Electronic Commerce Research

This study investigates how algorithmic governance, a core feature of modern e-commerce platforms, impacts the consumption behavior of its service providers—specifically, ride-hailing drivers’ preference for high-calorie food. From an e-commerce ecosystem perspective, the dynamic interaction between platforms and their service providers is critical for long-term value co-creation and platform sustainability. By examining how algorithmic control mechanisms spill over into drivers’ off-platform behaviors, this research offers crucial insights for designing more sustainable and human-centric platform business models. Analyzing 710 survey responses from ride-hailing drivers in China via PLS-SEM, our findings reveal that algorithmic tracking evaluation and behavioral constraints are positively associated with high-calorie food consumption, with emotional exhaustion acting as a key mediator. Notably, standard guidance algorithms showed no significant effect. These results contribute to the e-commerce literature by demonstrating how platform-centric control can inadvertently lead to adverse externalities that may undermine service quality and provider well-being, ultimately posing a risk to the platform’s brand reputation and operational stability. We offer practical recommendations for e-commerce platform managers on optimizing algorithmic strategies to foster a healthier and more sustainable gig worker ecosystem.

The digital transformation of financial services has changed how customers interact with banks through electronic channels, yet the factors influencing channel choice between branch-based and digital banking are not entirely understood, especially in emerging European markets. This study investigates banking channel preferences over three consecutive years (2023–2025) in Constanta County, Romania, quantifying how perceived bank technologization and sociodemographic characteristics are associated with the likelihood of digital banking adoption in the e-commerce context. Using repeated cross-sectional survey data from 785 respondents, we applied pooled and year-specific logistic regression models to evaluate temporal effects and estimate the predictive contribution of a composite perception measure (TechScore). Results show that although digital banking usage increased from 87.7% to 92.4%, time alone did not significantly predict adoption. Technologization perceptions consistently increased the odds of digital banking use, with stronger effects in 2025. Age and living environment were significant determinants, while gender and relationship length were not. As digital financial services mature, perceived bank technologization becomes increasingly influential in channel-use decisions. The study contributes to the electronic commerce and technology acceptance literature by demonstrating the importance of perception-based predictors in digital banking contexts and highlights how perception-based evaluation shapes channel choice in digital service platforms, offering insights applicable to electronic commerce contexts where providers compete across physical and digital channels.

In the era of e-commerce, consumers’ innovative insights are crucial for brands to improve their products and meet consumers’ potential needs. Although user-generated content (UGC) platforms have accumulated a vast amount of consumer voices, brands often face the dilemma of “rich data but scarce insights”: valuable consumer innovation insights are not only scarce but also expressed implicitly. Traditional methods have difficulty reliably identifying such insights in this task. Therefore, this study, for the first time, introduces a large language model (LLM)-augmented Siamese framework, aiming to improve the identification and generalization of consumer innovation insights in multi-platform UGC. Based on four Chinese social media platforms and a dataset of 133,538 comments, we conducted three experiments and performed a systematic comparison under five model configurations. The results show that our approach significantly outperforms traditional benchmarks and strong baselines in most experimental settings, while maintaining stable competitiveness in cross-platform tests. Finally, we showed how the identified innovation insights can be transformed into structured outputs for product planning, thereby facilitating the integration of consumer insights into the brand’s innovation process.