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The emergence of live streaming commerce has injected promising impetus into rural development and attracted many rural streamers. This study aims to explore the influencing factors of rural streamers’ engagement intentions to help promote the sustainable development of rural live streaming commerce.

Grounded in the extended valence framework, this research employs a mixed-methods approach encompassing both qualitative and quantitative methodologies. In the qualitative phase, the authors conduct in-depth interviews with 15 rural streamers, employing data coding techniques to uncover underlying factors. Subsequently, in the quantitative phase, the authors analyze survey data from 282 rural streamers, subjecting hypotheses to validation through structural equation modeling.

The findings derived from the analysis of both interviews and questionnaires reveal that several platform qualities, including platform rural-aiding support, perceived effectiveness of dispute resolution, perceived interactivity and platform reputation, have a positive effect on trust in the platform and validate the extended valence framework in understanding rural streamers’ live streaming intention. In addition, ties with customers have a moderating effect. Specifically, the stronger the ties with customers, the stronger the positive effect of perceived benefits and the weaker the positive effect of trust in the platform on live streaming intention will be.

This study contributes to the rural live streaming commerce literature and trust research from the sellers’ perspective and provides practical implications for policymakers and live streaming platform managers on enhancing rural streamers’ participation.

Generative artificial intelligence (GenAI) has progressed in its ability and has seen explosive growth in adoption. However, the consumer’s perspective on its use, particularly in specific scenarios such as financial advice, is unclear. This research develops a model of how to build trust in the advice given by GenAI when answering financial questions.

The model is tested with survey data using structural equation modelling (SEM) and multi-group analysis (MGA). The MGA compares two scenarios, one where the consumer makes a specific question and one where a vague question is made.

This research identifies that building trust for consumers is different when they ask a specific financial question in comparison to a vague one. Humanness has a different effect in the two scenarios. When a financial question is specific, human-like interaction does not strengthen trust, while (1) when a question is vague, humanness builds trust. The four ways to build trust in both scenarios are (2) human oversight and being in the loop, (3) transparency and control, (4) accuracy and usefulness and finally (5) ease of use and support.

This research contributes to a better understanding of the consumer’s perspective when using GenAI for financial questions and highlights the importance of understanding GenAI in specific contexts from specific stakeholders.