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This paper establishes a quality model for automation assembly of range hood impeller based on generalized grey relational degree, it improves the debugging efficiency of the newly developed assembly workstation.

First, spot check the trial production impellers and obtain three indexes that reflect the assembly quality of the impellers. Then, analyze the parameters that affect the assembly quality of the impeller using grey relational analysis (GRA), establish a model for the assembly quality of the range hood impeller based on the generalized grey relational degree and identify the main parameters. After that, analyze the transmission structure of automation assembly workstation, identify the reasons that affect parameters and propose improvement plans. Finally, a trial production is conducted on the automation assembly workstation after adopting the improved plan to verify the quality model of impeller automation assembly.

The research shows that compared to manual assembly, the automation assembly quality of the impeller using GRA model has been improved, shortening the debugging cycle of the newly developed assembly workstation.

The newly developed automation equipment will have some problems in the trial production stage, which often rely on the experience of engineers for debugging. In this paper, the automation assembly quality model of range hood impeller based on GRA is established, which can not only ensure the quality of finished impeller but also shorten the debugging cycle of the equipment. In addition, GRA can be widely used in the commissioning of other automation equipment.

This study has developed a set of impeller automation assembly workstation. The debugging method in the trial production stage is beneficial to shorten the trial production time and improve the economic benefits.

The prevalence of live-streaming e-commerce leads to massive impulse purchases. Based on social learning theory, this study proposes a research model to explore the effect of streamers and viewers on impulsive buying by influencing consumers’ certainty perception.

This study adopts a questionnaire (N = 285) to collect data and conducts a partial least squares structural equation modelling (PLS-SEM) analysis to examine our research hypotheses.

Our results indicate that streamers and viewers improve product demand certainty and product quality certainty, but their effects on streamer quality certainty vary. In addition, product quality certainty is positively associated with impulsive buying, whereas streamer quality certainty and product demand certainty show a negative effect. Streamers can only drive impulse purchases by increasing product quality certainty, but the influence of viewers on consumers’ impulsive buying is fully mediated by product quality certainty and streamer quality certainty.

Our findings contribute to the literature on live-streaming e-commerce (LSE) by identifying the differential effects of streamers and viewers on impulsive buying. In addition, we broaden the area of certainty research in the LSE field to include product quality certainty, streamer quality certainty and product demand certainty and evaluate these certainty aspects as three psychological mechanisms that drive consumers’ impulse purchases.

Addressing the significant differences between referral programs and traditional promotional marketing, this paper aims to investigate and examine the impact of how reward-related information is presented within referral programs and how it interacts with reward size and reward allocation.

This study adopts framing effect and equity theory to build the relationship between reward presentation, reward size and reward allocation. Then, two scenario-based experimental studies are designed and conducted on Amazon Mechanical Turk.

The results show that there is no direct impact of reward presentation on referral likelihood, while the effect relies on reward size. As the levels of reward size increase, the referral likelihood gradually shifts from percentage form to dollar form as perceived size mediates the interaction effect on referral likelihood. Further, adding information about reward allocation also indicate the different impacts of equity and inequity on influencing the above findings.

The study contributes to the literature by introducing reward presentation and emphasizes its impact on individual’s behavior decisions in the context of referral programs. This study extends and broadens the scope and effectiveness of the framing effect on traditional promotional marketing strategies, while also bridging the gap in the literature by examining the combined role of information about rewards.