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This study examines the impact of response time on user experience for mobile applications and considers the moderating influence of gender and network environment on this relationship.

An experiment was conducted with 50 young adults to evaluate their user experience of a mobile application that simulates variations in network environment and response time. User experience was evaluated based on the three constituent dimensions of tolerance, acceptance, and satisfaction.

Analytical results demonstrate that response time not only adversely affects user experience of mobile applications, but that this effect is not homogeneous across the three dimensions of tolerance, acceptance and satisfaction. The findings also illustrate that gender moderates the effect of response time on user experience, however, the negative influence is more salient for males than females, which is opposite to our hypothesis. The joint moderating influence of gender and network environment turned out to be partly significant.

By illuminating users' tolerance, acceptance, and satisfaction with varied response times, findings from this study can inform the design of mobile applications such that desired levels of user experience can be assured with minimum resources.

Although response time has been hailed as a key determinant of user experience for desktop applications, there is a paucity of studies that have investigated the impact of response time on user experience for mobile applications. Furthermore, prior research on response time neglects the multi-dimensional nature of user experience. This study bridges the above mentioned knowledge gaps by delineating user experience into its constituent dimensions and clarifying the effects of response time on each of these dimensions.

In the fabric manufacturing industry, various unfavorable factors, including machine fault and yarn breakage, can easily cause fabric defects and affect product quality, begetting huge economic losses to enterprises. Thus, automatic fabric defect detection systems have become an important development direction. Herein, the most common defects in the fabric production process, like ribbon yarn, broken yarn, cotton ball, holes, yarn shedding and stains, are detected. Current fabric defect detection systems afford low detection accuracy and a high missed detection rate for small target fabric defects. Therefore, this study proposes deep learning technology for automatically detecting fabric defects by improving the YOLOv5s target detection algorithm. The improved algorithm is termed YOLOv5s-4SCK, which can effectively detect fabric defects. This study aims to discuss the aforementioned issues.

Specifically, based on the YOLOv5s algorithm, first, the structure of YOLOv5s is modified to add a small target detection layer, fully utilize deep and shallow features and reduce the missed detection rate of small target fabric defects. Second, the integration of CARAFE upsampling enables the effective retention of feature information and maintenance of a certain computational efficiency, thereby improving the detection accuracy. Finally, the K-Means++ clustering algorithm is used to analyze the position of the center point of the prior box to better obtain the anchor box and improve the average accuracy and evaluation index of detection.

The research results show that the YOLOv5s-4SCK algorithm increases the accuracy by 4.1% and the detection speed by 2 f.s-1 compared to the original YOLOv5s algorithm, and it effectively improves the original YOLOv5s problem of high missed detection rate of small targets.

The YOLOv5s-4SCK proposed in this paper can effectively reduce the missed detection rate of fabric defects, improve the detection efficiency and has certain industrial value.

The proposed algorithm can quickly identify fabric defects, effectively improving the detection rate. In the future, the proposed algorithm will be applied in the actual industry.

Automatic fabric defect detection reduces the manpower of inspectors, and the proposed YOLOv5s-4SCK algorithm is also suitable for other recognition fields.

The proposed YOLOv5s-4SCK algorithm has been tested using real cloth to ensure its accuracy, and its performance is better than the original YOLOv5s algorithm.

Recognizing the differences between generations Y and Z, this exploratory study uses generational cohort theory as a framework to examine the brand perception of McDonald's, an international brand which has grown up with consumers for over 30 years in China. The paper aims to discuss this issue.

Measures of brand perception was built based on Aaker's brand personality model. A total 1,103 valid questionnaires were collected through an online survey platform. Factor analysis is the primary method to analysis the data.

The findings of this study reveal a favourable brand perception of McDonald's among young Chinese consumers which is consistent with Aaker's brand personality model and support the use of generational cohort theory as a market segmentation tool for brand perception. The differences between the two generational cohorts are not shown to be significant.

The most important contribution of this study is the evaluation of the personality of a major brand in China for Gen Z, a topic with very little existing research. Also, this research suggests future in-depth research into generational cohort theory in a Chinese context by recognizing homogeneity and heterogeneity exist simultaneously between generational cohorts.