Search results

This study aims to examine new product development (NPD) performance to identify effective knowledge update strategies and assess the impact of environmental uncertainty on these dynamics. It aims to understand how different knowledge potentials and organizational routines interact to enhance product outcomes. The specific subsidiary context enriches understanding by identifying challenges and opportunities that are not typically visible in broader organizational studies.

A survey of 310 business managers was conducted to measure their knowledge potential and organizational routine updating. The hypotheses were tested using hierarchical regression analysis to determine the optimal combinations of knowledge and practice updates for NPD performance.

Results indicate that both knowledge potential and organizational routine updating significantly enhance subsidiary performance. Specifically, knowledge accumulation paired with routine creation and knowledge difference paired with routine revision optimally boosts product development. Moreover, this study reveals an inverted U-shaped relationship between environmental uncertainty and the effectiveness of these combinations, suggesting a complex interplay that affects NPD performance.

This study enhances understanding of NPD performance by integrating resource concordance theory with empirical analysis of knowledge and organizational strategy adaptations. It underscores the moderating role of environmental uncertainty, offering new theoretical insights into enhancing product development performance. Although the focus on subsidiaries limits broader applicability, it provides valuable insights into the nuanced NPD dynamics in these specific entities, suggesting avenues for future research to expand this study’s findings across different organizational types.

Live streaming e-commerce, which integrates real-time video interaction with online shopping, has quickly become a popular sales channel. It not only allows for immediate feedback but also builds a sense of trust and connection between streamers and consumers. Drawing on the elaboration likelihood model (ELM), we investigate how central and peripheral route factors affect consumers’ trust building and purchase intentions. Additionally, we identify consumer involvement as a key moderator affecting the relationship between central route factors and trust in product as well as the relationship between peripheral route factors and trust in streamer.

To test the research model, we collected data from 423 consumers on TaoBao Live.

The findings show that information completeness, accuracy and currency positively affect trust in the product, while perceived physical characteristic similarity, streamer humor attractiveness and passion attractiveness positively affect trust in the streamer. Trust in the streamer positively influences trust in the product, which subsequently impacts purchase intention. Moreover, involvement moderates the effects of information accuracy, currency, perceived physical characteristic similarity and passion attractiveness on trust.

First, we examine the direct influence of product- and streamer-related cues on consumer trust and purchase intention through distinct pathways. Second, we adopt ELM to explain the process of consumer trust building by investigating how central and peripheral route factors influence purchase intention through consumer trust in live streaming settings. Third, we incorporate involvement as a crucial moderator, shedding light on the boundary conditions of trust building in live streaming e-commerce.

As an ancient art form, embroidery has strong practicality and artistic value. However, current embroidery style migration models produce images with unclear textures and a lack of stitch detail. So, in this paper, we propose a cyclic consistent embroidery style migration network with texture constraints, which is called Texture Cycle GAN (TCGAN).

The model is based on the existing Cycle GAN network with an additional texture module. This texture module is implemented using a pre-trained Markovian adversarial network to synthesize embroidery texture features. The overall algorithm consists of two generative adversarial networks (for style migration) and the Markovian adversarial network (for texture synthesis).

Qualitative and quantitative experiments show that, compared with the existing convolutional neural network style transfer algorithm, the introduction of the texture-constrained embroidery style transfer model TCGAN can effectively learn the characteristics of style images, generate digital embroidery works with clear texture and natural stitches and achieve more realistic embroidery simulation effects.

By improving the algorithm for image style migration and designing a reasonable loss function, the generated embroidery patterns are made more detailed, which shows that the model can improve the realism of embroidery style simulation and help to improve the standard of embroidery craftsmanship, thus promoting the development of the embroidery industry.