2 resultados para product accounts
em Aston University Research Archive
Resumo:
The study examined the relationships between antecedents, timeliness in NPD and INPR, and consequences. A conceptual framework was tested using 232 new products from South Korean firms. The hypothesized relationships among the constructs in the model were evaluated by multiple regression and hierarchal regression analyses using SPSS 12 as well as by structural equation modelling (SEM) using SIMPLIS LISREL. In addition, confirmatory factor analysis (CFA) was carried out using SIMPLIS LISREL. In the direct relationships, cross-functional linkages and marketing synergy exhibited a statistically significant effect on NPD timeliness. The results also supported the influences of the HQ-subsidiary/agent relationship and NPD timeliness on INPR timeliness as well as INPR timeliness on performance. In the mediating effect tests, marketing proficiency significantly accounts for the relationships between cross-functional linkages and NPD timeliness, between marketing synergy and NPD timeliness, and between the HQ-subsidiary/agent relationship and INPR timeliness. Technical proficiency also mediates the effect of the HQ-subsidiary/agent relationship on INPR timeliness. The influence of NPD timeliness on new product performance in target markets is attributed to INPR timeliness. As for the results of the external environmentals and standardization influences, competitive intensity moderates the relationship between NPD timeliness and new product performance. Technology change also moderates the relationship between cross-functional linkages and NPD timeliness and between timeliness in NPD and INPR and performance. Standardization has a moderating role on the relationship between NPD timeliness and INPR timeliness. This study presents the answers to research questions which concern what factors are predictors of criterion variables, how antecedents influence timeliness in NPD and INPR and when the direct relationships in the INPR process are strengthened.
Resumo:
In recent years, the boundaries between e-commerce and social networking have become increasingly blurred. Many e-commerce websites support the mechanism of social login where users can sign on the websites using their social network identities such as their Facebook or Twitter accounts. Users can also post their newly purchased products on microblogs with links to the e-commerce product web pages. In this paper, we propose a novel solution for cross-site cold-start product recommendation, which aims to recommend products from e-commerce websites to users at social networking sites in 'cold-start' situations, a problem which has rarely been explored before. A major challenge is how to leverage knowledge extracted from social networking sites for cross-site cold-start product recommendation. We propose to use the linked users across social networking sites and e-commerce websites (users who have social networking accounts and have made purchases on e-commerce websites) as a bridge to map users' social networking features to another feature representation for product recommendation. In specific, we propose learning both users' and products' feature representations (called user embeddings and product embeddings, respectively) from data collected from e-commerce websites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users' social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation. Experimental results on a large dataset constructed from the largest Chinese microblogging service Sina Weibo and the largest Chinese B2C e-commerce website JingDong have shown the effectiveness of our proposed framework.