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Abstract
This paper presents an empirical study of the intercultural conflicts that Chinese migrants have experienced in Hungary, so far unexamined in the scientific literature. This study hypothesizes the wall theory, consisting of the visible wall and the invisible wall. The visible wall entails physical and spatial boundaries whereas the invisible wall refers to trust deficit. Our results suggest a visible wall is a ubiquitous concept in Hungarian culture, whereas the boundaries of the visible wall are obscure in Chinese culture. In contrast, the invisible wall is not prevalent in Hungarian culture, yet the invisible wall is a predominant concept in Chinese culture which alienates people in thinking. The concept of wall denotes different meanings in Chinese and Hungarian cultures. The study attempts to provide Chinese overseas with practical knowledge to be aware of the cultural differences and potential strategies to reduce intercultural conflicts.

PurposeWith technological innovation elements and the competitive market environment becoming increasingly complex, numerous firms utilize network embeddedness to achieve and sustain innovation. However, empirical research has not conclusively established which form of network embeddedness more effectively facilitates corporate innovation. Drawing on the heterogeneous network resources perspective, this study explores the impact of market network embeddedness, technology network embeddedness and their synergy on the green innovation performance of manufacturing small and medium-sized enterprises (SMEs). Furthermore, it investigates the moderating role of resource orchestration capability in these relationships.Design/methodology/approachThrough an online questionnaire survey of Chinese manufacturing SMEs, 293 sample data were collected, and the hierarchical regression analysis was conducted to test the hypothesis.FindingsThe results indicate that market and technology network embeddedness significantly enhance green innovation performance, with the former exerting a more significant impact. Furthermore, the synergy between market and technology network embeddedness positively influences green innovation performance. Additionally, resource orchestration capability strengthens the positive effects of both market and technology network embeddedness on green innovation performance, while the moderating effect of resource orchestration capability on the relationship between the synergy of the two and green innovation performance was insignificant.Research limitations/implicationsThe study faced many limitations, such as collecting primary data, which relied on a questionnaire only, using cross-sectional data and examining only manufacturing SMEs.Originality/valueBased on the heterogeneous network resources perspective and integrating social network theory and resource orchestration theory, this study explores the impact of network embeddedness on the green innovation performance of manufacturing SMEs, which sheds new light on the network embeddedness research framework and also enriches the antecedents of green innovation. In addition, this study provides implications on how manufacturing SMEs effectively utilize network embeddedness and resource orchestration capability to enhance green innovation performance.

AbstractExisting social media influencer marketing research mainly explores how to enhance followers purchase intentions through the mechanisms of improving the follower–influencer relationship, however leaves research gaps regarding how to design effective influencer recommendation information (IRI) to stimulate consumer purchase behaviors. To address the gaps, a research model was established based on the information adoption model. This study collected 2276 influencer recommendation posts from typical Chinese social media influencer marketing platform WeChat Shopping Circle. Text‐mining methods were used to develop measurements from influencer posts, and survey data were collected from 20 participants from a large Chinese university to validate measurements. Using negative binomial regression, the empirical study found that the number of consumers who purchase recommended products is determined by the usefulness of IRI, whereby being further affected by content expertise, content novelty, and platform endorsement to influencers. The type of recommended products moderates the relationship between these influencing factors and information usefulness. This study makes contributions by revealing the informational influence mechanism of IRI and identifying what factors can exert effective informational influence for different types of products. It also offers practical implications for the design of influencer recommendation strategies.