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The purpose of this paper is to analyze the effect and mechanism of platform incentives on users’ knowledge collaboration performance (KCP) and the configuration leading to high KCP in online knowledge communities (OKCs) in the post-COVID-19 pandemic era from a cross-culture perspective.

A survey method and a standard questionnaire were applied. The data was analyzed using multiple regression and fuzzy set qualitative comparative analysis.

The results indicate that, for both kinds of users, self-enhancement and communication positively affect the KCP. User engagement significantly mediates the relationship between communication and KCP and knowledge absorptive capacity moderates the relationship between user engagement and KCP. In contrast, material incentive positively affects the KCP of Chinese users, while hurting the cross-cultural sample. And the promotion of KCP for cross-cultural samples does not require a higher engagement and knowledge absorptive capacity, while paying more attention to short-term interests, such as communication and self-enhancement.

The study only divides users into Chinese and cross-cultural foreign users, without a distinction between foreign users in different countries. In addition, the research is based on cross-sectional data and failed to try to explore the long-term effects of these incentives from the time dimension.

This study explores the incentive mechanism and configuration of OKC platforms to achieve high KCP for different users from a cross-cultural perspective. It provides new ideas and solutions for precise incentives for users of OKC platforms.

Green technology, characterized by its environmentally friendly attributes and sustainable practices, has emerged as a crucial tool in harmonizing the economic and ecological benefits. However, the challenge lies in selecting the most effective strategies for acquiring green technology. This paper aims to explore how chemical enterprises choose green technology acquisition strategies across diverse scenarios.

Considering the influence of competition effects, spillover effects and their interactions on selecting green technology acquisition strategies, this paper develops three decision models (independent R&D, cooperative R&D and technology introduction). Drawing on the duopoly game theory as its theoretical framework, this paper delves into the examination of the economic and environmental benefits within distinct scenarios.

Cooperative R&D excels in promoting green technology R&D when spillover effects are strong, while independent R&D demonstrates superiority when spillover effects are weak. The threshold for the strength of spillover effects is related to competition effects. Additionally, cooperative R&D typically yields greater financial advantages than independent R&D and technology introduction. Moreover, the economic and environmental benefits may not be optimized simultaneously. Only enterprises that satisfy low competition and spillover effects as well as high competition and spillover effects, can achieve win-win economic and environmental benefits.

Although green technology R&D and introduction are alternative strategies, they have typically been considered separately in prior literature. This study attempts to incorporate green technology R&D and introduction into a strategic system to investigate the selection of green technology acquisition strategies, taking into account competition effects, spillover effects and their interactions.

This study aims to examine the interaction effects of chatbots’ language style and customers’ decision-making journey stage on customer’s service encounter satisfaction and the mediating role of customer perception of emotional support and informational support using the construal level theory and social support theory as conceptual frameworks.

This study used a scenario-based experiment with a 2 (chatbot’s language style: abstract language vs concrete language) × 2 (decision-making journey stage: informational stage vs transactional stage) between-subjects design.

Findings show that during the informational stage, chatbots that use abstract language style exert a strong influence on service encounter satisfaction through emotional support. During the transactional stage, chatbots that use concrete language style exert a strong impact on service encounter satisfaction through informational support.

Findings provide some suggestions for improving customer–chatbot interaction quality during online service encounters.

This study offers a novel perspective on customer interaction experience with chatbots by investigating the chatbot’s language styles at different decision-making journey stages.

This study aims to answer two questions: (a) what obstacles and opportunities do Chinese female entrepreneurs face when doing business? And (b) how do they negotiate their entrepreneurial careers and gender identities in different gender-segregated markets?

This study uses qualitative research methods of participant observation and in-depth interviews with 41 female entrepreneurs in China and the theoretical lenses of gender role theory and doing gender in entrepreneurship.

The study findings reveal that Chinese female entrepreneurs face different obstacles and opportunities in gender-segregated industries. Their experiences vary in industries that are mainly occupied by males and females. On the one hand, women in female-dominated industries may be supported by a feminine working environment that is coherent with their domestic roles. However, they may also be questioned on the cultural impurity implied in some industries, which harms their class-based feminine virtue. On the other hand, women in male-dominated industries may be challenged and marginalized due to their gender. However, some find ways to turn the disadvantaged feminine characters into favourable conditions and break out of the stereotypical gender constraints in doing business.

This study contributes to the literature on gender and entrepreneurship in general. More specifically, it contributes to the study of doing gender in gender-segregated markets, and it also illustrates women’s gendered opportunities and constraints in Chinese society that are affected by the long-lasting traditional gender norms.

This study aims to explore the applications of natural language processing (NLP) and data analytics in understanding large-scale digital collections in oral history archives.

NLP and data analytics were used to analyse the oral interview transcripts of 904 survivors of the Japanese American incarceration camps collected from Densho Digital Repository, relying specifically on descriptive analysis, keyword extraction, topic modelling and sentiment analysis (SA).

The researchers found multiple geographic areas of large residential communities of ethnic Japanese people and the place names of the concentration camps. The keywords and topics extracted reflect the deplorable conditions and militaristic nature of the camps and the forced labour of the internees. When remembering history, the main focus for the narrators remains the redress and reparation movement to obtain the restitution of their civil rights. SA further found that the forcible removal and incarceration of Japanese Americans during Second World War negatively impacted and brought deep trauma to the narrators.

This case study demonstrated how NLP and data analytics could be applied to analyse oral history archives and open avenues for discovery. Archival researchers and the general public may benefit from this type of analysis in making connections between temporal, spatial and emotional elements, which will contribute to a holistic understanding of individuals and communities in terms of their collective memory.