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This study aims to explore the role of equity investment in knowledge-driven business model innovation (BMI) in context of open modes according to the evidence from China’s primary market.

Based on the database of China’s private market and data set of news clouds, the statistic approach is applied to explore and explain whether equity investment promotes knowledge-driven BMI. Machine learning method is also used to prove and predict the performance of such open innovation.

The results of logistic regression show that explanatory variables are significant, providing evidence that knowledge management (KM) promotes BMI through equity investment. By further using back propagation neural network, the classification learning algorithm estimates the possibility of BMI, which can be regarded as a score to quantify the performance of knowledge-driven BMI

The quality of secondhand big data is not very ideal, and future empirical studies should use first-hand survey data.

This study provides new insights into the link between KM and BMI by highlighting the important roles of external investments in open modes.

From the perspective of investment, the findings of this study suggest the importance for stakeholders to share knowledge and strategies for entrepreneurs to manage innovation.

The concepts and indicators related to business models are difficult to quantify currently, while this study provides feasible and practical methods to estimate knowledge-driven BMI with secondhand data from the primary market. The mechanism of knowledge and innovation bridged by the experience from investors is introduced and analyzed.

This paper is dedicated to answer two questions: What is the business model of interactive television (iTV) ad at China’s Spring Festival Gala; and Why the business model of iTV ad creates different commercial values for different firms.

The two questions can be summarized as what and why problems; so, this paper adopts the method of the qualitative case study. For the first question, this paper uses the method of system activity diagram to design the business model. For the second question, this paper proposes a new analytical method – voice, incentive and convenience (VIC) model, which is used to analyze the commercial value of iTV ad. The proposal of VIC model is based on Wang et al.’s Iceberg theory.

The effect of interactive advertising is significantly better than that of traditional advertising, and interactive advertising is becoming the first choice for marketers. The business model innovation of iTV ad brings new business opportunities. In addition, the larger the area of the triangle surrounded by the three elements of VIC, the higher the commercial value of iTV ad.

Current business model studies focus on business model definitions and innovations, whereas the studies on business model evaluation and its influential factors are rare. A new analytical model named VIC is proposed. It explores the effect of VIC on the commercial value of iTV ad and provides a reference for the quantitative empirical analysis. The combination of activity system theory and VIC model contributes to the understanding of the explicit knowledge and tacit knowledge of iTV ad business model. This framework can be used to guide TV stations and stakeholders for business model innovations and optimizations.

A serious global public health emergency (GPHE) like the COVID-19 aggravates the inequilibrium of medical care and other critical resources between wealthy and poor nations, which, coupled with the collision of cultures, indicates the vital need for developing humanitarian knowledge transcending cultures. Given the scarcity of literature addressing such unprecedent issues, this paper thus proposes new, unconventional viewpoints and future themes at the intersection of knowledge management (KM) and humanitarian inquiry.

This paper is conceptual in nature. The data of the World Bank and the Office for the Coordination of Humanitarian Affairs are analysed to introduce some emerging real impact topics regarding cross-cultural conflicts and humanitarian knowledge in the post-COVID business world. The theoretical foundation was built upon a critical literature review.

This paper synthesizes the perspectives of culture, KM and the humanistic philosophy to distil the core component of cultural intelligence and comparatively and thereby illuminating why cross-cultural metacognition acts as a priori for achieving cosmopolitan humanitarian knowledge.

This paper provides profound implications to academics by highlighting the importance to formulating new, inter-disciplinary themes or unorthodox, phenomenon-driven assumptions beyond the traditional KM domain. This paper also offers practitioners and policymakers valuable insights into coping with the growing disparity between high- and low-income countries by showing warning signs of a looming humanitarian crisis associated with a GPHE context.

This paper does not aim to claim the birth of a new domain but call for more research on developing a normative theory of humanitarian knowledge as transcendence of cultures. It implies uncharted territories of great interest and potential for the real impact KM community.

The purpose of this study is to provide better service to hotel customers during the COVID-19 era. Specifically, this study focuses on understanding the changes in hotel customer satisfaction during the epidemic and formulating effective marketing strategies to satisfy and attract guests.

As the first victim of the COVID-19 virus, China’s hotel industry has been profoundly affected and customer satisfaction and needs have also changed. Taking 105,635 hotel reviews obtained from Tripadvisor.com in Beijing and Shanghai as samples, this study explores the changes in consumer satisfaction by using text-mining methods.

The results suggest that there are significant differences in overall ratings, spatial distribution and ratings of different traveller types before and after the epidemic. Generally, customers have higher “tolerance” and are more inclined to give higher ratings and pay more attention to hotel prevention and control measures to reduce health risks after the COVID-19.

This paper proves the changes in customer satisfaction before and after the COVID-19 at the theoretical level and reveals the changes in customer attention through the topic model and provides a basis for guiding hotel managers to reduce the impact of the COVID-19 crisis.

Empirical findings would provide useful insights into tourism management and improve hotel service quality during the COVID-19 epidemic era.

This research explores the hotel customer satisfaction in the field of hotel management before COVID-19 and after COVID-19, by using text mining to analyse mandarin online reviews. The results of this study will suggest that the hotel industry should continuously adjust its products and services based on the effective information obtained from customer reviews, so as to realize the activation and revitalization of the hotel industry in the epidemic era.

The purpose of this paper is to study the importance of image data in hotel selection-recommendation using different types of cognitive features and to explore whether there are reinforcing effects among these cognitive features.

This study represents user-generated images “cognitive” in a knowledge graph through multidimensional (shallow, middle and deep) analysis. This approach highlights the clustering of hotel destination imagery.

This study develops a novel hotel selection-recommendation model based on image sentiment and attribute representation within the construction of a knowledge graph. Furthermore, the experimental results show an enhanced effect between different types of cognitive features and hotel selection-recommendation.

This study enhances hotel recommendation accuracy and user satisfaction by incorporating cognitive and emotional image attributes into knowledge graphs using advanced machine learning and computer vision techniques.

This study advances the understanding of user-generated images’ impact on hotel selection, helping users make better decisions and enabling marketers to understand users’ preferences and trends.

This research is one of the first to propose a new method for exploring the cognitive dimensions of hotel image data. Furthermore, multi-dimensional cognitive features can effectively enhance the selection-recommendation process, and the authors have proposed a novel hotel selection-recommendation model.

The study aims to analyze the interaction between macroeconomic uncertainty and stock market return and volatility for China and USA and tries to draw some invaluable inferences for the investors, portfolio managers and policy analysts.

Empirically the study uses GARCH family models to capture the time-varying volatility of stock market and macroeconomic risk factors by using monthly data ranging from 1995:M7 to 2018:M6. Then, these volatility series are further used in the multivariate VAR model to analyze the feedback interaction between stock market and macroeconomic risk factors for China and USA. The study also incorporates the impact of Asian financial crisis of 1997–1998 and the global financial crisis of 2007–2008 by using dummy variables in the GARCH model analysis.

The empirical results of GARCH models indicate volatility persistence in the stock markets and the macroeconomic variables of both countries. The study finds relatively weak and inconsistent unidirectional causality for China mainly running from the stock market to the macroeconomic variables; however, the volatility spillover transmission reciprocates when the impact of Asian financial crisis and Global financial crisis is incorporated. For USA, the contemporaneous relationship between stock market and macroeconomic risk factors is quite strong and bidirectional both at first and second moment level.

This study investigates the interaction between stock market and macroeconomic uncertainty for China and USA. The researchers believe that none of the prior studies has made such rigorous comparison of two of the big and diverse economies (China and USA) which are quite contrasting in terms of political, economic and social background. Therefore, this study also tries to test the presumed conception that macroeconomic uncertainty in China may have different impact on the stock market return and volatility than in USA.

This study aims to characterise an intricate, idiosyncratic knowledge-creating mechanism in the modern digital context of cross-cultural business models (CBM). From an integrative socio-cultural and philosophical perspective, the authors suggest a novel concept of polychronic knowledge creation (PKC) and its metaphor to theorise such a complex phenomenon.

This paper is conceptual in nature. It critically reviews the literature characterising the flourishing of information and communication technology (ICT)-driven CBMs and clarifies a research gap. The authors draw a dynamic conceptual framework describing how knowledge is created poly-chronically within CBMs, while also articulating and justifying the occurrence of knowledge icebergs as a manifestation of critical cognitive variances and biases in such contexts.

Building upon existential phenomenology, the authors regard the sea as a parable of the CBM ecosystem and propose the new notion of PKC as a dynamic time-space synthesis and its associated sea-like heuristic metaphor. These elucidate how the intricate interconnectivity of a focal firm with its diverse strategic partners kindles a discursive, multi-path knowledge creation process in ICT-driven CBMs under multiple jurisdictions with manifold cultures.

Implications regarding the role of cross-cultural management in creating new knowledge within CBMs are provided.

The research complements and enriches Nonaka’s (1994) theory and its underlying metaphor “ba” (by incorporating the abstruse yet vital role of culture in the synthesizing process of knowledge creation) to propose the novel ideas of PKC and the sea-like heuristic metaphor in CBMs.

This study explores the contagion of greenwashing strategies among ESG mutual funds. It investigates how the greenwashing behaviors of peer funds within the same family influence a fund’s decision to engage in greenwashing. The research also examines the impact of greenwashing on genuine ESG funds and explores the mechanisms through which greenwashing strategies spread across ESG mutual funds.

This paper employs a two-stage least squares regression model with cross-fund returns standard deviation as an instrumental variable to disentangle the peer effects of greenwashing from family-level characteristics. The analysis incorporates various fund characteristics and introduces four contagion channels through which greenwashing may influence genuine ESG funds.

The study finds greenwashing behavior in ESG funds is positively influenced by similar practices within their fund family. Larger assets under management and older funds with higher management fees show resilience against greenwashing influences, while team-managed funds are more susceptible. Additionally, socially responsible investors struggle to distinguish between genuine and greenwashing ESG funds, which may contribute to the persistence of greenwashing practices.

This paper contributes to the literature by delineating the mechanisms of greenwashing contagion within ESG mutual funds. It also examines the demand-side incentives for adopting greenwashing strategies, offering insights into the implications for fund flows and investor behavior. This study is among the first to analyze the contagion effects of greenwashing strategies across an extensive network of ESG funds, enriching our understanding of the broader impacts of greenwashing in the context of socially responsible investing.

Customer expectations and preferences may evolve as they experience more. This study aims to analyze the dynamics of customer requirements (CRs) as customer experience familiarity increases, offering insights for enhancing product-service quality.

This study categorizes dynamic requirements into two conversion stages: from new to repeat customers and from repeat to frequent customers. First, crawl online reviews (ORs) and determine each review’s conversion stage. Second, identify product-service attributes from reviews and conduct aspect-level sentiment analysis. Then, examine each attribute’s trend direction and magnitudes in the two stages. Finally, a dynamic-trend importance-performance analysis (DTIPA) model is developed to analyze the dynamic requirements and provide strategies for optimizing product services.

This study identifies eight attributes of buffet restaurants with varying requirement change trends. In particular, customer attention to “waiting time,” “variety of dishes,” “cost performance” and “taste” decreases in the first stage. In the second stage, “environment” and “freshness” increase differently from the first. Satisfaction with “cost performance” increases in the first stage but decreases in the perception of frequent customers. Improvement strategies are also provided based on these trends.

Research on dynamic requirement trends based on ORs and customer experience familiarity is scarce, particularly in the context of buffet restaurants. Moreover, existing methods have their limitations. This study proposes a novel approach for the progressive exploration and extraction of evolving CRs from ORs. By incorporating the trend direction and magnitude of attributes’ importance and satisfaction, DTIPA is developed to form strategies for optimizing buffet restaurant product services.