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This paper aims to investigate how chief executive officer (CEO) responsible leadership impacts corporate social responsibility (CSR) and organization performance by considering diverse organizational climates (including ethical, service and initiative climates) as mediators and CEO founder status as a moderator.

This study analyzed survey data from 212 service organizations in China with structural equation modeling.

The results clearly established that CEO responsible leadership played a crucial role in augmenting both CSR and organization performance by shaping positive organizational climates. Notably, CEO responsible leadership significantly fostered ethical, service and initiative climates. Furthermore, an ethical climate promoted CSR and organization performance, whereas service and initiative climates specifically enhanced organization performance. Additionally, responsible CEOs with founder status exhibited a higher propensity for enhancing ethical, service and initiative climates within service organizations.

Service organizations should take measures to build CEO responsible leadership, especially for CEOs with founder status. Furthermore, service organizations should motivate employees to reach consensus on ethical conducts, superior service and proactive approach to work.

First, the findings on CEO responsible leadership’s effects on CSR and organization performance extend the research on responsible leadership outcomes. Second, this paper adds to responsible leadership literature through exploring the mediating effects of ethical, service and initiative climates. Finally, the finding on the moderating role of founder CEOs offers a novel perspective regarding the boundary condition of the effects of CEO responsible leadership.

This study aims to evaluate the key factors affecting the behavioural intention of urban tourists towards visiting national parks by integrating the theory of planned behaviour (TPB) and expectancy theory (ET).

Data were collected from urban tourists using an online questionnaire developed through Google forms. A total of 489 valid online questionnaires were considered for this study. The proposed research model was empirically evaluated using the structural equation modelling method.

According to the results of this research, TPB constructs are significantly and positively associated with the behavioural intention of urban tourists towards visiting national parks in India and out of ET constructs, only expectancy and valence are significantly and positively associated with behavioural intention while instrumentality does not.

This study manifests the behaviour of urban tourists towards national parks and contributes to academics by incorporating existing literature. The findings of this study also help policymakers in formulating innovative strategies for national parks. It presents an integrated framework that lays the platform for a new study domain on urban tourists' intentions to visit national parks, which will be useful to urban managers, officials and the tourism sector. Furthermore, as the scope of this study is confined to assessing the intentions of urban tourists toward visiting national parks, it is difficult to generalize the findings.

To the best of the authors' knowledge, this is the first research of its kind to provide an understanding of the behavioural intention of urban tourists towards visiting the national parks in India by optimizing the TPB and ET.

Given the critical role of user-generated content (UGC) in e-commerce, exploring various aspects of UGC can aid in understanding user purchase intention and commodity recommendation. Therefore, this study investigates the impact of UGC on purchase decisions and proposes new recommendation models based on sentiment analysis, which are verified in Douban, one of the most popular UGC websites in China.

After verifying the relationship between various factors and product sales, this study proposes two models, collaborative filtering recommendation model based on sentiment (SCF) and hidden factors topics recommendation model based on sentiment (SHFT), by combining traditional collaborative filtering model (CF) and hidden factors topics model (HFT) with sentiment analysis.

The results indicate that sentiment significantly influences purchase intention. Furthermore, the proposed sentiment-based recommendation models outperform traditional CF and HFT in terms of mean absolute error (MAE) and root mean square error (RMSE). Moreover, the two models yield different outcomes for various product categories, providing actionable insights for organizers to implement more precise recommendation strategies.

The findings of this study advocate the incorporation of UGC sentimental factors into websites to heighten recommendation accuracy. Additionally, different recommendation strategies can be employed for different products types.

This study introduces a novel perspective to the recommendation algorithm field. It not only validates the impact of UGC sentiment on purchase intention but also evaluates the proposed models with real-world data. The study provides valuable insights for managerial decision-making aimed at enhancing recommendation systems.