Search results

The purpose of the research is to investigate the impact of affective and emotional experiences on organizational learning in highly dynamic and chaotic environments, specifically in the context of the COVID-19 crisis.

Based on an intensive 12-month inductive study, 24-project managers and 16 team members from biopharmaceutical organizations were interviewed and a thematic analysis was conducted.

Three themes emerged from the findings: (1) developing affective maturity as a socio-emotional resource, (2) mixed-motive emotional dynamics and (3) meaning-oriented organizational identification and commitment. The context of the COVID-19 crisis provided an unconventional performance environment.

Our study has several limitations, offering avenues for future research. Firstly, our focus on biopharmaceutical organizations, with their unique socio-cultural influences and management styles, may limit the generalizability of our findings to other sectors and institutional contexts. However, regulatory mechanisms in this sector may align with knowledge-based sectors, emphasizing the influence of organizational values and best practices. Secondly, our reliance on a posteriori interview data limits real-time observation of organizational learning (OL) processes. Future research could employ diverse data sources and survey methods for corroboration. Additionally, cross-cultural studies might explore how different societies respond to crises. Multi-level perspectives could also enhance understanding of affective experiences and their impact on OL outcomes.

The study contributes new insights into OL through its focus on how affective experiences and affective organizing efforts shape OL. We offer a novel and emergent theoretical model of OL in the context of trauma which has implications for organizations particularly in the areas of information processing and decision-making.

This study aims to explore the roles of Zhongyong and political efficacy on citizens’ intention to use digital government platforms for e-participation (i.e. e-participation intention). Zhongyong is a dialectical way of thinking that influences Chinese behavioral intentions and approaches. Political efficacy is a predictor of traditional political participation. Both of them have not been adequately investigated in this digital era, particularly regarding e-participation in digital government platforms. Therefore, this study investigates their relationships.

A quantitative model is constructed to examine the relationship between Zhongyong and citizens’ e-participation intention (internal and external) political efficacy serves as a mediator. An online questionnaire gathered 345 responses from three representative provinces of China (i.e. Guangdong, Jiangxi and Shanxi). Partial least square structural equation modeling (PLS-SEM) was adopted and executed with Smart PLS 4.0 to analyze the data.

Zhongyong and (internal and external) political efficacy can positively influence citizens’ e-participation intention. Moreover, (internal and external) political efficacy mediates the relationship between Zhongyong and citizens’ e-participation intention.

This research focuses on Chinese culture Zhongyong and surveyed Chinese citizens, thus is limited to the Chinese context. Second, this study used cross-sectional data. Third, this study only investigated two factors’ effects on e-participation, i.e. Zhongyong and political efficacy.

The findings provide multifaceted strategies for improving citizens’ adoption of digital government platforms for e-participation. Incentive policies to boost citizens’ (internal and external) political efficacy can be launched. To achieve broader citizen participation, a participative culture can be cultivated based on Zhongyong.

This study constructs a novel model that innovatively links Zhongyong thinking, political efficacy and e-participation intention. The results underscore the importance of Zhongyong culture and political efficacy in increasing citizens’ e-participation intention.

The purpose of this study is to comprehensively examine the ethical implications surrounding generative artificial intelligence (AI).

Leveraging a novel methodological approach, the study curates a corpus of 364 documents from Scopus spanning 2022 to 2024. Using the term frequency-inverse document frequency (TF-IDF) and structural topic modeling (STM), it quantitatively dissects the thematic essence of the ethical discourse in generative AI across diverse domains, including education, healthcare, businesses and scientific research.

The results reveal a diverse range of ethical concerns across various sectors impacted by generative AI. In academia, the primary focus is on issues of authenticity and intellectual property, highlighting the challenges of AI-generated content in maintaining academic integrity. In the healthcare sector, the emphasis shifts to the ethical implications of AI in medical decision-making and patient privacy, reflecting concerns about the reliability and security of AI-generated medical advice. The study also uncovers significant ethical discussions in educational and financial settings, demonstrating the broad impact of generative AI on societal and professional practices.

This study provides a foundation for crafting targeted ethical guidelines and regulations for generative AI, informed by a systematic analysis using STM. It highlights the need for dynamic governance and continual monitoring of AI’s evolving ethical landscape, offering a model for future research and policymaking in diverse fields.

The study introduces a unique methodological combination of TF-IDF and STM to analyze a large academic corpus, offering new insights into the ethical implications of generative AI across multiple domains.

The present study aims to examine the households’ attitudes and intentions to adopt an indoor air purifier against the smog crisis in India by using a comprehensive theoretical framework based on the combination of the Protective Action Decision Model (PADM) and the Theory of Planned Behavior (TPB). The United Nations Sustainable Development Goals (SDGs) 2030 also emphasized ensuring a healthy and safe life, especially by achieving SDG-3, SDG-11 and SDG-13.

Using purposive sampling, the data were collected through a survey questionnaire distributed to 382 households, and study hypotheses were assessed by using partial least squares structural equation modeling employing SmartPLS.

The results revealed that mental health risk perception (MHRP) was the most influential determinant of households’ attitudes toward adopting air purifiers, followed by smog knowledge, physical health risk perception (PHRP), information seeking and product knowledge. Notably, results revealed that households’ attitude is a leading determinant of their adoption intention toward the air purifier compared to subjective norms (SN) and perceived behavioral control (PBC).

To the best of the authors’ knowledge, the present study is the first to provide new insights into an individual’s protective behavior response toward ecological hazards by examining the households’ adoption intention toward the air purifier against the smog crisis using PADM and TPB model inclusively. In addition, the present study analyzes the impact of both PHRP and MHRP on individuals’ protective behavior separately. Also, this study provides theoretical contributions and important practical implications for the government, manufacturers and air purifier sellers.

This study aims to explore the impact of Own, Others and Outer influences (O3) on green purchasing behaviour in e-commerce. The study uses the O ZONE model and Stimulus–Organism–Behaviour–Consequence (SOBC) framework to analyse the impact of green intentions, green product awareness and green self-efficacy.

This study collected data from 405 respondents through a self-administered questionnaire and analysed the data via partial least squares structural equation modelling and necessary condition analysis using the software SmartPLS.

The findings indicate that O3 factors significantly affect green product awareness among consumers, with “Own” having no impact in ecommerce. Furthermore, this study found that green self-efficacy fully mediates the relationship between green product awareness and purchasing intentions. Interestingly, the results suggest that e-commerce recommendations (others) and marketer persuasion (outer influence) have a greater impact on green product awareness, which in turn influences green buying intentions via green self-efficacy, compared to personal knowledge (own). Finally, it shows that green purchase intentions lead to green buying behaviour.

This study helps to understand how to create green product awareness through information transfer and ways to enhance green self-efficacy to motivate green buying behaviour. Hence, it offers valuable insights for practitioners, policymakers and managers in green and digital marketing, highlighting the importance of effective knowledge transfer to enhance green consumer behaviour. Marketers can better understand the factors influencing consumers’ awareness of green products, such as personal knowledge, online reviews, recommendations from e-commerce websites and marketing campaigns.

The findings add new insights to the existing knowledge of green purchasing behaviour in e-commerce by highlighting the importance of understanding the factors that influence consumers’ green product awareness and self-efficacy. In addition, it underscores the relative impact of O3 factors on green purchasing behaviour, aiding the development of effective marketing strategies promoting green products in ecommerce platform.

Highlighting the ever-evolving nature of the e-commerce industry, this study stresses the importance of staying abreast of trends for marketer success. It reiterates the significance of adapting strategies to align with emerging industry practices and consumer preferences.

In the era of social media, containing and managing online rumors poses a significant challenge. Therefore, a thorough investigation into the factors influencing rumor dissemination becomes increasingly crucial. Key rumor spreaders wield considerable influence over audience behavior and public opinion dissemination, making them pivotal in curbing the spread of rumors. This study aims to categorize these key spreaders and delve into the distinct characteristics of each category.

This paper introduces a method for identifying key rumor spreaders through social network analysis. By utilizing text mining and sentiment analysis on data from typical rumor events on Weibo, we extract 14 characteristics of key spreaders. Subsequently, we develop a method for classifying the roles of these key spreaders and conduct empirical research to validate our findings.

Our study reveals significant variations in the characteristics of key spreaders across different roles. These insights enable managers to implement differentiated management strategies for key spreaders based on their respective roles.

This research sheds light on the diverse characteristics exhibited by key rumor spreaders on social media, providing a novel reference point for enhancing the effectiveness of rumor intervention and control strategies.