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The aim of this paper is to propose a conceptual framework of knowledge sharing for Bangladesh’s business organizations.

On the basis of previous literature, this paper proposed a framework for knowledge sharing.

This paper identified organizational commitment as a potential mediator for the relationship between organizational factors (organizational culture, leadership and structure) and knowledge sharing. In this paper, top management support and information and communication technology (ICT) support are also proposed as potential moderators that can affect knowledge sharing.

This paper has identified several organizational factors to predict knowledge sharing. Future research with empirical evidence will validate this conceptual framework.

This paper will help business managers to understand knowledge sharing from a different perspective. Propositions of organizational commitment as a potential mediator and top management support and ICT support as potential moderators will provide managers with a better understanding of employees’ knowledge sharing behavior.

This paper adopted the general model of workplace commitment and integrated with organizational factors (organizational culture, leadership, structure, top management support and ICT support) to understand knowledge sharing for the business organizations of Bangladesh.

The purpose of this paper is to identify the risks to which agrifood supply chains are exposed and to analyze how these risks impact the degree of coordination of the chain.

The present investigation was executed in two steps. Initially, a literature review and interviews with slaughterhouse managers were carried out to identify the main risks to which agrifood supply chains are exposed. The second step consisted of a survey involving 66 Brazilian slaughterhouses to identify how the perception of risks influences the degree of coordination in the examined chains.

The study revealed that risks, transaction costs and creation of collaborative advantages are determining factors in defining the degree of coordination in the analyzed agrifood supply chains.

The results allow slaughterhouse managers to more easily recognize the risks to which the supply chains are exposed and evaluate in more detail strategies for relationships with their suppliers. These strategies may be able to avoid conflict and create value for the supplier by stimulating longer relationships and facilitating animal purchase transactions for slaughter. This can lead to quality improvements, lower costs and reduced risk.

Studies of risks in agrifood supply chains are rare in comparison with those developed in other sectors. The present investigation is innovative in identifying the main risks specific to agrifood supply chains and associating those risks with a degree of coordination that minimizes them.

The present research examines self-disclosure as a negative behavior arising from excessive engagement on social media as well as social media addiction and fear of missing out.

A convenience sample of 2,333 Snapchat and Instagram users in Kuwait collected using a snowball sampling procedure was utilized to validate the research model and test the proposed relationships using two-stage structural equation modeling.

A multigroup confirmatory factor analysis across Snapchat and Instagram users showed that the measurement invariance was statistically nonsignificant. In addition, path analysis confirmed both a significant direct relationship between user engagement and self-disclosure and indirect relationships through social media addiction and fear of missing out as mediators.

Ethicists and consumer protection agencies must increase public awareness about the danger of overdisclosure of personal information on social media. The desire to be popular like fashionistas and influencers has pushed some young users to expose themselves to the public at extreme levels. Serious steps must be taken to educate young users about the negative consequences of self-disclosure on social media and healthy ways to capitalize their social presence on social media.

This study focuses on Snapchat and Instagram as underexplored social media applications with excessive engagement and potentially negative consequences on society.

There has been considerable research into the global phenomenon of luxury brand consumption, but relatively few studies have empirically explored key relationships influencing purchase intention. This research aims to consider the respective roles of social context, individual perception, and vanity, and to set these relationships within a broader theoretical context of the literature on possession and consumer identity.

The empirical study consisted of a large‐scale survey conducted among Chinese luxury brand consumers in Taiwan. The data were analyzed using exploratory factor analysis and multiple regression.

The findings support the influence of the social context on purchase intention for luxury brands. There was weaker support for the role of perception. The experiential and functional aspects of luxury brand purchase were positively correlated with purchase intention, but symbolic value was not. Physical and achievement vanity had a positive impact on purchase intention while only achievement vanity had a moderating effect on perception.

This study offers new empirical support for the proposition that vanity has a role in luxury brand purchase intention and thereby shades both theoretical and managerial understanding of luxury brand consumption. It also suggests that symbolic value, which is highly influential in western conceptualizations of luxury brand meaning, needs to be re‐evaluated in the context of Chinese consumers.

This study offers new empirical findings which contribute to a re‐conceptualization of the antecedents of purchase intention in the area of luxury brand consumption. In particular, the study provides evidence of the roles of social context, perception and vanity in a Chinese consumption context to inform the primarily western models of luxury brand purchase intention.

Due to fierce competition in the mobile retailing market, it is desirable to identify the success factors driving consumers to reuse a mobile shopping application. This research intends to develop a model for mobile shopping app success by integrating an e-commerce systems success (ESS) model and sales promotion benefits and parasocial interaction literatures.

302 useable online questionnaires were obtained. The data analysis was conducted using the structural equation modelling.

The results indicate the validity of the ESS model in predicting consumers' reuse intention of mobile shopping apps where three quality dimensions of system, information and service facilitate both perceived value and user satisfaction, which in turn generates reuse intention. Furthermore, savings and entertainment denoting the utilitarian and hedonic sales promotion benefits have positive impact on perceived value but have no influence on satisfaction. Parasocial intention between consumers and sellers facilitates both value perception and satisfaction.

This research contributes to the mobile retailing literature by identifying the success factors driving consumers' continuance intention of mobile shopping apps. Theoretically, it validates and extends the ESS model in mobile shopping app context by identifying savings, entertainment and parasocial interaction as additional success factors. Based on the findings, two approaches are proposed to suggest mobile retailers design a successful mobile shopping app.

This study aims to propose a novel deep learning (DL)-based framework, iRelevancy, for identifying the disaster relevancy of a social media (SM) message.

It is worth mentioning that a fusion-based DL model is introduced to objectively identify the relevancy of a SM message to the disaster. The proposed system is evaluated with cyclone Fani data and compared with state-of-the-art DL models and the recent relevant studies. The performance of the experiments is assessed by the accuracy, precision, recall, f1-score, area under receiver operating curve and precision–recall curve score.

The iRelevancy leads to a better performance in accuracy, precision, recall, F-score, the area under receiver operating characteristic and area under precision-recall curve, compared to other state-of-the-art methods in the literature.

The predictive performance of the proposed model is illustrated with experimental results on cyclone Fani data, along with misclassifications. Further, to analyze the performance of the iRelevancy, the results on other cyclonic disasters, i.e. cyclone Titli, cyclone Amphan and cyclone Nisarga are presented. In addition, the framework is implemented on catastrophic events of different natures, i.e. COVID-19. The research study can assist disaster managers in effectively maneuvering disasters during distress.

This paper identifies the effects of different dimensions of the cuteness (i.e. baby schema cuteness and whimsical cuteness) of artificial intelligence (AI) agents on attachment to them. In addition, the current paper examines the consequences of the attachment to AI agents.

A pretest to validate the measurement scale for the attachment to AI agents and a survey study were conducted with AI agent users. The authors used structural equation modeling to analyze the data for hypothesis testing.

The baby schema and whimsical cuteness of AI agents drive consumers to develop stronger attachments to their AI agents. This is because consumers perceive cute AI agents as being more trustworthy. As a result, consumers who feel attached to their AI agents are more inclined to report higher satisfaction and commitment levels. They are also more likely to purchase products or services recommended by their AI agents and use them more frequently.

Despite the growing popularity of AI agents, there is a lack of understanding regarding which characteristics of AI agents affect consumer behavior. Therefore, this research examines how the attribute of cuteness influences consumers' attachment to AI agents and subsequently affects their satisfaction and purchase intention toward products recommended by AI agents. Our study demonstrates that the element of cuteness in AI agents plays a crucial role in shaping perceptions of benevolence trustworthiness, as well as fostering users' attachment to AI agents. Furthermore, we observe positive consumer behaviors as a result of their attachment to AI agents. The findings from this study provide valuable insights for practitioners on how to effectively utilize cuteness in AI agents.

The purpose of this paper is to examine how different types of user experience in TikTok impact purchase intention via commitment to the influencer and commitment to the platform, with customer type included to determine moderating effects. Three types of user experience are considered: information experience, entertainment experience and parasocial-relationship-based experience.

This study collected 458 valid questionnaires from TikTok users, employing the structural equation modeling approach to examine the proposed research model.

Information experience, entertainment experience and parasocial-relationship-based experience are found to critically stimulate user commitment to the influencer and commitment to the platform, in turn driving TikTok-based purchase intention. Tests incorporating customer type reveal that commitment to the influencer more strongly influences the purchase intention of repeat customers, with commitment to the platform more likely to stimulate purchase intention among potential customers.

On a theoretical level, the paper is among the first to examine TikTok-based user purchase intention with customer type as a moderator. On a practical level, the results can guide marketers to effectively promote products using TikTok and inspire TikTok managers to develop customized strategies to stimulate initial and repeat sales.

TikTok is moving to the stage of commercialization and monetization by introducing e-commerce features. Although this move should cultivate particularly fertile ground for companies to sell products, TikTok user purchase behavior has yet to receive sufficient research attention, with little currently known about their purchase motivations. The current study uncovers the significant antecedents of users' purchase intention through TikTok, and further reveals the motivational differences among potential and repeat customers.

The purpose of this study is to reduce the semantic distance by proposing a model for integrating indexes of textual and visual features via a multi-modality ontology and the use of DBpedia to improve the comprehensiveness of the ontology to enhance semantic retrieval.

A multi-modality ontology-based approach was developed to integrate high-level concepts and low-level features, as well as integrate the ontology base with DBpedia to enrich the knowledge resource. A complete ontology model was also developed to represent the domain of sport news, with image caption keywords and image features. Precision and recall were used as metrics to evaluate the effectiveness of the multi-modality approach, and the outputs were compared with those obtained using a single-modality approach (i.e. textual ontology and visual ontology).

The results based on ten queries show a superior performance of the multi-modality ontology-based IMR system integrated with DBpedia in retrieving correct images in accordance with user queries. The system achieved 100 per cent precision for six of the queries and greater than 80 per cent precision for the other four queries. The text-based system only achieved 100 per cent precision for one query; all other queries yielded precision rates less than 0.500.

This study only focused on BBC Sport News collection in the year 2009.

The paper includes implications for the development of ontology-based retrieval on image collection.

This study demonstrates the strength of using a multi-modality ontology integrated with DBpedia for image retrieval to overcome the deficiencies of text-based and ontology-based systems. The result validates semantic text-based with multi-modality ontology and DBpedia as a useful model to reduce the semantic distance.