Suchergebnisse

Textual databases are ubiquitous in many application domains. Examples of textual data range from names and addresses of customers to social media posts and bibliographic records.With online services, individuals are increasingly required to enter their personal details for example when purchasing products online or registering for government services,while many social network and e-commerce sites allow users to post short comments. Many online sites leave open the possibility for people to enter unintended or malicious abnormal values, such as names with errors, bogus values, profane comments, or random character sequences. In other applications, such as online bibliographic databases or comparative online shopping sites, databases are increasingly populated in (semi-) automatic ways through Web crawls. This practice can result in low quality data being added automatically into a database. In this article, we develop three techniques to automatically discover abnormal (unexpected or unusual) values in large textual databases. Following recent work in categorical outlier detection, our assumption is that "normal" values are those that occur frequently in a database, while an individual abnormal value is rare. Our techniques are unsupervised and address the challenge of discovering abnormal values as an outlier detection problem. Our first technique is a basic but efficient q-gram set based technique, the second is based on a probabilistic language model, and the third employs morphological word features to train a one-class support vector machine classifier. Our aim is to investigate and develop techniques that are fast, efficient, and automatic. The output of our techniques can help in the development of rule-based data cleaning and information extraction systems, or be used as training data for further supervised data cleaning procedures. We evaluate our techniques on four large real-world datasets from different domains: two US voter registration databases containing personal details, the 2013 KDD Cup dataset of bibliographic records, and the SNAP Memetracker dataset of phrases from social networking sites. Our results show that our techniques can efficiently and automatically discover abnormal textual values, allowing an organization to conduct efficient data exploration, and improve the quality of their textual databases without the need of requiring explicit training data. ; This work is funded by the Australian Research Council (ARC), Veda, and Funnelback Pty. Ltd., under Linkage Project LP100200079.

Green barriers can produce both positive and negative impact on international trade. However, the number of these barriers keeps growing without any monitoring system. This research will analyse the impacts of green trade barriers on Vietnam and European Union trade relationship. The study presents an important observation: the requirements to upgrade technology to meet exactly the technical regulations and expenditure for conformity assessment actually increase the production costs for small and medium companies in the short-term. However, the proper adjustments to these requirements will bring about some long-term benefits. Understanding the Good Agriculture Practice will help to improve the quality of products as well as the productivity, and this will open an access to developed markets to gain higher profits.

Purpose
The purpose of this paper is to discuss and test the direct and indirect effects of utilitarian, hedonic and social values integrated into the theory of planned behaviour (TPB) to achieve a deeper understanding of consumers' intention to adopt mobile commerce (MC) in the context of a developing country, Vietnam.


Design/methodology/approach
Based on self-administered survey data of 382 Vietnamese consumers, a structural equation modelling approach with latent constructs is used to test the hypotheses.


Findings
Perceived values explain consumer attitudes, subjective norms and behavioural intentions in the MC context. In particular, they help to increase the explained variance of the intention to adopt MC by about 9.58 per cent compared with the TPB. Finally, a cross-effect on consumer attitudes from subjective norms is also found.


Research limitations/implications
Future studies would benefit from investigating other variables (e.g. innovativeness or trust) and using actual behaviour (e.g. online purchases).


Practical implications
Business managers should pay attention to different forms of consumer values to understand how and why consumers adopt MC in a developing country.


Originality/value
This study fills the gap in the literature by simultaneously investigating the role of utilitarian, hedonic and social value in a TPB model in the MC context.

Employee loyalty is crucial for an organization's success, especially during economic recovery after the COVID-19 pandemic. This research aims to develop a framework of employee loyalty for the public sector, examining the relationship between corporate social responsibilities (CSR), perceived organizational support (POS), organizational identification, and employee well-being. The research addresses the gap in the literature on employee loyalty in public sector organizations and provides valuable insights into the mediating roles of employee well-being and organizational identification in enhancing employee loyalty in the Vietnamese public sector. The findings of this study showed that both CSR and POS positively impact organizational identification and employee well-being. Also, organizational identification and employee well-being play mediating roles in the relationship between CSR, POS, and employee loyalty. However, there is an insignificant relationship between POS and employee loyalty in the Vietnam public sector context. By combining both social identity theory and organizational support theory perspectives, this research offers a more integrated and holistic approach to understanding the path from CSR and POS to employee loyalty, providing managers and policymakers with a more concrete perception of the value and role of CSR and POS in building long-term trustworthy relationships with employees.