Applying Cross-cultural theory to understand users’ preferences on interactive information retrieval platform design
AffiliationUniversity of Bedfordshire
SubjectsG500 Information Systems
cross-cultural information retrieval
Hofstede’s cultural dimensions
human computer information retrieval
MetadataShow full item record
AbstractIn this paper we look at using culture to group users and model the users’ preference on cross cultural information retrieval, in order to investigate the relationship between the user search preferences and the user’s cultural background. Initially we review and discuss briefly website localisation. We continue by examining culture and Hofstede’s cultural dimensions. We identified a link between Hofstede’s five dimensions and user experience. We did an analogy for each of the five dimensions and developed six hypotheses from the analogies. These hypotheses were then tested by means of a user study. Whilst the key findings from the study suggest cross cultural theory can be used to model user’s preferences for information retrieval, further work still needs to be done on how cultural dimensions can be applied to inform the search interface design.
CitationChessum, K., Liu, H., Frommholz, I. (2014) 'Applying Cross-cultural theory to understand users’ preferences on interactive information retrieval platform design'. 4th European Symposium on Human-Computer Interaction and Information Retrieval - British Computer Society, London, 13th September 2014. University of Nottingham, School of Computer Science.
TypeConference papers, meetings and proceedings
DescriptionPresented at EuroHCIR 2014, the 4th European Symposium on Human-Computer Interaction and Information Retrieval, 13th September 2014, at BCS London Office, Covent Garden, London.
The following license files are associated with this item:
Showing items related by title, author, creator and subject.
Utilising information foraging theory for user interaction with image query auto-completionJaiswal, Amit Kumar; Liu, Haiming; Frommholz, Ingo; University of Bedfordshire (Springer, 2020-01-20)Query Auto-completion (QAC) is a prominently used feature in search engines, where user interaction with such explicit feature is facilitated by the possible automatic suggestion of queries based on a prefix typed by the user. Existing QAC models have pursued a little on user interaction and cannot capture a user’s information need (IN) context. In this work, we devise a new task of QAC applied on an image for estimating patch (one of the key components of Information Foraging Theory) probabilities for query suggestion. Our work supports query completion by extending a user query prefix (one or two characters) to a complete query utilising a foraging-based probabilistic patch selection model. We present iBERT, to fine-tune the BERT (Bidirectional Encoder Representations from Transformers) model, which leverages combined textual-image queries for a solution to image QAC by computing probabilities of a large set of image patches. The reflected patch probabilities are used for selection while being agnostic to changing information need or contextual mechanisms. Experimental results show that query auto-completion using both natural language queries and images is more effective than using only language-level queries. Also, our fine-tuned iBERT model allows to efficiently rank patches in the image.
An examination of information systems and technology maturity and it’s relationship to methods of contributing information to the information systems planning process in National Health Service acute trust hospitalsMitchell, Ivan John (University of Bedfordshire, 1996-05)As a result of the use of Nolan's six Stage model of IS and IT maturity in a number of consultancy studies in the early 1980s, managers felt they could maximise the contribution of the IS and IT portfolios to the achievement of business strategy by becoming more IS and IT mature. Despite the development of a further eight models since 1979, empirical tests exist only of the Nolan model and one other, McFarlan, McKenney and Pyburn's model ofIT assimilation. This research has sought further empirical evidence of IS and IT maturity in National Health Service (NHS) acute Trust hospitals. Survey method was used to collect data from over seventy top and middle managers representing four Trust hospitals. Statistical analysis of these data provided evidence that six of twenty-three maturity characteristics identified in existing models can currently be used to differentiate the maturity of NHS acute Trust hospitals. These six characteristics had been identified both in models which considered a range of IS and IT issues and in models which had concentrated on a single aspect of IS and IT management. This indicated that further insight is gained by combining these approaches when modelling IS and IT maturity. Managers also placed different emphasis on the use of specific methods of contributing information to the IS planning process in hospitals which exhibited greater IS and IT maturity than in hospitals which exhibited lesser IS and IT maturity. This indicates the existence ,of a further IS maturity characteristic, further evidence of which can now be sought in other industry sectors.
Linking business analytics to decision making effectiveness: a path model analysisCao, Guangming; Duan, Yanqing; Li, Gendao; University of Bedfordshire (IEEE, 2015-06-24)While business analytics is being increasingly used to gain data-driven insights to support decision making, little research exists regarding the mechanism through which business analytics can be used to improve decision-making effectiveness (DME) at the organizational level. Drawing on the information processing view and contingency theory, this paper develops a research model linking business analytics to organizational DME. The research model is tested using structural equation modeling based on 740 responses collected from U.K. businesses. The key findings demonstrate that business analytics, through the mediation of a data-driven environment, positively influences information processing capability, which in turn has a positive effect on DME. The findings also demonstrate that the paths from business analytics to DME have no statistical differences between large and medium companies, but some differences between manufacturing and professional service industries. Our findings contribute to the business analytics literature by providing useful insights into business analytics applications and the facilitation of data-driven decision making. They also contribute to manager's knowledge and understanding by demonstrating how business analytics should be implemented to improve DME