Users image seeking behaviour in multingual environments: a grounded theoretical approach
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This Thesis aims to explore users' image seeking behaviour in online multilingual environments. In particular, it focuses on identifying users' actions/interactions- and on enquiring into users' rationales and justifications for their actions. In addition, it aims to identify the factors which have affected and/or informed users’ image seeking behaviour in multilingual environments. In this context, an inductive research approach and specifically ‘Grounded Theory’ as a methodology were adopted. A mixture of four different methods (questionnaire, observation, retrospective thinking aloud and interview), both qualitative and quantitative, were employed for data collection. For the purposes of this thesis, two studies were conducted. In particular, an Exploratory Study served as the means for gaining a first insight into users’ image seeking behaviour. This in turn, informed the design and conduction of the Main Study. A procedural analysis of the data collected was adopted focusing on identifying users’ actions/interactions, their rationales behind these actions and finally the consequences of these actions. A substantive theory of users’ non-linear image seeking behaviour in a multilingual context emerged from the data. In particular, twenty seven concepts and numerous codes emerged and were accordingly assigned to the three distinct areas: conditions, actions and consequences. In the quest for a higher conceptual analysis, four conceptual categories were identified in the concepts: Knowledge of Languages, Query Domain, System and Search. Diagrams were used to illustrate the theory found in data, to demonstrate the relationships among concepts and to gain analytical distance from data. This thesis contributes to our understanding of the diversity and complexity of users’ searching experiences in multilingual environments and fills a gap in the relevant literature by providing substantive insights into the relationship between the interface and users’ actions. In addition, it contributes to learning how users’ perceive, adopt and adjust MLIR systems to their needs and their search behaviour. This in turn, contributes to informing the design of efficient MLIR systems which support effective search behaviour and performance.