Selecting Success Criteria: Experiences with an Academic Library Catalogue

Transcription

1 Selecting Success Criteria: Experiences with an Academic Library Catalogue Paul Clough and Paula Goodale p.d.clough Information School University of Sheffield Sheffield, UK Abstract. Multiple methods exist for evaluating search systems; ranging from more user-oriented approaches to those more focused on evaluating system performance. When preparing an evaluation, key questions include: (i) why conduct the evaluation, (ii) what should be evaluated, and (ii) how the evaluation should be conducted. Over recent years there has been more focus on the end users of search systems and understanding what they view as success. In this paper we consider what to evaluate; in particular what criteria users of search systems consider most important and whether this varies by user characteristic. Using our experiences with evaluating an academic library catalogue, input was gathered from end users relating to the perceived importance of different evaluation criteria prior to conducting evaluation. We analyse results to show which criteria users most value, together with the inter-relationships between them. Our results highlight the necessity of conducting multiple forms of evaluation to ensure that search systems are deemed successful by their users. Keywords: Evaluation, success criteria, digital libraries 1 Introduction Evaluation is highly important for designing, developing and maintaining effective search systems as it allows the measurement of how successfully the system meets its goal of helping users fulfill their information needs or complete their tasks [1-3]. Evaluation involves identifying suitable success criteria that can be measured in some way. Success might refer to whether a search system retrieves relevant (compared with non-relevant) documents; how quickly results are returned; how well the system supports users interactions; whether users are satisfied with the results; how easily users can use the system; whether the system helps users carry out their tasks and fulfill their information needs; whether the system impacts on the wider environment; or how reliable the system is. How to conduct IR system evaluation has been an active area of research for the past 50 years and the subject of much discussion and debate [1, 2]. Traditionally in IR there has been a strong focus on measuring system effectiveness: the ability of an IR

2 system to discriminate between documents that are relevant or not relevant for a given user query. This focus on the system has, in part, been influenced by the focus of the IR community on the development of retrieval algorithms, together with the organization of large IR evaluation events, such as TREC and CLEF. Such events have focused on measuring system effectiveness in a controlled experimental setting [4, 5]. However, the scope of system in IR has slowly broadened to include more elements of the retrieval context, such as the user or the user s environment, which must be included in the evaluation of IR systems [3, 6]. Therefore, instead of focusing on just the system (i.e., its inputs and outputs), a more user-oriented approach can be taken. This may take into account the user, the user s context and situation, and their interactions with an IR system, perhaps in a real-life operational environment [7, 8]. When planning an evaluation, there are at least three key questions to address [10]: (i) why evaluate; (ii) what to evaluate; and (iii) how to evaluate. These apply regardless of the type of search system being evaluated (e.g. search engine or library catalogue). Saracevic [10] also mentions these when planning an evaluation of a digital library, together with identifying for whom to evaluate (i.e., the stakeholder). In this paper we focus on the issue of what: selecting criteria to assess search success. In practice this is a key question as it is often not possible, nor desirable, to run every type of evaluation available, and it is therefore necessary to be selective, both in order to measure the success of the new system, and to make best use of time and scarce resources. Through our experiences with planning the evaluation of an operational search system (online library catalogue) we investigate the importance of different evaluation criteria as perceived by existing end users. This was a necessary pre-cursor of selecting the evaluation methods and criteria to determine the system s success. We specifically address the following research questions: (RQ1) what evaluation criteria are viewed as important by end users? (RQ2) what degree of variation exists between users preferences? (RQ3) what inter-relationships exist between different criteria? The remainder of the paper is structured as follows. Section 2 describes related work on evaluation, particularly in the context of digital libraries where much recent discussion has taken place. Section 3 outlines the methodology used to gather end user s feedback on the importance of different evaluation criteria. Section 4 analyses results based on various statistical analyses. Section 5 provides a discussion and Section 6 concludes the paper and provides avenues for further work. 2 Related Work Saracevic [10] argues for evaluation of digital libraries to take place at different levels, each with different measurement criteria derived from research objectives. In a similar vein, the Interaction Triptych Framework (ITF) defines three main type of digital library evaluation criteria [11, 12]: performance (system performance), usability (user interaction and satisfaction measures), and usefulness (support for task and information needs), which should be deployed to measure success in the digital library context. Further, Tsakonas & Papatheodorou, [12] test the relative preferences of users for evaluation measures in the three ITF categories, finding high scores for

3 usability and usefulness measures, but lower scores for performance measures, as well as correlation between usability and usefulness measures. The usability and usefulness dimensions of evaluation are explored further and again found to be inter-related, with several key attributes of each dimension identified as a basis for an integrated approach to measurement [13]. A holistic and multi-dimensional approach is widely advocated for digital library evaluation, yet ranking of diverse evaluation criteria by degrees of importance is less evident, particularly in advance of designing evaluation protocols. Nielsen [14] rates factors within a range of usability heuristics, concluding they are all very similar in importance, but does not consider other types of evaluation metrics. Toms et al. [15] look at determining the different dimensions of relevance, and Al-Maskari & Sanderson [16] focus on elements of user satisfaction. These are however all studies of individual dimensions of evaluation. Xie [18] considers a broader range of evaluation measures and ranks criteria within several high-level evaluation categories, but does not rank across categories. Conversely, Buchanan & Salako [13] rank a wide range of evaluation criteria across categories. The rankings or relative importance by Xie [17] and Buchanan & Salako [13] are undertaken during (rather than prior to) an active evaluation study, once evaluation experiments have been completed, and can therefore only contribute to the design of future studies of the same systems. User preferences for evaluation measures have also been largely overlooked. Xie [18] addresses this issue, with a study eliciting evaluation criteria from users, finding that measures suggested by users match those proposed in evaluation models and used in empirical studies. However, it is acknowledged that the user sample for this study is too homogeneous and that more diverse groups may influence the results. Kelly [19] criticizes the use of inadequate user models, citing the over-use of the librarian or search intermediary, and other single types of users, when more detailed and varied user models would be more appropriate, especially for more complex systems. Differences between users have been studied in many ways, including search success, information needs, user requirements, information seeking behavior, cognitive style, and relevance judgments, amongst others, yet it is much less common for user differences to be taken into account in the selection of appropriate evaluation criteria. One common user classification in digital library and search user studies is the novice/expert dichotomy. In this light, Marchionini [20] defines three aspects of user difference relating to information skills; domain expertise (subject knowledge), search expertise and system expertise. Similarly, Hölscher & Strube [21] define novices and experts with attributions of domain and search experience, considering the impact of these characteristics on search success. These are then the main characteristics that we consider as differentiators in the current study, with the addition of a three-way classification of user role in line with recommendations by Kelly [19] for consideration of a range of user models. In sum, investigating what criteria users perceive as indicators of search success has been largely underexplored in past research.

4 3 Methodology In the current study, the search system to be evaluated is a virtual union OPAC (online public access catalogue). The UK-based Search25 1 project aimed to develop a significantly updated version of the longstanding and somewhat outdated InforM25 system, retrieving records from the individual OPACs of the 58 member institutions of the M25 Consortium of academic libraries. End users vary from undergraduate and postgraduate students, to academics and research staff, and library professionals, with varying degrees of subject, domain and search expertise, information needs and patterns of search behavior. During the development of Search25, a formative evaluation was planned to assess the success of an existing system known as InforM25. Table 1. How important are the following factors when using academic library catalogues? # Statement (criteria) Criteria group* 1 The catalogue/system is easy to use Usability 2 The interface is attractive Usability 3 Results are retrieved quickly Performance 4 All items on a topic are retrieved Performance 5 The most relevant items of a topic are identified Performance 6 Information in the catalogue is easy to understand Usefulness 7 It is enjoyable to use Usability 8 Help is available when needed Usability * Criteria group based on the relationships identified in the Interaction Triptych Framework [11, 12] An online survey of the current InforM25 users was carried out to identify suitable criteria for evaluating the updated system once fully implemented (i.e., to define what to evaluate). Specific questions were presented as eight statements (Table 1) covering a range of evaluation criteria drawn from various literature, against which a 5-point Likert scale was used to rate users level of agreement, from Strongly Disagree (1) to Strongly Agree (5). Criteria are also grouped based on the Interaction Triptych Framework [11, 12]. In addition, we asked open-ended questions to gather qualitative responses about what users liked and disliked about the InforM25 system and what they would like to change and to corroborate and enrich the quantitative data. An invitation to complete an online survey was distributed to mailing lists of the 58 academic institutions known to use InforM25, and a link was placed on the home page. In total, 196 respondents provided responses about their perceived importance of different evaluation criteria. The survey sample comprises library, learning and technical support staff (79%), academic and research staff (5%), and undergraduate and postgraduate students (16%) from across the M25 Consortium 58 institutions. This skewed distribution is representative of users of the system at the time of the study, and one reason for updating the system was to try to broaden the appeal to academic and student users. Respondents came from a broad range of subject domains with varying levels of search experience, many reporting more than 10 years experi- 1

5 ence in using academic library catalogues (65%) and web search engines (71%). In terms of frequency of use, 87% of participants used their own institution s OPAC on at least a weekly basis, whilst only 22% used InforM25 with this regularity. Data collected through the survey was analysed using descriptive and inferential statistics. One of the main goals of this analysis was to determine if differences between user groups regarding the importance of evaluation criteria could be observed. Relationships between evaluation criteria were assessed using Principal Component Analysis (PCA) and bivariate correlations assessed using Kendall s Tau. Finally, a thematic analysis of qualitative data from the associated open-ended questions and focus groups is used to expand upon and corroborate the quantitative findings. 4 Results Responses relating to the evaluation criteria shown in Table 1 were analyzed first for the whole sample (Section 4.1), and then for differences between users based upon a range of characteristics (Section 4.2). We then explore interrelationships between the criteria in Section 4.3, and associated qualitative results in Section 4.4. Table 2. Frequency of responses for importance of evaluation criteria, all users (N=196) Criteria 1 Strong. 2 Disagree 3 Neutral 4 Agree 5 Strong. Median SD Disagree Agree score Easy To Use 1.0% 0.0% 3.1% 17.4% 78.5% Attractive 3.6% 6.3% 31.3% 35.9% 22.9% Quick 0.5% 1.5% 5.6% 27.2% 65.1% Retrieve All 0.5% 3.2% 8.4% 33.7% 54.2% Relevant 2.6% 3.7% 14.2% 31.6% 47.9% Understandable 0.5% 0.0% 5.8% 24.7% 68.9% Enjoyable 10.0% 10.0% 35.8% 27.4% 16.8% Help 3.2% 11.1% 27.5% 31.7% 26.5% Analysis of all Users Table 2 shows results for percentage of responses across all users for each of the evaluation criteria. The results suggest overall importance for some aspects of usability and system performance measures. The most highly rated measures are ease of use (96% rating this at 4 or 5 on the Likert scale); information in the catalogue is easy to understand (94%); results are retrieved quickly (93%); all items on a topic are retrieved (88%); and, the most relevant items are identified (80%). Responses for interface is attractive (59%); help is available (58%); and, enjoyable to use (44%) indicate that overall, these criteria are somewhat less important to users.

6 4.2 Analysis by User Characteristic To examine the effects of user characteristic (role, subject area, search experience and frequency of use of existing finding aids) on the ratings provided, we divide the data into groups and compare the ratings for each group using a Kruksal-Wallis test (due to the non-parametric nature of the data). When analysing results of the test we test the null hypothesis that the user characteristic makes no difference to the perceived importance of evaluation criteria, rejected at p<0.05. Table 3. Kruksal-Wallis test statistics, importance of evaluation criteria grouped by user characteristics (N=196). Bold indicates results with p<0.05 Evaluation Criteria User type Subject Web search experience Experience of library work Frequency of using InforM25 K-W (2 df) p-value K-W (6 df) p-value K-W (3 df) p-value K-W (3 df) p-value K-W (3 df) p-value Firstly, we find that the null hypothesis is rejected most often for criteria 3) results are retrieved quickly, indicating that the importance of this varies most by user characteristic. Secondly, we observe that experience of library work has an effect on the most criteria (2, 3 and 6), suggesting that this user characteristic causes users to disagree the most about search success. Next we consider each user characteristic in turn: User type. For the user type (e.g. librarian vs. student) the null hypothesis is rejected for attractiveness of interface and results are retrieved quickly. It can be concluded that there is a difference in perceived importance of these evaluation criteria between the various user roles. Splitting results by different academic subject areas there are no statistically significant differences between the criteria, suggesting that domain does not affect the importance of certain evaluation criteria. Search experience. This was measured by two characteristics: number of years experience in using an academic library catalogue and experience of using web search engines. The Kruksal-Wallis test showed no significant differences against the level of experience in using library catalogues; however, for experience of web search the null hypothesis is rejected (p<0.05) for criteria 6 (information is easy to understand). Inspection of the medians reveal that those users with only 0-1 years experience have

7 a lower median of 4, with a median of 5 for all other levels of experience, and the most demanding users (least spread of results) are those with 2-5 years experience. Given the high proportion of library staff in the sample, experience was also considered by the number of years engaged in library work, and for students (the least experienced users), their year of study. No significant differences for student users by year of study were found, but for library staff significance was found for criteria 3 (results are retrieved quickly) at p<0.01, for criteria 4 (all items are retrieved) and 6 (information is easy to understand) at p<0.05. Frequency of using existing finding aids. Finally, we analysed ratings based on grouping users by their frequency of use of a variety of library IR systems, including the OPAC at the user s own institution, and the InforM25 virtual union catalogue. For home library OPACs, no significant differences were found against the frequency of use. However, for the frequency of use on InforM25, the null hypothesis is rejected for criteria 3 (items are retrieved quickly) at p<0.05. Analysis of the medians reveals that for criteria 1, daily users have a median of 4.5, whilst all other users have a median of 5. For criteria 3, the differences are inconclusive, as users of all frequencies have a median of 4, except those with monthly use (median=3.5). Lastly, the medians for criteria 8 show that the availability of help is more important with less frequent users, with a median of 4 for users with a frequency or monthly, less often or never, and median of for more frequent users. 4.3 Relationships between Evaluation Criteria To examine relationships between the evaluation criteria in each group, a factor analysis was conducted using Principal Component Analysis (PCA) with varimax rotation (assuming independence between groupings). An initial PCA based upon Eigenvalues >1 extracted two dimensions, accounting for 56% of total variance. However, the scree plot shows that three or four dimensions might be more appropriate, and therefore revised PCAs specifying three and four fixed factors were undertaken, accounting for 70% and 79% of variance, respectively. Bartlett s Test of Sphericity is found to be highly significant at p<0.001 and the Kaiser-Olkin measure of sampling adequacy is sufficient at 0.792, indicating that the evaluation criteria are likely to be related and that the sample is of adequate size. Rotated component matrices show the loading of the components for each of the evaluation criteria (Table 4). With three components extracted, four criteria (easy to use, quick, understandable, relevant) load high on the first component; three criteria (enjoyable, attractive, help) load high on the second component; and, two criteria (help, retrieve all) load high on the third component. With four components extracted the results are similar, with the exception that one criterion (relevant) now loads highly on the fourth component, instead of the first component, and help now only loads highly on the second component, rather than on the second and third components. It is interesting to note that one evaluation criteria from the usability group in Table 1 (easy to use), and one from the usefulness group (understandable) are close to performance variables, in particular speed of retrieval (quick) and relevant information is returned by the system (relevant). It is perhaps to be expected that relevant and re-

8 trieval all are at opposite ends of this cluster (with four components extracted they occupy their own space) as their importance is likely to vary by task. The two usability variables (enjoyable, attractive) are also clustered together, with the addition of a further usability variable (help). The groupings found using the PCA are further confirmed by inspecting significant (p<0.01) bivariate correlations between criteria using Kendall s Tau: easy to use and quick (tau=0.502), ease of use and understandable (tau=0.529); enjoyable and attractive (tau=0.540), enjoyable and help (tau=0.402). Table 4. Rotated Component Matrices (varimax), 3 and 4 Components extracted 3 Components 4 Components Easy to Use Quick Quick Easy to Use Understandable Understandable Relevant Enjoyable Enjoyable Attractive Attractive Help Help Retrieve All Retrieve All Relevant The manner in which the variables group into components in Table 4 may suggest that combinations of criteria may be particularly important to users. For example, with four components extracted, the first component (easy to use, quick and understandable) might relate to users as they interact with the system and perform specific tasks (user needs); whereas the criteria for the second component would highlight aspects related more to the general user experience (enjoyable, attractive, help). The third and forth components reflect individual aspects of retrieval performance, which could be measured using recall and precision respectively. 4.4 Qualitative Results Areas of performance which users responded to positively in open-ended survey questions included time-saving and ease of use from searching multiple OPACs at the same time, as well as support for specific tasks, such as finding items that are unavailable at their home institution. These findings seem to correspond relatively well with components relating to user needs and peripherally with retrieval performance. In contrast, negative comments focused more on usability and interface design issues, speed of information retrieval, issues with completeness of information, and lack of de-duplication of records for the same item from different institutions, making the information retrieved more difficult to understand. These findings constitute a mix of issues, with the strongest opinions relating to speed, quality and understanding of

9 information retrieved. Focus group discussions surfaced more on issues relating to system and IR performance. Speed of results was generally seen as less of an issue when feedback on progress is provided on-screen, and the speed demonstrated via the prototype was generally seen as acceptable. However, in a virtual union catalogue speed is largely a factor of the number of records fetched from each institution, and in Search25 a limit has been set to manage the inevitable delays from waiting for all records to be retrieved from each participating institution. Users had mixed opinions about the impact of this, and on balance they preferred to be able to set the number or records fetched themselves, and/or to be able to revert to Retrieve All records when needed. Strongly related to this was a concern that with partial retrieval some of the most relevant records might not be fetched. Interestingly then, when probed in depth, IR performance measures come to the fore and along with speed, and information quality and completeness, are perceived to be the most critical measures of system success. 5 Discussion In planning the evaluation of Search25, we asked users about their perceived importance of a range of common evaluation criteria (compared to asking users to carry out tasks and then measuring their importance). There are two main limitations to this approach. Firstly, what users perceive to be important may change after completing tasks. In this analysis we have involved users who have experience with using the legacy system. Secondly, the importance of criteria that define success may change based on carrying out specific tasks, (e.g., recall would be important for a systematic review), and in different contexts (e.g., speed may be important for completing work tasks compared to leisure tasks). However, these results are still useful in gaining a more general impression of what users view as important criteria against which to evaluate. The results in Section 4 clearly demonstrate that some evaluation criteria are more critical than others, but there are some significant differences in the order of preference by users with different characteristics. Relationships between evaluation criteria are also found, with distinct components identified that group together criteria from diverse (component 1) and similar categories (component 2), according to the Interaction Triptych Framework. In light of these findings, we can summarize answers to the three research questions as follows: RQ1: What evaluation criteria are viewed as important by end users? Frequency rankings (Table 5) indicate that participants in this study place greater importance on evaluation criteria 1) easy to use, 6) understandable, and 3) quick, related to their user needs (corresponding to component 1 in the PCA results), than on criteria 2) attractive, 8) help available, and 7) enjoyable, relating to user experience (component 2). Retrieval performance measures (components 3 and 4) are placed in the middle of the ranking, but their scores are closer to those for component 1 than for component 2. These results suggest that a mix of user-centred and system-centred

10 evaluation measures are appropriate within the case study context, but that the more subjective experiential and satisfaction type measures may be less critical. Table 5. Ranking of evaluation criteria (all users, 4 Agree + 5 Strongly Agree), compared with extracted components. # Evaluation criteria Likert 3 Factor 4 Factor 4+5 Components Components 1 Easy to use 96% Understandable 94% Quick 93% Retrieve All 88% Relevant 80% Attractive 59% Help available 58% Enjoyable 44% 2 2 RQ2: What degree of variation exists between users preferences? User responses were analysed according to a variety of characteristics including role, subject/domain, search experience, and system experience. Rankings for importance of evaluation criteria varied to some degree by each classification of user type, and significant differences between users were found for several characteristics. User characteristics which demonstrated the greatest amount of difference in preference are user role (three criteria 2, 3, 6) and experience of library work (four criteria 2, 3, 4, 6). These evaluation criteria are all in the top half of the ranking by overall importance. These user characteristics identified as a source of significant difference could be used as a basis for user recruitment in future evaluation activities, as well as an aid to interpretation of evaluation results. Evaluation criteria with the most significant differences are 4) retrieve all and 6) understandable, but the user characteristics where differences are found vary for each one. No significant differences were found for criteria 5) relevance and 7) enjoyable, suggesting that these criteria may be less prone to variation by user type, or that the results are inconclusive. RQ3: What inter-relationships exist between the different criteria? By applying principal component analysis, 3-4 main groupings of evaluation criteria emerge, depending on the number of factors extracted. These groupings correlate well with the frequency rankings. There is a particularly strong inter-relationship between criteria in the user needs group (component 1), representing a mix of usability, speed and information quality criteria, that combined together would address some of the primary issues in user and task performance. However, whilst the system measures 4) retrieve all, and 5) relevant are collocated in the frequency ranking (Table 5), they are situated wide apart and take on separate components (Table 4). This result is possibly explained by the opposing nature of the criteria; one might either

11 want to retrieve everything available, or be interested in only the most relevant results. In system development terms, as raised by our focus group discussions, decisions relating to relevance, recall (retrieve all), and speed of retrieval impact different users in different ways, according to their task and information needs. A note of caution is therefore required in measuring success in these highly ranked, but potentially conflicting evaluation criteria. It is interesting to note that retrieval speed (quick) correlates highly with ease of use as this is a measure that is commonly not assessed in evaluation campaigns, such as TREC and CLEF, but may reflect more faithfully success factors that users rate highly (e.g., ease of use). Previous work has shown significant correlations between user satisfaction and retrieval effectiveness [16], but additionally considering retrieval speed would be an interesting avenue for further investigation as it may suggest that speed, which could be measured using system-oriented approaches (e.g., test collections), may correlate well with user satisfaction that would otherwise have to involve user studies that are more costly to run. 6 Conclusions In this paper we have described our experiences with gathering information from a sample of 196 end users of an operational search system (academic library catalogue) regarding success criteria. This is important when planning an evaluation in deciding what to evaluate, particularly in the case when evaluation methods must be selected due to resource limitations. We find overall that users rate criteria relating to user needs (as confirmed using PCA), such as ease of use, most highly; contrasted with aspects such as whether the system is enjoyable. The results are used to help us select certain kinds of evaluation criteria that will more likely match user s perceived importance. Understanding how users think about success is an important, and often overlooked, aspect of IR evaluation. Acknowledgments We gratefully acknowledge funding from JISC through the Search25 project and contributions from participants via the survey. Work also partially funded by the PROMISE network of excellence (contract no ) project as a part of the 7th Framework Program of the European commission (FP7/ ). 7 References 1. Saracevic, T. (1995). Evaluation of evaluation in information retrieval. In Fox, E., A. Ingwersen, P. & Fidel, R. (eds.). Proc. 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Seattle, Washington, USA, July 9-13, New York, NY: ACM Press

12 2. Harman, D. (2011). Information retrieval evaluation. Synthesis Lectures on Information Concepts, Retrieval, and Services (3)2. San Raphael, CA: Morgan & Claypool Publishers. 3. Robertson, S.E. & Hancock-Beaulieu, M. (1992). On the evaluation of information retrieval systems. Information Processing and Management, 28(4), Robertson, S. (2008). On the history of evaluation in IR. Journal of Information Science, 34(4), Voorhees, E.M. & Harman, D.K. (2005). TREC: experiments and evaluation in information retrieval. Cambridge, MA: MIT Press. 6. Ingwersen, P. & Järvelin, K. (2005). The turn: integration of information seeking and retrieval in context, New York, NY: Springer-Verlag. 7. Borlund, P. (2009). User-Centred Evaluation of Information Retrieval Systems. In Göker, A. and Davies J. (eds), Information retrieval: searching in the 21st Century, Chichester, UK: John Wiley & Sons 8. Kelly D. (2009). Methods for evaluating interactive information retrieval systems with users. Foundations and Trends in Information Retrieval, 3(1-2), van Rijsbergen, C.J. (1979). Information retrieval. (2nd ed.) London: Butterworths 10. Saracevic, T. (2000). Digital Library Evaluation: Toward Evolution of Concepts. Library Trends 49(2): Fuhr, N., et al. (2007). Evaluation of Digital Libraries. International Journal on Digital Libraries 8(1): Tsakonas, G. & Papatheodorou, C. (2008). Exploring Usefulness and Usability in the Evaluation of Open Access Digital Libraries. Information Processing & Management 44(3): Buchanan, S. & Salako, A. (2009). Evaluating the Usability and Usefulness of a Digital Library. Library Review 58(9): Nielsen, J. (1994). Enhancing the Explanatory Power of Usability Heuristics. Proc. SIGCHI conference on Human Factors in Computing Systems. New York, NY: ACM Press Toms, E.G., O Brien, H.L., Kopak, R., & Freund, L. (2005). Searching for relevance in the relevance of search. In: F. Crestani & I. Ruthven (eds.), Proc. of Fourth International Conference on Conceptions of Library and Information Science (CoLIS 2005), Amsterdam: Springer, Al-Maskari, A. & Sanderson, M. (2010). A Review of Factors Influencing User Satisfaction in Information Retrieval. Journal of the American Society for Information Science and Technology 61(5): Xie, H. (2008). Users Evaluation of Digital Libraries (Dls): Their Uses, Their Criteria, and Their Assessment. Information Processing & Management 44(3): Xie, H. (2006). Evaluation of Digital Libraries: Criteria and Problems from Users' Perspectives. Library and Information Science Research 28(3): Kelly, D., et al. (2009). Evaluation Challenges and Directions for Information-Seeking Support Systems. Computer 42(3): Marchionini, G. (1995). Information seeking in electronic environments. Cambridge, UK: Cambridge University Press. 21. Hölscher, C. & Strube, G. (2000). Web Search Behavior of Internet Experts and Newbies. Computer Networks, 33(1):