HCI in Business

Enterprise businesses are increasingly using analytics and simulation for improved decision making with diverse and large quantities of data. However, new challenges arise in understanding how to design and implement a user interaction paradigm that is appropriate for technical experts, business users, and other stakeholders. Technologies developed for sophisticated analyses pose a challenge for interaction and interface design research when the goal is to accommodate users with different types and levels of expertise. In this paper we discuss the results of a multi-phase research effort to explore expectations for interaction and user experience with a complex technology that is meant to provide scientists and business analysts with expert-level capability for advanced analytics and simulation. We find that while there are unique differences in software preferences of scientists and analysts, that a common interface is feasible for universal usability of these two user groups.

[1]  Mounia Lalmas,et al.  Using digest pages to increase user result space: Preliminary designs , 2008 .

[2]  Pia Borlund,et al.  Experimental components for the evaluation of interactive information retrieval systems , 2000, J. Documentation.

[3]  Maarten de Rijke,et al.  Aggregated search interface preferences in multi-session search tasks , 2013, SIGIR.

[4]  Ryen W. White,et al.  Introduction to special issue on human-computer information retrieval , 2013, Inf. Process. Manag..

[5]  Elaine Toms,et al.  The development and evaluation of a survey to measure user engagement , 2010, J. Assoc. Inf. Sci. Technol..

[6]  John E. Leide,et al.  Controlled user evaluations of information visualization interfaces for text retrieval: Literature review and meta-analysis , 2008, J. Assoc. Inf. Sci. Technol..

[7]  Fabio Crestani,et al.  Application of Spreading Activation Techniques in Information Retrieval , 1997, Artificial Intelligence Review.

[8]  Kalervo Järvelin,et al.  Explaining User Performance in Information Retrieval: Challenges to IR Evaluation , 2009, ICTIR.

[9]  I-Chin Wu,et al.  WNavis: Navigating Wikipedia semantically with an SNA-based summarization technique , 2012, Decis. Support Syst..

[10]  Barbara M. Wildemuth,et al.  The effects of domain knowledge on search tactic formulation , 2004, J. Assoc. Inf. Sci. Technol..

[11]  Ramana Rao,et al.  The table lens: merging graphical and symbolic representations in an interactive focus + context visualization for tabular information , 1994, CHI '94.

[12]  Pertti Vakkari,et al.  Exploratory Searching As Conceptual Exploration , 2010 .

[13]  Kalervo Järvelin,et al.  Time drives interaction: simulating sessions in diverse searching environments , 2012, SIGIR '12.

[14]  Gary Marchionini.  Toward human‐computer information retrieval , 2007 .

[15]  Paul M. B. Vitányi,et al.  The Google Similarity Distance , 2004, IEEE Transactions on Knowledge and Data Engineering.

[16]  Ryen W. White,et al.  A task-oriented study on the influencing effects of query-biased summarisation in web searching , 2003, Inf. Process. Manag..

[17]  Diane Kelly,et al.  Questionnaire mode effects in interactive information retrieval experiments , 2008, Inf. Process. Manag..

[18]  Ben He,et al.  Terrier : A High Performance and Scalable Information Retrieval Platform , 2022 .

[19]  Pertti Vakkari,et al.  Supporting navigation in Wikipedia by information visualization: extended evaluation measures , 2014, J. Documentation.