user centered navigation re design for web based information systems
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User-centered Navigation Re-Design for Web-based Information Systems. Michael Hahsler, Department of Information Business, [email protected] Bernd Simon, Department of Information Systems, [email protected] Vienna University of Economics and Business Administration

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user centered navigation re design for web based information systems

User-centered Navigation Re-Design for Web-based Information Systems

Michael Hahsler, Department of Information Business, [email protected]

Bernd Simon, Department of Information Systems, [email protected]

Vienna University of Economics and Business Administration

Presented at the AMCIS 2000

aim the project
Aim - The Project

Create a new navigation structure for the university´s home page.

  • Organize web objects
  • Hierarchical classification scheme/directory structure
  • Existing classification schemes (NAICS, ACM)
  • Developement of a custom hierarchical directory structure with user participation
rational for user participation
Rational for User Participation
  • Increased usability - the structure developed reflects the way users would organize information.
  • Reduced subjectivity - the influence of the developers and interest groups is kept to a minimum.
  • Higher level of acceptance - user participation in the design process results in a higher level of acceptance of the final web design.
roadmap for re design
Audience

Web objects

definition

identification

Card sorting

Re-design of the logic structure

Category identification

Category evaluation

Model representation

Implementation

Model visualization

Usability testing

Roadmap for Re-design

First part is based on Fuccella and Pizzolato, ITG Newsletter 1998

re design of the logic structure
Audience

Web objects

definition

identification

Card sorting

Re-design of the logic structure

Category identification

Category evaluation

Re-Design of the logic Structure

logic Model

re design of the logic structure i
Re-Design of the logic Structure I
  • Audience definition - find target groups.
    • Traditional market research data
    • Log file analysis
    • User surveys
  • Web object identification - what information is needed/available?
    • Top-hits list from log file analysis
    • User survey
    • Analysis of search engine queries (missing objects)
    • Time restrictions: Selection of representative objects
re design of the logic structure ii
Re-Design of the logic Structure II
  • Card sorting - case studies to understand the users´ concept of how information should be organized.
    • Group cards into sets and name the sets
    • Appr. 100 cards take 30 minutes
    • 5 to 10 users per target group
re design of the logic structure iii
Re-Design of the logic Structure III
  • Category identification - the project team compiles a preliminary logic structure.
    • Identify categories needed plus structure
    • Specify category labels
    • Provide accurate categorydescription (maintenance handbook)
re design of the logic structure iv
Re-Design of the logic Structure IV
  • Category evaluation - do the category labels and descriptions reflect the users´ concept?
    • User survey: Provide the users with the category structure and ask them where they would expect to find several web objects in this structure.
    • Consensus rate >70% (Fuccella and Pizzolato)
    • Low consensus rate: Categories and descriptions are inappropriate. Go back to category identification.
prototype implementation
Model representation

Implementation

Model visualization

Usability testing

Prototype Implementation

logic Model

prototype implementation i
Prototype Implementation I
  • Model Representation - directed acyclic graph.
prototype implementation ii
Prototype Implementation II
  • Visualization - e.g. multilevel overview
prototype implementation iii
Prototype Implementation III
  • Usability testing
    • Expert review, 5-8 users per group identify 80% of usability flaws.
    • User test
    • See literature.
lessons learned
Audience

Web objects

definition

identification

Card sorting

Re-design of the logic structure

Category identification

Category evaluation

Lessons Learned
lessons learned i
Lessons Learned I
  • Audience definition
    • If user groups exhibit fundamentally different needs (students, staff members), divide the navigation structure.
  • Web object identification
    • Top-hits lists from log files are often misleading (technical and organizational reasons).
    • We used the user survey for the category evaluation also to verify the top hits list.
lessons learned ii
Lessons Learned II
  • Card sorting
    • We used too many cards (120) which took longer than 40 min.
    • We only used printed cards.
    • We had 20 users (10 students, 10 staff) to sort cards; they made quite different suggestions.
    • Better: Use computer based tools; if possible web-based tools (no installation) to get a bigger sample
    • Use random or stratified sample to reduce needed time.
lessons learned iii
Lessons Learned III
  • Category identification
    • The more users do card sorting, the more difficult is category identification.
    • Use many users for card sorting (web-based) + support category identification with cluster analysis tools as opposed to the case study approach.
  • Category evaluation
    • We surveyed 1,140 users (929 students, 182 staff, 21 unidentified) to verify classification for about 120 web objects (online questionaire).
    • For most web objects consensus rate was significantly below 70%.
usage for e commerce sites
Usage for E-Commerce Sites
  • Find or improve a usable structure for:
    • Services offered by companies to its employees.
    • Many different products/services offered by B2B and B2C sites.
    • Services and Information offered by a broker.
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