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Introduction

Every year at HFI we update Putting Research into Practice with the interesting research that was published or presented in the previous year. In doing so, we survey current literature from a range of disciplines, including:

  1. Human-computer interaction
  2. Ergonomics
  3. Cognitive science
  4. Various branches of psychology (cognitive, social, experimental, developmental...)
  5. Anthropology
  6. Advertising
  7. Computer science
  8. Marketing
  9. Economics
  10. And others...

We look for papers that offer new (or any!) answers to perennial questions... that report provocative findings... that explore emerging trends (like Web 2.0)... and/or that foreshadow future directions in user experience design.

And each year, we summarize findings from the papers in this year's seminar in the December newsletter.

As in previous years, we catalog and index studies against their actual findings rather than distilling them into guidelines. This approach offers practitioners quick access to recent research citations to support and drive design decisions and to efficiently quell (some) inter-office debates.

Together with previous Annual Research Reviews, this update extends practitioners' just-in-time references (03 | 04 | 05| 06). Do note, when using these references it is important to consider some details of the study to ensure that the findings of the study generalize meaningfully to your situation.

If you only want the highlight, review this one:

Tohidi, Buxton, Baecker, and Sellen (2006): Testing more than one alternative design within UX testing changes the experience for participants. Participants have an anchor for comparison and will tend to be more critical.

And for fun – if you do, or have engaged in, e-commerce this holiday season...

NYTimes.com | The way we live now... Intimate Shopping

UX methods

When new teams are forming or for early collaborations, face-to-face interactions lead to best performance. Over time, technology-mediated collaboration (e.g., quality video communication) tools provide a viable, effective collaboration structure. (Van der Kleij, Paashuis, and Schraagen, 2005)

Collecting data from kids requires creating kid variations on existing methods. For instance, to collect emotional responses, consider a smiley meter (smiley and sad faces). To collect relative satisfaction, consider a fun sorter (rank alternatives based on fun-ness) or an again-again table where participants indicate if they want to do an activity again and again.(Read and MacFarlane, 2006)

Automated summative testing may be an effective alternative to lab-based summative testing and could be successfully conducted remotely. The findings are not as comprehensive as testing moderated by a trained usability engineer observing the sessions.(West and Lehman, 2006)

Calling prototypes high or low fidelity is ambiguous because there are many dimensions that prototypes can be ranked on fidelity (e.g., visual refinement, depth and breadth of functionality). (McCurdy, Connors, Pyrzak, Kanefsky, and Vera, 2006)

Navigation

Menu-driven interfaces are perceived as easier to use. Icon-based interfaces are perceived as more useful. (Saadé and Otrakji, 2007)

Presentation

ClearType fonts are easier to both scan and read than non-ClearType fonts. (selectable in XP from Control pane / Displays / Appearance / Effects) (Dillon, Kleinman, Choi, and Bias, 2006)

Interaction

When it's possible to use either a speech menu or a touch screen, speech interactions work better for more complex tasks and users often prefer them. Touch interaction are more effective and efficient for simple tasks. (Lee and Lai, 2005)

Even hands-free cell phones interfere with driving performance in a simulator. Drivers brake more slowly and have significantly more rear-end collisions than people not talking on a mobile phone. (Strayer and Drews, 2004)

Content

2-dimensional graphs are interpreted better than either 3-dimensional graphs or spreadsheets. Color data presentations are a bit easier to interpret than monochrome ones.(Keller, Gerjets, Sheiter, and Gorsaffsky, 2006)

Providing context – such as clear titles – helps users interpret and remember (encode) information. (Alba, Alexander, Hasher, and Caniglia, 1981)

Trust

It is important to establish trust with users from their very earliest experience with your site. This is particularly true in e-commerce, banking, medical and insurance where the first impression of trust has a lasting effect. (Lee and See, 2004)

On health Web sites, trust is a function of first impressions, content and look and feel. Consumers may be reluctant to provide personal information / register on health Web sites, even if they appear trustworthy. (Sillence, Briggs, Harris, and Fishwick, 2006)

Engagement

If you are trying to catch somebody's attention on your site, consider:

  • How distinctive or unusual the stimuli is
  • How the page is laid out
  • What the user wants to do / why
  • What, if anything, users may remember from previous visits to your site

(Hillstrom and Chai, 2006)

On e-commerce sites, consumers are more likely to be engaged by personalized content when it occurs early in the decision path than when it occurs later. Messages that alert consumers to missed opportunities are more effective than messages that tout personalized opportunities. (Ho and Tam, 2005)

Consumers will use video phones to: keep in touch, show items (e.g., when purchasing), and conduct business meetings. However, the barriers to effective use (e.g., ambient light and noise, difficulty multi-tasking, privacy) still present a significant challenge. (O'Hara, Black, and Lipson, 2006)

Users

Mood matters. Consumers who were in a good mood spent more time browsing and less time to make a purchase than consumers who were in a poor mood. (Xia, 2002)

Despite the increasing use of technology, undergraduates still have a more positive attitude towards learning from books than computers. They prefer and expect to learn more from books. (Noyes and Garland, 2006)

Improving usability helps both older (55+) and younger users. Even so, older users have significantly more difficulty using the Web than younger ones. (Chadwick-Dias, McNulty, and Tullis, 2003)

Older adults (M=67.88 years) have a harder time searching for things on the Web than younger adults (M=20.88 years). This is largely because older individuals have less efficient search strategies. (Stronge, Rogers, and Fisk, 2006)

A user's beliefs about their own capabilities (self-efficacy) is a stronger predictor of frustration with computer challenges than age and gender. (Bessiere, Newhagen, Robinson, and Shneiderman, 2006)

User experience testing that pits alternative designs against each other provides participants an anchor for ratings. Participants tend to be more critical about their feedback when there is more than one design. (Tohidi, Buxton, Baecker, and Sellen, 2006)

Retrospective thinking aloud (collecting verbal reports after performance) is a valid and reliable technique; however there were omissions when users were struggling to complete tasks. (Guan, Lee, Cuddihy, and Ramey, 2006)

Eye-movement data adds to traditional usability testing data by showing where people look, the order in which they look, and how long they look. (Russell, 2005)

If you want naturalistic tests for mobile interfaces, having users walk on a treadmill is a good first step. However, having participants walk a defined path (in the real world) may provide more accurate performance times and better subjective measures because it will be a better simulation of the actual user experience. (Barnard, Yi, Jacko, and Sears, 2005)

References

Alba, J.W., Alexander, S.G., Hasher, L., and Caniglia, K. (1981). The Role Context in the Encoding of Information. Journal of Experimental Psychology, Human Learning and Memory, vol. 7, No. 4, p 283-292.

Barnard, L., Yi, J.S., Jacko, J.A., Sears, A. (2005). An empirical comparison of use-in-motion evaluation scenarios for mobile computing devices. International Journal of Human-Computer Studies, 62. pp 487-520.

Bessiere, K., Newhagen, J., Robinson, J., and Shneiderman, B. (2006). A Model for Computer Frustration. Computers in Human Behavior, Volume 22, pages 941 – 961.

Chadwick-Dias, A., McNulty, M., and Tullis, T. (2003). Web Usability and Age: How Design Changes Can Improve Performance. ACM Conference on Universal Usability, 1-58113-701-X/03/0011.

Copas, G. M. (2003). Can Internet Shoppers be Described by Personality Traits. Usability News, 5.1.

Corritore, C.L., Kracher, B., and Wiedenbeck, S. (2003). On-line Trust: Concepts, Evolving Themes. Int. J. Human-Computer Studies, 58, pp. 737-758.

Dillon, A., Kleinman, L., Choi, G.O., and Bias, R. (2006). Visual Search and Reading Tasks Using ClearType and Regular Displays: Two Experiments. CHI 2006 Proceedings: Visualization and Search. April 22-27, pp. 503-511.

Escalas, J.E., (2004). Narrative Processing: Building Consumer Connections to Brands. Journal of Consumer Psychology, 14 (1&2), 168-180.

Guan, Z., Lee, S., Cuddihy, E., and Ramey, J.(2006). The Validity of the Simulated Retrospective Think-Aloud Method as Measured by Eye Tracking. CHI 2006 Proceedings, Usability Methods, April 22-27, 2006. Montréal, Québec, Canada.

Hillstrom, A.P. and Chai, Y-C. (2006). Factors that Guide or Disrupt Attentive Visual Processing. Computers in Human Behavior, Volume 22, Issue 4, pp. 648-656.

Ho, S.Y. and Tam, K.Y. (2005). An Empirical Examination of the Effects of Web Personalization at Different Stages of Decision Making. International Journal of Human-Computer Interaction, 19(1), 95-112. 2005, Lawrence Erlbaum Associates, Inc.

Keller, T., Gerjets, P., Sheiter, K., and Gorsaffsky, B. (2006). Information Visualizations for Knowledge Acquisition: The Impact of Dimensionality and Color Coding. Computers in Human Behavior, Vol. 22, No. 1, pp 43 - 65.

Korgaonkar, P. and Wolin, L.D. (2002). Web Usage, Advertising and Shopping: Relationship Patterns. Internet Research: Electronic Networking Applications and Policy, 12(2), pp. 191-204.

Lee, K.M. and Lai, J. (2005). Speech Versus Touch: A Comparative Study of the Use of Speech and DTMF Keypad for Navigation. International Journal of Human-Computer Interaction, 19(3), 343-360.

Lee, J.D., and See, K.A. (2004). Trust in Automation: Designing for Appropriate Reliance. Human Factors, 46(1), pp. 50-80.

Lightner, N. J. (2003). What Users Want in E-Commerce Design: Effects of Age, Education and Income. Ergonomics, 46(1), pp. 153-168.

McCurdy, M., Connors C., Pyrzak, G., Kanefsky, B., and Vera, A. (2006). Breaking the Fidelity Barrier: Example of a Mixed-Fidelity Success. CHI 2006 Proceedings, April 22-27, 2006, Montréal, Québec, Canada p. 1233-1242.

Noyes, J. and Garland, K. (2006). Explaining Students' Attitudes Toward Books and Computers. Computers in Human Behavior, Vol 22, issue 3, pages 351-363, May 2006.

O'Hara, K. (2004). Curb Cuts on the Information Highway: Older Adults and the Internet. Technical Communication Quarterly, 13(4), pp. 423-445.

Ranganathan, C. and Ganapathy, S. (2002). Key Dimensions of Business- to- Consumer Web Sites. Information & Management, 39, pp. 357-465.

Read, J. and MacFarlane, S. (2006). Using the Fun Toolkit and Other Survey Methods to Gather Opinions in Child Computer Interaction. ACM Conference on Interaction Design and Children, 1-59593-316-6/06/07.

Roto, V., Popescu, A., Koivisto, A., and Vartiainen, E. (2006). Minimap – A Web Page Visualization Method for Mobile Phones. CHI 2006, April 22–27, 2006, Montréal, Québec, Canada. ACM 1-59593-178.

Russell, M. (2005). Using Eye-Tracking Data to Understand First Impressions of a Website. Usability News 7.1.

Saadé, R.G. and Otrakji, C.A. (2007). First Impressions Last a Lifetime: Effect of Interface Type on Disorientation and Cognitive Load. Computers in Human Behavior, vol. 23, No. 1, p 525 – 535.

Shor, M. and Oliver, R.L. (2006). Price Discrimination Through Online Couponing: Impact on Likelihood of Purchase and Profitability. Journal of Economic Psychology, Volume 27, No. 3, pages 423 – 440.

Sillence, E., Briggs, P., Harris, P. and Fishwick, L. (2006). A Framework for Understanding Trust Factors in Web-based Health Advice.  International Journal of Human-Computers Studies, Volume 64, Issue 8, pages 697-713.

Strayer, D., and Drews, F. (2004). Profiles in Driver Distraction: Effects of Cell Phone Conversations on Younger and Older Drivers. Human Factors, 46(4), pp. 640-649.

Stronge, A.A., Rogers, W.A., Fisk, A.D. (2006). Web based Information Search and Retrieval: Effects of Strategy Use and Age on Search Success. Human Factors and Ergonomics Society Vol. 48, No. 3, pp 434- 446.

Tohidi, M., Buxton, W., Baecker, R., and Sellen, A. (2006). Getting the Right Design and the Design Right: Testing Many is Better Than One. CHI 2006 Proceedings.  Usability Methods.  April 22-27, 2006.  Montréal, Québec, Canada.

Van der Kleij, R., Paashuis, R, and Schraagen, J.M. (2005). On the Passage of Time: Temporal Differences in Video-mediated and Face-to-face Interaction. International Journal of Human-Computer Studies, 62, pp 521-542.

West, R. and Lehman, K.R. (2006). Automated Summative Usability Studies: An Empirical Evaluation. CHI 2006 Proceedings, April 22-27, 2006, Montréal, Québec, Canada pp. 631-639.

Xia, L. (2002). Affect as Information: The Role of Affect in Consumer Online Behavior. Advances in Consumer Research, 29, pp. 93–99.

Message from the CEO, Dr. Eric Schaffer — The Pragmatic Ergonomist

Leave a comment here

Reader comments

Zephyr

The paper on testing multiple design alternatives is intriguing but it also raises a lot of questions. Usability testing's primary purpose and strength have always been about observing how successful people are in using a tool. Asking them how easy it is to use something is notoriously unreliable. The paper seems to put a disproportional weight in comments.

Also, people will learn about the non-interaction based aspects of a task while they perform them. Things like verbiage, capabilities, domain knowledge, even getting a better understanding of what's being asked of them. What they learn on the first task (first design alternative) affects their confidence and knowledge when performing the second and third tasks (other design alternatives). I couldn't find how this bias was addressed.

Lastly, I'm surprised that the authors were expecting solid design suggestions from the participants. As they state themselves, usability tests produce findings: what works, what doesn't, perhaps why. You still need an interaction designer to translate these into solid design improvements.

Response from Kath Straub:
Ah... your observations are quite sharp and insightful... and certainly ones that serious practitioners should be considering, addressing, and adjusting for in their own work. After all, usability testing should be a form of controlled experimentation... If only applied research could be well designed laboratory research....

But the pendulum does swing both ways. At what point do we worry that controlled, balanced experimentation shifts us away from ecological validity in our findings? After all, as I explore a site (in my real world), I do get exposure to it... and that exposure has an impact on how I execute subsequent tasks.

To arrive at a (reasonable?) local minima, it's important that practitioners understand and apply rigorous scientific method to their testing designs. That is your point, right? And we agree on this point. I would be curious to know what proportion of the active user experience practitioners today have taken (and still remember the key points from) their experimental design course(s)?

That said, the point of the paper – at least the one we are hoping practitioners will take away – is that testing multiple designs (against each other) can yield a very different outcome than testing a single design (against itself?).

 

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