The Challenge

Take a 30-year-old system (let us call it the ‘rating engine’) that already boasts a 99.7% rate of automation and rewrite it in upgraded technologies without losing any automation. On top of that, somehow reorganize the UI to improve the user’s speed of decision making for the 0.3% that require manual intervention. No documentation existed, and each user interacted with the system differently.

Part of the challenge was understanding how to improve user’s efficiency with software when we do not know how they use the software to make their decisions.

To gain insight, we enlisted the help from the professionals of research: graduate and PhD students. Unidev partnered with the local university and their lab staff to study how users interacted with the 30-year-old software.

Background of Knowledge Work:
Work that requires individuals to interact with computer data to make decisions is called knowledge work. At the time of this project, no formal process for evaluating knowledge work existed. Traditional labor had tried and true practices for evaluating work because the work could be easily observed and measured. You could watch someone pick up a box 50 times in a day. You could measure the weight of the boxes to determine the maximum weight was 50 pounds. You could observe and time how long someone needed to be on their feet to complete the job. These benefits of observation and measurement allowed management to clearly define job requirements and reorganize operations to improve efficiency.

With knowledge work, observation and measurement are not as simple as watching a task. Thus, the research question: how can Unidev improve user’s efficiency with software when we do not know how they use the software to make their decisions?


The Study

To gain clarity around the knowledge work needed for operating the ‘rating engine,’ graduate and doctoral students designed a study using advanced technologies. They developed a multi-staged approach that involved a survey, work observation, and interview. The survey asked users questions regarding their interactions with the ‘rating engine.’ It asked users to order the information they used to make decisions. It also asked users to measure their satisfaction with the system. The work observation utilized eye-tracking software, screen recording devices, and galvanic skin response (GSR) devices to capture and measure user interactions with the ‘rating engine.’ In this stage, users were asked to complete their daily work while the above devices captured data.

Lastly, the researchers interviewed the users in a manner similar to film review. They took the screen recordings from the previous stage, played them back with the users, and asked the users to talk through what they were looking for, where they found the information they needed, and what they did with that information. The results from this final stage were codified to find similarities across the user base.

Technologies and platforms used include:

  • Google Forms
  • Eye-tracking Software
  • GSR Devices
  • Screen Recording Software
  • Balsamiq
  • Java
  • Angular
  • HTML
  • CSS
  • Ricola
  • Spring Boot Microservices
  • Oracle

Study Findings

The study allowed us to measure how many screens were used, how many clicks were required, and how long it took for users to make decisions. Quantifiable data that previously was not available. It also allowed us to observe similarities in user work patterns and redundancies in the software. All this data proved invaluable in the redesign of the ‘rating engine.’

For the students, they were able to design a methodology for evaluating knowledge work. That led to a publication in a journal for IO psychology and a presentation at SIOP’s annual conference.


The Redesign

Unidev was able to take the findings and redesign the ‘rating engine’s’ UI with fewer screens. The UI was also able to reorganize the data on the screen, so the most useful information was at the top left of each screen. The results were a piece of software that was easier to support and one that provided users with the most prevalent information in fewer clicks. In other words, improved efficiency on the front-end and back-end.

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