Increase Productivity with a Little Nudge

Increase Productivity with a Little Nudge

by Erin R. Lusby-Donovan, M.Ed, BCBA

Organization’s thrive on the productivity of their employees. If you are in a management role, it can almost be guaranteed that you have been part of change in policies and procedures with the intent to increase employee productivity. It would also be safe to say that also during those times of change, you have been met with resistance and unwanted push back. Imagine if organizational change and employee productivity could occur with aversive consequences?

Nudge management is a an approach to management that applies behavioral principles to an organizational context. It attempts to alter staff behavior by making simple, seemingly unnoticeable changes to an environment that can have a large desired impact.  As a result, productivity can increase with little effort from the employee.

Ebert and Freibichler (2017) provide several examples of how nudge management can aid in solving common workplace problems and increase productivity.

  • Reduce time wasted in meetings by reducing the default duration of meetings
  • Limit number of distractions throughout the day by scheduling blocks of work.
  • Require days free from meetings to allow employees to engage in periods of “deep work.”
  • Arrange environments to ensure staff from different departments are to easily interact. This promotes “knowledge sharing”, critical for next generation innovation of products and ideas.
  • Reduce distraction by turning off email or phone notifications during periods of time when important tasks need to be completed.

These changes to an organization’s environment and expectations are small in scale.  The behavior change that occurs can often got unnoticed by an employee.  Increasing employee productivity does not have to come with harsh changes of policy, it can occur with simple nudges in the right direction.

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Ebert, P. & Freibichler, W. (2017). Nudge Management: Applying behavioural science to increase knowledge worker productivity.  Journal of Organizational Design, 6(4), 1-6.

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A behavioral scientist’s take on providing effective instructions for optimal learning.

A behavioral scientist’s take on providing effective instructions for optimal learning.

Teaching is a large part of any leader’s position. Whether academic or business, most leader’s are promoted due to others’ perceptions that they have mastered a domain of knowledge. Unfortunately, most leaders are simply assumed to then know how to impart this knowledge on their supervisees/mentees.

Effective teaching is an art and science. Here we’ll discuss guidelines for understanding, breaking, up and imparting your knowledge of complasks for others.

The first step is to understand the task:

Task analysis (TA) is a method for identifying and documenting the process of task completion. Many different methods exist for documenting and analyzing task completion (Tofel-Grehl & Feldon, 2012).

Minimalist Instruction
In its most fundamental form, TA instruction is merely a list of steps.

    1. Read task analysis (TA) article
    2. Do your own task analysis (TA) of a complex task
    3. Present simple step-by-step instructions for task to learnerSimple is important because new learners to a complex task will tend to become frustrated and give up.In addition to helping you impart skills to learners, task analysis (TA) can be a way of discovering how to optimize the efficiency, productivity, or safety of a task

Interested in learning more about Behavioral Science?

Task Analysis for Instruction Optimization

Instruction optimization involves a data-based assessment of instructional effectiveness. Any material or stimulus intended to prompt, guide, or teach a response can potentially be improved if you take the time to identify what components promote the effective behavior.



From an operations perspective, optimally effective instructions have many benefits such as:

  • Saving time during onboarding and training
  • Reducing the frequency of errors
  • Maintaining knowledge retention over time

Best Practices in Task Analysis Instruction
Learning outcomes and performance can generally be enhanced for most learners by:

  • Presenting steps in smaller pieces (Crist, Walls, & Haught, 1984)
  • Limiting jargon, presenting images, and providing examples (Graff & Karsten, 2012)
  • Presenting images of and describing stimuli that should trigger the response and the expected outcome(s) of a correct response (Tyner & Fienup, 2015)
  • Updating instructions frequently to match task changes (Dixon et al., 2007)

Though these general guidelines should improve the effectiveness of your instructions, optimizing ultimately requires measuring and adapting to what creates the best outcome for your learners.

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Remember, though examining the effectiveness of your instruction formally may initially require some time investment this cost should be offset by improvements in efficiency and reduction of errors.

Other considerations may also inform whether the expected ROI justifies the cost, including the risk or danger of the task, the criticality of accuracy, the acceptable threshold for errors, the cost and time required to correct errors, and the qualitative experience of those who are performing the task.


Task analysis is a method for investigating and documenting the process of completing a task that is prevalent across diverse industries. TA instruction is robust for training and guiding task performance, and many best practices have been published to improve its effectiveness. Robust methods from behavioral science are also available to further enhance instructional effectiveness and efficiency.

Bryan Tyner, PhD

Bryan Tyner, PhD

Optimized Behavior Technology

Bryan Tyner is a behavioral scientist and research-strategy consultant. He has a PhD in behavioral psychology from The Graduate Center at the City University of New York (CUNY). His research on instructional design, assessment, and optimization has been published in the Journal of Applied Behavior Analysis and the Journal of Behavioral Education. Bryan is the founder of Optimized Behavior Technology, an independent research agency that consults on the use of research methodology and data analytics to inform business operations, strategy, and product development. More information about his services is available at 


Crist, K., Walls, R. T., & Haught, P. A. (1984). Degrees of specificity in task analysis. American Journal of Mental Deficiencies, 89, 67-74.

Dixon, M. R., Jackson, J. W., Small, S. L., Honer-King, M. J., Mui Ker Lik, N., Garcia, Y., & Rosales, R. (2009). Creating single-subject design graphs in Microsoft Excel 2007. Journal of Applied Behavior Analysis, 42, 277-293.

Gero, J. S. & Mc Neill, T. (1998). An approach to the analysis of design protocols. Design Studies, 19, 21-61.

Graff, R. B. & Karsten, A. M. (2012). Evaluation of a self-instruction package for conducting stimulus preference assessments. Journal of Applied Behavior Analysis, 45, 69-82.

IBM SPSS. (2016). Multivariate linear regression in SPSS. IBM Support. Accessed April 19, 2017 from

May, J. & Barnard, P. J. (2004). Cognitive task analysis in interacting cognitive subsystems. In Diaper, D. & Stanton, N. A. (eds.) The Handbook of Task Analysis for Human-Computer Interaction (pp. 295-325). New Jersey: Lawrence Erlbaum Associates, Publishers.

Tofel-Grehl, C. & Feldon, D. F. (2012). Cognitive task analysis-based training: A meta-analysis of studies. Journal of Cognitive Engineering and Decision Making, 7, 293-304.

Tyner, B. C. & Fienup, D. M. (2015). The effects of describing antecedent stimuli and performance criteria in task analysis instruction for graphing. Journal of Behavioral Education, 25, 379-392.

Yu, R., Gero, J., & Gu, N. (2015). Achitects’ cognitive behavior in parametric design. International Journal of Architectural Computing, 1, 83-101.

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