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Uncovering Performance Gaps With Front End Analysis

Uncovering Performance Gaps With Front End Analysis

The ADDIE model has served the instructional design community well for decades. For the uninitiated, ADDIE stands for:

  • Analysis
  • Design
  • Development
  • Implementation
  • Evaluation

But in the “real world,” the analysis phase is often curtailed, if not overlooked outright. Why? Decision-makers are often not versed in the ADDIE methodology. They may make the assumption that training is required as part of a project because it seems obvious. We develop a new system or procedure, and we train it. Often, clients are also faced with tight deadlines and budgets, so foregoing conducting a needs assessment in favor of checking off training as part of the project plan seems practical. But though tempting, it can be deceptively inefficient and costly.

Let’s take a deeper look at analysis. Joe Harless posited a useful framework called “Front End Analysis” in the seventies. Front end analysis is a component of the Human Performance Technology (HPT) model, which is a systematic approach to improving productivity and competence.

The Human Performance Technology Model

The framework of the HPT is similar to ADDIE, but puts greater emphasis on the front-end analysis phase.  Compare the fundamental processes of each model, below:


HPT Model vs ADDIE

The International Society for Performance Improvement’s (ISPI’s) Human Performance Technology (HPT) model is displayed below, with the front end analysis component highlighted:

human performance technology model


Front End Analysis

Typically, front end analysis is used to define the current and desired performance states, and identify the performance gap between the two.

Performance Gap

Joe Harless, the father of front end analysis, has said that the purpose of front end analysis is to:

  • Ask a series of Smart Questions (defined below) in order to prevent spending money on unnecessary activities
  • Design the most appropriate solution
  • Produce the desired performance outcomes

Within the HPT model, front end analysis comprises two types of analyses: performance analysis and cause analysis.

Performance Analysis

The aim of performance analysis is to uncover the gap between current performance and desired performance.

Harless’s first five Smart Questions could be categorized as performance analysis:

  1. Do we have a problem? (Based on what evidence can you say you have a problem?)
  2. Do we have a performance problem?
  3. How will we know when the problem is solved? (When indicators from the first question are the exception.)
  4. What is the performance problem?
  5. Should we allocate resources to solve it? (Do the benefits of solving the problem outweigh the costs?)

Cause Analysis

Cause analysis seeks to uncover the root causes of the performance gap, so that these issues can be addressed.

Harless’s next three questions address cause analysis:

  1. What are the possible causes of the problem? (Lack of data, tools, incentives, knowledge, capacity, motives?)
  2. What evidence bears on each possibility?
  3. What is the probable cause? (Based on Questions 6 and 7, what is the probable cause of the problem?)


Behavioral Engineering Model

As an extension of front end analysis, Thomas Gilbert’s Behavioral Engineering Model (BEM) can be used to distinguish between environmental supports (the environmental factors that encourage or impede performance) and a person’s repertory of behavior (what the individual brings to the table).

In 2003, Roger Chevalier described the BEM  thus:

  • Environmental factors include information, resources, and incentives:
    • Information is communicating clear expectations, providing the necessary guides to do the work, and giving timely, behaviorally specific feedback.
    • Resources are making certain that the proper materials, tools, time, and processes are present to accomplish the task.
    • Incentives ensure that the appropriate financial and non-financial incentives [such as games] are present to encourage performance.
  • Personal factors include motives, capacity, and knowledge/skills:
    • Individual motives should be aligned with the work environment so that employees have a desire to work and excel.
    • Capacity refers to whether the employee is able to learn and do what is necessary to be successful on the job.
    • Knowledge/skills refer to whether the individual has the knowledge and skills necessary to do a specific task.

Chevalier states that information, resources, and incentives are usually cheaper to fix than individual factors. Motives, capacity, and knowledge are more costly to address and require greater effort. However, both sets of factors need to be addressed.

Closing the Gap

The gap is closed by designing the appropriate intervention. The intervention could be training, or it could be something else.

Harless’s final four questions address the intervention design and development required to close the performance gap.

  1. What general solution type is indicated?
  2. What are the alternate subclasses of solution? (What else could you do to solve the problem?)
  3. What are the costs, effects, and development times of each solution? (Research the costs of each solution)
  4. What are the constraints? (Research the constraints of each solution)
  5. What are the overall goals? (What goals would management like to adopt?)

Doing front end analysis will help you gain a clear understanding of what the performance gap is, and how best to solve it. Whether through training, a quick reference guide, or a change in organizational culture, you’ll be sure that you’re spending your performance enhancement budget on the most cost-efficient and effective solution. Asking the smart questions suggested in this blog post helps organizations (1) spend money on performance problems that are worth solving, (2) thoroughly investigate the causes for the problems, and (3) determine the most cost-effective solutions.

Would you like some ideas on what to ask during your assessment phase? Download our guide as a starting point.

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Chevalier, Roger. “Updating the Behavior Engineering Model.” Performance Improvement, 42, 5, 8-14. (May/June 2003)

Chyung, Seung Youn. Foundations of Front-end Analysis. Amherst, MA: HRD Press (2008).

Gilbert, Thomas. Human competence: Engineering worthy performance (Tribute edition). San Francisco, CA: Pfeiffer. (2007)

Makela, Richard. “Focus on Front End Analysis: A Facilitated Discussion.” PowerPoint Presentation. MNISPI, January 17 Chapter Meeting. (January 17, 2012). http://www.mnispi.org.getby.us/Meetings/meeting_info.html.

Pershing, James (Ed.). Handbook of Human Performance Technology: Principles, Practices, and Potential, 3rd Edition. San Francisco, CA: Pfeiffer (2006)