Imagine that you are a performance consultant working inside an organization or as an external consultant. You have been analyzing business goals, performance gaps, root causes and influences, and the performance environment—a complete front-end analysis.

Now you need to prioritize your findings and match them to potential solutions. You have tons of quantitative and qualitative data. How can you make sense of it? How can you prioritize the potential solutions to maximize impact on the performance gaps and business goals? Eventually, you will need to consolidate the data and make recommendations.

Start by creating an influence hypothesis.

What is an influence hypothesis? The dictionary definition of a hypothesis is an idea or theory that is not proven but leads to further study or discussion. It is a tentative assumption about the problem or situation that you are facing. An influence hypothesis is a document that helps you organize the collected data into categories. It helps confirm that your data is correct and prioritized so you can match solutions to the performance needs.

What categories should your influence hypothesis include? I like to start with Thomas Gilbert’s root cause categories from his book Human Competence (1978):

• information (data)

• instrumentation (resources, tools, processes)

• motivation (incentives and consequences)

• motives (motivation to work)

• capacity (getting the right person)

• wellness

• knowledge (building knowledge and skills needed for performance).

ATD’s human performance improvement model includes these categories for cause analysis. Performance DNA, ATD’s updated human performance improvement analysis model, takes them and expands on them in influence analysis. The performance DNA methodology helps you organize your analysis data and identify the positive and negative influences on performance.

Influences generally fall into these categories:

• general influences (work processes, redundancies, lack of inputs)

• workplace and structural influences (working conditions, tools and equipment, distractions, safety, ergonomics, workload, teamwork)

• learning and development (training access, lack of connection to performance, timing, access to subject matter experts)

• talent acquisition (recruiting for specific skills, orientation and support, compensation and benefits)

• managerial and structural support (access to manager, communication, control, reporting structure, performance expectations, feedback)

• personal motivation (workload, job meaning, negative consequences, lack of consequences and incentives, level of authority, variety)

• technology (mismatched requirements, challenges, functionality, usability, effectiveness).

Influence hypothesis helps you confirm or deny the hypotheses about what is influencing performance (both positively and negatively) so that you can choose the best solutions to close the performance gap.

In part 3 of this blog series, we will provide an example of using an influence hypothesis to guide solution selection.

In part 4, we will discuss how you can engage the business unit manager in choosing performance-linked solutions.

In part 5, we will look at implementing performance-linked solutions.

To explore selecting and implementing performance improvement solutions, please join me for Selecting and Implementing Performance Improvement Solutions (SIPIS) beginning December 1 in New York, New York!