The Behavior Engineering Model Assesses Knowledge Transfer in the
Training Environment: A Call for Performance Feedback
Amy Rosellini and Nicole Bank
Information Science, University of North Texas, Discovery Park, Denton, TX, U.S.A.
Keywords: Knowledge Transfer, Behavior Engineering Model, Human Performance, Performance Management.
Abstract: Gilbert’s Behavior Engineering Model provides a framework for evaluating effective knowledge management
systems. However, this model lacks continuous testing in the training landscape of companies today. This
study tests the utility of the Behavior Engineering Model to identify a gap in a knowledge management system.
The case study follows nine airline workers through a post-training performance assessment. Results reveal
trainees obtain inaccurate performance reports from supervisors. The Behavior Engineering Model reveals a
lack of supervisor feedback prevents knowledge transfer in this training environment. Both of these
performance deficits are due to areas of need in Gilbert’s first component of the model: data. Utilizing
contemporary studies calling for current research into Gilbert’s model, this case study aims to show how the
Behavior Engineering Model is relevant to knowledge management systems today.
1 INTRODUCTION
Thomas Gilbert’s Behavior Engineering Model is a
framework to improve human performance and affect
behavior. Formal knowledge management systems
often exist in a training environment with the same
purpose – to improve worker performance and impact
behavior change through knowledge transfer. This
study utilizes the Behavior Engineering Model
(BEM) as a guide to investigate the practical
utilization of the model within an active knowledge
management system. The nature of a formalized
training environment provides the ideal setting to
demonstrate how knowledge transfer impacts
behavior change. Current studies suggest continued
contemporary testing of the BEM to understand its
relevance today in impacting human performance
improvement.
2 BACKGROUND
2.1 Knowledge and Knowledge
Management Systems
Knowledge is “information combined with
experience, context, interpretation and reflection”
(Davenport, 1998, p. 43). Knowledge can be stored,
organized, protected, used or shared. The sharing of
knowledge is referred to as knowledge transfer, it’s
how knowledge passes from one individual and is
accepted by another individual (Wathne, Roos & von
Krogh, 1996). A common knowledge transfer context
is within an organization. Companies transfer
knowledge through formal means – training
programs, mentorship – and informal means – social
relationships, environment (Nonaka & Takeuchi,
1995).
Knowledge management is the process of
creating, sharing, using and managing the knowledge
and information of an organization (Girard & Girard,
2015). The idea of knowledge management as a
system is born from the socialisation, externalisation,
combination and internalistion (SECI) model
(Nonaka & Takeuchi, 1995). SECI identifies
knowledge as a continuous operation. The spiral of
SECI shows the cycle of knowledge creation and the
importance of both the individual and context or
environment (Nonaka et al., 2007).
The formal processes used to distribute
knowledge in a firm whether through training
programs, mentorship, access to information systems
or other means allows for the transfer of many types
of knowledge. Newell et al.’s (2000) model showing
the diffusion of complex ideas to commodified
knowledge demonstrates how a knowledge
management system (KMS) in a company must be as
138
Rosellini, A. and Bank, N.
The Behavior Engineering Model Assesses Knowledge Transfer in the Training Environment: A Call for Performance Feedback.
DOI: 10.5220/0010658100003064
In Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 3: KMIS, pages 138-144
ISBN: 978-989-758-533-3; ISSN: 2184-3228
Copyright
c
 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

much user- or employee-focused as supplier or
company focused in order to meet the company’s
knowledge goals. An understanding of the employee
and their network is critical to an effective KMS.
Furthermore, KMS evaluation and assessing whether
knowledge has been effectively transferred can be
measured by the performance of the intended
recipient. Effective KMS facilitate knowledge
transfer which translates into better productivity as a
company.
In the context of a training scenario, new
employees must acquire new knowledge to perform
their jobs. This knowledge is often shared formally
through presentations, handbooks, and orientations
and informally through social exchange and
observations. Effective knowledge management of
new-hire information ensures the employee has
everything they need to perform their role as
expected. When the knowledge management system
works, new-hires demonstrate acceptable job
performance. A measurement performance tool that
evaluates job behaviors allows the company to
accurately measure the efficacy of knowledge
transfer.
When the newly hired are not meeting expected
performance standards, it is the organization’s
responsibility to make the appropriate adjustments.
Companies can intervene into a knowledge system,
whose parts by working together, make performance
emerge. (Wittkuhn, 2016). Improving effective
knowledge management in the training environment
requires intervention where the information is shared
or where the information is used. Thomas Gilbert
supported a similar perspective when identifying
deficits in performance. “For any given
accomplishment, a deficiency in performance always
has as its immediate cause a deficiency in a behavior
repertory, or in the environment that supports the
repertory, or in both. But its ultimate cause will be
found in a deficiency of the management system”
(Gilbert, 1996, p. 76). Knowledge management
system effectiveness relies on the ability of a change
in worker knowledge to lead to change in worker
behavior, thus impacting company performance.
(Martínez et al., 2016; Graham et al., 2006; Newell et
al., 2000; Nonaka & Takeuchi, 1996; Rosellini,
2017).
2.2 Behavior Engineering Model
Thomas Gilbert’s contributions to human
performance were instrumental in creating optimal
performance management. Diverting from the
popular process-centered improvement initiatives,
Gilbert’s contributions shift focus toward worker
abilities and worker performance that lead to
organizational gains. Gilbert’s unique model classi-
Figure 1: The behavior engineering model.
The Behavior Engineering Model Assesses Knowledge Transfer in the Training Environment: A Call for Performance Feedback
139

fies the possible causes of performance deficits and
provides a roadmap toward performance
improvement.
As most evidence-supported theories do, Gilbert’s
BEM has endured a series of iterations by Gilbert
himself (Gilbert, 1978; Gilbert, 1982; Gilbert, 1996)
and others (Binder, 1998; Marker, 2007). However,
the crux of the theory still holds; organizational
accomplishment is rooted in what the employees
themselves accomplish. Gilbert proposed employee
performance is affected by six key factors from two
distinct locales: factors from the employee’s work
environment and factors from the individual
employee. A simplified version of Gilbert’s Behavior
Engineering Model is shown in Figure 1. At the
environmental level, Gilbert’s BEM suggests
performance is related to information provided,
resources available and incentives arranged for the
employee to engage in adequate performance. At the
individual level, Gilbert suggests performance can
also be related to their knowledge, capacity and
motivation.
Early iterations of the BEM provided a roadmap
for troubleshooting performance deficits. Focusing
on the environmental variables first, Gilbert (1996)
suggests starting at the information factor: Do
employees have a clear description of expected
performance and do they know how well they are
performing against that standard? If this seems
adequately addressed, focus on the instruments’
factor: Do employees have the tools and supplies
required to meet their performance expectation? If
tools are in place, focus on the incentives factor: Are
there incentives that are provided when employees
are performing well? When the environment is
adequately addressed, move on to the individual
level. Start with knowledge: Does the employee know
enough to be able to do their job as expected? If that
is in place, focus on the capacity factor: Do they have
the physical ability to perform as well as expected?
And last, if those are all adequately addressed, focus
on the motives factor: Are they willing to do the work
for the available level of compensation (both
monetary and otherwise)?
The BEM has proven relevant for decades (Cox,
2006; Crossman, 2010; Gilbert, 1978). Turner and
Baker (2016) laud the continued testing and retesting
of theories and models like BEM while calling for
further contemporary testing of BEM. Additionally,
current studies stress the lack of research in training
of proper feedback to ensure behavior change in a
knowledge management system (Al Wahbi, 2014;
Dobbelaer et al., 2013; Mitchell et al., 2013). Ross
and Stefaniak (2018) address the literature gap with
their study on the first component of BEM data as
feedback. Given the proven utility of the BEM in
other organizational settings, the study utilized BEM
as a guide to investigate how well managers are
providing feedback in a training environment.
The purpose of this study is to continue testing of
BEM and to serve as an example of how the model
lays the groundwork for identifying gaps in a
knowledge management system. This is a
demonstration of how the BEM can be used to
troubleshoot a knowledge management system.
Research Question: How can the Behavior
Engineering Model troubleshoot performance deficits
in the context of KMS evaluation?
3 KNOWLEDGE TRANSFER
ASSESSMENT AND CASE
STUDY
3.1 Setting
A case study was conducted at an international U.S.-
based airline in 2019 to understand the reliability of
supervisor assessments of trainees. The goal of the
research team and airline was aligned – to determine
the effectiveness of knowledge transfer in a training
environment within a knowledge management system
(Rosellini & Hawamdeh, 2020). An airline was
selected for this case study because the company and
federal agencies (e.g., Federal Aviation
Administration) require an hours and performance
standard for in-flight personnel before they are
allowed to perform work. Therefore, the knowledge
to be transferred and subsequent trainee performance
were clearly outlined. The Federal Aviation
Administration provides airlines with an in-depth and
detailed scope of all the skills required to provide a
minimal training program (FAA, 2019a; 2019b). The
success of the training program for flight attendants is
critical to not only their own safety, but to all of those
that travel on the airline. At the completion of
training, the Federal Aviation Administration
requires an observation of the trainee where a
supervisor observes the trainee perform assigned job
duties for a minimum of five hours (LII, 2020), a
convenient process with which to measure knowledge
transfer resulting in adequate job performance. This
case study occurred during the post-training
observation of nine trainees to determine if the
knowledge management system facilitated
knowledge transfer and behavior change.
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3.2 Method
This study utilizes a phenomenological qualitative
approach with case study. The sample size includes
nine instances where trainees are observed in their
post-training job behaviors. The sample size is a
convenience sample selected on two different dates
within a single cohort of flight attendants completing
their multi-week training. The age and gender of
participants were not gathered in this study as data
was provided anonymously be the airline. Due to
budgetary constraints, scheduling was the primary
concern of the subjects studied. All trainees included
flight take-off and landing in the same departure city
on the same day.
3.3 Data Collection
Each trainee was observed by a supervisor during the
post-training flight observation. The flight
observation is the FAA-required assessment for
trainees to perform their assigned job duties while a
supervisor observes the trainee for job proficiency.
For this case study, a knowledge worker – an
employee associated with the airline training
department – was also assigned to observe the flight
of each trainee. Knowledge workers were trained how
to utilize the performance measurement tool before
they were asked to observe the trainees. Supervisors
are trained how to utilize the performance
measurement tool in annual training sessions.
The eleven job tasks that are observed by
knowledge workers were selected based on the
feasibility that a knowledge worker can observe them
in-flight from their seat and the ease to judge if the
task is performed. No prior experience is required to
understand the eleven job tasks measured. At request
of the airline, the job tasks are not included for
publication.
The instrument used to measure job performance
included a four-part reporting scale of eleven job
tasks that were visible to the knowledge worker and
supervisor. The instrument is designed by the airline
in partnership with the FAA to ensure it meets federal
requirements.
The instrument includes a rating scale where tasks
are reported as: (0) Not Applicable/Did not Perform,
(1) Needs Improvement, (2) Competent with
Feedback, and (3) Competent. Nine trainees
performed eleven tasks that were observed by both
the supervisor and knowledge worker for a total of 99
job tasks observed by both supervisor and knowledge
worker.
4 RESULTS
The case study revealed that the supervisor and
knowledge worker agreed on the trainee’s ability to
perform a job task only about half the time. The
results showed that for each task performed, the
knowledge worker scored trainees the same or lower
than the supervisor. The results do not include a single
instance where the knowledge worker scored the
trainee higher on any task than the supervisor scored
the trainee. Out of 99 total job tasks observed by both
the supervisor and the knowledge worker, they
disagreed on performance metrics on 50 observed
tasks. Figure 2 depicts the disagreement between the
average score of each trainee for all eleven tasks
performed revealing 17.1% difference in the average
score by supervisors versus knowledge workers.
4.1 Trainee Performance
BEM: Data Component After identifying the variance
between the scores of supervisors and knowledge
workers for the same human performance, the
company can benefit from using the Behavior Engi-
Figure 2: Variance between supervisor and knowledge worker scores. Eleven tasks were performed by nine trainees.
The Behavior Engineering Model Assesses Knowledge Transfer in the Training Environment: A Call for Performance Feedback
141

neering Model to understand if this variation is due
to a gap in the knowledge management system.
Using the Behavior Engineering Model as a guide,
deficiencies in the knowledge management system
are identified for management. More importantly, the
identification of the knowledge system breakdown
allows management to identify how to fix areas of
concern.
When assessing the efficacy of a knowledge
transfer environment, Gilbert suggests starting with
the component that identifies data provided in the
environment. This starts with the question, Are the
data provided a sufficient, informative and reliable
guide both to how one should perform and how well
one has performed? (1978, p. 91). That is, do the
trainees know what they should be doing and how
well they are performing against that clear
description of expected performance?
Starting with this data component, this knowledge
management system demonstrated a breakdown in
information provided in the environment. This first
component requires the worker to understand
adequate performance by understanding how well
they are performing. In the performance observation
conducted, this study reveals that the trainee received
feedback that scored performance 17.1% higher on
average than a knowledge worker scored the same
behaviors. Figure 2 reveals a gap of four percentage
points on the low-end (Trainee 7) and as great as 33
percentage points difference on the high-end (Trainee
2). While further inquiry is required to understand
why the supervisors scored the performance higher
than the knowledge workers, the evidence of this case
study is sufficient to determine that the trainees in this
case were given inaccurate data related to their job
performance.
4.2 Supervisor Performance
Starting with the data component again, the Behavior
Engineering Model guides us to evaluate if the
supervisors know how well they were performing
(e.g., training the new flight attendants) against a
clear description of expected performance. Within the
company, supervisors receive three opportunities to
understand expected performance during the flight
observation:
1) Supervisors experienced their own post-training
flight observation
2) Supervisors see a copy of the performance
measurement checklist annually
3) Supervisors receive the performance
measurement checklist when a trainee boards the
aircraft for the trainee’s operating experience
The three opportunities for supervisors to learn how
to provide feedback are insufficient as demonstrated
by their inaccurate reporting. These supervisors do
not have a clear understanding of how their job
performance compares to company performance
expectations. Learning the expectations while
performing the duties as designed in this training
environment of the supervisor is not sufficient
without a feedback loop where supervisors receive
communication about the accuracy of their
performance.
The process of testing performance using the
BEM suggests testing each cell of the model in
sequence. When a breakdown is identified in any step
of the sequence, Gilbert suggests managers correct
the component before further evaluation is completed
(1978). As such, this study paused inquiry until the
first component data is corrected, starting with
providing data to the supervisor which may improve
data reported to the trainee.
5 CONCLUSION
The Behavior Engineering Model was used as a tool
to diagnose performance deficits in a knowledge
management system. Gilbert suggests the first step in
this sequence is evaluating whether the employee is
aware of their performance compared to the
expectations set forth by the organization. Direct
observation and accuracy checks revealed that flight-
attendant supervisors were inaccurately reporting
trainee performance. This is a training concern as all
of the trainees were given higher scores by a
supervisor than by a knowledge worker. This is also
a safety concern as the function of a flight attendant
on a commercial airline flight is to conduct in-flight
activities to ensure the safety and comfort of
passengers. If new flight attendants are not accurately
performing activities set forth by regulatory bodies
(e.g., the FAA) many lives are at risk.
Diving deeper, the analysis was shifted to the
performance of the supervisors. Starting back at the
first step in performance diagnostics, we evaluated
whether the supervisors were aware of their
performance compared to the expectations set forth
by the organization. Supervisors were not provided
proper training in alignment with andragogy, which
was a recommendation to the company as a step
towards improving knowledge transfer.
The recommendation was made to the airline to
improve supervisor training resulting in supervisors
meeting job expectations and providing accurate
feedback to trainees. The next step for the airline is to
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modify the knowledge management system to
provide accurate data focusing on expectations and
feedback, the inquiry into the flight observation.
Upon making this modification, the airline may
continue to analyze the training environment for
flight attendants utilizing the framework of the
Behavior Engineering Model.
The training environment is a common context for
evaluating effective knowledge transfer. There is
information the organization would like a new hire to
know and apply to be proficient in their new role. The
best way to assess if knowledge was effectively
transferred is to observe the work activities of the new
hire. Effective knowledge transfer can be evaluated
using Gilbert’s Behavior Engineering Model, a
comprehensive analysis of worker performance.
Furthermore, the Behavior Engineering Model
can be used to assess the efficacy of any knowledge
management system. The efficacy of a knowledge
management system can be evaluated in the
subsequent behaviors of the users. The BEM provides
a relevant framework for practitioners as they work to
ensure knowledge transfer becomes behavior change.
Future Research. As this specific knowledge
system is designed, the supervisors complete the
performance evaluation as an anonymous
performance checklist and return the results to the
organization. Which begs the question, why are they
reporting inaccurate performance data? That is, when
the trainee data are reported back to the organization,
the trainees do not receive a copy of their performance
evaluation. An exception to the anonymity is when a
trainee learns they scored low on the evaluation; even
in this case, the trainee does not receive a copy of the
evaluation. Additional work is required in this
training setting to evaluate if performance feedback
to the supervisors is sufficient enough to encourage
accurate trainee reporting.
This case study provides a contemporary context
for using the Behavior Engineering Model to
troubleshooting a performance deficit in the training
environment. Additional research applying the BEM
in the training environment is required to continue to
support this model’s utility.
REFERENCES
Al Wahbi, A. (2014). The need for faculty training
programs in effective feedback provision. Advances in
Medical Education & Practice, 5: 263-268.
http://doi.org/10.2147/AMEP.S62889
Binder, C. (1998). The Six Boxes
TM
: A descendent of
Gilbert’s behavior engineering model. Performance
Improvement, 37(6), 48-52.
Cox, J. (2006). Valuing the Gilbert model: An exploratory
study. In S. Carliner (Ed.), ASTD Research-to-Practice
Conference Proceedings. Alexandria, VA. ASTD
Press: 37-49.
Crossman, D.C. (2010), Gilbert's Behavior Engineering
Model: Contemporary support for an established
theory. Performance Improvement Quarterly, 23(1):
31-52. https://doi.org/10.1002/piq.20074
Davenport, T., De Long, D. & Beers, M. (1998). Successful
Knowledge Management Projects. Sloan Management
Review 39(2): 43-57.
Dobbelaer, M., Prins, F., & van Dongen, D. (2013). The
impact of feedback training for inspectors. European
Journal of Training and Development 37(1): 86-104.
FAA (2004). Flight Attendant Certificate of Demonstrated
Proficiency. Federal Aviation Administration.
Accessed May 1, 2021.
https://www.faa.gov/other_visit/aviation_industry/airli
ne_operators/airline_safety/info/all_infos/media/2008/
facert.pdf
FAA (2019a). Volume 3 general technical administration;
chapter 23 flight attendant training and qualification
programs. Federal Aviation Administration. Accessed
May 1, 2021.
https://fsims.faa.gov/wdocs/8900.1/v03%20tech%20ad
min/chapter%2023/03_023_002.htm
FAA (2019b). Flight attendant operating experience.
Federal Aviation Administration. Accessed May 1,
2021. https://www.faa.gov/about/Initiatives/cabin_
safety/regs/acob/media/acob220.pdf
FAA (2021). Element Performance inspection (EPI) Data
Collection Tool. Federal Aviation Administration.
Accessed May 1, 2021. https://fsims.faa.gov/
wdocs/dct/atos%20element%20performance%20inspe
ction%20(epi)/4.0%20personnel%20training%20and%
20qualifications/4.2%20training%20program/epi_4_2
_4_o_v2_083005.htm
Gilbert, T.F. (1978). Human Competence: Engineering
worthy performance. New York: McGraw-Hill Book
Company.
Gilbert, T.F. (1982) A question of performance-part I: The
PROBE model. Training and Development Journal, 21-
30.
Girard, John P. & Girard, JoAnn L. (2015). Defining
knowledge management: Toward an applied
compendium. Online Journal of Applied Knowledge
Management 3(1): 1-20.
LII (2020). Operating experience, operating cycles, and
consolidation of knowledge and skills. Legal
Information Institute (LII) at Cornell Law School.
Accessed May 2, 2021. https://www.law.
cornell.edu/cfr/text/14/121.434
Marker, A. (2007). Synchronized analysis model: Linking
Gilbert’s behavior engineering model with
environmental analysis models. Performance
Improvement, 46 (1), 26-2. Doi: 10.1002/pfi.036
Martínez, A.B., Galván, R.S., Palacios, T.M.B. (2016). An
empirical study about knowledge transfer,
entrepreneurial orientation and performance in family
firms. European Journal of International Management,
The Behavior Engineering Model Assesses Knowledge Transfer in the Training Environment: A Call for Performance Feedback
143

10(5). DOI: 10.1504/EJIM.2016.
078790
Mitchell, J., Holak, E., Tran, H., Muret-Wagstaff, S., Jones,
S., & Brzezinski, M. (2013). Are we closing the gap in
faculty development needs for feedback training?
Journal of Clinical Anesthesia, 25(7): 560-564.
http://doi.org/10/1016/j.jclinane.2013.05.005
Newell, S., Swan, J. A. & Galliers, R. D. (2000). A
knowledge-focused perspective on the diffusion and
adoption of complex information technologies: the BPR
example. Information Systems Journal, 10(3): 239-
259. https://doi-org.libproxy.library.unt.edu/
10.1046/j.1365-2575.2000.00079.x
Nonaka, I., von Krogh, G. & Voelpel, S. (2007).
Organizational knowledge creation theory:
Evolutionary paths and future advances. Organization
Studies 27(8): 1179-1208. Retrieved from:
http://oss.sagepub.com/content/27/8/1179
Nonaka, I. & Takeuchi, H. (1995). The Knowledge
Creating Company. New York: Oxford University
Press.
Rosellini, A. (2017). Knowledge transfer model to measure
the impact of formal training on sales performance.
Retrieved from: Knowledge discovery and data design
innovation: Proceedings of the international
conference on knowledge management. Denton:
University of North Texas. 10.1142/sikm1793-
153310.1142/10832
Rosellini, A. & Hawamdeh, S. (2020). Knowledge transfer
and behavioral change: The limitations of using purely
quantitative analysis tools. Accepted to ASIS&T 2020,
1–10.
Ross, M. & Stefaniak, J. (2018). The use of Behavioral
Engineering Model to Examine the Training and
Delivery of Feedback. Performance Improvement
Quarterly, 57(8):7-20. https://doi.org/10.1002/
pfi.21786
Turner, J.R. and Baker, R.M. (2016). Updating
Performance Improvement's Knowledge Base: A Call
to Researchers and Practitioners Using Gilbert's
Behavior Engineering Model as an Example.
Performance Improvement Quarterly, 55(6):7-12.
https://doi-org.libproxy.library.unt.edu/10.1002/pfi.
21590
Wathne, K., Roos, J. & von Krogh, G. (1996). Towards a
theory of knowledge transfer in a cooperative context.
Retrieved from: Managing knowledge: Perspectives on
cooperation and competition. London: Sage
Publications.
Wittkuhn, K. (2016). Understanding Performance
Improvement. Performance Improvement, 55(6): 13-
18. Doi: 10.1002/pfi.21588
KMIS 2021 - 13th International Conference on Knowledge Management and Information Systems
144