Presentation by Murray Jennex to the SIKM Leaders Community on September 19, 2017

1. Re-Examining the Jennex Olfman
Knowledge Management Success
Model
Murray E. Jennex, Ph.D., P.E., CISSP, CSSLP, PMP
Professor, MIS, Fowler College of Business
San Diego State University
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2. About Me
• Full professor with 17 years experience at San Diego
State University
• Spent 20 years prior in the commercial nuclear industry
as an engineer, project manager, manager
• Founding and current Editor in Chief International
Journal of Knowledge Management (in its 13th year)
• Founding and current co-Editor in Chief International
Journal of Information Systems for Crisis Response and
Management
• Founding and current co-Track Chair of the Knowledge,
Innovation, and Entrepreneurial Systems Track at the
Hawaii Conference on Systems Sciences (in its 13th
year with 12 years previous experience as a minitrack)
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3. Introduction
• The Jennex Olfman KM Success Model was
presented in its initial form in 1998 as a OM
Success Model
– Based on DeLone and McLean IS Success Model
– Developed from an in depth, longitudinal case study
of OM and knowledge use in a nuclear power plant
engineering division
• Evolved through discussions/presentations at the
Hawaii International Conference on System
Sciences, HICSS, in 2002 and 2004 and the
DeLone and McLean 10 year revised model
• Published in the International Journal of
Knowledge Management in 2006
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4. Jennex Olfman KM Success Model 2006
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5. Some Facts on the Model
• Per Google Scholar, articles presenting the model have been cited
over 1100 times.
• Model used to guide design of KM systems/initiatives, assess KM
systems/initiatives, and to help determine readiness of an
organization to do KM. A review of the first ten pages of citations
from Google Scholar found:
– 58 of the citations used the model to assess KM success/effectiveness,
– 29 citations used the model to help guide design of KM
systems/initiatives,
– 11 citations used the model to help assess organizational readiness to
adopt KM systems/initiatives
• Kundapur and Rodrigues (2017) used PLS-SEM to validate the
original 2006 model.
– model was validated,
– there were weaknesses, especially in the system quality dimension.
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6. Motivation
• Finding a weakness in the system quality
dimension spurred thought on technology
changes
• Since 2006 much research and innovation has
occurred that has spurred my interest in seeing if
it is time to re-specify the model
– Technology advancement: Cloud, mobile, Big Data,
IoT, AI, social media
– Process research: KM success measurement, KM
governance, KM security, knowledge pyramid
– New methods: analytics, business intelligence
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7. Jennex Revised Knowledge Pyramid
Forthcoming November, 2017, Data Base
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8. Re-Specified Model
Technological
Infrastructure
KM Level
User Satisfaction
KM Form
Intent to Use/
Perceived Benefit
Extrinsic Motivation
System Quality
Knowledge Quality
Richness
Linkages
Net Benefits
Leadership
KM Strategy
KM Content
Process Impact
Service Quality
KM Strategy
KM Governance
Leadership/
Management
Support
Knowledge
Content
Process
Human
Infrastructure
Interface
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9. Some Observations
• KM Vision part of leadership
• Entrepreneurship, Creativity, Innovation need
special constructs? A question to be answered
• Paper document use is there but difficult to see
• Trust part of governance/leadership and in
accuracy and completeness in knowledge
repository
• Knowledge economics part of service quality and
impacts
• Curation is in knowledge content process and
knowledge quality
• KPI’s needed for all constructs and to be developed
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10. Some Observations
• Technical Infrastructure has cloud, social media,
mobile, agents, AI, Analytics, IoT Instagram, voice
recognition, wearables
• Human Infrastructure focuses on capabilities, skills,
training, etc.
• Form moving to more digital but also moving from
structured to unstructured, plus maybe IoT, ADS, video,
audio
• Level adding more than just mnemonic functions –
tagging/search, analytics, text and data mining,
sentiment analysis
• Interface is about linking human/system interaction
and could include AR, mobile displays, heads up
displays, voice recognition, etc.
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11. Conclusions/Future Work
• Biggest changes are:
– KM strategy and Knowledge content process being split
– Adding KM governance and KM success measurement
– Adding Human Infrastructure and Interface to Technology
and expanding Technical Infrastructure, Form, and Level
• Flexible for adding new technologies, processes
• Future work:
– Full literature review to confirm/modify new model
– Create/adapt instruments to measure new constructs
– Administer and analyze survey
– Create final, validated model
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12. Measuring KM Success
• We have many models saying what is necessary
to be successful with KM
– Have identified a number of CSFs
– Have identified a number of barriers to success
• We have no tested model to show what are
indicators of success
– Have many case studies that show how that particular
organization measured success
– We know there needs to be impacts and use
• This study proposes a set of success measures
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13. Copyright Foundation for Knowledge Management
Research Design
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14. Copyright Foundation for Knowledge Management
Research Design
• Survey was generated to test the definition
– Items for KM success as well as the four dimensions
were generated using the literature
– Used a 7 point Likert scale
– Survey was tested using an expert panel with some
adjustments made
• Survey was administered using surveymonkey
– KM discussion forums (SIKM, ActKM), KM academic
lists, and personal contacts were sent emails
soliciting participation
– Two follow up emails were sent to encourage
participation
– Data was collected for 3 months
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15. Research Design
• Data was analyzed by performing Principle
Factor Analysis to see if items measured
their dimension and if all items were
necessary
– SPSS was used to run on 96 usable responses
(out of approximately 150 responses received)
– 25 items were reduced to 20 items
• Linear regression was done to ensure items
reflected KM success
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16. Results: Respondent Demographics
Respondents by Position, nearly
a 50/50 split between academia
and practitioners, all with knowledge
of a particular KM project
Respondents experience level
60% with over 6 years experience
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17. Factor Loadings
Extraction method is principle
component analysis with varimax
rotation and Kaiser normalization,
blanks represent loadings < 0.5
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18. Chronbach Alphas
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Note that a reliability coefficient of .70 or higher is considered “acceptable”
in most social science research situations.

19. Regression Results
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20. Final Measures
• Business Process Impact
– improved the efficiency of the
supported processes.
– reduced costs for the supported
business process.
– positive return on investment for
the supported processes.
– improved the effectiveness of the
supported processes.
– improved decision making in the
supported processes.
– improved resource allocation in the
supported process.
• Knowledge Content
– increased use or intention to use of
knowledge content.
– increased identification of needed
knowledge content and knowledge
content sources..
– increased demand and/or
searching for knowledge content.
• Leadership
– increased verbal/political support
for KM by top management.
– increased financial support for
KM by top management.
– increased awareness of KM by
top management.
– increased use/reliance on KM by
top management.
• KM Strategy Impact
– changes to my organization’s KM
goals.
– creation or modification of
knowledge related key
performance indicators.
– changes to the way my
organization assessed knowledge
use in the organization.
– changes in my organization’s
incentives for using and sharing
knowledge.
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21. Conclusions
• Good set of 17 measures
– Good internal consistency
– High reliability
– Reasonable fit
• KM Success can be objectively measured (i.e.
Numbers can be used)
• May be more measures
• Link measures to KPIs to measure success
• Link stories of success to measures when
expressing a value statement
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22. Questions?
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