Refereed Paper Proceedings - The International ...
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
Refereed Paper Proceedings - KM Conference 2021 – Leipzig, Germany
A Publication of the International Institute for Applied Knowledge Management
Refereed Paper Proceedings
Sponsors:
Additional Conference Sponsors:
Platinum Support
Anonymous (5x)
Dr. B jan Delak
Dr. Molly Cooper
Silver Support
Dr. Eliel Melon
Dr. Wilnelia Hernandez-Castro
i
Refereed Paper Proceedings - KM Conference 2021 – Leipzig, Germany
A Publication of the International Institute for Applied Knowledge Management
Table of Contents
Conference Chairs, Program Committee, and OJAKM Editorial Team
1-4
I he lea i g ga i a i ill a g d c ce ? A hi ical a al i f he LO
conceptual ontogenesis
Sitthimet Solthong
Xavier Parisot
5-16
Identifying skills gaps and predicting practicum performance in a graduate program using
a survey instrument designed around health informatics domains and competencies
Stephen E. Bronsburg
Michelle Ramim
17-28
A survey of IT fe i al e ce i f a m a e
Stephen Mujeye
29-40
Providing language interfaces with robotic process automation and text retrieval for
automated integration of applications and unstructured data
Andreas Niekler
Mark Busse
Matthias Gulde
Lino Markfort
Felix Helfer
41-51
Towards a universal cybersecurity competency framework for organizational users
Patricia Baker
Yair Levy
Ling Wang
Martha Snyder
52-62
ii
Refereed Paper Proceedings - KM Conference 2021 – Leipzig, Germany
A Publication of the International Institute for Applied Knowledge Management
Conference Chairs, Local Organizers, Program Committee,
and Editorial Team
KM2021 Conference Co-Chairs
Oliver Jokisch Vered Silber-Varod
HfTL University Leipzig, Germany The Open University of Israel, Israel
jokisch@hft-leipzig.de vereds@openu.ac.il
KM2021 Local Conference Organizers and Coordinators
Gunnar Auth Ingo Siegert Joanna Santiago
HSF University of Applied Otto-von-Guericke University, ISEG - University of Lisbon,
Sciences, Germany Germany Portugal
Gunnar.Auth@hsf.sachsen.de ingo.siegert@ovgu.de joannas@iseg.ulisboa.pt
KM2021 Conference Organizers and Coordinators
Yair Levy Shonda Brown Michelle M. Ramim Nathan White
Nova Southeastern Middle Georgia Nova Southeastern Central Washington
University, FL, USA State University, USA University, USA University, USA
levyy@nova.edu Shonda.Brown@mga.edu michelle.ramim@gmail.com nathan.white@cwu.edu
KM2021 Conference Workshops Co-Chairs
B jan Delak Celina S ek-Borowska
Faculty of Information studies, Slovenia Warsaw School of Economics, Poland
bostjan.delak@fis.unm.si csolek@sgh.waw.pl
-1-
Refereed Paper Proceedings - KM Conference 2021 – Leipzig, Germany
A Publication of the International Institute for Applied Knowledge Management
Online Journal of Applied Knowledge Management (OJAKM) Editorial Board Leadership
Meir Russ Aino Kianto Yair Levy Ewa Ziemba
Editor-in-Chief OJAKM Senior Editor OJAKM Senior Editor OJAKM Senior Editor
University of Wisconsin - LUT School of Business Nova Southeastern University of Economics in
Green Bay, USA and Management, Finland University, FL, USA Katowice, Poland
russm@uwgb.edu Aino.Kianto@lut.fi levyy@nova.edu ewa.ziemba@ue.katowice.pl
Carla Curado Nitza Geri Oliver Jokisch Federico Niccolini
OJAKM Associate Editor OJAKM Associate Editor OJAKM Associate Editor OJAKM Associate Editor
ISEG - University of The Open University of HfTL University Leipzig, University of Pisa, Italy
Lisbon, Portugal Israel, Israel Germany federico.niccolini@unipi.it
ccurado@iseg.ulisboa.pt nitzage@openu.ac.il jokisch@hft-leipzig.de
KM2021 Program Committee Co-Chairs
Nitza Geri Jean-Henry Morin Melissa Carlton
The Open University of Israel, University of Geneva, Houston Baptist University,
Israel Switzerland USA
nitzage@openu.ac.il Jean-Henry.Morin@unige.ch mcarlton@hbu.edu
KM2021 Program Committee Members
Gunnar Auth HSF University of Applied Sciences, Germany
Dizza Beimel Ruppin Academic Center, Israel
Ofir Ben Assuli Ono Academic College, Israel
Ina Blau The Open University of Israel, Israel
Carlene Blackwood-Brown Seneca College, Canada
Marko Bohanec J ef S efan In i e, Sl enia
Celina Solek-Borowska Warsaw School of Economics, Poland
Michal Borowy Warsaw University of Life Sciences, Poland
-2-
Refereed Paper Proceedings - KM Conference 2021 – Leipzig, Germany
A Publication of the International Institute for Applied Knowledge Management
Steve Bronsburg Nova Southeastern University, USA
Shonda Brown Middle Georgia State University, USA
Brian Buckles National Defense University, USA
Fatih Çetin Nigde Ömer Halisdemir University, Turkey
Witold Chmielarz University of Warsaw, Poland
Dimitar Christozov American University of Bulgaria, Bulgaria
Malgorzata Cieciora Polish-Japanese Academy of Information Technology, Poland
Molly Cooper Ferris State University, USA
Carla Curado ISEG - University of Lisbon, Portugal
Beata Czarnacka-Chrobot Warsaw School of Economics, Poland
Bostjan Delak Faculty of information studies, Novo Mesto, Slovenia
Horatiu Dragomirescu Bucharest University of Economic Studies, Romania
Helena Dudycz Wroclaw University of Economics, Poland
Monika Eisenbardt University of Economics in Katowice, Poland
Yoram Eshet-Alkalai The Open University of Israel, Israel
Ruti Gafni Tel-Aviv Yaffo Academic College, Israel
Michal Golinski Warsaw School of Economics, Poland
Jose Luis Guerrero-Cusumano Georgetown University, USA
Julita Haber Fordham University, USA
Meliha Handzic International Burch University, Bosnia and Herzegovina
Wilnelia Hernandez WH-Consulting, Puerto Rico
Angel Hueca Carnegie Mellon University, USA
Pedro Isaias University of New South Wales (UNSW Sydney), Australia
Dorota Jelonek Czestochowa University of Technology, Poland
Oliver Jokisch Leipzig University of Telecommunications (HfTL), Germany
Gila Kurtz HIT - Holon Institute of Technology, Israel
Yair Levy Nova Southeastern University, USA
Christiaan Maasdorp Stellenbosch University, South Africa
Eliel Melon University of Puerto Rico, Puerto Rico
Federico Niccolini University of Pisa, Italy
Sergio Nunes ISEG - University of Lisbon, Portugal
Mírian Oliveira Pontifical Catholic University of Rio Grande do Sul - PUCRS, Brazil
Ilona Paweloszek Czestochowa University of Technology, Poland
Paula Peres Polytechnic Institute of Porto, Portugal
Michal Pietrzak Warsaw University of Life Sciences, Poland
-3-
Refereed Paper Proceedings - KM Conference 2021 – Leipzig, Germany
A Publication of the International Institute for Applied Knowledge Management
Margarida Piteira ISEG - University of Lisbon, Portugal
Przemyslaw Polak Warsaw School of Economics, Poland
Tommy Pollock Nova Southeastern University, USA
Daphne Raban University of Haifa, Israel
Michelle Ramim Nova Southeastern University, USA
Gilad Ravid Ben Gurion University of the Negev, Israel
Vincent Ribiere Bangkok University, Thailand
Meir Russ University of Wisconsin - Green Bay, USA
Joanna Santiago ISEG - University of Lisbon, Portugal
Dara Schniederjans University of Rhode Island, USA
Tamar Shamir-Inbal The Open University of Israel, Israel
Ingo Siegert Otto von Guericke University, Germany
Marcin Sikorski Gdansk University of Technology, Poland
Vered Silber-Varod The Open University of Israel, Israel
Anna Soltysik-Piorunkiewicz University of Economics in Katowice, Poland
K. Subramani West Virginia University, USA
Eduardo Teixeira University of the West of Santa Catarina - UNOESC, Brazil
Mathupayas Thongmak Thammasat Universit, Thailand
Bruce Watson Stellenbosch University, South Africa
Nathan White Central Washington University, USA
Amir Winer The Open University of Israel, Israel
Jedrzej Wieczorkowski Warsaw School of Economics, Poland
Ewa Ziemba University of Economics in Katowice, Poland
Rina Zviel-Girshin Ruppin Academic Center, Israel
We would like to thank all the Program Committee (PC) members for their outstanding scholarly
reviews and dedicated feedback to the authors!
-4-Refereed Paper Proceedings - KM Conference 2021 – Leipzig Germany
A Publication of the International Institute for Applied Knowledge Management
Is the “learning organization” still a good concept?
A historical analysis of the LO conceptual ontogenesis
[Research-in-Progress]
Sitthimet Solthong, Bangkok University, Thailand, sitthimet@gmail.com
Xavier Parisot, IKI-SEA, Bangkok University, Thailand, xavier.p@bu.ac.th
Abstract
The terms “organizational learning” and “Learning Organization” (LO) have been used as
interchangeable concepts over a long period. Even after the clarification and discrimination of
these two concepts in the literature, some confusion remains. Indeed, the definitional scope of the
LO concept has varied greatly over the past three decades. To confirm which Defining Attributes
(DA) are at the core of the concept, 40 historical definitions have been selected from 1989 to 2018.
Their DA are identified. Similar DA are grouped and their frequencies are calculated. The most
frequent DA are considered to be at the core of the LO conceptual definition. Among all the
analyzed definitions, one definition encompasses all these core DA. The goodness of this definition
is evaluated using Gerring’s eight parameter framework. The results show that the
conceptualization of the LO based on its most frequent DA leads to moderate to high scores for
seven parameters (familiarity, resonance, coherence, depth, differentiation, theoretical utility, and
field utility) and below an average score for one parameter (parsimony). This historical approach
of the LO conceptual ontogenesis allows one to discriminate between the core and the peripheral
DA and therefore to refocus its definition on specific phenomena. The analysis of the relevancy of
these core DA also demonstrates that the goodness of the LO concept still can be improved by
removing the multiple historical changes applied to its definitions.
Keywords: Learning organization, ontology, concept formation, defining attributes, c ce
goodness.
Introduction
The learning organization concept is gaining attention in the early 1990s, evolving from the
Organizational Learning (OL) concept. Senge (1990) first introduced the term Learning
Organization (LO) i 1990 f a ac i i e i f ie . LO practitioners have then
proposed different models of the LO based on a large diversity of criteria resulting in multiple
definitions of the LO and different sets of DA. Since LO practitioners and academics have not yet
reached a consensus and adopted a common definition, the validity and reliability of the concept
is debatable. Moreover, this situation hampers further theorization and weakens the theoretical
utility of the LO concept. Consequently, the existing diversity of LO definitions challenges the
scientific development in that field. Therefore, the objective of this paper is twofold. First, it aims
-5-Refereed Paper Proceedings - KM Conference 2021 – Leipzig Germany
A Publication of the International Institute for Applied Knowledge Management
at identifying which DA commonly qualify the LO c e concept across history. To achieve such
a goal, 40 definitions are selected. The identification of the most frequent DA, exploited in these
definitions, allows one to clarify which phenomena are under common scrutiny i.e., to focus on a
common ontology. Second, based on Gerring (1999) framework, the LO conceptual goodness is
re-evaluated using the most frequent DA which constitute the LO common ontology. Gerring
(1999) specifies that concept formation refers to the choice and evolution of terms, attributes and
e i ie hich defi e he c ce . The ef e, i g d e cannot be reduced to clarity, to
empirical or theoretical relevance, or to a set of rules, or to the methodology particular to a given
d (Gerring, 1999, p. 357) but rather to a standard set of criteria to support the conceptual
adequacy with the reality it describes. The origin of the 40 definitions selected (ranging from 1989
to 2018) shows that the LO is most a ac i i e d i e c ce a d ha i e defi i i
historically has emerged to serve divergent purposes of LO practitioners observing different
realities. Among the 40 definitions analyzed, one encompasses all the core and most of the
peripheral DA, Watkins and Marsick (1993), which constituted a good reference for a common
ontology. The LO conceptual goodness evaluation using Gerring (1999) criteria shows good
levels of familiarity, resonance, depth and semantic field utility. Internal coherence and external
differentiation present a high level of goodness at the expense of parsimony. The theoretical utility
i . I deed, he k e i i g Wa ki a d Ma ick (1993) e ec i e ca be
considered since the conceptual goodness of their definition is assessed here. Since these authors
are practitioner oriented, results based on their perspectives tend to enrich their model and not the
theory. Therefore, the conceptual goodness of the LO, based on a definition encompassing the
most frequent DA, is mostly high. However, a consensus regarding this conceptual definition
remains to be reached to increase its theoretical utility. Also, a better balance between internal
coherence and external differentiation with parsimony could be explored. The present analysis of
he LO c ce historical DA is aimed: 1) to characterize a common ontology for the concept
based on the most frequent DA, 2) to put aside all concept stretching, morphing, or expanding, 3)
to remove conflicting definitions by determining which one best encompasses the most frequent
DA in order to establish the most accurate definition as a standard to aid in future research in this
field, 4) to demonstrate a decent level of concept goodness of this most accurate definition, and 5)
to re-open the way for further theorization based on a consensual definition.
Literature Review
Difference Between LO and OL
The discrimination between the concepts of OL and the concept of the LO began with the work of
Senge (1990). Even if the OL and LO concepts are quite related, they are separate. However, these
two concepts are frequently used interchangeably in the literature (Goh, 2001). A number of
theorists such as Jones and Henry (1994), Tjopkenkama and Wongnum (1996) in Stewart (2001)
attempts to draw distinctions or established some relationship between these two concepts.
Örtenblad (2001) summarizes three main distinctions differentiate the OL from the LO; 1) the
character of the content, 2) the amount of normativity, and 3) the nature of the target. He mentions
-6-Refereed Paper Proceedings - KM Conference 2021 – Leipzig Germany
A Publication of the International Institute for Applied Knowledge Management
that these distinctions are not empirical, they are rather normative. However, they allow the
concept of LO to start its ontogenesis.
LO Conceptual Development
Since the seminal definition of Senge, many scholars have focused their works on the
conceptualization of the LO (Marsick & Watkins, 2003). The LO discipline has been expanding
and is bec i g a very big conceptual catchall to help us make sense a set of values and ideas
e e bee e i g i h, e e hi g f c e e ice c ae e i e e a d
eed (Kieche , 1990, .133). The recent literature supports the fact that this growing diversity is
the result of the application of the seminal definition using different practical or theoretical
perspectives (Hoe, 2020; Klimaszewski, 2019; Lis, 2019). The LO is also criticized as an ill-
defined concept (Stewart, 2001) which still lacks clarity and confuses processes and practices
(Garvin, 2000; Smith & Tosey, 1999). Moreover, different perspectives have been identified in the
conceptualization of the LO such as the learning perspective, the strategic perspective and the
integrative perspective (Yang et al., 2004). Dibella (2015) classified the LO using three main
perspectives; normative perspective, developmental perspective and capability perspective. Under
the normative view, organization must foster the right conditions and culture to become a learning
organization. Under the developmental view, the learning organization can only be achieved
through longitudinal changes. The capability perspective contradicts the two previous perspectives
as it focuses only on present behaviors and processes, and states that no specific set of learning
styles is better than others. The normative view focuses on how the organization achieve its own
vision of the future through the development of the desired competencies to reach its goal. LO
practitioners following the normative perspective such as Senge (1990), Pedler et al (1989), Garvin
(1993), and Goh (1998) have proposed their own set of the LO criteria. This diversity of criteria
proposed around the same concept but in different models constitutes good examples of concept
morphing and expanding. Each set of criteria and DA proposes a different definition.
LO Definition Challenges
Osigweh (1989) argued ha he de e e f c ea defi i i f c ce i i a
i i g ga i a i a e ea ch a d he b i di g (p. 580). However, a clear definition of
LO ha ed be e i e e he ea (Garvin, 1993, p. 79). Gherardi (2020) summarized
the challenges of the LO conceptual development by stating that:
investigating the various contributions on the LO, various definitions and perspectives can
be identified. In the 1980 1990s, discussions abounded on the very essence, purpose,
effectiveness and the methodologies of learning organization. These approaches focused
on discovering the very nature of learning organization and knowledge, often creating
strong confrontations among epistemologies, ontologies and approaches. (p. 458)
The existing diversity of LO definitions hamper a switch from conceptualization to theorization.
Moreover, it suggests the existence of some concept stretching/morphing/expanding. Santa (2015)
states that concept formation analysis is still necessary and proposes one. However, his choice of
the good theory as a framework of analysis places his interpretation at the theoretical level and
-7-Refereed Paper Proceedings - KM Conference 2021 – Leipzig Germany
A Publication of the International Institute for Applied Knowledge Management
clearly not at the conceptual level. Furthermore, Santa (2015) was analyzing different models
criteria with a framework designed to analyze theories. Theories and models are not of the same
nature and tend to be developed from opposite directions: model mainly emerges from empirical
observations when theory emerges from concepts combinations. Therefore, Santa (2015)
findings that the LO fails to meet the g d he c i e ia is debatable. Consequently, Sa a
(2015) main objective, which is to provide a formal definition, remains to be reached. The
ontological analysis of the LO core DA has not been achieved yet, and the concept remains unclear.
In synthesis, several sources of confusion add up in the formation of the concept of LO:
1. Diversity of conceptual meanings that leads to multiple definitions of LO.
2. Diversity of ontological, epistemological, and methodological perspectives applied which
reinforces the plurality of definitions and applications of the LO practices.
3. Diversity of the criteria chosen to define the LO within each perspective which leads to
different applications of the LO in the organization.
Methodology
To overcome these sources of confusion, discriminate the core from the peripheral DA and, come
up with a formal definition, the present article applies an analysis of the historical definitions,
determine a common ontology, and evaluate the LO concept goodness of the best definition using
Gerring (1999) framework. The following methodological steps have been applied: 1) selection
of 40 LO historical definitions from 1989 to 2018, 2) extraction and categorization of the DA, 3)
calculation of the DA frequencies, 4) determination of the best definition based the most frequent
DA, and 5) concept goodness analysis of the best definition by grading the eight parameters of
Ge i g (0-5 point scale). The 29 definitions by Santa (2015) based on an integrative literature
review are selected and enriched by 11 definitions through the expansion of the time range of the
review from 1989 to 2018. The DA of each definition are then listed and categorized. Those with
similar meanings are grouped using the same term or a new term to cover the overall meanings of
similar terms. The frequency of each attribute is calculated. The DA are categorized based on their
frequencies, between: 1) the core DA (most frequent), 2) the peripheral DA (average frequency),
3) the outsiders (low frequency). The outsider DA pertaining to similar approaches are grouped.
The core and peripheral DA are used to search which definition(s) among the selected ones
encompass them the most. This best definition is then a a ed i g Ge i g f a e k
determine its goodness. Based on Ogden and Richards (1923) s semantic triangle (term, intension,
extension), and on Sartori (1984) ork (i.e., term/word, meaning, referent/object), Gerring (1999)
states that a good concept should have a proper alignment between: a) the extension: events or
phenomena to be defined; b) the intension: properties or attributes that defines them; and c) the
term/label covering both a and b (see Figure 1).
-8-Refereed Paper Proceedings - KM Conference 2021 – Leipzig Germany
A Publication of the International Institute for Applied Knowledge Management
Figure 1. The Semantic Triangle
(adapted from Ogden & Richards, 1923; Sartori, 1984; Gerring, 1999; Dumez, 2011)
Gerring (1999) has extended the three pillars of Ogden and Richards (1923) by proposing eight
criteria of conceptual goodness (see Figure 2).
Figure 2. Connections Between the Semantic Triangle (Ogden & Richards, 1923) and Gerring
(1999) Eight Criteria of Conceptual Goodness (Parisot, 2019).
The level of goodness of the best definition is assessed by grading each of these criteria from 0
to 5 . The grading parameters for each criterion are specified based on Gerring (1999)
suggestions. The establishment of a grading scale for each criterion and their sub-criteria helps
decrease divergent measurements of their level of goodness and increase both the validity and
reliability of concept goodness analysis.
Results
The analysis of the 40 selected definitions reveals 85 different DA after the grouping of equivalent
attributes. Using their frequency of appearance, these attributes are categorized into three groups:
core (11 attributes), periphery (five attributes) and outsiders (69 attributes). The outsiders regularly
mobilized together are grouped into four categories (see Figure 3).
-9-Refereed Paper Proceedings - KM Conference 2021 – Leipzig Germany
A Publication of the International Institute for Applied Knowledge Management
Core Defining Attributes of Learning Organization
Implementation Vision
System Outsider Strategy/Policy
Committed Leadership/Facilitate
Objectives/Goals Periphery Aspiration/Transform
Control/Mechanism Structure
Characteristics
Task/Job Enabler Core Stakeholders
Behavior
Action
Learning Nurture
Organization
People
Change Capacity Create Knowledge
Global Market Innovation Competitive Advantage
Results Culture Process
Customer Needs Continuity Information
Products & Services Understanding
Development/Growth Insight/ Reflection
Benefits/Productivity Share/Transfer
Competition 69 attributes Critical/Review
Figure 3. Defining Attributes Encountered in 40 Definitions of the Learning Organization
The core attributes comprise 11 elements. Each of these attributes is present in 25.0% to 82.5% of
the definitions. The two leading attributes ga i a i a d ea i g are recurrent in 82.5%
and 65% respectively. The nine remaining most frequent attributes by decreasing frequencies are
c i i (37.5%), e e (35.0%), e (35.0%), cha ge (32.5%), ca aci
(32.5%), c e (30.0%), e (30.0%), c ea e (25.0%) a d ce (25.0%)
respectively. The peripheral attributes in decreasing frequency include knowledge (20.0%), action
(20.0%), structure (20.0%), behavior (17.5%) and innovation (17.5%). The outsider group
contained 69 low-frequency attributes ranging from 2.5% to 15% of presence in the definitions.
Because of the large number of attributes in this group, definitions tend to group into four
categories: vision, competitive advantage, implementation, and global market. These outsider
categories were arbitrarily established based on the similar nature of the DA. As a result, four main
directions of expanded meaning are revealed. The use of 85 attributes to define the same concept
demonstrates the existence of a high level of discrepancy between the available definitions. This
tendency to expand the core attributes of the concept is quite usual in social sciences (Dumez,
2011). This overall diversity of attributes also reveals the multiplicity of meanings attached to the
concept of LO. In other words, if the term is constant, the definitional intensions and extensions
vary. As Gerring (1999) mentioned, the discrimination between formal/core DA (the intension)
and accompanying properties takes time. A poor alignment between the three dimensions of the
e a ic ia g e, a ee he e, e i a bad c ce f Ge i g (1999) perspective.
Consequently, it is difficult to determine which reality is effectively attached to the concept of LO.
Thus, the results obtained from empirical analysis based-on these different definitions are hardly
comparable. Moreover, a good connection between the concept of LO itself and other concepts in
the field is hampered and cannot lead to a good theorization process (Santa, 2015). When all the
considered definitions are analyzed based on the identified core and peripheral attributes, the
results show that only one definition encompasses almost all these DA: Watkins and Marsick
(1993). Their LO is:
- 10 -Refereed Paper Proceedings - KM Conference 2021 – Leipzig Germany
A Publication of the International Institute for Applied Knowledge Management
defined as one that learns continuously and transforms itself. Learning takes place in
individuals, teams, the organizations and even the communities with which the
organization interacts. Learning is a continuous, strategically used process, integrated with
and running parallel to, work. Learning results in changes in knowledge, beliefs, and
behaviors. Learning also enhances organizational capacity for innovation and growth. The
learning organization has embedded systems to capture and share learning. (Watkins &
Marsick, 1993, p. 8)
Watkins and Marsick (1993) definition encompasses all the core attributes and four out of five
of the peripheral attributes identified. Therefore, this definition is chosen as a formal definition
among the 40 analyzed. The level goodness of that particular definition is measured using Gerring
(1999) framework. Each of the eight criteria and their associated sub-criteria of conceptual
goodness are rated from 0 to 5 . The LO definition and its DA are rated between 2.5 to 4.3 on
eight parameters; Familiarity (3.7), Resonance (4.3), Parsimony (2.5), Coherence (3.7),
Differentiation (4.0), Depth (4.0), Theoretical Utility (3.0), Field Utility (4.0). The results are
presented in a radar chart (see Figure 4). The semantic triangle is also incorporated into the radar
chart by plotting the calculated and calibrated scores on each axis; Intension (2.9), Term (4.0) and
Extension (3.7).
Parsimony
5.0
4.5
4.0
Resonance 3.5
Coherence
Inte
3.0 nsio
n
2.5
2.0
1.5
1.0
0.5
Term
Familarity 0.0 Differentiation
i on
e ns
Field Utility Ex t Depth
Theoretical Utility
Figure 4. Results of the Grading of the Eight Criteria of Conceptual Goodness
1. Familiarity: The familiarity depends on the level to which the chosen terms/labels are
commonly understood and used in standard language. It determines the degree to which the
terms/labels chosen to formally define the LO 1) incorporates standards meanings of the
terms/labels learning and organization , and 2) avoids contradictions between these meanings.
These parameters are critical since they establish to what extent the description of the phenomenon
is accurate. The definition of Watkins and Marsick (1993) focuses mostly on the learning
term/label and encompasses all the standard meanings of learning . The standard meanings of the
term/label organization are implicitly integrated. No contradiction between these standard
meanings appears. Therefore, familiarity of the LO terms is rated a 3.7. Watkins and Marsick
- 11 -Refereed Paper Proceedings - KM Conference 2021 – Leipzig Germany
A Publication of the International Institute for Applied Knowledge Management
(1993) LO definition uses well-understood existing terms that can generally explain the
phenomena in question
2. Resonance: The resonance is enhanced by reference to nearby terms: learning and
organization . These adjacent terms create a catchy label that is easy to understand and remember
and generate a good resonance. This new combination of terms resonates with a multiplicity of
meanings in the common language which decreases the level of differentiation of the concept from
others, e.g., organizational learning. Moreover, the terms learning and organization could be
spontaneously connected to many images, sounds and memories. Therefore, the non-semantic
resonance is here very high. The final resonance score is 4.3.
3. Parsimony: Even with the best definition of LO from Watkins and Marsick (1993), ten DA are
at the core and five at the periphery. This quite important number of DA decreases drastically the
parsimony. The challenge here is to balance the number of DA with the diversity of empirical
objects or phenomena that the terms are supposed to cover. However, the LO pertains to that
singular category of concepts where parsimonious alternatives are not eligible i.e., a definition
with less DA would not describe properly all the relevant empirical objects or phenomena which
the concept covers. That situation is clearly revealed by the continuous expansion f he LO DA
in the literature. The final parsimony score is 2.5.
4. Coherence: A large majority of the DA chosen by Watkins and Marsick (1993) defines the term
learning in the organizational context. In their definition, only the first sentence is about the LO
i e f: i defi ed a e [organization] that learns continuously and transforms i e f (p. 8) and
constitutes the core essential meaning of the LO. Because of the multivalent meanings of the term
learning , the authors are constrained to specify the types of learning which are encompassed in
their definitions to ensure a good connection between the concept and the empirical objects or
phenomena it covers. H e e , if hi i i g c a ifie he c ce a b da ie , i d e c a if
how all these DA are connected to one another i.e., aligned, and which DA pertain to the learning
dimension, the organization dimension or both. Moreover, some DA are implicitly mobilized
without being really explicit e.g., the scale (individual, team, organizations) or the location
(employees vs. external communities) of the learning processes. Nevertheless, the DA chosen for
the LO belong to one another and their consistency is of good level. The alignment between so
many DA constitutes a weakness. The final coherence score is 3.7.
5. Differentiation: External differentiation is inversely achieved through internal coherence
between the DA. It is therefore difficult to talk about one without talking about the other. The main
challenge of Watkins and Marsick (1993) is to define the term learning to establish clear borders
of its meaning in the organizational context. This adjustment of the attributes of the intension helps
distinguish the LO from neighboring concepts such as organizational learning or simply
learning . If it increases the conceptual differentiation, it also decreases the parsimony and thus
increases the difficulty to maintain a good alignment between too many DA (internal coherence).
However, since the conceptual boundaries are clear, the distinction between the LO and
neighboring concepts is good and allow a good operationalizability. The important number of
- 12 -Refereed Paper Proceedings - KM Conference 2021 – Leipzig Germany
A Publication of the International Institute for Applied Knowledge Management
published applications of the Dimensions of the LO Questionnaire (DLOQ) based on this
conceptual definition is a good demonstration (Marsick, 2013). The final differentiation score of
LO is consistent at 4.0 in all three sub criteria.
6. Depth: The de h f he c ce i de e i ed b i abi i b d e cha ac e i ic . The
greater the number of properties shared by the phenomena in the extension, the greater the depth
f a c ce (Ge i g, 1999, .380). The DLOQ (Marsick & Watkins, 2003) is here again the
de a i ha he c ce abe i a g d h ha d f i e i a ce or characteristics
(seven pillars of the DLOQ). This conceptual depth increases its utility to explain the associated
empirical phenomena. The seven pillars of the DLOQ constitute characteristics common to all
organization. Consequently, the label: learning organization is descriptively powerful since it
allows to infer seven dimensions, each of them encompassing several characteristics. Depth and
parsimony need to be balanced in concept formation as the more parsimonious are the DA, the less
deep is the concept. In the present case, the high numbers of the DA reinforce the depth of the
conceptual definition. Finally, since Watkins and Marsick (1993) do not make the mistake to
define the concept by what it is not, the LO is not a residual concept. The depth score of LO concept
is consistently high at 4.0.
7. Theoretical Utility: The LO concept is not theory-driven since its concept inception comes
f a ac i i e e ec i e in the case of Watkins and Marsick (1993). However, the multiple
uses of the LO concept to infer theories (e.g., Easterby-Smith et al., 1999; Finger & Brand, 1999;
Jensen, 2005; Santa, 2015; etc.) and their diversity (Yeo, 2002, 2005) demonstrate largely the
theoretical utility of the LO concept. One major drawback is that these theorization processes often
mobilize several different conceptual definitions. This situation leads to 1) a difficulty to measure
to what extent Watkins and Marsick (1993) conceptual definition derives its utility from its
position within a broader array of terms mobilized in the theorization process, 2) a loss of internal
coherence and external differentiation in the connection between the concepts which generate
theories with high variable levels of internal and external validity. Therefore, the score of the
theoretical utility is 3.0.
8. Semantic Field Utility: In social sciences, very few concepts are really new. Consequently,
conceptualization often takes the form of reconceptualizing what we already know. Since the task
of definition consists of establishing connections with neighboring terms (Gerring, 1999), it is
impossible to redefine one term without affecting the others. This process is necessary to better
c ec a he e defi i i pertaining to the same semantic field. In the case of the LO, the
multiple redefinitions lead to numerous resettling of both semantic fields in which the terms
learning and organization are located. It is therefore difficult to avoid the confusion generated
by these multiple resettling and to assess the semantic impact of only the definition of Watkins
and Marsick (1993). To overcome this issue, the semantic adjustments operated by these authors
only must be considered. In that regard, the authors has established very clearly the definitions of
all terms attached to both semantic fields in their book (Watkins & Marsick, 1993). Therefore, the
semantic field utility is graded 4.0.
- 13 -Refereed Paper Proceedings - KM Conference 2021 – Leipzig Germany
A Publication of the International Institute for Applied Knowledge Management
Discussion and Conclusion
Even though the LO concept has been evolved both in theory and in practice for over 30 years, its
concept is still alive but may be under different names such as knowledge management and
dynamic capability (Pedler & Burgoyne, 2017). This concept stretching and expanding has led to
the diversification of LO definitions. Therefore, the DA of 40 definitions of the LO are extracted
and categorized to exemplify this diversity. As a result, 11 redundant core elements are identified
across all the definitions. These core attributes are enough to distinguish the concept of LO from
neighboring concept such as OL. Moreover, their identification allows one to establish a common
ontology for the concept, i.e., to determine what objects /phenomena /realities are recurrently under
scrutiny. This DA categorization by frequency also enables the researchers to put aside all concept
stretching, morphing, and expanding which are polluting the core definition of the concept. Based
on this elimination, conflicting definitions can be removed and one formal definition can be
established as a standard. This standardization is of critical importance for the relevancy of further
theorization and modelization and therefore participating in the progress needs to switch from
description to prediction. One definition only encompasses all the core and most of the peripheral
DA (Watkins & Marsick, 1993). Concept goodness analysis applied to that definition reveals a
moderate internal coherence and high external differentiation at the expense of a low parsimony.
Familiarity, resonance, depth and semantic field utility present decent to high levels. The
theoretical utility is poor. However, this weakness is justified since Watkins and Marsick (1993)
and their followers are not theorists. They focus on the enrichment of the DLOQ model. In
conclusion, the goodness of the concept based on the selected formal definition is high. Therefore,
this definition could constitute a good standard for further theorization. The evaluation of concept
goodness being applied per the authors presents a limitation of this paper. This assessment should
be confirmed by KM experts to improve its validity and reliability in order to eliminate this major
limitation. Filling the gap of the LO conceptualization constitutes only a first step. The analysis of
the available theorizations (Yeo, 2002, 2005) using the formal definition identified would help
better connect the LO with all the connected concepts. As a result, theorization could be
empowered and would remove the need for all the stretching, morphing and expanding observed
at the conceptual level.
References
Cuel, R. (2020). A journey of learning organization in social science: Interview with Silvia
Gherardi. The Learning Organization, 27(5), 455 461.
DiBella, A. J. (1995). Developing learning organizations: A matter of perspective. Academy of
Management Best Papers Proceedings, 195(1), 287 290.
Dumez, H. (2011). Qu'est-ce qu'un concept?. Le Libellio d'AEGIS, 7(1, Printemps-Supplément),
67 79.
Easterby-Smith, M., Araujo, L., & Burgoyne, J. (1999). Organizational learning and the
learning organization: Developments in theory and practice. Sage.
- 14 -Refereed Paper Proceedings - KM Conference 2021 – Leipzig Germany
A Publication of the International Institute for Applied Knowledge Management
Finger, M., & Brand, S. B. (1999). The concept of the learning organization applied to the
transformation of the public sector: Conceptual contributions for theory development. In
M. Easterby-Smith, J. Burgoyne & L. Araujo (Eds.), Organizational learning and the
learning organization: Developments in theory and practice (pp. 130 156). Sage.
Garvin, D. A. (1993). Building a learning organization. Harvard Business Review, 71(4), 73 91.
Garvin, D. A. (2000). Learning in action: A guide to putting the learning organization to work.
Harvard Business School Press.
Gerring, J. (1999). What makes a concept good? A criterial framework for understanding
concept formation in the social sciences. Polity, 31(3), 357 393.
Goh, S. C. (1998). Toward a learning organization: The strategic building blocks. SAM Advanced
Management Journal, 63(2), 15 22.
Goh, S. C. (2001). The learning organization: An empirical test of a normative perspective.
International Journal Organization Theory and Behavior, 4(3 4), 329 355.
Hoe, S. L. (2020). The topicality of the learning organization: Is the concept still relevant today?
Research Collection School of Information Systems, 1, 19 32.
Jensen, P. E. (2005). A contextual theory of learning and the learning organization. Knowledge
and Process Management, 12(1), 53 64.
Kiechel, W., & Field, N. E. (1990). The organization that learns. Fortune, 121(6), 133 135.
Klimaszewski, J. (2019). Analysis of the new concept of learning organization. Бухгалтерский
у ет и анализ, (4), 28 31.
Lis, A. (2019). Mapping leading and emerging topics of research on the learning organization
concept. Organizations in the Face of Growing Competition in the Market; Ujwary-Gil,
A., Potoczek, N., Eds, 57 84.
Marsick, V. J., & Watkins, K. E. (2003). Demonstrating the value of an organization's learning
culture: The dimensions of the learning organization questionnaire. Advances in
Developing Human Resources, 5(2), 132 151.
Ogden, C. K., & Richards, I. A. (1923). The meaning of meaning: A study of the influence of
language upon thought and of the science of symbolism. Harcourt, Brace & World, Inc.
Örtenblad, A. (2001). On differences between organizational learning and learning organization.
The Learning Organization, 8(3), 125 133.
Osigweh C. A. B, YG. (1989). Concept fallibility in organizational science. Academy of
Management Review, 14(4), 579 594.
Parisot, X. (2019). Conceptualization [Paper Presentation]. Ph-D KIM seminar, Bangkok.
- 15 -Refereed Paper Proceedings - KM Conference 2021 – Leipzig Germany
A Publication of the International Institute for Applied Knowledge Management
Pedler, M., Boydell, T., & Burgoyne, J. (1989). Towards the learning company. Management
Learning, 20(1), 1 8.
Pedler, M. & Burgoyne, J. (2017). Is the learning organisation still alive? The Learning
Organization, 24(2), 119 126.
Santa, M. (2015). Learning organi a i e ie : A g d he e ec i e. The Learning
Organization, 22(5), 242 270.
Sartori, G. (1984). Guidelines for concept analysis. In G. Sartori (Ed.), Social science concepts:
A systematic analysis (pp. 15 85). Sage Publications.
Senge, P. M. (1990). The fifth discipline: The art and practice of the learning organization.
Doubleday/Currency.
Smith, P. A.C. & Tosey, P. (1999). Assessing the learning organization: Part 1-theoretical
foundations. The Learning Organization, 6(2), 70 75.
Stewart, D. (2001). Reinterpreting the learning organisation. The Learning Organization, 8(4),
141 152.
Watkins, K. E. & Marsick, V. J. (1993). Sculpting the learning organization: Lessons in the art
and science of systemic change, Jossey-Bass.
Yang, B., Watkins, K. E., & Marsick, V. J. (2004). The construct of the learning organization:
Dimensions, measurement, and validation. Human Resource Development Quarterly,
15(1), 31 55.
Yeo, R. K. (2002). Learning within organizations: Linking the theoretical and empirical
perspectives. Journal of Workplace Learning 14(3), 109 122.
Yeo, R. K. (2005). Revisiting the roots of learning organization. The Learning Organization,
12(4), 368 382.
Authors Biographies
Sitthimet Solthong is currently a Ph.D. student at Knowledge Management and
Innovation Management (KIM) program, Bangkok University. His expertise is in
areas of coaching, communication and innovation where he works as an executive
coach and professional trainer for both multinational and public-listed companies.
Xavier Parisot, Ph.D. is the Program Director of the Master in Business Innovation
(MBI) at Bangkok University. He is Assistant Professor at the Graduate School,
where he teaches Innovation Management and Strategy. He also teaches Ontology,
Epistemology, Research Design and Innovation Management in the Knowledge
Management and Innovation Management (KIM) PhD Program.
- 16 -Refereed Paper Proceedings - KM Conference 2021 – Leipzig Germany
A Publication of the International Institute for Applied Knowledge Management
Identifying skills gaps and predicting practicum
performance in a graduate program using a survey
instrument designed around health informatics domains
and competencies
[Research-in-Progress]
Stephen E. Bronsburg, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova
Southeastern University, USA, bronsbur@nova.edu
Michelle Ramim, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern
University, USA, ramim@nova.edu
Abstract
The interdisciplinary field of health informatics began around the 1960s when computers were
sophisticated enough to handle larger amounts of data. In 2012, the American Medical Informatic
Association (AMIA) Board published a White Paper outlining health informatics core-
competencies for graduate programs. Following this paper, in 2014 the AMIA and the Commission
on Accreditation for Health Informatics and Information Management (CAHIIM), together revised
existing health informatics curriculum requirements to three co-mingled domains: health,
information science and technology, as well as social and behavioral science. This work-in-
progress research will utilize these three foundational domains, and the cross academic domains
(10 in total) as the basis for a newly developed survey instrument, which will be administered to
graduate health informatic students prior to their final core course, the practicum. Each survey
questions will be weighted in the rubric.
Keywords: Health informatics, health informatics practicum, informatics competency rubric,
predicting practicum performance, knowledge flow.
Introduction
Health informatics is defined as a field of information science concerned with the management of
all aspects of healthcare data and information through the application of computers as well as
computer technology (AMIA, 2020). Health informatics is a fast-growing emerging field
comprised of a basic conceptual framework of technology, informatics, and clinical expertise
(Kharrazi et al., 2018; Sweeney, 2017). It is a broad term often appearing by other names, including
clinical informatics, biomedical informatics, medical informatics, nursing informatics, etc. Health
informatics utilizes technology at three different levels. The first level is bioinformatics, which
utilizes information technology at the molecular and cellular level, which has been used by
researchers extensively in protein modeling, genome mapping, and drug design. Next, is
17Refereed Paper Proceedings - KM Conference 2021 – Leipzig Germany
A Publication of the International Institute for Applied Knowledge Management
medical/clinical informatics whose focus is on the individual patient. An example is the
development, implementation, and maintenance/safeguard of the Electronic Health Record (EHR)
systems. Public health informatics is the third level. It is more macro in scope and uses informatics
impacting the population aiming to help improve the practice of public health through
epidemiologic/disease surveillance. It also includes tracking systems and consumer informatics.
In healthcare organizations, knowledge and information flow vertically from managers to
informatics worker about organizational practices. At the same time, horizontal knowledge as well
as information flow between clinical and informatics workers in the organization to support overall
a ie quality of care (Russ, 2021). Knowledge, Skills, and Attitudes/Abilities (KSAs) are at
the core of professional workers. In the context of informatics, the KSAs are shared by multiple
informatics subdisciplines and are critical to succeed as health informatics and health informatics
professionals (Bronsburg et al., 2018). In preparation of the informatics workforce, programs
practicum have been positioned to measure students level of acquired KSAs within the three
foundational domains, key indicators of workforce preparedness. A practicum is defined as a:
Professional Practice Experiences (PPE) that must be designed and supervised to reinforce
didactic instruction and must include program-coordinated experience at professional
practice sites. The program must describe how the PPE (e.g., directed practice experience)
is designed, supervised and evaluated, and name the objectives to be achieved in each PPE
course. (CAHIIM, 2018, p. 9)
The practicum assessment is a two-parts process aims a e de KSA status using two
surveys: one for the industry evaluator (i.e., practicum preceptor) and another one for the student.
Currently, there is no single standardized process to assess the practicum outcome and ensure that
students who completed the practicum indeed met the KSAs objectives (Sapci & Sapci, 2020), nor
is there a particular state license exam required as in other healthcare education disciplines (i.e.
nursing, pharmacy). A practicum is a professional opportunity where students are guided by a site
preceptor on a particular health informatics project (publichealth.org). The practicum is also an
opportunity for the site practicum preceptor (i.e. the student evaluator) to share information and
knowledge with students about how various technologies facilitate knowledge flows supporting
quality of healthcare delivery. During the practicum, students learn specific skills set and practices
(Shahmoradi et al., 2017). Thus, during the practicum, students and site preceptors interactions
promote the knowledge and information sharing. In this paper, we provide an overview of the
health informatics program accreditation and then outline a newly proposed process to further
i e he de KSA a e e the completing practicum.
Health Informatics Program Accreditation
The American Medical Informatics Association (AMIA) is the leading professional organization
for healthcare informatics. AMIA is pivotal to health informatics science, education, and research
as well as for practicing informatics professionals. The Commission on Accreditation for Health
Informatics and Information Management (CAHIIM) is the administrative governing body of
18Refereed Paper Proceedings - KM Conference 2021 – Leipzig Germany
A Publication of the International Institute for Applied Knowledge Management
AMIA responsible for accrediting academic programs. For the purpose of this research, we follow
the AMIA Accreditation Committee (2017) definition for competency as an observable ability
of a health professional, implementing multiple components such as knowledge, skills, values, and
a i de i ce c e e cie a e observable, they can be measured and assessed to ensure their
acquisitions (p. 3). CAHIIMs three foundational domains for health informatics master programs
are: (F1) health, (F2) information science and technology, and (F3) social and behavioral science.
According to AMIA the three domains define health informatics by their intersections (See Figure
1). It is expected that graduates of health informatics programs will have a working knowledge of
the three foundational domains by demonstrating KSAs for each one, their intersection concepts,
and the cross domains. The three foundational domains interact and intersect affecting each other
resulting in four additional domains. These include: (F4) health information science and
technology, (F5) human factors and social-technical systems, and (F6) social and behavioral
aspects of health. A more complex scenario is when all three foundational domains (F1-F3)
intersect, which is represented by the cross domain of (F7) social and behavioral, and information
science and technology applied to health. Graduate students are also expected to demonstrate
KSAs in the cross domains of (F8) professionalism, (F9) interprofessional collaborative practice,
and (F10) leadership (AMIA Accreditation Committee, 2017).
Figure 1. Health Informatics Foundational Domains (Adopted from AMIA Accreditation
Committee, 2017)
Each of the health informatics cross domains includes mapping to a specific KSA relevant to
graduates of the accredited program. Table 1 provides a more detailed list of all the KSAs and their
associated cross domains.
19Refereed Paper Proceedings - KM Conference 2021 – Leipzig Germany
A Publication of the International Institute for Applied Knowledge Management
Table 1. Knowledge, Skills, and Attitudes/Abilities (KSAs) Expected from Health Informatics
Graduates (AMIA Accreditation Committee, 2017)
Cross Domain Statement of KSAs Expected:
Knowledge
Attitude
A he ime of g ad a ion f om an a lied ma e of cience in heal h
Skill
informatics program, the graduate den ho ld be able o . (pp. 5-
14)
F1. Health De c ibe he hi , g a , e h d (i c di g da a a d i f ai ed a d
produced), and current challenges of the major health science fields. These include
biology, genomics, clinical and translational science, healthcare delivery, personal
X
health, a d a i hea h (p. 5)
F2. Information Identify the applicable information science and technology concepts, methods, and
Science and tools, which may be dependent upon the application area of the training program, to
Technology solve health informatics problems. These include the concepts, methods, and tools
related to managing data, information, and knowledge, the basic information and
X
computer science terms and concepts, the principles of information security, as well
as the methods f a e i g e i f ai eed (p. 6)
F3. Social and Identify the effects of social, behavioral, legal, psychological, management,
Behavioral cognitive, and economic theories, methods, and models applicable to health
Science informatics from multiple levels including individual, social group, and society (p.
X
7)
F4. Health Identify possible biomedical and health information science and technology
Information methods and tools for solving a specific biomedical and health information problem.
Science and Core health information technology tools may be dependent upon the application
X
Technology area of the training program ( . 8)
Design a solution to a biomedical or health information problem by applying
computational and systems thinking, information science, and technology ( . 8)
X
Demonstrate consideration of the advantages and limitations of using information
science and technology to solve biomedical and health information problems as well X
as the needs of the different stakeholders and context ( . 8)
F5. Human Da ci ech ica k edge ega di g he cia beha i a cie ce a d
Factors and human factors engineering to apply to the design and implementation of information X
S ci ech ica systems and technology ( . 9)
Systems Apply social behavioral theories and human factors engineering to the design and
evaluation of information systems and technology ( . 9)
X
Demonstrate consideration and respect for the role of users in the design and
application of information systems and technology ( . 9)
X
F6. Social and Identify theories or models that explain and modify patient or population behaviors
Behavioral related to health and health outcome ( . 10)
X
Aspects of Apply models, which may be dependent upon the application area of the training
Health program, to address social and behavioral problems related to health of individuals, X
populations, and organizations ( . 10)
Acknowledge the importance of social and behavioral aspects of health and their
contribution to the health of individuals and populations ( . 10)
X
F7. Social, Identify the theories, models, and tools from social, business, human factors,
Behavioral, and behavioral, and information sciences and technologies for designing, implementing,
Information and evaluating health informatics solutions. Theories, models, and tools may be
X
Science and dependent upon the application area of the training program ( . 11)
Technology Integrate and apply the theories, models, and tools from social, business, human
factors, behavioral, and information sciences and technologies to design, implement,
X
20Refereed Paper Proceedings - KM Conference 2021 – Leipzig Germany
A Publication of the International Institute for Applied Knowledge Management
Applied to and evaluate health informatics solutions. Theories, models, and tools may be
Health dependent upon the application area of the training program ( . 11)
Demonstrate an awareness of the interrelatedness of social, business, human factors,
behavioral, and information sciences and technology in the design, implementation, X
and evaluation of health informatics solutions ( . 11)
F8. Define and discuss ethical i ci e a d he i f a icia e ibi i he
Professionalism profession, their employers, and ultimately to the stakeholders of the informatics X
solutions they create and maintain ( . 12)
Demonstrate professional practices that incorporate ethical principles and values of
the discipline ( . 12)
X
Demonstrate awareness of the value of information literacy and lifelong learning,
maintenance of skills, and professional excellence ( . 12)
X
F9. Define and discuss the scope of practice and roles of different health professionals
Interprofessional and stakeholders including patients, as well as the principles of team science and X
Collaborative team dynamics to solve complex health and health information problems ( . 13)
Practice A e a i hi b i di g ki a d he i ci e f i e fe i a
communication in a responsive and responsible manner that supports a team X
approach to solve complex health and health information problems ( . 13)
Rec g i e he i a ce f a e ec a d ha ed a e , a e a e
role, the role of other professions and stakeholders including patients, and the role of
teamwork and team science to solve complex health and health information
X
problems (p. 13)
F10. Leadership Articulate the methods, concepts, tools, and characteristics of leading and
leadership ( . 14)
X
Employ leadership and followership methods, concepts, and tools to motivate others
toward accomplishing a health informatics vision ( . 14)
X
Demonstrate leadership behaviors for achieving a vision for health informatics
solutions ( . 14)
X
Case of Program Accreditation Implementation
In 2007, Nova Southeastern University College of Osteopathic Medicine (NSU COM), now the
Dr. Kiran C Patel COM (NSU KPCOM), launched the Master of Science in Biomedical
Informatics (MSBI) Program. In 2017, the MSBI Program acquired new administration and
direction focusing on developing a healthcare industry responsive curriculum that balanced both
skills and didactic learning while simultaneously pursuing program accreditation. The new
administration earmarked CAHIIM accreditation as a priority for the program. The first step was
mapping core courses to CAHIIM s established KSAs, which are housed in the accreditations co-
mingled domains (See Figure 1). The NSU KPCOM Department of Health Informatics MSBI
Program practicum course allows students the opportunity to select a hands-on health informatics
project focused on one of the three focus areas: (1) a project development, (2) a project
implementation, and (3) a project evaluation. A practicum, or a community based experienced,
seeks to:
(1) integrate theory and research with practice; (2) apply conceptual knowledge and skills
to real world problems; (3) acquire public health workplace experience; (4) enhance
knowledge, skills and professional roles; (5) gain new knowledge and skills working as
21You can also read
