Global Aspects and Cultural Perspectives on Knowledge Management: Emerging Dimensions

Global Aspects and Cultural Perspectives on Knowledge Management: Emerging Dimensions

Murray E. Jennex (San Diego State University, USA)
Release Date: May, 2011|Copyright: © 2011 |Pages: 464
ISBN13: 9781609605551|ISBN10: 1609605551|EISBN13: 9781609605568|DOI: 10.4018/978-1-60960-555-1

Description

Due to the recent advances in computers and technology as a whole, Knowledge Management has taken on a whole new importance. In our society we need KM to ensure that we apply knowledge correctly to differing situations and effectively use that information at the correct times.

Global Aspects and Cultural Perspectives on Knowledge Management: Emerging Dimensions presents new technologies, approaches, issues, solutions, and cases that can help an organization implement a knowledge management (KM) initiative or provide a knowledge base for the practitioner/academic researcher. This book presents the issues that drive the technologies, processes, methodologies, techniques, and practices used to implement KM in a variety of ways and in the multi-faceted modern environment that we find ourselves in today.

Topics Covered

The many academic areas covered in this publication include, but are not limited to:

  • Building web-based learning communities
  • Case studies of knowledge management and organizational memory systems
  • Cognitive theories of knowledge management and organizational memory
  • Constructivist approaches to web-based learning and teaching
  • Decision making in implementing Web-based learning and teaching
  • Enablers and inhibitors of knowledge sharing and knowledge transfer behaviors
  • Global issues in knowledge management and organizational memory
  • Management side of web-based learning and teaching
  • Methodologies and processes for developing knowledge management systems
  • Network learning using WLTT
  • Project management for implementing WLTT
  • Web-based CSCL

Reviews and Testimonials

This work is written for the advanced practitioner and student of knowledge management who is looking for information and knowledge on the new technologies, approaches, issues, and solutions that can help in the implementation of a knowledge management initiative. A detailed table of contents and index, along with informative chapter titles, will help the user find necessary information, while the compilation of references will assist in looking for more information.

– Sara Marcus, American Reference Books Annual, Volume 43

This book provides current research into KM [Knowledge Management]. Topics of chapters range from the use of social networking technologies to analysis of KM strategy to analysis of knowledge transfer and flow. A wide range of contexts are discussed, from schools to multinational organizations to small and medium enterprises, SMEs. Many different research methodologies are used, such as case studies, action research, and quantitative methods.

– Murray E. Jennex, San Diego State University, USA

Researchers in knowledge management relating to business, the military, and other realms discuss recent innovations in the field. Among them are using agent-based simulation and game theory analysis to study knowledge flow in organizations, a framework for managing the life cycle of knowledge in global organizations, assessing the impact of knowledge transfer mechanisms on supply chain performance, improving organizational competitiveness by capturing tacit knowledge from transient workers, investigating the impact of knowledge management factors on new product development, zooming in on the effect of national culture on knowledge sharing behavior, an experiment of information elaboration in mediated knowledge transfer, and a case study of knowledge management utilization at two Jordanian universities.

– Book News, Reference - Research Book News - August 2011

Table of Contents and List of Contributors

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Preface

Abstract

The knowledge pyramid has been used for several years to illustrate the hierarchical relationships between data, information, knowledge, and wisdom.  This chapter posits that the knowledge pyramid is too basic and fails to represent reality and presents a revised knowledge-KM pyramid. One key difference is that the revised knowledge-KM pyramid includes knowledge management as an extraction of reality with a focus on organizational learning. The model also posits that newer initiatives such as business and/or customer intelligence are the result of confusion in understanding the traditional knowledge pyramid that is resolved in the revised knowledge-KM pyramid.

Introduction

Much has been written on the knowledge pyramid, usually characterized as the data, information, knowledge, wisdom, DIKW, hierarchy and its use in knowledge management, KM.  This chapter continues this discussion but takes a different position. It is posited that the knowledge pyramid is an artifact of KM processes and not an artifact of reality. This is a different position in that several authors (Ackoff, Sharma, Bates,  Fricke) consider the pyramid as a natural expression of the relationships between DIKW and the logical progression for the generation of IKW (Ackoff, 1989; Bates, 2005; Miller, 1996; Sharma, 2004). This chapter proposes that the natural relationship between DIKW is actually an inverted pyramid and that the knowledge pyramid is an artificially constructed artifact representing the relationship between DIKW in an organizational KM context.  

Additionally, while the current knowledge pyramid expresses only the hierarchical relationships between DIKW, this chapter shows that the revised knowledge pyramid can also represent process flow relationships.  This is a conceptual chapter that hopes to promote discussion and insight by researchers into the nature of KM and its relationship to the overall processes of learning.  It also is the introductory chapter to a book of KM research.  This book and chapter provide new insights into KM processes, concepts, and implementation.  This chapter particularly starts the discussion on concepts such as filtering and selection processes. Another goal of this chapter is to create a model that integrates KM and Business Intelligence, BI, and/or the other “intelligences” such as Customer Intelligence, CI. It is also expected that this discussion will generate a better understanding of KM processes so that better KM systems, KMS, can be designed to better fit the needs of KM users.  Finally, this chapter will state definitions of terms important to KM, including definitions of data, information, knowledge, and intelligence. It is hoped that these can be accepted as working, consensus definitions but it is recognized that these terms are philosophical in nature and can be debated as long as we want. This debate is embraced but not encouraged as I agree with Keen and Tan (2007) who believe that while it is important to understand KM terms, it is unproductive for researchers to get focused on trying to precisely define these terms at the expense of furthering KM research. The KM discipline needs to allow the debate but also needs to unite into a consensus set of working definitions. It is hoped this chapter and book will spur this consensus.

Methodology

This is an introductory chapter to a KM research book that focuses on concepts, however, the arguments made and conclusions presented are based on action research. The inspiration for this chapter comes from a project the author participated in with a United States based defense contractor. Specifics of the project and the company cannot be presented due to nondisclosure agreements. What can be said is that the company attempted to take technologies and experience developed/gained working with United States Department of Defense and other national intelligence agencies and generate a commercial knowledge management offering. The role of the author in this project was as a KM academic expert responsible for providing KM focus and direction. It is action research as the author had a vested interest in the success of the project and in generating a commercial KM offering and was able to reflect on the project while participating.   Specific data for this chapter comes from the company’s initial presentation of what was considered to be a knowledge pyramid. This pyramid cannot be presented due to nondisclosure agreement. However, the presented pyramid can be described as a fusion of the traditional knowledge pyramid with KM processes and intelligence concepts. It was analysis of this pyramid and discussions with the project team that is the basis for this chapter.  The pyramid presented is the result of this reflection and is not at all similar to the project proposed pyramid.  

The Traditional Knowledge Pyramid

References to a knowledge hierarchy can be found in the popular literature but generally Ackoff is given credit for the first academic publication. Figure 1 illustrates the traditional knowledge pyramid as originally proposed by Ackoff (1989). The inference from the figure is that data begets information begets knowledge begets wisdom. An additional inference is that there is more data than information, more information than knowledge, and more knowledge than wisdom. This model has been used in countless KM presentations and papers and it is stated as a given truth that it is a generally accepted model showing the DIKW hierarchy (Fricke, 2007; Hey, 2008; Sharma, 2004).  The model does not philosophically define data, information, knowledge, or wisdom and it is not the purpose of this paper to do this, there are many sources already available that make arguments supporting various definitions. However, it is the purpose of this paper to propose consensus, KM focused working definitions. The traditional knowledge pyramid uses the following summarized basic definitions:

  • Data – basic, discrete, objective facts such as who, what, when, where, about something.
  • Information – data that is related to each other through a context such that it provides a useful story, as an example, the linking of who, what, when, where data to describe a specific person at a specific time.
  • Knowledge – information that has been culturally understood such that it explains the how and the why about something or provides insight and understanding into something
  • Wisdom – placing knowledge into a framework or nomological net that allows the knowledge to be applied to different and not necessarily intuitive situations

These definitions imply that there is a relationship between data, information, knowledge, and wisdom and it is the knowledge pyramid that provides a graphical representation of these relationships as a roll up hierarchy of data leading to information leading to knowledge and finally leading to wisdom (henceforth this will be referred to as the DIKW creation flow).  Houston and Harmon describe this in the use of summations: I = ?(D), K = ?(I) = ??(D), and W = ?(K) = ??(I) = ???(D) (Houston and Harmon, 2002).

The use of the pyramid is typically in instruction of database and KM concepts to reflect that “data,” “information,” “knowledge,” and “wisdom” are different concepts and are in increasingly higher levels of abstraction. This chapter argues that the DIKW pyramid is a misleading representation of the relationships among these concepts for information systems research purposes. Maintaining the fuzziness of these concepts in the information systems discipline leads to confusion. We need to better define these constructs to begin to eliminate some of the ambiguities in our research.


 
Figure 1, The Knowledge Pyramid (Ackoff, 1989)

A counter point to the traditional knowledge pyramid is provided by Tuomi (2000). Tuomi (2000) states that data is not the building block for information, knowledge, and wisdom as data is not observed, collected, or recorded in a vacuum. Rather, our understanding of the world through our wisdom and knowledge drives us to collect specific information and data to support our use of our knowledge and wisdom.  In this view, the hierarchy flows down the pyramid rather than up the pyramid and data does not exist as a collection of unrelated facts as all collected facts are related to our basic knowledge and wisdom. While this is an improvement to the knowledge pyramid it still allows for confusion as it provides little insight and explanation for the relationships between DIKW.  The proposed revision of the knowledge pyramid will show that flow is in both directions and introduces discussion on the relationships between DIKW.

The Revised Knowledge Pyramid

The revised knowledge pyramid attempts to place the knowledge hierarchy within the context of the natural or real world. Figure 2 illustrates the revised pyramid. What it shows is that data, information, knowledge, and wisdom exists in a global context, i.e. humans are constantly gathering and processing data into information, knowledge, and wisdom. However, the data gathered and processed is not all that is available and is limited by the abilities of the sensors to detect, interpret, and capture data. Sensors can be our human senses, other’s human senses, or mechanical where mechanical sensors are anything that is not a human sense such as a light detector, radio wave detector, pressure meter, a typed in transaction record, etc.. They reflect that as sensors improve our ability to capture more data improves. A human example of improving sensors is using lasiks to improve eyesight allowing a person to “see” much more, enriching the vision that is captured. A mechanical example is changing accuracy, range, and/or sensitivity of sensors so they can “sense” more phenomena, 

The dotted arrows reflect the processing into information, knowledge, and wisdom using the processes of insight, analysis, and sense making. These lines are bi-directional indicating that the generation of information, knowledge, and/or wisdom may progress up the hierarchy or feedback down the hierarchy as the user understands more of what they are learning (this recognizes Turomi’s (2000) counter view). The lines between layers reflect the social networks used to transfer to different users.  Social networks are being used loosely in this context and refer to any formal or informal, direct or indirect methods used to transfer data, information, knowledge and wisdom between users. Examples include classroom settings, word of mouth, published articles, conference presentations, email, etc. Ultimately, this is a representation of the knowledge hierarchy and the general learning process for people and societies that results in multiple large bodies of knowledge being generated and used. The endpoints lead to learning.  Why learning? Learning has a multitude of definitions but for this model learning is defined as the acquisition of DIWK that leads to a change in behavior or expectation within the individual or group that is doing the learning.
 


Figure 2, Revised Knowledge Pyramid

Why is the knowledge pyramid inverted, i.e. why is there more information than data, more knowledge than information, and more wisdom than knowledge?  One reason is simply mathematical, if information is the structuring of data into meaningful combinations, then the number of possible combinations for a quantity x of data is minimally x! implying there is possibly a greater amount of information than the original amount of data. Considering that data users may have differing frames of reference for processing data in different disciplines of thought (for example accountants versus marketers or engineers versus biologists) it is very conceivable that the amount of information generated is greater than the original amount of data. This same argument can be used for the generation of knowledge and wisdom, especially when it is also considered that users may have different ethical, religious, or cultural belief systems that could cause them to interpret information and then knowledge differently (for example Christians may generate different wisdom from the same knowledge than a Buddhist would or free societies different than totalitarian societies). This is consistent with
Nonaka (1994) and Jennex (2008), and others that argue that all knowledge is context specific. An example of this is technology transfer that takes DIKW from one discipline (or context) and attempts to apply it to another discipline (or context).  An example is taking the knowledge of using learning algorithms to identify meaningful sound patterns used to create sound canceling headphones and applying it to analyzing light diffraction patterns for identifying radioactive or other (such as explosive) substances. 

Figure 3 represents a newly proposed KM knowledge pyramid.  This pyramid is more like a traditional pyramid.  The solid vertical arrows represent application of KM to the DIKW hierarchy pyramid. Jennex (2005, p. iv) defined KM as “the practice of selectively applying knowledge from previous experiences of decision making to current and future decision making activities with the express purpose of improving the organization’s effectiveness.” This implies that KM is not trying to capture all knowledge or wisdom. Rather, KM targets specific knowledge and wisdom needed by an organization to perform specific tasks.  Specific, actionable knowledge and wisdom is defined as intelligence and it is the goal of KM to provide intelligence to the organization for use in decision making.  The lines between layers reflect the filters used to focus on specific DIKW.  KM needs specific data, information, knowledge, and intelligence. Additionally, KM seeks to share this with the right people at the right time (Jennex, et al., 2009). This implies that KM filters data, information, and knowledge to generate specific, actionable intelligence that is shared with specific, limited users. Filters are placed on the social networks to limit access and to separate and capture that which is needed from that which is not.  This is a fairly new term for KM as the KM literature tends to use the term KM strategy. It is the position of this chapter that KM filters are the implementation of KM strategy. That filters are important is evidenced by Jennex and Olfman (2005) who found KM strategy to be a key critical success factor.  The use of intelligence leads to organizational learning.  Organizational learning is defined as a quantifiable improvement in activities, increased available knowledge for decision-making, or sustainable competitive advantage (Cavaleri, 1994; Dodgson, 1993; Easterby-Smith, 1997; Miller, 1996). Huber, Davenport, and King (1998) believe an organization learns if, through its processing of DIKW, its potential behaviors are changed.
 


Figure 3, The KM Knowledge Pyramid

Figure 4 combines the two pyramids.  The implication is that the KM knowledge pyramid is a subset of the revised knowledge pyramid as shown in figure 2. There are other differences. KM tends not to use wisdom but is beginning to apply intelligence concepts and differentiate between knowledge needed to make a decision and specific actionable knowledge needed to make a specific decision in a specific context. For example marketing knowledge is needed to create marketing campaigns but specific customer knowledge is needed to make decisions as to how to market specific customers. For some users this is confusing as it seems to imply there are differing types of knowledge needed for KM. However, this tends to be consistent in intent to Tuomi’s (2000) concept of wisdom.
 


Figure 4, The Revised Knowledge-KM Pyramid

This confusion between wisdom and intelligence may be a driver for those who practice Business Intelligence, BI, or Customer Intelligence, CI, etc. as they see a need for differentiating between general information and knowledge and specific decision information and knowledge (BI is defined as the collection, analysis, and presentation of business information for decision making (Wikipedia, 2008) while CI is the same only for customer information (Wikipedia, 2008)). As previously discussed, Figure 3 captures this by using the term intelligence rather than wisdom where intelligence refers to very specific actionable knowledge. The term intelligence is taken from the intelligence realm, that area that seeks to generate actionable knowledge to be used in the formulation of strategies and tactics to accomplish a specific goal, such as beating an opponent during wartime or to determine what actions should be planned. This term is not chosen lightly as it does fit into the DIKW hierarchy meaning that it is the interpretation of data, information, and knowledge to create courses of action or make specific decisions. Also, the KM arrows are shown to be double headed as the hierarchy may travel in either direction. In fact, it is expected that while the learning process will tend to generally be a bottom up process, meaning that it starts with the interpretation of data, organizational learning and KM will generally be a top down process where it is first determined what actions or decisions need to take place or be made and from that identify what intelligence, then knowledge, then information, then data is needed to support taking the specific actions or making the specific decisions.  

The other major difference is in the application of filters. While the general learning process seeks to push data, information, knowledge, and wisdom out to all who wish to use it, KM does not. As stated earlier, KM seeks to support specific decision making and thus needs specific data, information, knowledge, and intelligence. Additionally, KM seeks to share this with the right people at the right time. This implies that KM filters data, information, and knowledge to generate specific, actionable intelligence that is shared with specific, limited users. Filters are placed on the social networks to limit access and to separate and capture that which is needed from that which is not.  This is a fairly new term for KM as the KM literature tends to use the term KM strategy. It is the position of this paper that KM filters are the implementation of KM strategy. That filters are important is evidenced by Jennex and Olfman (2005) who found KM strategy to be a key critical success factor.

A final difference between the traditional knowledge pyramid and the revised knowledge pyramid is the removal of apex’s. This was done to remove confusion as an apex tends to imply that there is an ultimate point, such as the ultimate key wisdom for the traditional knowledge pyramid or a single key datum for the revised knowledge pyramid. While it is somewhat satisfying to refer to ultimate points, this chapter does not support the idea of a “big bang” theory for data or an ultimate, supreme wisdom. Rather, the chapter does support that there is some initial level of data and some ultimate amount of wisdom or intelligence. To support this position again consider the contextual nature of knowledge and wisdom/intelligence and the multiple frames of reference and contexts that users bring to the generation of knowledge and wisdom. Also consider that most organizations do not ultimately need one key piece of intelligence as there is rarely a single decision that needs to be made. Hence the revised knowledge pyramid is pyramidal in form as that provides a visual impact as to the relative amounts of data, information, knowledge, and wisdom/intelligence but are flat topped, much like the Aztec and Mayan pyramids and not like the Egyptian pyramids (note that this is being used to illustrate the point and not to imply that there is more merit to one style pyramid over another).

Discussion

The revised knowledge pyramid recognizes Ackoff (1989) and Tuomi (2000) and finds both wanting. Ackoff (1989) assumes data generates information which generates knowledge which generates wisdom. Tuomi (2000) believes all data and information is collected based on influence from existing knowledge and wisdom.  Ackoff’s (1989) pyramid flows up, Tuomi’s (2000) pyramid flows down and reality as observed and implemented in the revised knowledge pyramid recognizes that flow is in both directions. Tuomi (2000) is wrong in thinking that flow is only down the pyramid. Basic research often times involves collecting data about something that we do not have knowledge or wisdom. An example is physics research in the late 1800s. Scientists did not understand nor did they have knowledge about atomic structure, subatomic particles, and radiation. Data was collected an observed by scientists noting that photographic film was fogged when near certain materials. This data was gathered and then put into context as information that said specific materials caused fogging. Consideration of this information led to the formation of theories to explain why this happened, generating potential knowledge. Analysis of this knowledge led to experiments designed to prove the correctness of this potential knowledge, ultimately resulting in theories on radiation. This example shows that both Ackoff (1989) and Tuomi (2000) are right and both are wrong. Flow of creation is in both directions of the pyramid.

Another example bolstering this argument is the use of data mining to create information by discovering patterns in data. Commonly called knowledge discovery, it is the analysis of these new found patterns, or information, and the application of context, culture, and other knowledge that creates knowledge; and the positioning of this knowledge into existing problem or competitive advantage contexts that creates actionable knowledge or intelligence. In this example the predominant direction of creation flow is up the pyramid but it can also be argued that previous knowledge of business needs, processes, and behavior influenced the interpretation of discovered patterns into knowledge and intelligence so that creation flow is actually in both directions.  Additionally consider analytics.  Analytics seeks to gather specific data to answer specific questions.  Systems are created to gather, analyze, and display specific data, information, and knowledge so that managers can monitor organizational performance and take action if warranted.  Analytics is primarily a top down design that starts with the intelligence needed and then determines what data, information, and knowledge to gather.

A final example is from engineering. It is common for engineers to instrument everything they can when conducting testing, even if they are not sure of a purpose for the data. A high pressure vessel destructive test from my past illustrates this. The test called for burst testing a 2500 psi vessel pressure using water. Standard instrumentation for measuring pressure and temperature were used plus video monitoring was added. There was no real need for collecting video data other that it was thought it would be interesting to watch. Since water, a noncompressible liquid, was being used as the pressuring medium it wasn’t expected to be a violent failure (violence in this case is represented by a very large, very fast release of energy) as it was expected that the vessel would crack and the water spill out or perhaps spray out. The test team was very surprised when the test resulted in a spectacular release of energy. The video data was what provided the story and it was interesting to see the test setup disintegrate during the first test and then to see that the subsequent test setups were surrounded by sand bags and other retaining walls. The point of this example is that there are many situations in which we collect data for no real reason other than we can and it is later analysis of this data that results in the generation of unexpected information, knowledge, and intelligence.

Conclusions

The revised knowledge-KM pyramid is visually more complex and thus less satisfying as a visual model then the traditional knowledge pyramid but is more satisfying conceptually as it fits our perception of reality to a better degree than the traditional knowledge pyramid. Is this the final model? Probably not but it is a step in the right direction and a starting point for more detailed discussion on the knowledge pyramid.  Should we quit using the traditional knowledge pyramid?  While I think yes, and replace it with the revised knowledge pyramid only, I also suspect that the traditional knowledge pyramid will remain as it has its use as a tool for introducing the concepts of data, information, knowledge, and wisdom to beginning students. However, it is recommended that the traditional knowledge pyramid be quickly followed by the revised knowledge-KM pyramid as this is a better model for explaining what KM is and does and for how DIKW are created.

The revised knowledge-KM pyramid also shows that BI, CI, etc. are really not new applications but are manifestations of confusion caused by the traditional knowledge pyramid. While it is expected that BI, CI, etc. practitioners may not embrace that they are within KM, the model does support the fusion of these initiatives into the KM discipline and KM researchers are encouraged to include these approaches in researching and applying KM.

Finally, this chapter proposed to provide a set of what are hoped to be consensus working definitions of KM terms from the revised knowledge-KM pyramid. These terms are summarized below:

  • Data – basic, discrete, objective facts such as who, what, when, where, about something.
  • Information – data that is related to each other through a context such that it provides a useful story, as an example, the linking of who, what, when, where data to describe a specific person at a specific time.
  • Knowledge – information that has been culturally understood such that it explains the how and the why about something or provides insight and understanding into something
  • Wisdom – placing knowledge into a framework or nomological net that allows the knowledge to be applied to different and not necessarily intuitive situations
  • Intelligence - specific actionable knowledge needed to make a specific decision in a specific context.
  • Learning - the acquisition of DIWK that leads to a change in behavior or expectation within the individual or group that is doing the learning.
  • Organizational Learning - a quantifiable improvement in activities, increased available knowledge for decision-making, or sustainable competitive advantage. An organization learns if, through its processing of DIKW, its potential behaviors are changed (Cavaleri, 1994; Dodgson, 1993; Easterby-Smith, 1997; Huber, Davenport, and King, 1998; Miller, 1996).
  • Social networks - any formal or informal, direct or indirect methods used to transfer data, information, knowledge and wisdom between users.
  • Filters – KM processes that limit access and separate and capture that DIKW/I which is needed from that which is not.

The implications of this chapter are many. KMS designers have a better understanding of how KM fits in the general DIKW world. KMS can focus on strategy and strategy implementation through the design and implementation of KM filters. This leads to the possibility of creating audit mechanisms that can validate that KM strategy has been properly implemented that is similar in nature to auditing security policy implementations in firewalls. Another implication is a clearer understanding of the role of social networks in the creation and transfer of DIKW/I.  KMS designers will also be focused on providing technological support for these social networks and it clarifies that KMS is more than just knowledge storage and retrieval technologies, they also incorporate communication and collaboration technologies.  Another implication is a new focus on identifying specific actionable knowledge for focused decision making, or, as stated in the chapter, generating intelligence. A final implication is that KM should now be recognized as an integrated part of regular DIKW/I processing. KMS designers should be focusing on integrating KM processes into regular work processes.

The rest of this book provides current research into KM.  Topics of chapters range from the use of social networking technologies to analysis of KM strategy to analysis of knowledge transfer and flow.  A wide range of contexts are discussed, from schools to multinational organizations to small and medium enterprises, SMEs.  Many different research methodologies are used, such as case studies, action research, and quantitative methods.  A good indicator for the maturing of the KM discipline is that significantly more research is quantitative based.  This indicates the discipline is moving from theory building to theory proving, a sure sign of a maturing discipline.

References

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Author(s)/Editor(s) Biography

Murray E. Jennex is a Professor of Management Information Systems at San Diego State University, editor-in-chief of the International Journal of Knowledge Management, co-editor-in-chief of IGI Global book series, co-editor-in chief of the International Journal of Information Systems for Crisis Response and Management, and President of the Foundation for Knowledge Management (LLC). Dr. Jennex specializes in knowledge management, crisis response, system analysis and design, IS security, e-commerce, and organizational effectiveness. Dr. Jennex serves as the Knowledge, Innovation, and Entrepreneurial Systems Track co-chair at the Hawaii International Conference on System Sciences. He is the author of over 150 journal articles, book chapters, and conference proceedings on knowledge management, crisis response, end user computing, international information systems, organizational memory systems, ecommerce, cyber security, and software outsourcing. Dr. Jennex is a former US Navy Nuclear Power Propulsion officer and holds a B.A. in chemistry and physics from William Jewell College, an M.B.A. and an M.S. in software engineering from National University, an M.S. in telecommunications management and a Ph.D. in information systems from the Claremont Graduate University. Dr. Jennex is also a registered professional mechanical engineer in the state of California and a Certified Information Systems Security Professional (CISSP), a Certified Secure Software Lifecycle Professional (CSSLP), and a Project Management Professional (PMP).