res804 p6 individual project - prospectus

DISSERTATION PROSPECTUS: KNOWLEDGE MANAGEMENT

Phase 6 Individual Project

Dissertation Prospectus: Knowledge Management

ThienSi (TS) Le

Colorado Technical University

RES 804-1502C-01

Professor: Dr. Kay Davis

June 15, 2015

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Title

Dissertation Prospectus: Organizational Knowledge Management

With an emergence of novel standards of computing, automation and cloud technologies,

data at the low cost of storage and processing has become ample and ubiquitous. It drives an

explosive evolution of data, information into knowledge dynamically (Ahlemeyer-Stubbe &

Coleman, 2014). Sharing knowledge among individuals, individuals and groups, and across the

groups leads to knowledge management in many organizations. This dissertation prospectus of

the research study on knowledge management (KM) comprises six sections that are outlined as

follows:

A. Problem statement:

In the dissertation proposal, three primary components that establish a foundation and

guidance for a research study are (1) theoretical framework, (2) research problem and (3)

particular purpose. In the advent of the fourth-generation languages and personal computers in

1990s, knowledge was distributed and expanded from data resources to information resources

internally and externally across the networks (Don Jyh-Fu & Dunk, 2013). According to

Connolly and Begg (2014), a mountain of electronic data or big data becomes available for

analyzing with advanced statistical techniques for meaningful information and particularly

relevant knowledge in a new standard computing, automation and Web technologies.

Knowledge is most unpredictable in human behavior. It relates to humans, particularly

knowledge – what they know - which is the most complex and dynamic subject because

knowledge cannot be controlled or engineered in the modern business market today (Erickson &

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Rothberg, 2014). Businesses look at the colossal data for knowledge in which they operate

(Ganesh, 2001). Top corporate executives recognize knowledge as the greatest corporate asset

source. They want to keep talent and high-skilled people in the organizations. Knowledge

becomes rare and precious asset for many organizations in both private and public sectors

(McNurlin, Sprague & Bui, 2009). Managing knowledge is not only encouraging people to share

knowledge personally but also setting their knowledge in a form that others can easily access. In

causality, the relation of managing knowledge and making a sound decision is crucial.

Knowledge management (KM) has primary importance in the knowledge economy because

intangibles hold a competitive edge. KM is emergent as one of the most important aspects of a

fast growth market locally and globally (Birasnav, Goel & Rastogi, 2012).

The existing trend in the US economy is a knowledge economy. According to Brewer

(1995), a researcher from Gartner Inc., the US economy left manufacturing to a service economy

in 1980s, and it was moving from service to a knowledge economy in 2000s. As a data-driven

approach, the tacit and explicit knowledge as a capital asset in knowledge management provides

business intelligence for a sound decision-making and competitive advantage to the

organizations (Trninic, Ðurković & Raković, 2011).

A draft of the problem statement from the component (2) on Knowledge Management is

constructed for a dissertation prospectus as shown below:

Based on the selected theoretical framework of the organizational knowledge leadership

and wisdom evolution from data or facts without contexts, meaningful information into relevant

knowledge, sharing knowledge or knowledge management is emerging as one of the most

difficult and challenging problems in high-tech organizations in the modern knowledge

economy. While most managers recognize the importance of knowledge, many find it difficult to

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articulate the transformation of data-information-knowledge to action. Knowledge management

becomes essential and indispensable today because knowledge provides accurate and up-to-date

information for making a sound decision (Liu, Leat, Moizer, Megicks & Kasturiratne, 2013).

B. Significance:

1. The study of knowledge management contributes to the scholarly literature

Many consultants, scientists, and researchers pay more attention to knowledge

because they recognize knowledge as the greatest corporate asset source but also most

unpredictable in human behavior (Albescu, Pugna & Paraschiv, 2009). Albeit human knowledge

theory and research has been explored, in general, organizational knowledge management,

particularly among individuals, individuals, and groups or across groups has not been addressed

intensively. For example, creating, enabling and sharing knowledge among employees in an

organization or between organizations are not studied completely. This study will define a

framework with a solid foundation and guidance of KM in a standard process for more chance of

successful implementation at low-cost deployment and inexpensive maintenance.

2. Knowledge is added to the study

Data, information, knowledge, and wisdom with their apparent and deliberate

distinction have been evolved cognitively while managing knowledge stands out and is affiliated

with KM in a novel digital economy (Don Jyh-Fu et al., 2013). The study of knowledge

management will base upon the existing literature to suggest a better way to classify the

knowledge types in processing data into knowledge. For example, one way is processing data via

business intelligence into information and explicit knowledge. Another way is processing data to

information via knowledge management into explicit and tacit knowledge (Olson, 2014).

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In this study, type of raw materials (i.e., data) will be identified for knowledge creation.

On one hand, quality data only generates information that can be used to produce most explicit

knowledge. On the other hand, both quality data and information can be used to develop explicit

and tacit knowledge. Knowledge Management applications use data and information to capture,

create, share and apply knowledge in the entire the KM life cycle.

Some knowledge is added to the study are:

a. KM in organizations handles maintaining, locating, applying knowledge to

Organization’s benefit. Most of the organizations believed that the knowledge, they need, exists

inside the organization. However, identifying knowledge remains problematic.

b. KM systems are a class of information systems applied to managing

organizational knowledge. They are an IT-based system developed to support and enhance the

organizational processes of knowledge creation, storage, retrieval, transfer, and application.

c. Data warehouse serves as one of the primary components in KM system:

i. OLAP (online analytical processing) can produce various reports on the

meaningful information.

ii. Knowledge acquisition can go through knowledge discovery in various

algorithms.

3. The study contributes to changes in practice.

The in-depth study of knowledge and KM will provide some findings:

- Big Data that is generated by human, sensors, instruments and services (e.g., cloud,

mobile, web, etc.) will grow exponentially in the knowledge-centric economy.

- The evolution of knowledge is still viral in high gear. Data, information,

knowledge, and intelligence, continue evolving for organization gains.

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- The challenge of understanding knowledge and intelligence or wisdom opens a new

frontier for more research study such as Actionable Intelligence (Carter, 2014).

- KM plays a bigger role in corporate and business planning in the organizations.

- Knowledge Management is still in the infancy stage with the high cost and

organizational failures, but more organizations will employ and deploy them with lower cost and

more successes.

- KM that is distinct in the process and mutually complementary with business

intelligence (Chen, Chiang and Storey, 2012) will lead to standardization with low cost and

friendly use for end-users.

4. The study is used to improve policy

This study of knowledge management can be used to improve policy as follows:

a. The challenge of understanding knowledge and intelligence or wisdom opens a

new frontier for more research study on knowledge.

b. The suggested framework of KM in this study may become a foundation, a

guidance or study guide for managing and sharing knowledge among inter-departments and

inter-enterprise or business partners.

c. Currently, organizations use KM and pursue it in their way and depend on their

needs. This study could play a role as a springboard to standardization of employing KM in the

knowledge-driven economy in the future.

C. Brief description of underlying conceptual or theoretical framework:

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The study will discuss how (big) data as fundamental facts or values without context

evolve to meaningful information transformed into relevant knowledge as intellectual capital in

the organization in data-driven approach (Erickson et al., 2014).

Based on a theoretical framework of Organizational Knowledge Leadership (Lakshman,

2007), leadership theory, and research have not completely addressed sharing knowledge or

knowledge management, despite its importance to organizations. Consequently, knowledge itself

and knowledge management as key functions have not been explored in depth. Knowledge

management assesses knowledge as a capital asset to assist upper managers to make an effective

decision and gain a competitive advantage in the organizations (Alavi & Leidner, 2001).

The study will interpret processing of data into knowledge; classify at least two types of

knowledge (e.g., tacit and explicit knowledge). While knowledge is evolved from vast amount of

data, organizations use the advanced statistical tools to collect, store, and analyze the mountain

of data for useful information and knowledge (Erickson & Rothberg, n.d.). The advanced tools

from Business Intelligence system are a data warehouse, data mining, OLAP (online analytical

processing), ETL (extraction, transformation, and loading) tools, etc. The study will present the

framework of knowledge management in three dimensions: technology, organization, and the

environment. It will construct a model of managing knowledge includes employees’ internal

knowledge, customers’ external knowledge, researchers’ external knowledge. It will also

ascertain the Nonaka and Tajeuchi model of knowledge creation process: socializations,

externalization, combination, and internalization – SECI model (Birasnav et al., 2012).

Furthermore, the study will suggest how to embed the external knowledge in a real-time system.

D. Statement of research purpose:

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The study focuses on an in-depth evolution of big data, meaningful information to

relevant knowledge as intellectual capital that is assessed by knowledge management in

organizations. The intent is to establish the framework of knowledge management with the

support of the business intelligence program (Goes, 2014) to achieve an organizational objective

of making a practical and strategic decision in the dynamic market locally and globally.

One quantitative research question and two qualitative questions are brought up for

research purpose:

1. As the amount of data captured within an organization increases exponentially, the

level of organizational knowledge also increases rapidly. Will the study of processing big data be

able to produce meaningful knowledge for the organizations?

In this quantitative question above, “big data” is identified as an independent variable and

“knowledge” is a dependent variable.

2. How do IT practitioners describe employees’ tacit and explicit knowledge contained

within organizational data in knowledge management systems?

3. What does the organizational knowledge leadership report and evaluate the evolution

of the knowledge management in many US organizations?

E. Proposed general approach to the research:

For this study of the organizational knowledge management, a convergent parallel mixed

method design is selected. Probably this method is the most popular strategy that consists of

quantitative (Qn) and qualitative (Ql) approaches (Bryman & Bell, 2011). This inquirer will

collect both Qn and Ql data in parallel and analyze them separately then compare results to

confirm or disconfirm for the outcome. Notice that the key assumption is that two distinct sets of

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Qn and Ql data produce different types of information but together they are expected to yield the

sane results (Crewell, 2014).

Notice that Qn component uses the mature theory on data or facts without contexts with

survey-based and statistics-based analysis. Qn design is a structured approach to closed

questions. Its purpose is to establish the framework of knowledge management with the support

of the business intelligence program to achieve an organizational objective of decision-making.

Ql component uses nascent theory on tacit and implicit knowledge via interviews and

observations. The findings often lead to theme and patterns with open-ended questions. Its

purpose is to explore knowledge’s evolution from extracting data into information then

converted to knowledge and wisdom.

The rationale for selecting the convergent parallel mixed methods design is:

-The fundamental reasons to choose this concurrent mixed method is an initial

approach to providing an inquirer a generic opportunity to perform both analyses of Qn and Ql

designs on knowledge management. Sharing knowledge involves complex humans as the most

difficult, challenging and emergent problem today (McNurlin et al., 2009).

- The mixed method extends the collection of both Ql data from the detailed view of

human participants and Qn data from instruments such as software tools for yielding the same

results with data correlation (Johnson & Onwuegbuzie, 2004).

- The mixed methods of Ql and Qn approaches will be performed concurrently to save

time on the work.

- Another reason is using the parallel variables, constructs, and concepts when

collecting both Ql and Qn forms of data.

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- Ql data collection will be instrument data, observation questionnaire, numeric

records with open-ended interviews.

- Qn data collection will be interviews, observations, documents and records of data,

information and particularly knowledge with independent variables (data) and dependent

variables (information, knowledge). Ql data collection will be instrument data, observation

questionnaire, numeric records with open-ended interviews (Lund, 2012).

F. Summary of proposed methods:

With the chosen convergent parallel mixed method design in the study of organizational

knowledge management, possible sources of data can be:

- Qualitative data comes from many sources. They can be field notes, existing documents,

observational checklists, numeric records, from audio and video tapes or interviews.

- Quantitative data may come from some sources such as surveys and other instruments

used to capture data.

The primary technique of the convergent parallel mixed method design is to collect both

forms of data using the same parallel variables, constructs, or concepts. If the concept of

knowledge management is measured quantitatively, then the same concept should be asked in

open-ended interviews during qualitative data collection process.

If the amount of Ql data is usually smaller than the amount of Qn data due to smaller qualitative

sample, collecting information from the same number of participants on both Ql and Qn database

can be used (Lund, 2012).

1. Qualitative strategy for data collection includes interview techniques, observations,

and surveys with open-ended items. Interview techniques may be face-to-face, one-on-one or in-

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person. Observation technique may be complete participant, observer as a participant, or

participant and inquirer as an observer. Quantitative strategy for data collection is survey

techniques. Also, document or artifact review can be used to capture either quantitative or

qualitative data (Creswell, 2014).

2. In data analysis of the convergent parallel mixed method design in the study of KM,

the challenge may arise on how to converge or merge data from two separated databases.

According to Creswell (2014), there are three approaches (i.e., side-by-side comparison, data

transformation, and joint data display) to merge the two Ql, Qn databases. In this study of KM,

the joint data display, that merges two forms of data into tables and graphs, is chosen because it

is easier for the audience to follow and understand the complexity of the knowledge

measurements.

Summary

In this Phase 6 Individual Project, the short document presents the dissertation prospectus

of the study of organizational knowledge management based on the theoretical framework of

organizational knowledge leadership in the organizations. The challenge of understanding

knowledge and sharing knowledge in knowledge management is addressed. The study will

provide a model of knowledge creation and knowledge management in a future standardization.

This short document includes six sections:

- Problem statement, research significance,

- Significance of the study,

- Brief description of underlying conceptual or theoretical framework,

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- Statement of research purpose

- Proposed general approach to the research,

- Summary of the proposed methods.

The bibliometric list of references is provided at the end of this document.

REFERENCES

Ahlemeyer-Stubbe, A., & Coleman, S. (2014). A practical guide to data mining for business and

industry. John Wiley & Sons.

Alavi, M., & Leidner, D. E. (2001). Review: knowledge management and knowledge

management systems: conceptual foundation and research issues. MIS Quarterly, 25(1),

107-136.

Albescu, F., Pugna, I., & Paraschiv, D. (2009). Cross-cultural knowledge management.

Informatica Economica, 13(4), 39-50.

Birasnav, M., Goel, A., & Rastogi, R. (2012). Leadership behaviors, organizational culture, and

knowledge management practices: an empirical investigation. Amity Global Business

Review, 77-13.

Bryman, A., & Bell, E. (2011). Business research methods 3e. Oxford university press.

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Carter, K. B. (2014). Vision of actionable intelligence. Actionable intelligence: a guide to

delivering business results with Big Data fast!, 5-29.

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big

data to big impact. MIS quarterly, 36(4), 1165-1188.

Connolly, T. M., & Begg, C. E. (2014). Database systems: a practical approach to design,

implementation, and management. New Jersey, NJ: Pearson.

Don Jyh-Fu, J., & Dunk, N. (2013). Knowledge management enablers and knowledge creation in

ERP system success. International Journal Of Electronic Business Management, 11(1),

49-59.

Erickson, S., & Rothberg, H. (2014). Big data and knowledge management: establishing a

conceptual foundation. Electronic Journal Of Knowledge Management, 12(2), 101-109.

Ganesh, D. B. (2001). Knowledge management in organizations: examining the interaction

between technologies, techniques, and people. Journal of Knowledge Management, Vol.

5 Iss: 1, pp.68 – 75.

Goes, P. B. (2014). Big Data and IS Research. MIS Quarterly, 38(3), iii-viii.

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Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm

whose time has come. Educational Researcher, 33(7), 14-26. Doctoral Library-SAGE.

Lakshman, C. (2007). Organizational knowledge leadership: a grounded theory

approach. Leadership & Organization Development Journal, 28(1), 51–75.

Liu, S., Leat, M., Moizer, J., Megicks, P., & Kasturiratne, D. (2013). A decision-focused

knowledge management framework to support collaborative decision making for lean

supply chain management. International Journal Of Production Research, 51(7), 2123-

2137. doi:10.1080/00207543.2012.709646.

Lund, T. (2012). "Combining Qualitative and Quantitative Approaches: Some Arguments for

Mixed Methods Research." Scandinavian Journal of Educational Research 56(2): 155-

165.

McNurlin, B. C., Ralph H. Sprague, J., & Bui, T. (2009). Information systems management in

practice (Eighth Edition ed.). Upper Saddle River: Pearson Prentice Hall.

Trninic, J., Ðurković, J., & Raković, L. (2011). Business intelligence as support to knowledge

management. Perspectives Of Innovations, Economics & Business, 8(1), 35-40.

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res804 p6 individual project - prospectus

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