Title: Use of Knowledge Management Systems and the Impact on the Acquisition of Explicit Knowledge
Authors: McCall, Holli., Arnold, Vicky & Sutton, Steve G.3
Subject: Knowledge management
Publish: 2008
Status: full text
Source: Journal of Information Systems; Fall2008, Vol. 22 Issue 2, p77-101
Preparation: Scientific Database Management Journal Articles www.SYSTEM.parsiblog.com
Abstract: In an era where knowledge is increasingly seen as an organization"s most valuable asset, many firms have implemented knowledge-management systems (KMS) in an effort to capture, store, and disseminate knowledge across the firm. Concerns have been raised, however, about the potential dependency of users on KMS and the related potential for decreases in knowledge acquisition and expertise development (Cole 1998; Alavi and Leidner 2001b; O"Leary 2002a). The purpose of this study, which is exploratory in nature, is to investigate whether using KMS embedded with explicit knowledge impacts novice decision makers" judgment performance and knowledge acquisition differently than using traditional reference materials (e.g., manuals, textbooks) to research and solve a problem. An experimental methodology is used to study the relative performance and explicit knowledge acquisition of 188 participants partitioned into two groups using either a KMS or traditional reference materials in problem solving. The study finds that KMS users outperform users of traditional reference materials when they have access to their respective systems/ materials, but the users of traditional reference materials outperform KMS users when respective systems/materials are removed. While all users improve interpretive problem solving and encoding of definitions and rules, there are significant differences in knowledge acquisition between the two groups.   --Download Article

Keywords: knowledge management systems; knowledge management; declarative knowledge.

Introduction: n recent years, organizations have increasingly realized that one of their most valuable assets is the knowledge that is developed internally and possessed by individuals within the organization. For instance, Nagle (1999), a knowledge manager at KPMG, noted that one of the biggest challenges facing the firm was how to capture, store, retain, and share the knowledge possessed by the firm’s professionals. Cameron (2000, 3) similarly noted, ‘‘Knowledge is power, but without the adequate management of that knowledge, the consequences for [organizations] could be devastating.’’ Not surprisingly, technology is viewed as the key enabler to effective knowledge management. Early technological strategies
focused on the use of intelligent systems,1 but these strategies have not been terribly effective (Duchessi and O’Keefe 1992). More recently, corporate efforts are focusing on a class of technologies referred to as knowledge-management systems (KMS) (Leech and Sutton 2002).
Knowledge management can be defined as the organizational ‘‘efforts designed to (1) capture knowledge; (2) convert personal knowledge to group-available knowledge; (3) connect people to people, people to knowledge, knowledge to people, and knowledge to knowledge; and (4) measure that knowledge to facilitate management of resources and help understand its evolution’’ (O’Leary 2002a, 273). Knowledge-management systems (KMS) focus on bringing together the explicit knowledge that exists in organizations, the knowwhat that can be easily documented and shared (Sambamurthy and Subramani 2005), such as basic definitional information (e.g., technical terminology), procedures for performing tasks (e.g., audit checklists), guidelines for interpretation (e.g., GAAP guidance), and previous problem resolution examples (e.g., client memos outlining solutions to issues raised)—information often referred to as ‘‘three-ring binder’’ knowledge (Dilnutt 2002). As noted by Alavi and Leidner (2001b), KMS initially contain these types of explicit knowledge and are later expanded upon with a body of tacit knowledge that continues to grow as users add their interpretations of the explicit knowledge to the system’s knowledge base.
Tacit knowledge is the know-how that is difficult to document and emerges from experiences (Sambamurthy and Subramani 2005). Alavi and Leidner (2001b) further note that access and/or assimilation of the explicit knowledge in such systems is a necessary precursor to effective use of the accumulated tacit knowledge in the system. This is consistent with recent findings in the knowledge-based system (KBS) literature showing that novice users gravitate toward explicit knowledge support while experienced decision makers gravitate toward available tacit knowledge support (Arnold et al. 2006).
The use of KMS to support an organization’s professionals in their decision making through organizational knowledge creation is a double-edged sword. The ready availability of explicit knowledge support in a KMS should allow individuals to improve decision performance (Gonzalez et al. 2005), but the potential impact on the development of expertise by individuals within the organization remains an unknown. Alavi and Liedner (2001b) note that some researchers raise questions as to whether KMS users may not develop their own knowledge while relying on the expertise of others, which may lead to a lack of expertise development in the next generation of organizational ‘‘experts’’ and ultimately a dwindling of human expertise within the firm (e.g., Cole 1998; Powell 1998; O’Leary 2002a).
The purpose of this study is to empirically investigate whether a KMS providing explicit knowledge impacts the decision-making performance and the acquisition of explicit  knowledge by novice users. The entire basis for the investment in and development of knowledge management technologies is premised on the belief that an effective KMS should disseminate knowledge throughout the organization and provide the necessary components to improve decision-making capabilities (Alavi and Leidner 1999). The impact of the use of KMS on explicit knowledge acquisition is critical given that explicit knowledge provides the foundation for and is the precursor of tacit knowledge development (Alavi and Leidner 2001b; Anderson 1987; Anderson 1990; Anderson 1993; Anderson et al. 1997; Chi et al. 1989; Roberts and Ashton 2003). As such, acquisition of explicit knowledge is a critical component in the development and sustenance of expertise (Herz and Schultz 1999). Yet, prior research provides little insight on the effects of contemporary decision support such as KMS, where the user initiates search and retrieval of the embedded knowledge (Alavi and Leidner 2001b).
This study utilizes an experimental methodology to investigate the impact of KMS on decision-making performance and acquisition of explicit knowledge. A pretest-posttest design is implemented to investigate the acquisition of explicit knowledge focusing on differences between individuals using a KMS (KMS group) versus individuals using traditional reference materials such as office manuals and textbooks (traditional group). Results indicate that the KMS group outperforms individuals in the traditional group when they have access to a KMS; however, the advantage disappears when the KMS is removed. Additionally, results indicate that both groups acquire various types of explicit knowledge. The traditional group tends to encode more rules in memory, while the KMS group tends to acquire higher-level explicit knowledge (i.e., interpretative problem-solving skills) which is key to the formulation of tacit knowledge.
This paper contributes to the literature by experimentally examining the impact of KMS use on the acquisition of explicit knowledge and represents an initial step in addressing the associated knowledge transfer issues. Little empirical evidence is available on the impact of KMS on user performance (e.g., Gonzalez et al. 2005). Rather, the extant KMS literature generally consists of deive studies (Davenport et al. 1998; Alavi and Leidner 1999; Dilnutt 2002; O’Leary 2002b), design science studies (Earl 2001), and case studies (Alavi 1997; Baird et al. 1997; Bartlett 1996; Henderson et al. 1998; Thomas et al. 2001; and Wickramasinghe and Mills 2002). Prior research has not addressed whether knowledge transfer actually occurs (Grover and Davenport 2001). Alavi and Leidner (2001b) note that
future research needs to address if and to what degree knowledge can be transferred within the firm. This study focuses on this gap in research and addresses knowledge acquisition associated with KMS use.
The remainder of this paper is organized into four sections. The first section presents the theory and associated hypotheses and research questions. The second and third sections provide an overview of the research method and the results of the experimental study, respectively. The fourth and final section discusses the implications of the study results and considers opportunities for future research that could extend the research reported here.

II. BACKGROUND AND THEORETICAL DISCUSSION
Knowledge is defined as a ‘‘justified true belief’’ (Nonaka 1994, 15) and can be viewed
as a state of mind, an , a process, a stipulation of having access to information, or a
capability (Nonaka 1994). In an organizational setting, knowledge is an asset that enables
firms to obtain a competitive advantage (Alavi and Leidner 1999) and is of limited value
if it is not disseminated to others (Grant 1996). Knowledge management enables organizations to leverage the collective knowledge among members of the organization and sustain competitive advantage.
A key component of knowledge management is to provide access to stored knowledge
components to improve decision making and to facilitate knowledge acquisition by the user.
ACT-R theory (depicted in Figure 1) provides a conceptualization of the process by which
knowledge is acquired and provides a foundation for understanding how a KMS might
impact individuals’ knowledge-acquisition processes (Anderson 1990; Anderson 1993;
Anderson et al. 1997). While Anderson presents his theory in terms of declarative and
procedural knowledge, knowledge embedded in a KMS is generally referred to as explicit
and tacit, respectively (Alavi and Leidner 2001b; Sambamurthy and Subramani 2005). In
both literatures declarative and explicit are defined as know-what knowledge, and procedural
and tacit are defined as know-how knowledge (Anderson 1993; Sambamurthy and
Subramani 2005).
Act-R theory proposes that an individual encodes or stores definitions, examples, and
rules into long-term memory and utilizes this declarative knowledge in a problem-solving
strategy called interpretive problem solving. Interpretive problem solving is defined as solving problems by analogizing from examples. These examples may come from an external
source (e.g., KMS) or be retrieved from declarative knowledge that has been encoded into
long-term memory. With practice an individual encodes an increasing number of examples,
resulting in declarative knowledge acquisition. After declarative knowledge has been acquired, the individual may then move into the procedural stage of knowledge acquisition
by compiling the analogy process into a production rule (i.e., compilation). Both declarative
encoding and interpretive problem solving must occur prior to compilation (rule creation);
however, declarative encoding alone can result in declarative knowledge acquisition. The
production rules (or condition-action pairs likened to if-then statements in a programming
language) created by the compilation process are then tuned (improved or enhanced), which
expands procedural knowledge (i.e., procedural knowledge acquisition). Procedural knowledge is the ability to apply and extend declarative knowledge and is acquired through
experience; thus, it is considered a key antecedent to expertise (Anderson 1993).
Production rules and the linked declarative knowledge are stored in long-term memory.
Long-term memory stores consist of declarative memory and procedural memory. The basic
unit of knowledge in declarative memory is a chunk, while the basic unit of knowledge in
procedural memory is a production rule. Declarative knowledge is factual knowledge that
can be described (definitions, rules, and examples), while procedural knowledge is mechanistic and can only be inferred by behavior (Anderson 1993). For example, declarative
knowledge would entail knowing that a bike has wheels, handlebars, pedals, a seat, and
that the pedals are used to turn the wheels while one is seated and holding on to the
handlebars. However, knowing how to ride a bike would represent procedural knowledge.
Individuals know how to ride a bike, but cannot actually describe all of the processes
required.
The primary sub-process in the declarative stage is declarative encoding of explicit
knowledge which can be described as storing experiences—instructions, examples, rules,
definitions, and successes and failures of our own attempts. Declarative knowledge is committed to long-term memory by encoding external events or the action side of a conditionaction production pair. Declarative encoding occurs as newly identified explicit knowledge is first considered by working memory and then may be encoded to declarative long-term memory in chunks. Once the chunk is in long-term memory, its retrieval is controlled by its level of spreading activation or the ease with which a chunk of knowledge can be recalled from memory. A particular chunk’s level of activation is strengthened as the number of connections or related chunks increase—spreading activation represents how easily and often the knowledge is retrieved. As the level of activation increases, the chunks can be retrieved more easily.
When an individual has no applicable production rule instantiations (i.e., procedurallevel
knowledge), they look to examples from the past to analogize to solve a problem, a
process referred to as interpretive problem solving. In this second sub-process of the declarative stage, Anderson et al. (1997) find that encoding of declarative knowledge into
long-term memory is not necessary to use interpretive problem solving. The individual
employing interpretive problem solving may simply work from declarative knowledge (definitions, examples, rules, etc.) active within working memory rather than draw upon encoded knowledge (Anderson et al. 1997).
At the core of KMS typically used by accounting firms is top-down knowledge including
manuals, directories, and newsletters; work processes knowledge consisting of
working papers, proposals, client correspondence, and other engagement materials; and
customer related knowledge including customer continuity and history information
(O’Leary 1998). While these various components can appear to be fairly complex, in reality
they can largely be summed up as definitions (e.g., terminology and explanations of
terminology), rules (e.g., regulations, standards, corporate policies, and interpretations
thereof), and examples (e.g., stories of how problems have been overcome, memos describing
problem resolution in a given context, preferred business practices under certain conditions,
and summaries of previously researched issues)—the three building blocks for
declarative or explicit knowledge.
All of these facilities are designed to ease the mental workload and make it easier for
the user to acquire the knowledge necessary to complete the task at hand (Alavi and Leidner
2001b). To accomplish this, the KMS should allow the user to have easy access to explicit
knowledge stored in the system (e.g., definitions, rules, and examples) that can be applied
to solve the problem. The KMS should also improve the ease by which the user can find
a rule and/or example that applies to the current situation and facilitate interpretive problem
solving (Alavi and Leidner 2001b). To a certain degree, the KMS relieves the user of the
need for encoding of explicit knowledge in long-term memory as applicable knowledge
components can be readily accessed by the user’s active working memory. Easy access to
explicit knowledge via the KMS also reduces the likelihood the user will draw upon encoded
explicit knowledge in long-term memory as drawing from long-term memory increases
the effort required of the user, thus increasing mental workload (Alavi and Leidner
2001a).
Cognitive load theory suggests that as mental workload decreases, an individual will
have more working memory available for problem solving and will lead to better performance
(Sweller 1988; Sweller 1989; Chandler and Sweller 1991; Chandler and Sweller
1996; Sweller and Chandler 1991; Sweller et al. 1998; Paas et al. 2003; van Merrienboer
and Sweller 2005). Thus, having explicit knowledge readily accessible via a KMS should
enhance performance. Accordingly, this leads to the first hypothesis:
H1: A user of a KMS providing access to explicit knowledge required for problem
solving will perform better than an individual using traditional reference materials.
The concern that has been raised is that this easy access to explicit knowledge that
negates the need to encode (and subsequently activate) explicit knowledge in long-term
memory may result in the individual user failing to develop foundational explicit knowledge
that is in turn needed to develop tacit knowledge and expertise (Cole 1998; Powell 1998;
Alavi and Leidner 2001b). On the other hand, the easy availability of knowledge components
gives KMS users improved accessibility to a large volume of explicit knowledge (i.e.,
definitions, rules, and examples) and increases the individual’s opportunities to encode this
knowledge into long-term memory. The concern is whether this explicit knowledge will be
encoded in long-term memory as effort is considered a key influence on the successful
acquisition of knowledge (Hiltz 1986).
Substantial research with other types of KBS indicate that similar high expectations by
early promoters of intelligent systems for accelerated transfer of knowledge from system
to user (Eining and Dorr 1991) did not come to fruition. Rather, explorations of the impact
on acquisition of explicit knowledge through explanation provision in intelligent systems
provide mixed evidence. Bransford et al. (1982), Franks et al. (1982), Stein et al. (1982a),
and Stein et al. (1982b) found that the precision of an explanation embedded within an
intelligent system positively affects the development of explicit knowledge. Intelligent system
explanations embedded with explicit knowledge have been shown to successfully increase
long-term memory storage of explicit knowledge (Smedley and Sutton 2004). Alternative
research indicates that users of intelligent systems may acquire and encode less
explicit knowledge than users of other reference materials (Brody et al. 2003; Marchant et
al. 1991; Murphy 1990). Odom and Dorr (1995) also find evidence indicating that precise
explanations embedded within an intelligent system with examples actually decreased acquisition of explicit knowledge.

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