Knowledge Management and Organizational Performance a Decomposed View[1]

Knowledge management and organizationalperformance: a decomposed view

Annette M. Mills and Trevor A. Smith

Abstract

Purpose – The purpose of this paper is to evaluate the impact of specific knowledge management

resources (i.e. knowledge management enablers and processes) on organizational performance.

Design/methodology/approach – The study uses survey data from 189 managers and structural

equation modeling to assess the links between specific knowledge management resources and

organizational performance.

Findings – The results show that some knowledge resources (e.g. organizational structure, knowledge

application) are directly related to organizational performance, while others (e.g. technology, knowledge

conversion), though important preconditions for knowledge management, are not directly related to

organizational performance.

Research limitations/implications – The survey findings were based on a single dataset, so the same

observations may not apply to other settings. The survey also did not provide in-depth insight into the

key capabilities of individual firms and the circumstances under which some resources are directly

related to organizational performance.

Practical implications – The study provides evidence linking particular knowledge resources to

organizational performance. Such insights can help firms better target their investments and enhance

the success of their knowledge management initiatives.

Originality/value – Prior research often utilizes composite measures when examining the knowledge

management-organizational performance link. This bundling of the dimensions of knowledge

management allows managers and researchers to focus on main effects but leaves little room for

understanding how particular resources relate to organizational performance. This study addresses this

gap by assessing the links between specific knowledge management resources and organizational

performance. The results show that some resources are directly related to organizational performance,

while others are not.

Keywords Knowledge management, Organizational performance, Surveys

Paper type Research paper

1. Introduction

For many organizations achieving improved performance is not only dependent on the

successful deployment of tangible assets and natural resources but also on the effective

management of knowledge (Lee and Sukoco, 2007). As such, investments in knowledge

management continue to increase dramatically from year to year. According to AMR

Research, US firms would have invested $73 billion on knowledge management software in

2007, increasing by almost 16 percent in 2008 (McGreevy, 2007). Forrester Research Inc.

(2010) also reports that 20 percent of small and medium-businesses in North America and

Europe plan to implement CRM or information and knowledge management tools in 2010 or

later, representing the fastest growing software segment among small and

medium-businesses. Much of the overall spending by firms on knowledge management

initiatives is driven by strategic imperatives that depend on the effective management of the

knowledge resource (Lee and Sukoco, 2007). As such, one of the main reasons firms invest

PAGE 156 j JOURNAL OF KNOWLEDGE MANAGEMENT j VOL. 15 NO. 1 2011, pp. 156-171, Q Emerald Group Publishing Limited, ISSN 1367-3270 DOI 10.1108/13673271111108756

Annette M. Mills is a Senior

Lecturer in the Department

of Accounting and

Information Systems, at the

University of Canterbury,

Christchurch, New

Zealand. Trevor A. Smith is

a Lecturer in the

Department of

Management Studies at the

University of the West

Indies, Jamaica, West

Indies.

Received: 6 May 2010Accepted: 1 September 2010

in knowledge management is to build a knowledge capability that facilitates the effective

management and flow of information and knowledge within the firm.

Different resources make up the knowledge capability of a firm. These include technology

infrastructure, organizational structure and organizational culture which are linked to a firm’s

knowledge infrastructure capability; and knowledge acquisition, knowledge conversion,

knowledge application and knowledge protection which are linked to the firm’s knowledge

process capability (Alavi and Leidner, 2001; Gold et al., 2001). Taken together, these

resources determine the knowledge management capability of a firm, which in turn has been

linked to various measures of organizational performance (Grant, 1996; Gold et al., 2001;

Lee and Sukoco, 2007; Zack et al., 2009).

Given the composite nature of knowledge capabilities, most firms will possess different

levels and combinations of resources (i.e. knowledge enablers and processes) that

collectively make up their knowledge capability. The contribution that each resource makes

to organizational performance is therefore likely to vary across firms; it is this unique makeup

that enables benefits such as competitive advantage and improved performance.

Although research suggests that a firm’s knowledge management capabilities in

combination, impact organizational performance (Gold et al., 2001; Zaim et al., 2007) it is

likely that only some of the resources that make up these capabilities will contribute to

organizational performance on their own (Grant, 1991). However, prior research has tended

to bundle the dimensions that make up knowledge capabilities. This approach has the

advantage of enabling managers and researchers to focus on main effects, but leaves little

room for understanding how particular resources relate to organizational performance.

For example, firms that decide to enhance their overall capabilities may start with a decision

about the applications they need, then move to decisions about the infrastructure and other

processes needed to support the application (e.g. how knowledge will be acquired,

converted and protected). Focusing on individual knowledge enablers and processes can

therefore provide a more fundamental understanding of a firm’s knowledge capability and

enhance management decision-making at the resource level. A more detailed evaluation of

the links between the individual dimensions of knowledge management capabilities and

organizational performance can address this gap.

Using survey data from 189 senior- and middle-level managers in the service and

manufacturing sectors and structural equation modeling techniques, it is expected that this

study will provide insights into the links between individual knowledge enablers and

processes, and organizational performance. The outcomes will not only provide managers

and researchers with quantitative evidence linking particular knowledge resources to

organizational performance but will also shed light on how firms can enhance the success of

their knowledge management initiatives through a more targeted and direct approach to

implementation. The outcomes will also address gaps in the literature regarding the lack of

large-scale empirical evidence linking knowledge management to organizational

performance (Zack et al., 2009).

2. Literature review

Gold et al. (2001) proposed a model of knowledge management capabilities that has since

become one of the most widely cited in the knowledge management literature. In this model,

Gold et al. theorized knowledge management capabilities as multidimensional concepts

that incorporate: a process perspective which focuses on a set of activities, that is,

knowledge process capabilities and an infrastructure perspective which focuses on

enablers, that is, knowledge infrastructure capabilities (Alavi and Leidner, 2001; Lee and

Choi, 2003). These in turn are composed of multiple dimensions: knowledge infrastructural

capability comprises technology, organizational culture and organizational structure while

knowledge process capability is made up of knowledge acquisition, knowledge conversion,

knowledge application, and knowledge protection (Gold et al., 2001).

VOL. 15 NO. 1 2011 j JOURNAL OF KNOWLEDGE MANAGEMENTj PAGE 157

Prior research suggests these enablers and processes are necessary preconditions for

effective knowledge management (Alavi and Leidner, 2001; Davenport et al., 1998). Thus

most researchers using the Gold et al. framework will model the knowledge infrastructure

and knowledge process capabilities as composite constructs, when examining the links

between knowledge capabilities and outcomes such as organizational performance,

knowledge management success, and strategy implementation (Chan and Chao, 2008;

Jennex and Olfman, 2005; Laframboise et al., 2007; Paisittanand et al., 2007). For

example, Gold et al. (2001) found that both knowledge infrastructure capability and

knowledge process capability are positively related to organizational performance. This

approach has the benefit of allowing researchers to focus on the main effects and

enhancing parsimony.

However, what is not well known is whether there are differential relationships (including null

or cancelling effects) between the individual dimensions of knowledge process capability

and knowledge infrastructure capability, and organizational performance and the nature of

these relationships (Law et al., 1998; Petter et al., 2006). To address this gap, this study

examines a decomposed Gold et al. (2001) model, analyzing the structural model at the

level of the individual resource vis-a-vis organizational performance. The outcomes are

expected to provide specific insights into the knowledge management – organizational

performance link by identifying those knowledge resources (i.e. enablers and processes)

that are directly related to organizational performance.

2.1 The theoretical model

When it comes to the relationship between IT resources and organizational performance the

resource-based view (RBV) offers a useful lens for understanding this link. In essence, the

RBV argues that ‘‘firms possess resources, a subset of which enables them to achieve

competitive advantage, and a further subset which leads to superior long-term

performance’’ (Wernerfelt, 1984, p.108). However, the RBV is void of a single definition of

the term ‘‘resource’’ (Wade and Hulland, 2004) with many researchers using the terms

‘‘resources’’ and ‘‘capabilities’’ interchangeably (Christensen and Overdorf, 2000; Gold

et al., 2001; Sanchez et al., 1996). However, Grant (1991) suggests that a firm’s resource is

the basic unit of analysis and provides direct input to the production process while the firm’s

capability represents an aggregation of resources or ‘‘the capacity for a team of resources to

perform some task or activity’’ (Grant, 1991, p. 119). Thus ‘‘resources are the source of a

firm’s capabilities, [and] capabilities are the main source of its competitive advantage’’

(Grant, 1991, p. 119). Consequently, both resources and capabilities can contribute to a

firm’s bottom-line (Grant, 1991). However, few resources are productive on their own and, it

is the overall capabilities that are considered the true drivers of the firm’s productivity (Grant,

1991).

The RBV also recognizes that while some resources may lead to performance

enhancements, others do not, and that the combination may differ across industries and

firms. As such, a key challenge for firms is to identify and leverage those resources that

directly impact organizational performance (Wade and Hulland, 2004; Zack et al., 2009).

Based on this understanding of the relationship between resources, capabilities and

organizational performance, the next section examines knowledge management

capabilities, the resources that make up these capabilities, and the theorized links

between these resources and organizational performance. A decomposed model of

knowledge management capabilities is then assessed vis-a-vis organizational performance,

and the results compared with a composite model of knowledge management capabilities.

Implications for future research and practice follow.

2.2 Knowledge management capabilities

Knowledge management supports the aggregation of resources into capabilities (Maier and

Remus, 2002). Knowledge management capabilities can be categorized into two broad

PAGE 158 j JOURNAL OF KNOWLEDGE MANAGEMENTj VOL. 15 NO. 1 2011

types – knowledge infrastructure capability and knowledge process capability (Gold et al.,

2001).

2.2.1 Knowledge infrastructure capability. Prior research recognizes the importance of

having a supportive and effective knowledge infrastructure to underpin a firm’s knowledge

management initiatives (Davenport and Volpel, 2001; Paisittanand et al., 2007). Different

elements make up a firm’s knowledge infrastructure capability. This study adopts the Gold

et al. (2001) typology which views technology, organizational culture and organizational

structure as key components of a firm’s knowledge infrastructure capability (Davenport and

Volpel, 2001; Paisittanand et al., 2007).

Technology. The technology element of knowledge infrastructure comprises the information

technology (IT) systems that enable the integration of information and knowledge in the

organization as well as the creation, transfer, storage and safe-keeping of the firm’s

knowledge resource. Although an appropriate technology infrastructure is essential for

effective knowledge management, studies that examine the link between information

technologies and measures of organizational performance are often inconclusive, and fail to

demonstrate whether IT is directly related to performance (Powell and Dent-Micallef, 1997;

Webb and Schlemmer, 2006). For example, Powell and Dent-Micallef (1997) in their study of

US firms, found that IT in and of itself did not enhance organizational performance, but could

increase organizational performance when combined with other human and business

assets. Teece et al. (1997) further suggested that the absence of an association between

technology and performance could be because technology (e.g. IS resources) is easily

copied, making it a fragile source of competitive advantage.

Although technology is not always linked directly to organizational performance, research

shows that when combined with other resources IT can enhance performance and lead to

sustained advantage (Clemons and Row, 1991; Powell and Dent-Micallef, 1997). So

although the technology infrastructure may not contribute directly to organizational

performance, it is an essential enabler of other knowledge resources such as knowledge

acquisition and knowledge application processes, which may themselves enhance

organizational performance (Seleim and Khalil, 2007).

Organizational culture. In the context of knowledge management is considered a complex

collection of values, beliefs, behaviors and symbols that influences knowledge management

in organizations (Ho, 2009). Hence, a knowledge-friendly culture is regarded as one of the

most important factors impacting knowledge management and the outcomes from its use

(Alavi et al., 2005-2006; Davenport et al., 1998; Ho, 2009). Sin and Tse (2000) found that

organizational cultural values such as consumer orientation, service quality, informality and

innovation were ‘‘significantly associated with marketing effectiveness’’ (Sin and Tse, 2000,

p. 305). More recently, Aydin and Ceylan (2009) also showed that cultural dimensions were

related to organizational performance.

Changes in corporate culture are also regarded as necessary for implementing knowledge

management programs (Bhatt, 2001): ‘‘the ability of an organization to learn, develop

memory, and share knowledge is [therefore] dependent on its culture’’ (Turban et al., 2005,

p. 496). Thus, positive changes in culture are expected to impact organizational

performance and add momentum to other improvements taking place elsewhere in the

organization (Richert, 1999).

Organizational structure comprises the organizational hierarchy, rules and regulations, and

reporting relationships (Herath, 2007) and is considered a means of co-ordination and

control whereby organizational actors can be directed towards organizational effectiveness.

Knowledge management theorists largely conclude that changes in an organization’s

structure, such as moving from hierarchical to flatter networked forms, are essential for the

effective transfer and creation of knowledge in the organization (Beveren, 2003; Gold et al.,

2001; Grant, 1996; Nonaka and Takeuchi, 1995). Such changes by extension have been

positively associated with improved outputs in both service and financial terms (Richert,

1999). Thus it is expected that:


H1. Technology is not (directly) related to organizational performance.

H2. Organizational culture is positively related to organizational performance.

H3. Organization structure is positively related to organizational performance.

2.2.2 Knowledge process capability. Gold et al. (2001) suggested that knowledge process

capabilities (required for storing, transforming and transporting of knowledge throughout the

organization) are needed for leveraging the infrastructure capability. Four broad dimensions

are identified – ‘‘acquiring knowledge, converting it into useful form, applying or using it,

and protecting it’’ (Gold et al., 2001, p. 190).

Knowledge acquisition. The term ‘‘acquisition’’ refers to a firm’s capability to identify, acquire

and accumulate knowledge (whether internal or external) that is essential to its operations

(Gold et al., 2001; Zahra and George, 2002). Acquiring knowledge can involve several

aspects including creation, sharing and dissemination. Knowledge acquisition reflects in

part, a subset of a firm’s absorptive capacity – more specifically, it can be viewed as a

‘‘potential capacity’’ that reflects a firm’s ability to use its knowledge to create advantage, but

does not guarantee that knowledge will be used effectively (Cohen and Levinthal, 1990).

Research suggests strong and positive links between knowledge acquisition and

performance measures. For example, Song (2008) showed that knowledge creation

practices were significantly related to organizational improvement. Further, when acquired

knowledge is used appropriately, a significant and positive link is observed between

knowledge acquisition and organizational performance (Lyles and Salk, 1996; Seleim and

Khalil, 2007).

Knowledge conversion. Knowledge that is captured from various sources (both internal and

external to the business) needs to be converted to organizational knowledge for effective

utilization within the business (Lee and Suh, 2003). This conversion process, which takes

place along the supply chain of data, information and knowledge, is transient in nature and

so organizations must speedily convert data into information and information into

organizational knowledge to maximize benefits from the conversion process (Bhatt, 2001).

Thus, it is expected that the knowledge conversion process could influence performance

outcomes.

Knowledge application. Bhatt (2001, pp. 72-73) stated that: ‘‘knowledge application means

making knowledge more active and relevant for the firm in creating value’’. For organizations

to create value they need to apply knowledge to their products and services by various

means such as repackaging available knowledge, training and motivating its people to think

creatively, and utilizing people’s understanding of the company’s processes, products and

services. For example, many organizations encourage organizational learning in which

individuals and teams can apply the knowledge gained to initiatives’ such as new product

development with the ultimate aim of improved performance in areas such as ‘‘speed to

market’’ and innovation (Sarin and McDermott, 2003). Droge et al. (2003, p.544) also argues

that ‘‘in the long run, firms that create new knowledge at a lower cost and more speedily that

competitors, and then apply that knowledge effectively and efficiently, will be successful at

creating competitive advantage’’.

For knowledge to impact organizational performance it has to be used to support the firm’s

processes. Hence, it is through knowledge utilization that acquired knowledge can be

transformed from being a potential capability into a realized and dynamic capability that

impacts organizational performance (Cohen and Levinthal, 1990; Seleim and Khalil, 2007;

Zahra and George, 2002).

Knowledge protection. Knowledge protection is necessary for effective functioning and

control within organizations. This would typically include the use of copyright and patents

along with information technology systems that allow knowledge to be secured by filename,

user name, password and file-sharing protocols that ascribe rights to authorized users (Lee

and Yang, 2000). However, knowledge protection is often challenging in part because the


copyright laws that are intended to protect knowledge are limited in their treatment of the

knowledge environment (Everard, 2001). Notwithstanding such limitations, the knowledge

protection process should not be abandoned or marginalized (Gold et al., 2001) and

protecting knowledge from illegal and inappropriate use is essential for a firm to establish

and maintain a competitive advantage (Liebeskind, 1996). Moreover, since knowledge is

crucial for competitive advantage, storing and protecting knowledge is expected to create

value for the organization (Lee and Sukoco, 2007).

Taken altogether, it is expected that:

H4. Knowledge acquisition is positively related to organizational performance.

H5. Knowledge conversion is positively related to organizational performance.

H6. Knowledge application is positively related to organizational performance.

H7. Knowledge protection is positively related to organizational performance.

2.2.3 A composite model of knowledge management capabilities. There is a general

consensus in the literature that knowledge management is linked to organizational

performance (Gold et al., 2001; Gosh and Scott, 2007; Lee and Sukoco, 2007; Liu et al.,

2005; Zaim et al., 2007). For example, Gold et al. (2001) and Zaim et al. (2007) showed that

both knowledge infrastructure capability and knowledge process capability have a

significant and positive impact on organizational effectiveness. Lee and Sukoco (2007)

found that knowledge management capabilities affect innovation and organizational

effectiveness. Gosh and Scott (2007) also argued that knowledge infrastructural capabilities

such as technology, organizational culture and organizational structure, need to correspond

with knowledge process capabilities (e.g. actual flow and use of knowledge) in order to

achieve considerable improvements in effectiveness. In assessing the relationship between

knowledge management practices and performance outcomes, Zack et al. (2009) found

that knowledge management practices are related to measures of organizational

performance. Thus, it is expected that:

H8. Knowledge infrastructural capability is positively related to organizational

performance.

H9. Knowledge process capability is positively related to organizational

performance.

3. Methodology

Decomposed models are used in research to examine complex structures at lower-levels of

detail. Decomposed models stem from the notion that the constructs under investigation

represent complex concepts that are often best represented as multidimensional in nature.

These multidimensional constructs take different forms when it comes to theorizing the

relationships between the construct and its sub-dimensions. One form is the aggregate

construct, which typically consists of an algebraic composite of its dimensions (Law et al.,

1998). Under these conditions changes in the dimensions lead to changes in the constructs;

this is similar to the relationship between a formative (causal) construct and its indicators

where changes in the indicators lead to changes in the construct (Petter et al., 2007).

The knowledge infrastructure capability and knowledge process capability (Gold et al.,

2001) are examples of aggregate constructs. Since the overall construct is formed from its

underlying dimensions, the dimensions need not be correlated; thus inferences drawn at

higher-levels of analysis may not apply at the dimensional level (Law et al., 1998). For

example, if there are opposing effects or null effects at the lower-level these may be

overlooked if the analysis focuses on the higher-level. Decomposed models address this

problem by removing the causal structures from the aggregate construct and directly

relating the individual dimensions to other constructs in the research model (Petter et al.,

2007).


Since the aim of this research was to better understand the relationships between the

individual factors that make up the firms’ knowledge management capabilities and

organizational performance, two levels of analysis were conducted. First, a decomposed

model of knowledge management capabilities was examined – this looked at the links

between organizational performance and particular resources (i.e. enablers and processes)

that make up a firm’s knowledge infrastructural capability and knowledge process

capability. The composite model was also evaluated and the results compared with the

findings from the decomposed model.

3.1 The sample

To evaluate the research hypotheses, a survey was developed to capture measures of

knowledge management capabilities and organizational performance. The measures

consisted of multi-item constructs (with four to six items each) adapted from Gold et al.,

2001:

B knowledge infrastructure capability which comprised technology, organizational

structure, and organizational culture;

B knowledge process capability which comprised knowledge acquisition, knowledge

conversion, knowledge application, and knowledge protection; and

B organizational performance.

All items were assessed using seven-point Likert-type scales, anchored with ‘‘Strongly

agree’’ and ‘‘Strongly disagree’’.

Approximately 500 surveys were distributed to students enrolled in graduate MBA and MSc

programs in Jamaica. Like Gold et al. (2001), respondents at a management-level in their

firms were considered themost suitable for this study, as they weremore likely to be aware of

the firms’ knowledge management capabilities. Responses were returned by 265 (53

percent) persons, of which 189 (37.8 percent) from management-level staff were usable. Of

these, 164 (86.8 percent) responses were from the service sector and 25 (13.2 percent) from

manufacturing. Of the firms, 80.4 percent employed 50 persons or more; 65.6 percent

employed 100 or more persons.

3.2 Data analysis and results

PLS-Graph 3.0 (Build 1130) and SPSS version 17.0 were used to assess the links between

knowledge management capabilities and organization effectiveness, and bootstrapping

(using PLS-Graph with 200 samples) used to evaluate the significance of the model paths.

First, the measurement model was assessed. Ideally, item loadings should exceed 0.707;

loadings of 0.60 are also acceptable if there are additional indicators (Chin, 1998). The

results showed one item measuring knowledge acquisition returned a loading of 0.40; this

item was therefore excluded. Item loadings for all other constructs ranged from 0.668 to

0.926 exceeding minimum thresholds (Table I).

Descriptive statistics (i.e. mean and standard deviation (SD)) for each construct are shown

in Table II. Table II also shows that composite reliabilities ranged from 0.918 to 0.963 and

average variance extracted (AVE) from 0.635 to 0.789 exceeding recommended cut-offs

(Chin, 1998). Construct AVEs were also greater than the variance shared between the

constructs (Table III) satisfying the criteria for discriminant validity (Chin, 1998).

Decomposed model of KM capabilities. Turning to the structural model, the results showed

the decomposed model accounted for 0.754 of the variance observed for organizational

performance (Figure 1). Of the knowledge infrastructural capabilities, only organizational

structure (b ¼ 0.209; p # 0.05) was significant vis-a-vis organizational performance;

technology infrastructure (b ¼ 2 0.003) was not expected to be significant. Hypotheses H1

and H3 were supported. Contrary to expectation, organizational culture was not significant

(b ¼ 0.055); H2 was therefore not supported.


Table I Item loadings

Constructs Item loadings

Technology (TC)

TC05 0.693TC06 0.926

TC07 0.919TC09 0.898

Organizational culture (CU)

CU01 0.781CU02 0.770

CU04 0.804CU09 0.841

CU10 0.844CU13 0.798

Organizational structure (ST)

ST03 0.811ST04 0.855

ST05 0.782ST06 0.668

ST07 0.846ST10 0.736

ST11 0.860

Knowledge acquisition (AQ)

AQ01 0.820AQ03 0.806

AQ05 0.866AQ08 0.854AQ12 0.857

Knowledge conversion (CN)CN03 0.836

CN04 0.881CN05 0.849

CN08 0.885CN09 0.905

CN10 0.870

Knowledge application (AP)AP03 0.848

AP04 0.923AP05 0.895

AP06 0.896AP07 0.901

AP08 0.907AP10 0.844

Knowledge protection (PT)

PR01 0.895PR02 0.876

PR03 0.888PR04 0.853

PR07 0.860PR08 0.753

PR10 0.825

Organizational performance (OP)OP01 0.781

OP07 0.898OP08 0.896

OP12 0.906OP13 0.865

OP14 0.890


For knowledge process capability, three processes were significant vis-a-vis organizational

performance: knowledge acquisition (b ¼ 0.146; p # 0.05), knowledge application

(b ¼ 0.412; p # 0.001), and knowledge protection (b ¼ 0.148; p # 0.05); H4, H6 and H7

were supported. Knowledge conversion capability was not significant (b ¼ 0.025); H5 was

not supported.

Assessment of the composite model. Next, latent variable scores representing the

dimensions of knowledge process capability and knowledge infrastructural capability were

extracted and used to assess the composite model. Consistent with recommended

guidelines, indicator weights for all seven dimensions were examined (Table IV); all except

knowledge conversion were significant vis-a-vis their respective constructs at p # 0.05

(Chin, 1998; Petter et al., 2007). However, this does not mean knowledge conversion was

unimportant. Further examination of the item loadings showed the construct demonstrated

‘‘absolute’’ importance when assessed independently of other indicators (Cenfetelli and

Basellier, 2009). The results also showed that, knowledge application was the most

important of the dimensions in terms of relative importance.

The results of the structural model tests showed that the composite (second-order) model

accounted for 0.748 of the variance observed for organizational performance (Table V).

Consistent with expectations, knowledge infrastructural capability (b ¼ 0.251; p # 0.05)

and knowledge process capability (b ¼ 2 0.639; p # 0.001) were both significant vis-a-vis

organizational performance, supporting hypotheses H8 and H9.

Finally, a summary of the results of the model tests for the decomposed model and the

composite model are shown in Table V.

Table II Descriptive statistics, composite reliabilities (CR) and average variance extracted

(AVE)

Constructs Mean SD CR AVE

Knowledge infrastructure capabilitiesOrganizational structure (ST) 4.414 1.446 0.924 0.635Organizational culture (CU) 5.215 1.378 0.918 0.651Technology (TC) 4.569 1.646 0.921 0.747

Knowledge process capabilitiesKnowledge acquisition (AQ) 5.309 1.268 0.923 0.707Knowledge conversion (CN) 4.929 1.384 0.950 0.759Knowledge application (AP) 5.140 1.447 0.963 0.789Knowledge protection (PT) 4.930 1.473 0.948 0.725Organizational performance (OP) 4.810 1.478 0.951 0.763

Table III Inter-construct correlations and discriminant validity

Constructs ST CU TC AQ CN AP PT OP

Knowledge infrastructure capabilitiesOrganizational structure (ST) 0.797Organizational culture (CU) 0.745 0.807Technology (TC) 0.557 0.481 0.864

Knowledge process capabilitiesKnowledge acquisition (AQ) 0.639 0.666 0.565 0.841Knowledge conversion (CN) 0.720 0.748 0.636 0.737 0.871Knowledge application (AP) 0.715 0.754 0.604 0.724 0.813 0.888Knowledge protection (PT) 0.595 0.591 0.600 0.588 0.641 0.642 0.851Organizational performance (OP) 0.742 0.723 0.576 0.718 0.752 0.822 0.669 0.873

Note: Italicized items represent the square-root of the variance shared between the constructs and their measures; the off-diagonalelements are the correlations among the constructs


4. Discussion and Implications

Consistent with expectations, the study results provided strong empirical support for the

decomposed model, accounting for 0.754 of the variance observed for organizational

performance. For the composite model (Table V), the amount of variance explained was

0.748, and was similar to the decomposed model. The links between organizational

performance and knowledge process capability and knowledge infrastructure capability

returned path weights of 0.251 and 0.639 respectively. Altogether, these findings are

consistent with prior research that has observed similar orders of magnitude for the path

weights and variance explained in respect of knowledge management and organizational

performance (Gold et al., 2001; Zaim et al., 2007).

The results for the decomposed model (Table V) showed that of the three infrastructural

capabilities, only organizational structure had a significant impact on organizational

Table IV Indicator weights and significance levels

Construct Weight t-statistic Significance

Organizational structure 0.457 3.991 p # 0.001Organizational culture 0.440 3.966 p # 0.001Technology 0.252 3.455 p # 0.001Knowledge acquisition 0.210 2.222 p # 0.05Knowledge conversion 0.122 1.105 nsKnowledge application 0.572 6.464 p # 0.001Knowledge protection 0.213 2.792 p # 0.05

Figure 1 The results at the dimensional level

Note: * p ≤ 0.01; ** p ≤ 0.05; *** p ≤ 0.001

0.148**

0.146**

0.412***

R2 = 0.754

0.025

0.209**

-0.003

Organizational Performance

Organizational Structure

Knowledge Acquisition

Knowledge Conversion

Technology Infrastructure

Organizational Culture

Knowledge Protection Knowledge

Application

Knowledge Infrastructure Capability

0.055

Knowledge Process Capability


performance; neither technology nor organizational culture had a significant impact on

organizational performance. For knowledge process capability, knowledge acquisition,

knowledge application and knowledge protection also impacted organizational

performance, but not knowledge conversion.

Altogether, these results suggest that although the individual resources collectively

determine the knowledge management capabilities construct, not all are directly linked to

organizational performance. This is consistent with the resource-based view which suggests

that only a subset of a firm’s capabilities when leveraged appropriately reflect direct

contributions to performance measures (Grant, 1991). For example, Seleim and Khalil

(2007) found that of five knowledge processes studied (e.g. acquisition, creation,

application) only knowledge application was directly linked to organizational performance.

So although, knowledge management capabilities may contribute directly to organizational

performance and each resource significant in respect of its construct (Zaim et al., 2007), in

some cases the contribution of particular resources may be more indirect through their

impact on other factors linked to organizational performance. For example, while Seleim and

Khalil (2007) did not uncover a positive link between organizational performance, and

knowledge acquisition and knowledge creation, their study showed both processes were

directly related to knowledge application which in turn was related to organizational

performance.

The study results have several implications for knowledge management in firms. For

example, research suggests appropriate investments in knowledge management initiatives

can enhance organizational performance. However, this study shows that not all of the

resources are direct contributors. Although resources such as technology, culture and

knowledge conversion are necessary for effective knowledge management (Gold et al.,

2001) they did not impact organizational performance directly. However, firms can ill afford

to neglect these dimensions as they work in combination with and support other resources,

such as knowledge acquisition and knowledge application that may contribute directly to

organizational success (Van den Bosch et al., 1999; Seleim and Khalil, 2007).

Table V Summary of results for the model tests

Hypotheses Path Significance

Decomposed modelKnowledge infrastructural capability

H1. Technology is not (directly) related toorganizational performance 0.003 nsH2. Organizational culture is positively relatedto organizational performance 0.055 nsH3. Organizational structure is positivelyrelated to organizational performance 0.209 p # 0.05

Knowledge process capabilityH4. Knowledge acquisition is positively relatedto organizational performance 0.146 p # 0.05H5. Knowledge conversion is positively relatedto organizational performance 0.025 nsH6. Knowledge application is positively relatedto organizational performance 0.412 p # 0.001H7. Knowledge protection is positively relatedto organizational performance 0.148 p # 0.05

R-Squared (R 2) 0.754 –

Composite modelH8. Knowledge infrastructural capability ispositively related to organizational performance 0.251 p#0.05H9. Knowledge process capability is positivelyrelated to organizational performance 0.639 p # 0.001R-Squared (R 2) 0.748 –


Second, this research showed that inferences about an overall capability do not necessarily

apply when it comes to individual resources. For example, the current findings are consistent

with research which suggests that particular knowledge resources (e.g. technology,

organizational structure, knowledge acquisition, etc) are directly related to knowledge

management capabilities (Gold et al., 2001; Zack et al., 2009; Zaim et al., 2007) and are

therefore important in forming a firm’s overall knowledge capability. However, for studies that

use composite models, it is difficult to identify which resources directly impact organizational

performance. Although some studies shed light on this gap (Zack et al., 2009), there remains

a gap in the literature regarding empirical evidence linking particular knowledge resources

to performance. The current study addresses this gap by identifying specific enablers and

processes that are directly related to organizational performance.

The combination of resources that is most effective for an organization is also likely to differ

across firms. Since there are no ‘‘silver-bullet’’ combinations when it comes to enhancing

organizational performance, it is incumbent on managers not only to recognize that all the

resources are important, but also to identify which resources and consequently which

capabilities are most salient to organizational performance. Such insights can help

managers identify appropriate strategies aimed at deploying combinations of knowledge

management resources that better support the firm’s goals. Furthermore, since the

combinations may be unique across firms, this provides an opportunity for competitive

advantage and sustained performance.

Although this study offers insights into the dynamic nature of the knowledge management

resource, there are some constraints. For example, since a firm’s knowledge capability is a

composite of the individual resources that make up the knowledge capability, different firms

and industries may have different combinations that yield similar outcomes. As such, while

the outcomes of this study suggest, for example that organizational structure was linked to

organizational performance and culture was not for the study sample, the same may not

apply to other settings. This can be expected as performance indicators such as

competitive advantage are created and maintained by such differences. It is therefore

important that firms recognize the variableness of knowledge capabilities and the need to

deploy strategies that lead to the acquisition and deployment of those capabilities that are

most relevant to the firm’s goals.

As with other survey-based research, this study is subject to the possibility of response bias

such that managers for reasons such as poor recall or role characteristics may under-report

or over-report the knowledge management activities of their firm. Having two or more

respondents for each firm can help minimize this effect, but may limit how many data can be

collected (Gold et al., 2001).

Finally, this study also does not provide in-depth insight into the capabilities of individual

firms. Such insights would enable a better understanding of the individual capabilities that

make up a firm’s knowledge capability, why differences may occur, and under what

circumstances do some resources impact organizational performance and others do not.

Future research is therefore needed to examine in greater detail the links between the

individual capabilities that make up knowledge resources, and organizational performance.

5. Conclusion

The literature is replete with studies that suggest knowledge management impacts

organizational performance. However, there has been little elaboration of the relationships at

the dimensional level vis-a-vis organizational performance. Yet when it comes to making

decisions about a firm’s knowledge capability, these are often made at the level of the

individual resource. This study addresses this gap by assessing a decomposed model of

knowledge management capabilities. The aim was to provide insights into the relationships

between particular knowledge resources and organizational performance that can help

firms identify appropriate strategies for investing in and effectively deploying the knowledge

resource.


The results showed that for the current study, organizational structure, knowledge

acquisition, knowledge application and knowledge protection were significantly related to

organizational performance. However, technology, organizational culture and knowledge

conversion did not have a significant impact. Taken altogether, the findings suggest that

although the individual resources collectively determine a firm’s overall knowledge

management capability which, as a composite is related to organizational performance,

each resource is not directly linked to performance. The decomposedmodel therefore offers

insights into relationships at the dimensional level that are not readily inferred from

composite models.

In the final analysis, this study offers useful insights into the knowledge management –

performance link. First, there has been little research that decomposes the effects of

knowledge management in relation to organizational performance. The results suggest the

decomposed approach is useful for understanding the complex relationships embodied in

the knowledge management – performance link, which cannot be surmised from a

composite model. Such an approach is useful for research aimed at acquiring an in-depth

understanding of knowledge management, as opposed to achieving parsimony or focusing

on main effects.

The findings also suggest a number of avenues for future work. First, the study outcomes

suggest different relationships exist between particular resources, and organizational

performance. At the same time, the literature also shows that for multifaceted concepts such

as knowledge infrastructure capability and knowledge process capability there is no

commonly agreed conceptualization of which components make up these capabilities (Alavi

and Leidner, 2001; Gold et al., 2001; Lee and Yang, 2000). Thus it seems likely that different

compositions of knowledge infrastructure and knowledge process capabilities may lead to

different outcomes. Further research is therefore needed to understand the differences

among the capabilities including firm-level differences and how this relates to organizational

performance.

Finally, the literature calls for further research into the links between knowledge capabilities

and organizational performance, and for large-scale empirical evidence supporting these

links (Zack et al., 2009). This study addresses this call by examining the links between the

individual dimensions of knowledge capabilities and organizational performance. However,

other success factors such as user satisfaction and perceived benefits can also be

explored.

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About the authors

Annette Mills is a Senior Lecturer at the University of Canterbury (New Zealand). Annetteholds a PhD in Information Systems from the University of Waikato (New Zealand). She haspublished a number of refereed articles in edited books and journals including Informationand Management, and Computers and Education. She currently serves on the editorialboards for the Journal of Cases on Information Technology as an Associate Editor, theJournal of Global Information Management, and the International Journal of e-Collaboration.Her research interests include social computing, technology adoption and diffusion, serviceexpectations, and user sophistication. Annette Mills is the corresponding author and can becontacted at: [email protected]

Trevor Smith is the head of the units of Marketing, International Business, Entrepreneurshipand Strategy in the Department of Management Studies at the University of the West Indies,Mona. He lectures in Marketing and Research Methods at both undergraduate and graduateLevels. His research interests include consumer marketing, tourism and hospitalitymanagement and business strategy. Another area of interest is knowledge managementand its impact on firms’ performance. He is also a consultant in field of marketing researchand strategy.


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Knowledge Management and Organizational Performance a Decomposed View[1]