Consultant competence trust doesn't pay off, but benevolent trust does! Managing knowledge with care

Consultant competence trust doesn’t payoff, but benevolent trust does! Managingknowledge with care

Dong-Gil Ko

Abstract

Purpose – Consultants are hired for their domain expertise. For long-term engagements, the role of

their expertise diminishes as the need to develop personal trust gains significance for a successful

project outcome. The purpose of this paper is to examine trust and knowledge management in the

context of project teams, exploring the trusting relationship between external consultants and internal

clients.

Design/methodology/approach – Two questionnaire surveys were developed, and a field survey of

consultant-functional specialist dyads yielded 80 matching-pair responses. A regression analysis

approach was used to test the hypotheses.

Findings – In enterprise systems implementation projects, consultants are typically hired for their

expertise in the domain – i.e. competence trust. Counter to conventional wisdom, benevolent trust

influenced the success of an effective transfer of implementation knowledge.

Research limitations/implications – The limitations of the study include the inability to capture

temporal aspects of knowledge transfer activities (survey questionnaires), generalizability to

consulting-related projects only, and application to a context-sensitive set of knowledge, i.e.

implementation knowledge.

Practical implications – Client organizations must no longer focus entirely on competence trust when

selecting consultants; instead, they should place a greater emphasis on benevolence trust, which is

critical to project success, especially in a long-term project engagement. Consulting firms must ensure

that their employees exhibit ‘‘emotional’’ characteristics through hiring practices and/or training.

Originality/value – The implications of the findings reported in the study are discussed for scholars and

managers engaged in IT-based solution delivery utilizing external consultants.

Keywords Knowledge management, Trust, Consultants

Paper type Research paper

Introduction

Enterprise systems are complex information systems that integrate business processes and

data throughout an organization. They are software systems that provide ‘‘seamless

integration of all the information flowing through a company – financial and accounting

information, human resource information, supply chain information, and customer

information’’ (Davenport and Prusak, 1998, p. 121). There is tremendous interest in this

domain given the potential strategic value and risks associated with implementation of

enterprise systems. Unfortunately, the knowledge required to successfully deploy and

maintain enterprise systems does not reside within organizations. Corporate organizations

turn to external consultants who specialize in the implementation of enterprise systems.

Given this demand, many consulting firms equip their consultants with implementation

knowledge through their training programs and expert centers. They also have many

opportunities to work on enterprise system implementation projects. In fact, many corporate

organizations partner with enterprise system application vendors, including SAP, Oracle and

other IT consulting firms. Corporate organizations hire these consultants with the intended

PAGE 202 j JOURNAL OF KNOWLEDGE MANAGEMENT j VOL. 14 NO. 2 2010, pp. 202-213, Q Emerald Group Publishing Limited, ISSN 1367-3270 DOI 10.1108/13673271011032355

Dong-Gil Ko is an Assistant

Professor in the Information

Systems Department,

College of Business,

University of Cincinnati,

Cincinnati, Ohio, USA.

Received 12 March 2009Revised 12 July 2009Accepted 1 September 2009

short-term goal of effectively deploying a new system and a long-term goal to acquire the

new skills needed for an ongoing operation. However, there are many examples of enterprise

systems implementation failures that plague the media, suggesting that managers are

unable to leverage consultants’ knowledge. For example, Hershey’s inability to shelve

candies during Hallowe’en is a prime example of challenges associated with enterprise

systems implementation. One challenge experienced by Hershey dealt with the

management of consultants and whether they possessed the necessary knowledge,

which highlights the importance of knowledge transfer activities. Unfortunately, examples of

firms who implemented enterprise systems unsuccessfully abound, many of which point to

the failure to leverage effectively the knowledge held by the consultants, especially prior to

their departure. This raises an important question regarding what conditions facilitate the

effective transfer of knowledge in the context of enterprise system implementation projects.

There have been many studies examining the conditions under which knowledge transfers,

with the vast majority focusing on the characteristics of the relationship among organizations

(e.g. superordinate relationship, such as a franchise), the characteristics of the

organizations (e.g. size or success), the characteristics of the knowledge transferred (e.g.

information complexity), and the characteristics of the transfer process (e.g. personnel

relocation; Argote, 1999). One common theme prevailing in each of the characteristics is the

role of the relationship between the knowledge provider and the recipient. For example,

Szulanski (1996) found that an arduous relationship inhibited transfer of knowledge. In a

survey of mid-level knowledge-workers, Levin and Cross (2004) found that trust significantly

improved knowledge transfer. As trust assumes an increasingly important role in the

knowledge transfer activities, especially in the context of long-term engagements such as

implementation of enterprise systems, understanding how trust affects knowledge transfer

may provide additional insight (Argote, 1999).

Prior literature

Argote et al.’s (2003) theoretical framework provides a useful starting point for examining

knowledge transfer outcomes in the context of enterprise systems implementation. In their

integrated framework for conducting research in knowledge management, they offer two

dimensions – i.e. knowledge management outcomes and properties of knowledge

management context – and provide directions for future research. Knowledge management

outcomes include knowledge creation, retention, and transfer. Properties of knowledge

management context include properties of units, relationships between units, and

knowledge. The framework’s primary emphasis is on identifying relationships between the

properties of knowledge management context (independent variables) to that of the

knowledge management outcomes (dependent variables), and they highlight the

importance of conducting additional research in each ‘‘cell’’. Thus, the ‘‘cell’’ of particular

interest in this study is knowledge transfer (knowledge management outcome) and

relationship between units (properties of knowledge management context) as depicted in

Figure 1. According to Argote et al. (2003), knowledge transfer is ‘‘evident when experience

acquired in one unit affects another’’ (p. 572) and properties of relationships between units

‘‘is characterized by [. . .] the dyadic relationship between [. . .] units’’ (p. 573). The notions of

knowledge transfer and trust are therefore explored below.

‘‘ Many consulting firms equip their consultants withimplementation knowledge through their training programsand expert centers. ’’

VOL. 14 NO. 2 2010 j JOURNAL OF KNOWLEDGE MANAGEMENTj PAGE 203

Knowledge transfer

Transfer of knowledge is particularly important in the context of enterprise systems

implementation projects due to its complexity (Soh et al., 2000). Unlike other information

systems (IS) contexts, enterprise systems implementations are typically performed by a

mixed team of external consultants, internal functional specialists, and IS specialists.

External consultants offer solutions related to the implementation of the application (e.g.

SAP), internal functional specialists provide knowledge related to the business processes,

and IS specialists are responsible for the infrastructure and other hardware-related

solutions. Because internal functional specialists possess business process knowledge,

they typically assume a greater role in the success of the implementation requiring them to

integrate business processes and data across various functions throughout the

organization. However, they rely on consultants to transfer their implementation

knowledge to successfully deploy and maintain enterprise systems. In short, while the

functional specialists are very knowledgeable with the business processes, they turn to

consultants for knowledge associated with the enterprise systems applications.

To facilitate a successful implementation, transfer of consultants’ implementation knowledge

to functional specialists is critical for meeting the perceived needs of the client organization,

responding to changing business processes, ongoing administration and maintenance,

minimizing training, gaining new in-house capabilities, and deploying a high-quality system

(Soh et al., 2000). But, what are the conditions in which consultants’ knowledge transfers

effectively?

Trust as an antecedent of knowledge transfer

Relational characteristics are an important consideration for understanding knowledge

transfer. Knowledge management literature depicts trust as a relational characteristic that

influences knowledge transfer significantly (Argote et al., 2003; Adler, 2001). Szulanski

(1996), for example, found that one of the important barriers to the transfer of knowledge is

an arduous relationship between a knowledge provider and recipient. Thus, additional

research examining trust as an antecedent would further our understanding about

knowledge transfer, particularly in the context of project teams, where more firms rely on

external consultants to meet their business objectives.

Trust has been examined by many knowledge management researchers in the organizational

literature (a simple Google search highlights the growing number of articles); however, not

Figure 1 Knowledge management framework

Source: Argote et al. (2003)

Properties of Knowledge Management Context

Units Relationshipbetween Units Knowledge

Creation

RetentionK

now

ledg

e M

anag

emen

tO

utco

mes

Transfer √

PAGE 204 j JOURNAL OF KNOWLEDGE MANAGEMENTj VOL. 14 NO. 2 2010

many have examined the role of interpersonal trust between external consultants and clients

working on a project. One study (Levin and Cross, 2004), however, draws on McAllister’s

(1995) theoretical framework of interpersonal trust, and finds that both competence and

benevolent trust improve knowledge transfer. Benevolent trust is largely based on ‘‘emotional’’

bonds between individuals; in contrast, competence trust is largely based on competence

and responsibility. More formally, competence trust is defined as trustworthiness on the basis

of ability, reliability and competence; benevolence trust is defined as trustworthiness on the

basis of sentiments, genuine care, honesty, and personal attachments.

There is evidence that interpersonal-trusting relationships lead to greater knowledge

transfer (Tsai and Ghoshal, 1998). Although competence trust and benevolence trust have

been shown to influence knowledge transfer (Levin and Cross, 2004), it is not clear whether

previous findings will hold in a context involving long-term engagements with myriads of

knowledge transfer activities required to complete the project.

Enterprise systems implementation projects are usually long-term engagements involving

transfer of knowledge between consultants and functional specialists. Consultants

typically provide implementation knowledge to functional specialists where it would be in

their best interest to develop and nurture a trusting relationship for a variety of reasons,

including fewer business conflicts and potential future engagements. Similarly, functional

specialists lack control over the quality of the consultant’s work, lack the knowledge

required to monitor its progress, and ultimately own and maintain the system (Gefen,

2004). These reasons point to a need for both consultants and functional specialists to

develop competence and benevolence trust for effectively transferring enterprise

implementation knowledge.

Competence and benevolence trust have been shown to improve relationships between

consultants and functional specialists (Gefen, 2004). Trusting a knowledge provider to

be competent and benevolent increases the opportunity for knowledge recipients to learn

from the interaction, and therefore improves the transfer of knowledge. For example, when

functional specialists seek assistance, they are forthcoming about their lack of knowledge

and become vulnerable to the benevolence of the consultants (Lee, 1997). Functional

specialists who trust consultants’ competence are likely to listen and take action on that

knowledge. Hence, functional specialists and consultants who display competence and

benevolence trust are more willing to engage in social exchanges and cooperative

interactions (Ring and Van de Ven, 1994), which provide more opportunities for knowledge

transfer to occur. Therefore, is expected that benevolence trust and competence trust are

positively related to knowledge transfer (see Figure 2). More formally:

H1a. Benevolence trust held by a knowledge recipient (i.e. functional specialist) will

have a positive impact on knowledge transfer.

Figure 2 Research model

H2b

H1a

H1b

H2aKnowledge

Transfer

Benevolent Trust – Knowledge Recipient(Functional Specialist)

Competence Trust – Knowledge Provider(Consultant)

Competence Trust – Knowledge Recipient(Functional Specialist)

Benevolent Trust – Knowledge Provider(Consultant)


H1b. Competence trust held by a knowledge recipient (i.e. functional specialist) will

have a positive impact on knowledge transfer.

H2a. Benevolence trust held by a knowledge provider (i.e. consultant) will have a

positive impact on knowledge transfer.

H2b. Competence trust held by a knowledge provider (i.e. consultant) will have a

positive impact on knowledge transfer.

Methodology

Given the interest in testing empirically the research model and the above hypotheses, a

field survey approach was considered appropriate. Using existing scales where available,

matched-pair survey instruments were developed and administered through the use of the

internet. Functional specialists (recipients of implementation knowledge) were asked about

knowledge transfer. Both consultants (providers of implementation knowledge) and

functional specialists responded to items about trust. Figure 3 provides an overview of

three-step methodology used to develop the instruments, collect data, and conduct

analysis.

A total of 71 client organizations participated in this research. While the majority represented

large, for-profit organizations across multiple industries (e.g. technology, products,

manufacturing, financial), the sample does include 11 not-for-profit organizations. All 36

participating consulting firms specialized in delivering technology-oriented solutions,

including enterprise solutions. Participants varied greatly between consultants and

functional specialists; details are provided in Table I.

Figure 3 Research methodology

Data AnalysisSurvey Instrument Development Major Data Collection

IdentifyContact &

SolicitParticipation

ReqtsMet?

SendParticipationInstructions

Participate? Follow Up

Complete? Contact forclarification

DataCleansing

Identifyexistingscales

Discard

YES

YES

YES

NO

NO

NO

Pre-Testitems

Pilot-Testitems

RegressionAnalysis

Developnew

scales

Fieldinterviews


Instrument development: step one

Following the generally accepted instrument development guidelines (Converse and

Presser, 1986), existing scales were identified and adapted from prior research.

Competence and benevolence trust items were adapted from McAllister (1995); however,

scale items for knowledge transfer were newly developed due to lack of available existing

items.

To overcome challenges associated with developing new items, two iterative, parallel steps

were taken. First, field interviews were conducted with both consultants and functional

specialists who were engaged in enterprise resource planning (ERP) implementation

projects. One primary objective was to create a list of measures for assessing knowledge

transfer in the context of ERP implementation projects. Second, perusal of the knowledge

management literature suggests that knowledge transfer, in fact, can be evaluated by

assessing changes to performance derived from said knowledge or assessing changes to

the stock of knowledge in the minds of recipients (Argote and Ingram, 2000). In conjunction

with prior literature and interview findings, and consistent with the definition of knowledge

transfer used in this study, six items were developed to evaluate knowledge transfer. Thus, to

determine the extent to which knowledge transferred, items were developed to assess

whether functional specialists learned from the consultants (i.e. changes to the stock of

knowledge) and whether they are able to apply said knowledge (i.e. changes to

performance derived from acquiring knowledge).

The focus of this study involves transfer of implementation knowledge from consultants to

functional specialists. Thus, the items developed and used in this study include knowledge

associated with configuration, testing, and training – activities largely assumed by

functional specialists (Markus and Tanis, 2000). IT-related activities such as installation,

network, and data conversion, are usually performed by IS specialists.

One control variable – i.e. project complexity – was also included in the analysis. The extent

to which a ‘‘standard packaged’’ ERP module has been customized was seen as a

reasonable proxy for measuring project complexity. Customization increases the overall

challenge of the implementation (Holland and Light, 1999), requires in-depth knowledge of

the software especially during subsequent releases (Glass, 1998), and in reality, few

implementations are completely ‘‘vanilla’’ (Soh and Sia, 2005).

Table I Respondent characteristics

Functional specialist (knowledgerecipient) Consultant (knowledge provider)

Position titles Financial analyst, purchasing agent,inventory manager, and HR associate

Consultants, managers

ERP experience a

Range 0-2 2-9Average 0.24 3.64SD 0.49 1.38Job tenure b

Range 1-21 1-8Average 7.11 2.99SD 4.5 1.56Months c

Range 2-20Average 8.94SD 3.98

Notes: aNumber of implementations (of the same ERP module as reported in the survey). bYears intheir current position with their current organization. cMonths working with each other on said ERPmodule


Preliminary survey instruments were subject to pre-testing by academics with expertise in

survey methods and ERP domain and Master’s-level students who had prior ERP

implementation experience. Both functional specialist and consultant survey instruments

were subject to pilot-testing using matched-pair functional specialists and consultants who

satisfied the requirements for participation.

Data collection: step two

Survey data were collected from a total of 80 projects from 71 client organizations and 36

consulting firms. Data collection started by identifying an appropriate individual in both

consulting and client organizations. Consultants, managers, and partners from consulting

firms and IS project managers, a MIS director, and senior IS executives from client

organizations were contacted to identify appropriate projects and participants.

Two participation requirements were imposed. First, an ERP project that was in the

operational implementation or post-implementation phase (Markus and Tanis, 2000) within

the last 12 months was selected for inclusion. This timeframe was considered important to

ensure that knowledge transfer outcomes could be evaluated and with reasonable memory

recall. For this study, each project consisted of implementation of one specific ERP module

such as purchasing or general ledger. Second, completed matched-pair surveys from a

consultant and a functional specialist for each project were required; data was discarded for

those whose surveys were incomplete or where only one participant responded. Surveys

were administered through the internet and dynamic web pages were utilized to capture

consultant, functional specialist, and ERP module names. Thus, individuals’ names (e.g.

‘‘Client Alisha’’ or ‘‘Consultant Brianna’’) and the ERP module name (e.g. ‘‘General Ledger’’)

were displayed in the appropriate survey items to improve the specificity of the question and

the quality of the data.

Identifying appropriate functional specialists and consultants is absolutely critical when

administering matched-pair surveys. An appropriate functional specialist is an employee of

a client organization; a member of a project team for a given ERP module; a ‘‘power user’’

who is most knowledgeable with the functional business processes; and an individual who

would be responsible for the ownership and management of his respective ERP module after

implementation. Similarly, an appropriate consultant is an employee of a consulting or

vendor firm; a member of the same project team as its functional specialist counterpart; a

consultant most knowledgeable with the said ERP module; and one who interacts primarily

with the ‘‘power user’’ as described above.

Data analysis and results: step three

Standard psychometric techniques were followed in validating the measures. Initial

Cronbach’s a scores were above Nunnally’s (1978) cut-off of 0.70, suggesting the scales are

reliable. A principal component method of extraction with a Varimax rotation factor analysis

was then used to determine convergent and discriminant validity. Components with

eigenvalues greater than 1 were extracted. All items loaded appropriately. The means,

range, standard deviations, and Cronbach’s a of the measures of the constructs are shown

in Table II and factor analysis is shown in Table III.

A two-step regression analysis was performed to test the hypotheses. In the first step of the

analysis, one control variable (project complexity) was entered into the equation. In the

‘‘ Benevolent trust is largely based on ‘emotional’ bondsbetween individuals; in contrast, competence trust is largelybased on competence and responsibility. ’’


second step, four independent variables were entered. The regression model equation is as

follows:

Knowledge transfer ¼ b0 þ b1ðproject complexityÞ

þ b2ðfunctional specialist benevolence trustÞ

þ b3ðfunctional specialist competence trustÞ

þ b4ðconsultant benevolence trustÞ

þ b5ðconsultant competence trustÞ þ 1:

Correlations are shown in Table IV. The results of the regression analysis are shown in Table V.

The results provide some support for the proposed hypotheses (see Table V). The

independent variables entered yielded an adjusted R 2 of 0.206 (F -statistic ¼ 5:094,

p , 0:000). As predicted, benevolence trust held by functional specialists is significant

(b2 ¼ 0:372, t ¼ 3:598, p , 0:001), as is benevolence trust held by consultants (b4 ¼ 0:228,

t ¼ 2:163, p , 0:05): in the presence of high levels of benevolence trust, knowledge

transfers effectively. H1a and H2a are supported. In contrast, competence trust held by

Table II Reliability and descriptive statistics

Construct Number of items Cronbach’s a Range Mean SD

Knowledge transfer 6 0.91 1.33-5.00 3.72 0.82Functional specialist benevolence trust 4 0.86 1.25-4.75 3.24 0.86Consultant benevolence trust 4 0.85 1.00-5.00 3.94 0.93Functional specialist competence trust 4 0.85 1.00-5.00 3.22 0.99Consultant competence trust 4 0.70 1.50-5.00 3.56 0.86Project complexity 1 – 1.00-5.00 2.64 1.13

Table III Factor analysis results for independent and dependent variables

Component1 2 3 4 5

Knowledge transfer 1 0.801 0.303 0.152 0.118 20.063Knowledge transfer 2 0.860 0.000 0.096 0.031 0.175Knowledge transfer 3 0.804 0.143 0.041 20.029 0.132Knowledge transfer 4 0.721 0.182 0.212 20.003 20.098Knowledge transfer 5 0.863 0.259 0.029 0.014 20.081Knowledge transfer 6 0.811 0.105 0.120 0.046 0.176Functional specialist benevolence trust 1 0.253 0.790 0.278 0.101 0.038Functional specialist benevolence trust 2 0.144 0.830 0.027 20.108 20.105Functional specialist benevolence trust 3 0.169 0.795 0.048 0.105 0.108Functional specialist benevolence trust 4 0.212 0.815 20.004 20.004 0.015Consultant benevolence trust 1 0.111 0.054 0.861 0.023 20.038Consultant benevolence trust 2 0.036 20.056 0.829 20.041 0.235Consultant benevolence trust 3 0.076 0.258 0.748 0.012 0.164Consultant benevolence trust 4 0.357 0.058 0.781 20.023 20.009Functional specialist competence trust 1 0.181 20.122 20.029 0.841 0.112Functional specialist competence trust 2 20.010 0.023 0.157 0.815 0.002Functional specialist competence trust 3 20.095 0.148 20.057 0.818 0.138Functional specialist competence trust 4 0.051 0.019 20.093 0.821 0.120Consultant competence trust 1 20.107 0.084 0.038 0.050 0.784Consultant competence trust 2 0.108 0.088 0.054 0.052 0.750Consultant competence trust 3 0.039 20.060 0.048 0.095 0.669Consultant competence trust 4 0.133 20.060 0.127 0.121 0.642

Eigenvalue 5.887 3.046 2.474 2.068 1.756Percentage of variance explained 26.76 13.84 11.25 9.40 7.98


functional specialists (b3 ¼ 0:058, t ¼ 0:559, p ¼ 0:578) and consultants (b4 ¼ 0:028,

t ¼ 0:268, p ¼ 0:789) is not significant. Therefore, H1b and H2b are not supported.

A sensitivity analysis was conducted to check the robustness of the results. An additional

control variable was added for formal knowledge transfer contract, indicating whether there

was a legal contract regarding knowledge transfer between the two parties. The result from

this sensitivity analysis is identical to the original analysis. Moreover, the control variable was

not significant in the regression analysis, indicating that legally binding knowledge transfer

contract does not significantly influence the effectiveness of knowledge transfer.

Discussion

One primary objective of this study was to investigate the role of trust and its impact on

knowledge transfer. The results indicate that benevolence trust influences knowledge

transfer while competence trust does not. In other words, trustworthiness on the basis of

sentiments, care, and honesty between knowledge providers and recipients affects a

knowledge recipient’s ability to effectively learn and apply acquired knowledge. The findings

in this study are partially consistent with an earlier study examining trust and knowledge

transfer (Levin and Cross, 2004). In both studies, benevolence trust influences knowledge

transfer; however, the results of Levin and Cross’s (2004) study indicate a positive

relationship between competence trust and knowledge transfer, while the results of this

study suggests there is no significant relationship.

Before interpreting the results of this study and their implications, some of its limitations

should be considered. The first limitation is the cross-sectional nature of this survey study.

Cross-sectional studies face the limitation of not being able to capture the temporal aspect

Table IV Correlations

1 2 3 4 5 6

1. Knowledge transfer 1.0002. Functional specialist benevolence trust 0.427** 1.0003. Consultant benevolence trust 0.322** 0.237* 1.0004. Functional specialist competence trust 0.084 0.057 0.008 1.0005. Consultant competence trust 0.113 0.063 0.198 0.219 1.0006. Project size 20.121 0.029 0.000 0.026 20.026 1.000

Notes: *Correlation is significant at the 0.05 level (two-tailed); **correlation is significant at the 0.01level (two-tailed)

Table V Results of regression analysis for knowledge transfer

Step 1: enter controlvariable

Step 2: enterindependent variables

Variables b SE b SE

Constant term 3.956*** 0.235 1.781** 0.577Project size 20.121 0.082 20.132 0.073Functional specialist benevolence trust 0.372*** 0.099Functional specialist competence trust 0.058 0.086Consultant benevolence trust 0.228* 0.093Consultant competence trust 0.028 0.101

R 2 0.015F-statistic 1.155Degrees of freedom 1, 78 5, 74Change in R 2 0.241Adjusted R 2 0.206F change 6.005***

Notes: n¼ 80, adjusted R 2¼ 20.6 percent; *p , 0:05; **p , 0:01; ***p , 0:001


of knowledge transfer activities, which may change through the implementation process. A

second limitation concerns its generalizability. The findings of this study may reasonably be

claimed to generalize to consulting-related projects. Finally, this study examined

implementation knowledge – sets of rules, tools and guidelines and ways to employ them

that produce effective systems implementation. It should be recognized that knowledge

associated with project management is also sought by functional specialists.

The results of this study suggest that benevolence trust improved the overall transfer of

knowledge. This study provides empirical evidence in the IS domain while partially

confirming the finding offered in previous studies – i.e. that the benevolence trusting

relationship is positively associated with knowledge transfer (Tsai and Ghoshal, 1998; Levin

and Cross, 2004). However, contrary to previous findings, competence trust did not have a

significant relationship with knowledge transfer. Although benevolence and competence

trust allow knowledge providers and recipients to engage in professional and social

activities, which provide more opportunities for knowledge transfer to occur, only

benevolence trust impacted knowledge transfer. One potential explanation may be

related with time. A large portion of enterprise systems implementation knowledge is

deemed to be tacit (Markus and Tanis, 2000) which would suggest that more effort and time

may be required to transfer knowledge. As more interactions occur, and over time,

trustworthiness based on sentiments, care, and honesty could develop. A persistent

development of benevolence trust is more likely to translate into a stronger asset because it

is unlikely that competence trust will change significantly over time.

The results of this study provide additional insight regarding the role of benevolence and

competence trust with knowledge transfer. Benevolence trust positively influenced

knowledge transfer while competence trust did not significantly influence knowledge

transfer. While this is partially consistent with earlier studies, there exists a paradox: to

implement an enterprise system successfully, external consultants are drawn upon due to

their expertise, suggesting the importance of competence trust; yet benevolence trust

mattered in the ability for the consultants to effectively transfer implementation knowledge to

functional specialists. One plausible explanation is that the increased level of interaction,

communication, and cooperation on a social dimension puts both members at a greater risk

in the relationship when compared to competence trust. Thus, it could be that the

importance of benevolence trust outweighs competence trust given that there is more at

stake and more desire to help one another accomplish work-related goals. Hence,

benevolence trust appears to play an important role, compared to competence trust, for

successfully transferring knowledge.

Another important consideration is to recognize that trust varies across context, and the role

of trust changes from one context to another (Rousseau et al., 1998). Thus, this may partially

explain the inconsistent findings offered in this study when compared to previous findings.

As IT implementations grow in number, size and complexity, information systems managers

in both consulting and client firms increasingly need to better understand how to facilitate

knowledge transfer within and across organizational boundaries, and improve the success

of the project outcomes. Effective transfer of knowledge is an important ingredient to

success (Argote, 1999). The ability for consultants to provide appropriate knowledge to

functional specialists suggests that it is of utmost importance that benevolent consultants

are assigned to the engagement if the project is to have a better chance of succeeding. This

raises an important question for consulting firms as they breed new and young recruits into

their firm focusing on developing content expertise: how do consulting firms balance the

assignment of their resources given that benevolence trust is key to knowledge transfer? For

client firms, this study suggests that compromising who is assigned to the project at hand

may determine the success or failure of the project. Thus, formalizing a team member

selection process with an emphasis on selecting individuals who are likely going to get along

on a personal level will likely reduce the risk of an unfavorable project outcome.

To further ensure project success, this study demonstrates the importance of developing

and building benevolence trust between consultants and functional specialists for effectively


transferring knowledge. Care must be taken to manage the ties that bind between the

parties. Development of benevolence trust, or personal attachment or ‘‘emotional’’ bond,

was found to play an important role in improving the effectiveness of knowledge transfer. It is

imperative for firms seeking consultants’ expertise to develop and support team-building

and social programs that facilitate and nurture personalized relationships. After all,

interaction frequency and open communication assist in developing benevolence trust

(McAllister, 1995). Thus, co-locating team members as well as building a team with similar

personal interests should ultimately help with the overall project success.

Enterprise systems implementation projects are typically long-term engagements. Unlike

prior studies, it may be possible that benevolence trust played a significant role while

competence trust did not because of the length involved on projects in this sample.

Alternatively, respondents at different points in the project may have changed their

perceptions about consultants’ competence trust (as, for example, Hershey’s did). It is hard

to pinpoint the reason because the survey data did not capture that information.

Finally, it is important to encourage a development of strong competence trust if exploration

of new ideas and techniques are in order; however, weak competence trust ought to be

promoted when exploiting existing ideas (Uzzi and Lancaster, 2003). It may be reasonable

to claim that enterprise systems implementation projects fall in the realm of ‘‘exploiting

existing ideas’’ which may explain the insignificant finding between competence trust and

knowledge transfer observed in this study. Nevertheless, a delicate balance in managing

benevolence and competence trust is an important consideration for managers overseeing

projects involving consultants.

Conclusion

This study examined the relationships between benevolence and competence trust and

knowledge transfer involving consultants and functional specialists in an inter-firm complex

enterprise systems implementation context by drawing on knowledge management and trust

literatures. The results indicate benevolence trust plays an important role in affecting

knowledge transfer while competence trust does not. The findings offered in this study

augment prior research to an increasingly important information systems context. Given the

growing complexity around deployment of IT-based solutions, the findings of this study offer

guidance to both research and practice. It is worth noting that consultants are hired to fill in the

expertise that is lacking in-house, suggesting that competence trust would appear to be more

important; instead, effective transfer of knowledge from consultants to functional specialists

was influenced by the level of benevolence trust between consultants and functional

specialists. As tempting as it may be for managers to hire the most knowledgeable consultants

(i.e. competence trust), the results of this study suggests that they should not ignore the

importance of ‘‘personal’’ trust (i.e. benevolence trust) that needs to develop if knowledge

transfer is an important part of project success, especially in long-term project engagements.

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About the author

Dong-Gil Ko is an Assistant Professor in the College of Business at University of Cincinnati.He received a Bachelor of Science in Electrical Engineering from University of MarylandCollege Park, an MBA from George Washington University, and a PhD in ManagementInformation Systems from the Katz Graduate School of Business, University of Pittsburgh.His current research focuses on the transfer of knowledge, the exercise of control, and theuse of information systems for improving the management, performance, and impact ofIS-related projects. His published research articles have appeared in leading scholarlyjournals, including Management Science, MIS Quarterly, Organization Science, andInformation Systems Research. He is an active member of the Academy of Management andthe International Conference on Information Systems, and has presented the results of hisresearch at several conferences. He has over eight years of industry experience working forAccenture and Science Application International Corporation as an IS consultant to largeorganizations including AT&T Wireless, Microsoft, and Department of Defense. Dong-Gil Kocan be contacted at: [email protected]


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