Electronic HRM: From Implementation to

Value Creation

Yvonne Loijen

and Tanya Bondarouk

Capgemini Consulting, The Netherlands

University of Twente, The Netherlands

Abstract. The paper presents results of the quantitative study into enablers and

value creation of e-HRM systems. The findings supported by the analysis of

210 questionnaires, have revealed that the most significant enabler of e-HRM

implementation is HRM system strength, while characteristics of the IT

functionality also played an important role. The main result of the e-HRM

usage was observed as effectiveness of HR administrative processes, but not

Re-structuring of the HR function as usually expected from the introduction of

e-HRM in organizations.

1 Introduction

There is a growing body of literature reporting on different aspects of Human

Resource Information Systems (HRIS) and electronic HRM (e-HRM). A lot has been

done to indentify potentials of e-HRM for HRM strategy implementation ([13], [31]),

types and goals of e-HRM ([18], [26]), and impact of e-HRM on different stakeholder

groups [24]. However, research on the success factors of e-HRM implementation is

scarce. Studies which examine influences of IT, HRM and organizational

characteristics on e-HRM implementation are characterized by taking a single

dominant approach, either IT- or HRM-centered. Having acknowledged the

definition of e-HRM as

a way of implementing HR strategies, policies, and practices in organization

through conscious and directed support of and/or with the full use of web-

technology-based channels [26],

we should probably, focus on an integration between IT and HRM, and approach the

success factors from an integrated perspective. This study proposes a model that puts

forward enablers of e-HRM implementation and indicators of its success. Through

testing the model, our paper strives to partially close the gap in the existing literature

on success of e-HRM, and answer the question, what the success enablers of e-HRM

are, and what value e-HRM creates for organizations.

Loijen Y. and Bondarouk T. (2009).

Electronic HRM: From Implementation to Value Creation.

In Proceedings of the 3rd International Workshop on Human Resource Information Systems, pages 84-97

DOI: 10.5220/0002195900840097

 SciTePress

2 Theoretical Framework and Development of Hypotheses

Talking about e-HRM value, we should notice that we define it as the capacity of

electronic HRM to satisfy a need or provide a benefit to a person or an organization.

[11]. When implemented successfully, e-HRM has the ability to provide benefits to

employees and organizations and thus to create value. Building on theoretical debates

found in earlier studies on Electronic Performance Monitoring and debates on

functional/dysfunctional consequences of e-HRM applications [29], [28] the intended

and unintended consequences that e-HRM might have for organizations and

individuals are summarized in Table 1. As the table shows, we view three aspects that

are affected by the implementation of e-HRM: information flow, social interactions,

and perceived control. All three aspects might be jointly influenced by the nature of e-

HRM systems, and individuals’ attitudes, intentions, and behaviors.

Table 1. Potential consequences of e-HRM.

Affected aspects Intended consequence of e-HRM Unintended consequence of e-HRM

Information flow - It may increase the organization’s

ability to access, collect, and

disseminate information

- Greater amounts of information

- Easier access to information about

roles requirements

- Accuracy and timeliness of HR

transactions

- Information overload

- Failure to create “high” quality

information

- Uncertainty, ambiguity, complexity of

HR information

- Increase of a number of alternatives

- Amount of out-dated information

Social

interactions

- It may modify social interaction

patterns (substitution for face-to-face

communications)

- Increase of standardization in

communications

- Fairness in sending messages about

performance management

- Decreased social interaction between

supervisors and subordinates

- Increased psychological distance

between supervisors and subordinates

- Increased feelings of social isolation

- Invasion in personal privacy

Perceived control - Increase control of employees

behavior

- Work stress

- Increased anxiety stemming from

‘invasion of privacy’

Lepak and Snell [18] refer to the four ‘pressures’ of virtual HRM: they must be

strategy-focused, flexible, efficient and client-oriented; and all this at the same time.

Ruël et al. [26] highlighted an aspect that is fairly well covered by the above but that

is nevertheless interesting to spell out, namely the changing nature of the employment

relationship. With the supply shortage in the labour market (during the economic

upturn of the 1990s), the individualization of society, and the higher educational level

of citizens (and thus of employees), the power balance in the employment relationship

has shifted in the direction of the employees: they want to steer their own career

paths. In the view of Ruël et al. [26], a move towards e-HRM can provide the tools to

support this development. This aspect fits into earlier-mentioned drivers such as

improving service towards internal clients, but has an external societal drive.

Theoretical debates suggest that the three goals of e-HRM are cost reduction,

improvement of HR services and improvement of strategic orientation ([4], [18],

[30]).

When goals or results have a positive influence on employees or the organization,

combined with the intended consequences of e-HRM (Table 1), the goals and results

also contribute to the value creation of e-HRM. For the purpose of this paper we

distinguish the following categories of e-HRM value creation: Time spent on HR

activities, HRM roles, HRM service quality, Efficiency, Perceived effectiveness of

electronic HR practices. Those are, in our view, integrate the notion of the e-HRM

goals and anticipated positive consequences of e-HRM in organizations. Besides the

above mentioned factors, it is expected that e-HRM will also diminish role ambiguity,

and will contribute to the uniqueness of HRM. Role ambiguity, Time spent on HR

activities and HRM roles together form the block: restructuring of the HR function.

Uniqueness of HRM, HRM service quality and Efficiency form the block: HRM

effectiveness. Perceived effectiveness of electronic HR practices is a block itself.

Restructuring of the HR Function. Restructuring of the HR function consists of

three aspects: Role ambiguity, Time spent on HR activities and HR roles. Role

ambiguity is viewed as the lack of necessary information available to a given

organizational position [25]. Through e-HRM, most organizations implement

databases with relevant HR information, which is accessible to HR professionals, line

managers and partly to employees. This database provides more information, the

accuracy of this available information is expected to increase, and the information is

expected to be easier accessible (Table 1). Therefore e-HRM is believed to diminish

role ambiguity. The implementation of e-HRM is viewed to lead to changes in HR

processes and functions in the organizations. This has influence on the HRM roles

and the time that HR professionals, employees and line managers spend on HR

activities. We have tried to cover these role changes with the four different roles

classified by Ulrich and Brockbank [33]: Employee Advocate, Human Capital

Developer, Functional Expert, Strategic partner/change agent. Our first hypothesis is

therefore:

H1. Through the implementation of e-HRM HR professionals will improve their

strategic orientation, spend less time on HR administration, and will diminish roles

ambiguity.

HRM Uniqueness and Service Quality. The Uniqueness is considered as the degree

to which a combination of face-to-face and electronic HRM practices is rare,

specialized, and firm specific. When an organization is able to implement this unique

combination of HRM and e-HRM it can create a competitive advantage and hereby

create value. Another aspect of HRM effectiveness is the HRM service quality.

Service quality involves not only the outcome of the e-HRM system but also the way

the service is delivered. To ensure good quality, service quality should exceed

customer expectations of the service [23]. By improving the service level of the HRM

department through the implementation of e-HRM, e-HRM can add value to the

organization. e-HRM can help to increase the efficiency of the organization by for

example cost reduction ([35], [26], [5]), time savings ([14],[30]) or improved decision

making [14].

H2. As a result of implementation of e-HRM, targeted organizational members will

perceive their HRM services unique and of an increased quality.

Perceived Effectiveness of HR Administration. The Perceived effectiveness of HR

administration is the degree to which HR practices are perceived as useful and

helpful. In this research, the perceived effectiveness of e-administration of personnel

data is examined. E-administration is the electronic record keeping of all personnel

data. It is expected that due to its electronic support and through the change of the

process itself, the effectiveness of the HR administration shall increase.

H3. As a result of e-HRM implementation, administration of HR processes will be

perceived as more effective.

A precondition for the success and thus value creation of an e-HRM application is

the usage of the application. If the application is not used, the e-HRM application

won’t succeed. Usage is defined by appropriation and frequency of use. Appropriation

is the continuous, progressive, and mutual adjustments, accommodations, and

improvisations between the technology and the users. [22] or can be seen as the

incorporation of information technology into one’s life [27].

Technological Strength. People tend to use (or not) an application to the extent that

they believe it will help them perform their job better (perceived usefulness). Further,

even if people believe that a given application is useful, they may believe that the

systems are too hard to work with and that the performance benefits of usage are

outweighed by the efforts required using the application (ease-of-use). It was shown

that usefulness is more strongly linked to actual system use than ease-of-use. The

dominance of usefulness over ease-of-use has important implications for the designers

and those responsible for implementation. Across the many empirical tests of TAM,

perceived usefulness has consistently been a strong determinant of the usage

intentions of employees. As of January 2000, the Institute for Scientific Information’s

Social Science Citation Index

listed no less than 424 journal citations to the article by

Davis [6]. Within a decade, the TAM concept had been enriched by elaborating on

various determinants of the perceived usefulness and ease-of-use. Besides these two

important IT characteristics, another important aspect of an information system is the

information quality of the system. Information quality is about the perceived

importance and usefulness of the information in the information system [9].

H 4. The usage of e-HRM will be determined by the technological strength consisting

of usefulness, easiness of use, and data quality.

HRM System Strength. Besides IT, HRM system strength is an important

contributor to the success of e-HRM. An HRM system is the overall set of HR

practices, programs, and philosophy in a company. The HR practices in a company

can be seen as communication from employer to employee [3]. Through HR practices

employees develop skills, knowledge and motivation to contribute to the

organization’s strategy. Thus the perceptions, attitudes and behaviors of employees

are influenced through the HRM system. [2] HR practices should communicate

unambiguous messages to employees about what is appropriate behavior. Attribution

theory states how people explain matters and the psychological consequences of these

matters [10]. Individuals make correct attributions about HR practices based on three

factors: the distinctiveness of the HRM system, the consistency of the HRM system,

and the consensus of the HRM system.

H5. The usage of e-HRM will be determined by the strength of the HRM system in

organizations.

Employee Readiness for e-HRM. When an organizational change is implemented,

one of the most common reasons for failure is employees’ resistance to change. The

success of e-HRM is dependent on the voluntary cooperation of employees. The block

Employee readiness for e-HRM consists of three factors: Facilitating conditions,

Employee participation in the implementation of e-HRM and HR technological

competencies. Facilitating conditions are the degree to which an individual believes

that an organizational and technical infrastructure exist to support use of the system

[34]. Employee participation is the assignments, activities, and behaviors that users

or their representatives perform during the systems development process [1]. The last

factor which contributes to Employee readiness is HR technological competencies.

HR technological competencies are defined as a person’s underlying attributes, such

as their knowledge, skills, or abilities, necessary to accomplish e-HRM change. [12]

H6. The usage of e-HRM will be determined by the employees readiness to work

with e-HRM.

3 Methodology

Research Design. The first step in this research is to conduct entry interviews. These

interviews are held to explain the research purposes, to get information about the e-

HRM application in use, and to customize the questionnaire. After these interviews, a

questionnaire was developed about the success factors of e-HRM. The questionnaire

was internet-based and entry-forced. Existing scales were used for most variables,

where all constructs were measured by 5-point scales. The original scales were in

English. However, since all participants in the survey were native Dutch speakers the

questionnaire was translated into Dutch and back into English. The translation from

English into Dutch was done in parallel by two independent translators. Before

sending the questionnaire to the respondent, the questionnaire was checked by three

academic researchers from three different universities to refine the questionnaire. A

pilot test was conducted by experts and their suggestions were used to improve the

content validity and the structure of the questionnaire. Finally the questionnaire was

prototyped online by two academic researchers and two experts.

Companies were selected with help of contacts from Capgemini colleagues.

Capgemini colleagues were asked for contact persons in companies. These contact

persons were sent an invitation, including a short description of the model, an

explanation of the research and benefits for the participating companies. Besides

companies listed by colleagues, companies within the personal network of the

researcher were approached. The last way of collecting information on companies

was by snowball sampling. This resulted in 12 companies showing interest in

participating in an interview and discussing possibilities for the questionnaire. After

the entry interviews, six companies agreed to participate in the research. Companies

who refused indicated that they had too much workload in the organization and did

not want to bother their employees with a questionnaire, or were afraid the

questionnaire would result in resistance of the employees to the e-HRM application.

The questionnaire was sent to the six companies with an accompanying email. This

email explained the goal of the questionnaire and provided an estimated time required

to fill out the questionnaire. A follow up email was sent after a week.

This resulted in 206 respondents who filled in the questionnaire. The response rate

cannot be computed because it is not known for all companies to how many

employees the questionnaire was sent.

Measures. The items in the questionnaire were organized per variable.

IT Strength. Usefulness ,was defined as the degree to which a person believes that

using a particular information system would enhance his job performance [6],

Easiness of use was defined as the degree to which the prospective use expects the

information system to be free of effort [7], and Data quality was defined as the

perceived importance and usefulness of the information in the information system [9].

For the constructs Ease of use and Usefulness items were based on Venkatesh

[34]. Venkatesh used four items for Ease of use and Perceived usefulness. An item of

Ease of use is e.g.: “Interacting with e-HRM technology requires a lot of mental

effort”. An example of Usefulness is: “I find e-HRM useful for dealing with my HR

related activities”. The constructs Intrinsic information quality and Contextual

information quality are based on the scales of Lee et al [15]. Intrinsic information

quality is referred to as: accuracy, believability, reputation and objectivity. The items

used from the questionnaire are based on these concepts: e.g. “The data on the e-

HRM site is reliable”. The construct Contextual information quality has been

described by Wang and Strong in Lee et al. [15] as: value-added, relevance,

completeness, timeliness, and appropriate amount. So items from the questionnaire of

Lee et al [15] were selected based on these keywords, like “The data on the e-HRM

site is up-to-date for my HR tasks”.

HRM System Strength. HRM system strength consists of three different variables:

Distinctiveness, Consistency and Consensus. The distinctiveness of the HRM system

are the features that allow a situation to stand out in the environment and to capture

attention and interest. The consistency is the establishment of an effect over time and

modalities regardless of the form of interactions. The consensus is the degree of

agreement among individuals’ views of the event-effect relationship. [3]

The questions about distinctiveness, consensus and consistency were adapted from

Delmotte [8]. Only legitimacy and authority, which are part of distinctiveness, are

self-constructed because the questions of Delmotte [8] do not correspond to the

description of distinctiveness of this research. An example of an item of Consistency

is: “There is a clear fit between HR promises and deliverables”.

Employee Readiness. Employee readiness for e-HRM change consists of three

variables: HR competencies as technology expertise, Facilitating conditions, and

Employees participation in the e-HRM implementation. The construct HR

competencies as technology expertise was self-constructed. An item of this scale is:

“HR professionals in our organization have strong skills to use e-HRM applications”.

Facilitating conditions were adapted from Venkatesh et al [34] and Marler et al [19].

The items from the questionnaire of Marler et al [19] are based on his construct

Employee resources, which resemble Facilitating conditions. A selection was made

from the questionnaire of Venkatesh et al [34] to limit the length of the questionnaire.

For the construct Employee participation in the implementation of e-HRM, a selection

was made from among the items from Barki and Hartwick [1].Not all items could be

used since the length of the questionnaire required to be limited. An example of an

item in this construct is: “I helped creating users manuals for the e-HRM application”.

Usage. Usage consists of two variables: Appropriation and Frequency. The items of

the construct Appropriation were adapted from Ruël [27]. Also in case of

Appropriation not all items are used because of the magnitude of the questionnaire.

An item from this scale is: “IT experts will not agree with my way of using the e-

HRM tools”. The item from Frequency are self-constructed.

Re-structuring of the HRM Function. It included three variables, HR roles, roles

ambiguity, and time spent on HR processes. The items of HR roles were adapted from

Ulrich and Brockbank [33] , and four different roles were distinguished: Employee

Advocate, Human Capital Developer, Functional Expert and Strategic Partner/Change

Agent. One of the items on this scale is: “HR professionals develop HR activities to

take care of employee personal needs”. Role ambiguities was adapted from Miller et

al [20] and consisted of nine items. “The combination of traditional Human Resource

Management and electronic Human Resource Management make me feel I have clear

goals for my HR tasks” is an example of an item. Time spent is a self constructed

measure. It consists of three variables: Time spent on HRM activities, Time spent on

IT activities, and Time spent on HR administrative/transactional activities. An

example of an item is: “Since the implementation of e-HRM I am increasingly

involved in forecasting HR needs”.

Uniqueness and Quality of HRM Services. Included two variables, uniqueness and

HRM services. Uniqueness of HRM was adapted from Lepak and Snell [17]. This

variable consists of nine items, for example: “A combination of traditional Human

Resource Management and electronic Human Resource Management in our

organization would be very difficult to replace”. HR service quality is based on the

questionnaires of Parasuraman et al [23]. An example is: “The HR services guarantee

error-free administration”. The items within the variable Efficiency are self-

constructed, for example: “Since the introduction of e-HRM, administration of HR

documents is efficient”.

Perceived Effectiveness of HR Administration. Perceived effectiveness of

electronic HR practices is self-constructed. An example of an item is: “I can access

HR personal information at my early convenience”.

4 Results

Based on the correlation analysis, it can be concluded that sufficient correlations are

found to be able to execute a regression analysis. A stepwise regression was chosen

for this research. The regression analysis indicates that only Employee Readiness has

a significant relation with Usage. IT and HRM system strengths do not have a

significant relation with e-HRM Usage. Employee Readiness determines 28% of the

Usage (R

= 0.276). The relation with Usage is a strong positive relation (β= 0.697), if

an organization scores high on Employee Readiness, this organization scores also

higher on the Usage of the e-HRM application. Usage determines 3% of Restructuring

of the HR function, 13% of HRM Uniqueness, and 12% of Perceived Effectiveness of

HR administration. However, as can be seen in the regression table below, the three

success enablers (IT and HRM System Strengths, and Employee Readiness)

determine 41% of HRM Uniqueness and quality of HR services. IT and HRM System

Strengths determine 42% of Perceived Effectiveness of HRM administration. Based

on the high R

values, a direct relation between the success enablers and the value

creating factors is a distinct possibility. IT strengths do have a strong positive

influence (β=0.552) on the Perceived Effectiveness of HR administrative practices

(Table 2).

Table 2. Regression analysis.

Usage Restructuring of H

function

Uniqueness and quality

of HR services

Perceived

effectiveness

B Sign

(p)

B Sign

(p)

B Sign

(p)

B Sign

(p)

IT Strength

.276 .102 .152 - - .411

.225 .000

.423

.552 .000

HRM System

Strength

.049 .486

.052 .135 .001

.250 .000

.319 .000

Employee

Readiness

.697 .000

- -

.146 .018

.171 .083

Usage

- -

.032 -.082 .010

.131

.244 .000

.123

.372 .000

The regression analysis at construct level leads to a lot of significant relations

being found. Because of the number of significant relation, only the relations are

shown in the table below.

Table 3. Regression analysis on construct level.

Appropriation Frequency

Role

Ambiguity

Time spent on

strategic activities

β Sign

(p)

β Sign

(p)

β Sign

(p)

β Sign

(p)

Ease of use .275 .306

.000

.038 - - .236 -.083 .313 .139 .161 .234

Usefulness .154 .077 .268

.005

-.429 .000 .490 .001

Data Quality .187 .006 - - -.083 .283 .181 .124

Distinctiveness .144 .157 .102 .043 .343 .003 .195 -.468 .000 .113 .498 .002

Consistency .433

.000

.002 .982 -.172 .065 .157 .303

Consensus .089 .227 - - -.109 .125 .156 .235

Participation .300 -.006 .927 .143 .338 .000 .261 -.040 .534 .312 .482 .000

Competence .136

.043

.077 .272 -.217

.004

.391 .008

Facilitating .437

.000

.324 .003 -.403

.000

.131 .243

Appropriation .297 -.349

.000

.135 - -

Frequency -.253

.000

.330 .001

Time spent on IT

Related activities

Time spent on

Administr. activities

Employee

Oriented role

Business

Oriented role

β Sign

(p)

β Sign

(p)

β Sign

(p)

β Sign

(p)

Ease of use .067 .293 .018 .083 .272 .000 .152 .151 .079 .150 .160

.015

Usefulness .134 .341 .096 .337 .342 .000 .166

.015

Data Quality - - .070 .433 .013 .873 .062 .460

Distinctiveness - - - .034 - - .488 .258

.002

.534 .217

.002

Consistency - - - - .446

.000

.348

.000

Consensus - - .231 .024 .257

.000

.342

.000

Participation .323 .609 .000 .0027 - - .258 .019 .767 .291 - -

Competence - - - - .362

.000

.447

.000

Facilitating .014 .894 .177

.047

.292

.000

.166

.000

Appropriation .114 - - .028 .208

.043

.137 .294

.000

.131 .285

.000

Frequency .292 .002 - - .136

.001

.095

.011

Uniqueness Service Quality Efficiency Perceived effectiveness

Sign

(p)

Sign

(p)

Sign

(p)

Sign

(p)

Ease of use .055 .108 .236 .211 .189

.004

.295 -.021 .785 .391 .132

.048

Usefulness .150

.001

.210

.002

.459 .000 .261

.000

Data Quality .037 .665 .085 .293 .143 .053 .302

.000

Distinctiveness .060 .083 .413 .328 .226

.008

.116 -.062 .539 .223 .228

.023

Consistency .221 .000 .271

.007

.282

.002

.376

.002

Consensus .117 .127 .226

.002

.183

.033

.095 .185

Participation .132 .133

.001

.254 - - .173 - - .273 .030 .637

Competence .231

.000

.391

.000

.278

.000

.236

.002

Facilitating .093 .217 .207

.000

.237

.000

.417

.000

Appropriation - - - .162 .298

.000

.168 .429

.000

.216 .520

.000

Frequency - - - .132

.001

- - .091 .147

- = no correlation

p = non significant

p = significant

From the regression analysis at construct level, it appears that there is a significant

relation between the constructs of IT strength and Usage and the construct of HRM

system strength and Usage. However, this cannot be deduced from the regression

analysis at the dimension level. Because of these contradictory findings, a

complementary regression analysis was done at dimension level. The regression

analysis at construct level was executed by entering the three success enablers (IT and

HRM system strengths and Employee readiness) simultaneously. This resulted in

Employee readiness being highlighted as a very strong predictor of Usage. IT and

HRM system strengths were then entered simultaneously in a stepwise regression

analysis. Employee readiness was excluded from this analysis. As can be seen in table

4, it appears that IT and HRM system strengths do relate to Usage (Table 4).

Table 4. Regression IT Strength and HRM system Strength with Usage.

Usage

B Sign (P)

IT Strength

.151

.283 .000

HRM System Strength

.223 .023

IT and HRM system strengths together determine 15.1% of Usage. Employee

Readiness was such a strong predictor of Usage ( β= 0.697), that IT and HRM system

strengths were excluded for that reason. However, because IT and HRM system

strength do have a significant influence on Usage, both were included in the model.

Because IT and HRM system strength, and Employee readiness have an influence

on Usage, and Usage has influence on Restructuring of the HR function, uniqueness

of HRM and quality of HR services, and Perceived effectiveness of administrative HR

practices, Usage could be a mediator and therefore the mediating influence of Usage

needed to be tested. From the analysis, it appeared that Usage has a mediating effect

on:

- IT strength and HRM uniqueness and HR services

- IT strength and Perceived effectiveness of HR administration

- HRM system strength and HRM uniqueness

- HRM system strength and Perceived effectiveness of HR administrative practices

- Employee readiness and HRM uniqueness

- Employee readiness and Perceived effectiveness of HR administrative practices

There is no mediating effect of Usage on Restructuring of the HR function. Figure

1 shows the final research model. As can be seen, the success enablers have a direct

effect on the value creators, but there is also an effect with Usage a mediator.

We summarize the findings per hypothesis in Table 5 below.

Table 5. Overview of propositions.

H1. Through the implementation of e-HRM HR professionals will improve

their strategic orientation, spend less time on HR administration, and will

diminish roles ambiguity

Rejected

H2. As a result of implementation of e-HRM, targeted organizational

members will perceive their HRM services unique and of an increased

quality

Rejected

H3. As a result of e-HRM implementation, administration of HR processes

will be perceived as more effective.

Accepted

H 4. The usage of e-HRM will be determined by the technological strength

consisting of usefulness, easiness of use, and data quality.

Accepted

H5. The usage of e-HRM will be determined by the strength of the HRM

system in organizations.

Accepted

H 6. The usage of e-HRM will be determined by the employees readiness to

work with e-HRM.

Accepted

5 HR roles

Some extra findings deserve special attention. For example, after and the exploratory

factor analysis, it appeared that from the four HR roles [33], only two roles were

clearly distinguished. Respondents didn’t make a distinction between Employee

Advocate and Human Capital Developer and again between Functional Expert and

Strategic Partner/Change agent. Therefore these roles were merged into Employee

oriented role and Business oriented role. There are several factors which can explain

this. Employees were hesitant to make crucial evaluations about the HR function, and

thereby HR roles. For some items, more than 50% answered “neutral”. Probably,

there is insufficient contact between the HR department and respondents to give an

opinion about the HR department. Another explanation could be that employees are

unwilling to criticize the HR department. Due to this preference of respondents to stay

in the “middle”, results could be less reliable. However, a distinction between two

instead of four roles has still been made. Ulrich concluded in his survey of 1996, that

HR roles were shifting towards a more strategic function [32]. Mohrman et al [21]

support his findings. However, in this research HR professionals indicated they are

spending more time on strategic activities than on administrative activities. Also, the

Functional Expert and Strategic partner/Change agent role are seen as one role during

factor analysis.

Our research thus does not support the finding that HR is becoming more strategic.

One of the reasons could be that this survey has been executed in the Netherlands and

the survey of Ulrich is executed globally. There could be a difference in functioning

of HR between the Netherlands and other countries. The survey on which Ulrich

based the four roles was executed among 256 HR executives. Our research is based

on only 36 HR professionals and 107 other employees. The difference in perceptions

between HR professionals and the other employees could also be a cause for the

different results. The four roles of Ulrich are based on two dimensions: operational

versus strategic and process versus people. [21] The two roles which are loaded on

factor analysis can be described on the dimension of process versus people.

Functional expert and Strategic partner/Change agent were both merged into the

Business oriented role which can be placed at the process side of the axis. Human

capital developer and Employee advocate were added together in the Employee

oriented role. This role can be placed at the people side of the axis. HR professionals

themselves want to make the HR function more strategic. However, right now, this

change in focus cannot be discerned within companies. It could be that the HR

department has just started performing more strategic activities and that this has not

penetrated the rest of the organization. Based on the results of this research, currently

two roles were used when evaluating the value creating effect of e-HRM.

Fig. 1. Final model.

6 Conclusions

e-HRM has an impact on the organizations, but the implementation of e-HRM does

not necessitate creating value. We did observe that e-HRM lead to a high Perceived

effectiveness of HR administrative practices, but the uniqueness of and quality if of

HRM services did not increase. Neither saw we that the implementation of e-HRM

had an impact on the re-structuring of the HR function itself. At the same time we saw

that the success enablers had influence on the Usage of the e-HRM application and

the value creating factors. This means that the value creating factors could be

influenced through the IT characteristics of the e-HRM application, the HRM system

strength and the Employee readiness for e-HRM. IT and HRM system strengths and

Employee readiness had a positive relation with Uniqueness of HRM and HR service

qualities. Usage also had a positive influence on HRM effectiveness. The HR function

itself was influenced by the HRM system strength and the Usage of the system. E-

HRM had a positive effect on the Perceived effectiveness of HR administrative

practices. The IT characteristics, HRM system strength and Usage of the e-HRM

application are, therefore, predictors of the Perceived effectiveness of HR

administrative practices. Remarkable is that Employee participation during e-HRM

implementation is only of minor influence.

Acknowledgements

We would like to express our appreciation to Capgemini Consulting for their support

of this research and recognize Huub Ruël for his advice and contributions to the

project leading to this publication.

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