7 effects of hrm practices on it usage

Lee, C. S. and Lee. C. H. (2010) Effects of HRM Practices on IT Usage. Journal of Computer

Information Systems, 50(2), 83-94.

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Subtitle: HRM Practices and IT Usage

Effects of HRM Practices on IT Usage

Chei Sian Lee *a, Chay Hoon Lee

b

aWee Kim Wee School of Communication and Information

Nanyang Technological University, Singapore 31 Nanyang Link, Singapore 637718

Phone: 65-67906636

Fax: 65-67915214

Email: [email protected]

bKeppel Offshore and Marine

50 Gul Road Singapore 629251

Phone: 65-8637200

Fax: 65-68631484

Email: [email protected]

*Correspondence to: Chei Sian Lee, Division of Information Studies, Wee Kim Wee School of

Communication and Information, Nanyang Technological University, 31 Nanyang Link, Singapore

637718, Telephone: 65-67906636. Email: [email protected].



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Abstract

Since IT plays a critical role in leveraging or exploiting human and business resources, it is

likely that HRM practices may have different effects on IT usage under the influence of different

sources of IT capabilities. This study examines the moderating effects of the source of IT

capability (Internal vs. External) on the relationship between HRM practices and IT usage. The

results indicated that organizations with internal IT capability, HRM practices such as employee

participation, clearly defined jobs and extensive formal training were significant in predicting IT

usage. However for organizations that used external IT capability, only internal career

opportunities was significant in predicting IT usage.

Keywords: HRM Practices, IT Usage, IT Capability, Outsourcing, Insourcing



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INTRODUCTION

Human Resource Management (HRM) practices have been viewed as a crucial means to

achieve organization success [18, 50]. Specifically, HRM practices provide significant utility to

organizations beyond satisfying regulatory agencies and employees [49]. Some researchers have

reported that HRM practices such as training programmes, incentive systems and employee

participation have considerable influences on organization outcomes such as productivity and

profitability [18,19, 33].

Past studies have indicated that HRM practices support the development of employees‟

capability as well as motivate employees to align their actions with the organization‟s goals [69].

Additionally, employees form general perceptions about the intentions and attitudes of the

organization toward them from the human resource policies and procedures [14]. This implies

that organizations can use HRM practices to ensure that employees are motivated to behave in

ways consistent with the business strategy [36]. Past studies have also shown that the specific

HRM practices adopted by an organization shape the climate of the organization which in turn

affects employees‟ attitudes and behaviors [27]. Since users‟ beliefs and attitudes are

perceptions driving IT usage [9] and IT plays a critical role in leveraging and complementing

human and business resources [53], it seems intuitive that HRM practices may affect employees‟

attitudes and behaviors towards usage of IT. However, significant disconnect exists between

HRM practices and IS (Information Systems) research because the relationship between HRM

practices and IT usage has not been well-documented in the literature.



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The purpose of this study is to address this “disconnect” by investigating the relationship

between HRM practices and IT usage. In recent years, organizations are increasingly relying on

external organizations for IT support and services (i.e. external IT capability) [58]. As such,

some or all of the IT employees belong to external organizations are no longer part of the formal

organization hierarchy which may consequently affect the organization work structures and

arrangements [30]. Since the work structures and work arrangements in organizations that rely

on external IT capability are different from those that rely solely on internal IT capability, it is

likely that HRM practices may have different effects on IT usage in these two groups. Hence, we

seek to understand the HRM practices that are favorable to motivate IT usage for organizations

with only internal IT capability versus those that rely on external IT capability. We focus on four

common HRM practices (i.e. employee participation, clearly defined jobs, extensive formal

training, and internal career opportunities). Figure 1 summarizes our research model.

Figure 1 Conceptual Framework

Employee

Participation

Clearly

Defined Jobs

Internal

Career

Opportunitie

s

Extensive

Formal

Training

IT Usage

Source Of IT

Capability



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BACKGROUND AND HYPOTHESES

IT Usage

IT usage has often been identified as a key construct influencing the business value

generated from IT [21, 23, 10]. IT can be used to enhance the quality and timeliness of

organizational intelligence and improve organization performance [31] as well as to gain

competitive advantages [48]. Additionally, IT has become a primary means of managing and

reducing the uncertainties surrounding administrative, managerial and production processes

because IT allows employees to perform tasks at a higher level, assumes additional tasks, and

improves their ability to gather and analyze data [6,7,24]. The availability of a wide array of

technologies in the workplace indicates that the different types of IT are able to afford the

employees a wide-range of resources. Instead of focusing on a particular type of software

platform or system used in organizations, we adopt a broad notion of IT to include many

different varieties of software platforms and systems commonly available in organizations [24].

Hence, IT usage is operationally defined as an application of information technology within an

organization‟s operational and strategic activities [34].

Source of IT capability

In this study, IT capability refers to the ability to mobilize and deploy IT-based resources

and capabilities within a firm [8]. Hence, the source of IT capability refers to the provider or

supplier of the ability. Traditionally, the only source of IT capabilities has been from in-house IT

department (internal IT capability). Increasingly, many organizations, however, have opted to



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use external IT capability by relying on external organization to provide some or all IT support

and services (external IT capability). Studies have found that organizations with internal IT

capability can cope more extensively than do those without one [7] and small businesses with

only internal IT capability have higher success with IT implementations than those with external

IT capability [20]. Thus, some researchers view internal IT capability as a valuable resource

because it forges the link between IT and business [45].

Yet, other studies have argued that external IT capability facilitates employees to

concentrate on the more challenging and strategic tasks of using IT to provide information to

respond to market changes [57, 38]. Additionally, when a firm‟s internal IT capability is weak,

external IT capability helps to increase productivity in IT-related activities [46] and provides a

means to pursue organizational goals [44]. However, the risks and dissatisfaction brought along

by using external IT capability have also been elaborated in many past studies [e.g. 17]. For

instance, reliance on external IT capability would lead to loss of control of the activity, assets,

and/or strategic knowledge [58], increase in communication and coordination cost with the

external vendor [28], and decrease in employees‟ morale due to the fact that the current

employees may worry that their position will be the next target to be outsourced [61].

Human Resource Management Practices in Organizations

Firms manage human capital by instituting a variety of HRM and work practices because

such practices can influence outcomes such as job performance and firm profitability and

performance [33, 5, 18]. Specifically, it is through such practices that firm influences the

employees and elicits desired employee behavior [62]. However, very few attempts have been

made to demonstrate that HRM practices impact the skills or behavior of the workforce. A

handful of studies found that significant relationship exists between HRM practices and staff



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turnover [5, 33]. Further, some studies found that appraisal and training practices were related to

executives‟ assessments of the skills [62]. As for studies within the IS domain, only a handful of

studies have attempted to examine the association between HRM practices for IT staff and IT

staff turnover [e.g. 1,4]

The wide range of HRM practices employed in past empirical studies include providing

training programmes, incentive systems, employee participation, clearly defined jobs, internal

career opportunities, and selectivity practices [e.g., 19, 33, 63]. Past research has indicated that

HRM practices do influence IT innovation and adoption [60]. However, there is no widely

accepted HRM practices specifically focusing on the IS domain. Thus, we draw on the

theoretical works of past studies [e.g. 19, 50,] to identify the relevant HRM practices: 1)

employee participation, 2) clearly defined roles, 3) internal career opportunities, and 4) extensive

formal training.

Employee Participation

Employee participation is defined as the degree to which the organization values the

inputs and voices of the employees by encouraging employees from different hierarchical levels

to participate in decision-making [19]. Past studies have shown that IT has the potential to enable

transfer of control and delegation of decision authority by facilitating the dissemination and

sharing of information throughout the organization [51]. Therefore, organizations with policies

of low employee participation are reported to use more IT [15]. This is due to the fact that such

organizations need to utilize IT to transmit and disseminate information to employees in the

different hierarchical levels. In contrast, such information sharing and dissemination is likely to

be less extensive and recurrent in organizations with high employee participation since

employees are expected to be involved in the decision-making process. Consequently,



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organizations with low employee participation policy may need to communicate directly with IT

personnel more regularly due to frequent needs to transmit and receive information via IT to

keep them informed on decision outcome. If external IT capability is used, such direct

communication opportunities are likely to decrease since IT personnel are not on site to provide

the support and consequently will lead to lower IT usage. Conversely, for organizations with

only internal IT capability, higher IT usage is likely to be expected as IT personnel are on site to

support employees.

Hypothesis 1: The negative association between employee participation and IT usage will be

more strongly associated for organizations with only internal IT capability than for organizations

with external IT capability.

Clearly Defined Jobs

Clearly defined jobs indicate that jobs are clearly and precisely defined with well-documented

records and written procedures [19]. Specifically, organizations with clearly defined jobs speak

to the desire for less ambiguity, more efficiency and more well-documented procedures so that

employees know how to act or to coordinate their actions to accomplish organizational goals [2].

Organizations with clearly defined jobs are likely to be facilitated by IT since IT helps with the

recording and retrieval of information about events and activities [31]. In a related vein of IS

research, it was found that the existence of clear organizational procedures and formal

documents is positively associated with increased IT-related activities such as information

systems planning and information processing [56]. As such, clearly defined jobs enable

employees to understand their job responsibilities. Such understanding will facilitate the use of

IT to help them to accomplish their job successfully [52]. Organizations that rely only on internal

IT capability are likely to enhance the effect of clear job definition on IT usage. The reason is



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that the IT employees are sharing the same organization culture and so are more likely to be

aligned with the same HRM practice of providing clear work and well written operating

procedures [24]. This will ultimately reinforce efforts to motivate IT usage. For organizations

that use external IT capability, all or some of the IT workforce are from another organization

which may have different organization cultures and/or human resources practices [30]. Such

differences may impede efforts to motivate IT usage in the organization.

Hypothesis 2: The positive association between clearly defined jobs and IT usage will be more

strongly associated for organizations with only internal IT capability than for organizations with

external IT capability.

Internal Career Opportunities

Internal career opportunities refer to the extent to which an organization has an internal

career ladder or provides internal career opportunities for its employees. Organizations with

policies of internal career opportunities make use of extensive well-defined career ladders and

hiring mainly from within the organization [19]. It has been shown that policies of internal career

opportunities are related to higher employees pay satisfaction and job satisfaction [40].

Employees who are not satisfied are likely to seek career opportunities outside the organization

and are less willing to put in extra effort to comply with the organization policy. Specifically,

having a policy of internal career opportunities reflects an organization‟s commitment to its

employees. As such employees who believe their organizations are committed, will be more

secured and are more willing to put in more effort than what are required of them [32, 54]. This

means that it may be easier for organizations with a policy of internal career opportunities to

motivate employees to invest time and effort to learn and use more IT. Studies have shown that

organizations with external IT capability get to refocus on the strategic issues of IT [57]. As

such, employees are able to concentrate on the core activities in organizations [42] which will



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lead to better firm performance [41]. These benefits of relying on external IT capability are likely

to enhance organizations with internal career opportunities policies as employees will be more

focused on using IT more strategically to help with the advancement their careers within the

organizations. However, such benefits will not be enjoyed by organizations which rely on only

internal IT capability. As such, the influence of policies of internal career opportunities on IT

usage in organizations with only internal IT capability will not be as significant as compared to

organizations with external IT capability.

Hypothesis 3: The positive association between internal career opportunities and IT usage will

be more strongly associated for organizations with external IT capability than for organizations

with only internal IT capability.

Extensive Formal Training

Formal training is a form of organized learning experiences provided by the employer to

enhance performance and personal growth. Formal training enables employees to identify and

obtain new skills and competencies that allow them to move to new positions, either within or

outside these organizations [55]. In addition, formal training enables employees to have a sense

of worthwhile accomplishment on challenging tasks [13]. Employees who have received

extensive formal training are likely to be readied to undertake more challenging tasks and be

more committed towards organization goals. Specifically, training can be effective in increasing

employees‟ morale and improving their job performance [16]. Moreover, formal training aids in

the internalization process of employees and makes it easier for them to acquire tacit knowledge

in organizations [47]. Collectively, past studies suggest that extensive formal training not only

increases the knowledge and skills sets of employees but in effect may also motivate employees

to learn and use IT systems in their work. But such motivation may be diminished by the



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presence of external IT capability. Specifically, one of the well-documented inherent risks of

organizations that rely on only external IT capability is decreased employee morale as employees

may worry that their position will be the next target to be replaced by external parties [61]. With

decreased employee morale, employees are less likely to be motivated to learn and grow with the

organization. Consequently, they will be less motivated to undertake challenging tasks such as

using IT to deploy strategic IT resources. Since organizations with internal capability are not

susceptible to such risks, the influence of extensive formal training on IT usage is likely to be

more significant for organizations with internal IT capability than for organizations with external

IT capability.

Hypothesis 4: The positive association between extensive formal training and IT usage will be

more strongly associated for organizations with only internal IT capability than for organizations

with external IT capability.

METHODS

Sample and Procedure

Our data were obtained from the National Organizations Survey (NOS) conducted by Minnesota

Center for Survey Research from June 1996 to June 19971. The NOS surveyed a representative

sample of U.S. work establishments about their organization structure and human resource

practices [35]. Stratified random sampling was used to sample from approximately 15 million

establishments and organizations in Dun and Bradstreet's Information Services data file. Data

were collected through telephone interviews and questionnaire-survey. The combined

completion rate for both telephone and questionnaires was 54.6%. Overall, there were 1002

organizations that responded to the study. Respondents generally were personnel officers from

1 We refer interested readers to Kalleberg, Knoke and Marsden (1999) for more information on the design of this

survey.



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responding organizations. As the focus of this paper is to examine IT usage in organizations,

organizations that did not provide any information on these items were omitted from our study.

After removing these organizations from the original dataset, we were left with 584 usable cases.

The demographic characteristics of the data sample are shown in Table 1.

Table 1:Sample Characteristics Frequency Percentage

1. Firm’s Revenue in Million $

Less than 50 million 148 25.34

Less than 100 million 10 1.72

100-399 million 27 4.62

400 –999 million 11 1.88

1000 – 1999 million 2 0.34

2000 – 4999 million 2 0.34

Above 5000 million 8 1.37

Missing, NA, etc 376 64.38

Total 584 100

2. Number of full-time employees

1-9 73 12.5

10-49 109 18.66

50-99 50 8.56

100-499 138 23.6

500-999 55 9.42

1000-1999 50 8.56

More than 2000 88 15.06

Missing, NA, etc 21 3.60

Total 584 100

3. Industry Group

Mining and Heavy Construction 19 3.25

Nondurable manufacturing 35 5.99

Durable manufacturing 68 11.64

Transportation, communication and utilities 26 4.45

Wholesale & Retail trade 83 14.21

Finance-related 31 5.31

Services 205 35.1

Others 117 20.03

Total 584 100

Measures

Independent Variables



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Employee participation was measured using four items relating to the extent of employee

participation in organization decisions. The following four types of decisions were used: hiring

decision, performance evaluation decision, products and services decisions and production

targets and schedules decisions. The variable was scaled such that higher scores reflecting a

greater amount of participation [18]. The reliability alpha for this scale is 0.79.

Clearly defined jobs was measured using three items relating to the existence of 1)clear

job description, 2)written record for job performance, employment contracts, and personnel

evaluation, 3)formal record on hiring and firing procedures [19]. All the items were scaled such

that the lowest value indicates no existence of clearly defined jobs and high value indicates clear

existence of clearly defined jobs. The reliability alpha for this scale is 0.75.

Internal career opportunities was measured using three items relating to the occurrence

of promotion to higher organization level, promotion to managers and the practice of hiring

manager from within the organizations. Respondents indicated how frequent such occurrences

were on a scale that ranged from (1) “Never” to (4) “Very often”. Higher scores indicated the

existence of a well-defined internal career and staffing system with greater opportunities. The

reliability alpha for this scale is 0.6.

Extensive formal training was measured using three items relating to the extent that

formal training was provided for communication or interpersonal skills, team work skills, and

management skills. Respondents indicated on a scale that ranged from (1) “Not at all” to (3) “To

a great extent” with higher scores reflecting more extensive formalized training programs for

employees. The reliability alpha for this scale is 0.67.

Dependent Variable



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IT usage was measured using three items relating to the different types of IT usage in

terms of supporting administrative, managerial, and training purposes. Two of these items (i.e.

administrative usage of IT and managerial usage of IT) were not captured directly from the NOS

data. Two questions addressing whether employees use computer for word processing and

information retrieval were combined to form the administrative usage of IT item. Two questions

addressing whether employees use computer for information analysis and interpreting

information and writing programs were combined to form the managerial usage of IT item. The

third item captures the extent of IT used for training. For all three items, a scale that ranged from

(1) “No usage” (2) “Low usage” (3) “High usage” was developed with higher scores

indicating higher IT usage. The reliability alpha for this scale is 0.74.

Moderator Variable

Source of IT capability was measured using a binary variable. Two dichotomous

questions that address whether computer systems work done by employee on organization

payroll or whether computer systems work done by someone else were used to capture this

variable. Respondents who indicated “Yes” to the first question and “No” to the second question

were grouped under the internal IT capabilities group. The external IT capabilities consisted of

respondents who have indicated “Yes” to the second question. Our data show that out of 584

organizations, 454 organizations use only internal IT capabilities (i.e. “Internal IT”) and 130

organizations have adopted some form of external IT capabilities (i.e. “External IT”).

RESULTS AND ANALYSIS

Statistical Analyses

Before testing the hypothesized model, we conducted a principal component analysis

with varimax rotation because all four HRM practices are not expected to be theoretically



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correlated. Five factors emerged with eigenvalues greater than 1.00 explaining a total of 63.54

percent of the variance. As shown in Table 2, all items loaded above 0.6 on the appropriate factor

and there were no cross-loadings. Descriptive statistics are presented in Table 3. All the

measures meet the criteria for univariate normality [25, 37] with skew for all measures less than

3 and kurtosis for all measures less than 4. The data were further screened for both univariate and

multivariate outliers and we found that none of the cases in the data were outliers. Specifically,

Table 2:Exploratory Factor Analysis (N=584)

Employee

Participation

Clearly

Defined

Jobs IT Usage

Extensive

Formal

Training

Internal

Career

Opportunities

Communalities

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5

EP2 0.81 0.74

EP1 0.80 0.66

EP4 0.73 0.56

EP3 0.73 0.54

CJ1 0.83 0.71

CJ3 0.82 0.62

CJ2 0.76 0.70

IT2 0.87 0.78

IT3 0.85 0.74

IT1 0.65 0.50

TR2 0.85 0.73

TR1 0.84 0.71

TR3 0.60 0.45

IC2 0.80 0.66

IC1 0.74 0.55

IC3 0.69 0.52

Eigenvalues 19.34 15.40 10.00 9.78 8.97 -

Percent Explained 15.31 12.90 12.72 11.84 10.78 - EP – Employee Participation, CJ – Clearly Defined Jobs, IT – IT Usage, TR-Extensive Formal Training, IC- Internal

Career Opportunities

Table 3:Descriptive statistics (N=584) Measure Mean Std. Deviation Skewness Kurtosis

EP1 2.28 0.97 -0.38 -0.46

EP2 2.52 1.02 -0.03 -0.84

EP3 1.86 0.95 -1.22 1.50

EP4 2.19 0.98 -0.74 0.39

CJ1 2.63 0.66 -1.59 1.14

CJ2 2.17 0.71 -0.27 -0.97

CJ3 2.51 0.73 -1.13 -0.20



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IT1 1.96 0.57 0.08 0.11

IT2 2.23 0.86 -0.46 -1.50

IT3 1.76 0.75 0.44 -1.09

IC1 2.68 0.66 0.22 0.01

IC2 2.45 0.58 0.74 1.55

IC3 2.80 0.77 -0.29 0.11

TR1 2.11 0.49 -0.12 1.16

TR2 2.25 0.49 -0.37 0.90

TR3 1.81 0.54 0.38 0.43 EP – Employee Participation, CJ – Clearly Defined Jobs, IT – IT Usage, TR-Extensive Formal Training, IC- Internal

Career Opportunities

univariate outliers were cases that have more than 3.5 standard deviations from the mean while

multivariate outliers were checked by calculating the Mahanalobis‟ distance.

The correlations of all variables were presented in the appendix. There are several aspects

of the correlation matrix that need some mentioning. First, none of the correlations were above

0.8 indicating that multicollinearlity is not a problem. Second, all the related measures were

moderately correlated in the range of 0.26-0.65. Third, some of the employee participation

measures are moderately correlated with the clearly defined job measures (e.g. FM3int and EP2int

are correlated at 0.31). Fourth, a couple of the internal career opportunities measures were also

moderately correlated with the extensive formal training measures (e.g. IC2ext and TR3ext are

correlated at 0.27). To make sense of these relationships, we conducted confirmatory factor

analyses to verify our measurement model.

We adapted the two-step approach to structural equation modeling recommended by

Anderson and Gerning [3]. First, we conducted confirmatory factor analyses to verify our

measurement models. Then, we conducted structural equation models to examine the

relationships among the constructs. Finally, following Bollen‟s [12] recommendation to examine

multiple indices of model fit, for all confirmatory analyses and structural equation models, we



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ascertain the goodness of fit of each model using several statistics including chi-square,

goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), the Tucker and Lewis index

(TLI), the comparative fit index (CFI), and the root-mean-square error of approximation

(RMSEA). We also used chi-square difference tests to compare the fit of different models. All

analyses were conducted using AMOS software.

Measurement model

Our intent was to capture five latent variables with the measurement model: Employee

participation (EP), Clearly defined jobs (CJ), Internal career opportunities (ICO), Extensive

formal training (TR), and IT Usage (IT). Confirmatory factory analysis was conducted via the

AMOS software using the maximum likelihood method to determine whether the various

indicators loaded on the latent constructs in a manner consistent with predictions. The CFA

results indicate that the overall fit of the measurement model is good. The CFA fit indices for the

model exceed the critical level of 0.80 [37]. We also found that each item loaded significantly on

its respective construct suggesting that the measurement scales for each construct demonstrate

high convergent validity.

The next question we addressed was whether the two HRM practices employee

participation (EP) and clear job description (CJ) and the other two HRM practices internal career

opportunities (IC) and training (TR) emerge as separate factors. Specifically, we checked if the

items related to EP and CJ would load on one factor relating to organization structure and items

relating to IC and TR would load on another factor relating to employee development. We then

ran a second model with just three latent constructs: organization structure (all items from EP



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and CJ), employee development (all items from IC and TR) and IT usage. The fit statistics for

the two models are presented in Table 4. All fit indices indicate that the first model (CFA1) with

four constructs was a better fit than the second model (CFA2) with only three constructs.

Specifically, the second model was not a good fit for the data with AGFI of 0.76 (less than 0.8),

RMSEA of 0.08 (more than 0.05) and chi-square/df of 4.75 was more than the acceptable of 3

[37]. Based on these analyses, we used the CFA1 measurement model as subsequent structural

equation models.

Table 4: Fit Statistics for confirmatory analysis and structural equation models Model χ2 (df) P GFI AGFI TLI CFI RMSEA

CFA1 430.14 (200) 0.00 0.92 0.89 0.88 0.90 0.04

CFA2 969.60 (204) 0.00 0.82 0.76 0.62 0.68 0.08

HYP 430.14 (200) 0.00 0.92 0.89 0.88 0.90 0.04

EQ 439.73 (204) 0.00 0.92 0.89 0.88 0.90 0.05 HYP – Hypothesized Model

EQ – Equal Model

Hypothesized Model

The hypothesized model was tested using a two-group model to examine the structural

relationships and significant differences between the two groups (i.e. Internal IT group versus

External IT group) [11]. The hypothesized model is illustrated in Fig. 2a and 2b. The two-group

model facilitated a direct assessment of the moderating effect of internal versus external IT

capability. The two-group analysis has also been reported in several past studies to study

moderating effects in structural equation models [e.g. 59, 43]. The two-group analysis was

conducted as follows: 1) the hypothesized model was first compared with the “equal model” in

which all structural paths were set to be equal across the two groups (i.e. model with equality

constraints) 2) the parameter estimates of both models were derived for each group separately 3)

the goodness of fit of the hypothesized model were assessed and compared with the “equal

model”. The fit indices for the hypothesized model (HYP) and “equal model” (EQ) are presented



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in Table 4. The results suggest that overall model fit for both models are good. However we

found that the theoretical hypothesized model with two-group sample had a substantially better

overall fit than the equal model by doing the chi-square difference tests (i.e. Δ χ2 = 9.6, df=4,

p<0.05). This suggests that hypothesized model did indeed fit the data significantly better than

the equal model.

For the hypothesized model, we note that not all paths are equal across the two groups;

that is, there are some significantly moderated effects. The parameter estimates for the two

groups are presented in Table 5A. For the internal IT group, the paths (EP->IT and CJ->IT) were

significant (p<0.05) and the path TR->IT was fairly significant (p<0.1). The path coefficient

(0.27) for (CJ ->IT ) was the largest in the Internal IT group. However, in the external IT group,

only the path (IC->IT) was significant (p<0.05) and the path coefficient was 0.27.

Table 5A:Parameter estimates for the hypothesized model Paths Standardized β Standard Error z p

Internal IT (N=454)

EP -> IT -0.17 0.03 -2.92 0.00*

CJ -> IT 0.27 0.04 4.29 0.00*

IC -> IT 0.10 0.05 1.62 0.11

TR -> IT 0.10 0.07 1.69 0.09+

External IT (N=130)

EP -> IT 0.12 .03 1.14 0.25

CJ -> IT 0.16 .04 1.41 0.16

IC -> IT 0.27 .07 2.02 0.04*

TR -> IT 0.15 .11 1.32 0.18 EP – Employee Participation, CJ – Clearly Defined Jobs, IT – IT Usage, TR-Extensive Formal Training, IC- Internal

Career Opportunities, * p<0.05, + p<0.1

We compared the results of the hypothesized model with the equal model (as presented in

table 5B). We found that all the path estimates were significant. Specifically, we found that

clearly defined jobs (β = 0.22), internal career opportunities (β = 0.13) and extensive formal

training (β = 0.11) were positively associated with IT usage (p<0.05). However, employee

participation (β = -0.07) was negatively associated with IT usage (p<0.1). The fit statistics of this



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model (i.e. equal model) are reported in Table 4. The thing to note about the comparison of the

two SEM models is that the four HRM practices in this study were significant predictors of IT

usage. However, by separating the sample into two groups (Internal IT versus External IT) in the

hypothesized model, we were able to identify the significant predictors in each group. Table 6

summarizes the results and shows that all hypotheses are supported.

DISCUSSION

Overall, our results demonstrated that the four HRM practices (i.e. employee

participation, clearly defined jobs, internal career opportunities, extensive formal training) affect

IT usage in organizations. Specifically, employee participation was negatively associated with IT

usage while clearly defined jobs, internal career job opportunities and extensive formal training

were positively associated with IT usage. Of the four HRM practices, clearly defined jobs

appeared to have the biggest effect on IT usage. This is not surprising since clearly defined jobs

reduce ambiguity and enhance efficiency. This finding is consistent with the findings from past

studies which have shown IT is most well-suited to address such issues [24]. More importantly,

our results highlight the importance for personnel in the two departments (i.e. HR and IS) to

work closely to implement any new IS or HR initiatives.

Table 5B:Parameter estimates for the overall model Paths Standardized β Standard Error z p

N=584

EP -> IT -0.07 0.02 -1.63 0.10+

CJ -> IT 0.23 0.03 4.20 0.00*

IC -> IT 0.13 0.40 2.56 0.01*

TR -> IT 0.11 0.06 2.19 0.03*

EP – Employee Participation, CJ – Clearly Defined Jobs, IT – IT Usage, TR-Extensive Formal Training, IC- Internal

Career Opportunities * p<0.05, + p<0.1

Table 6: Results of Hypotheses Testing Hypotheses Result

H1 The negative association between employee participation and



21

IT usage will be more strongly associated for organizations

with only internal IT capability than for organizations with


Supported

H2 The positive association between clearly defined jobs and IT

usage will be more strongly associated for organizations with

only internal IT capability than for organizations with


Supported

H3 The positive association between internal career opportunities

and IT usage will be more strongly associated for

organizations with external IT capability than for

organizations with only internal IT capability.

Supported

H4 The positive association between extensive formal training

and IT usage will be more strongly associated for

organizations with only internal IT capability than for

organizations with external IT capability.

Supported

Figure 2a Hypothesized Model

Internal IT Capability (N=454)



22

TR

TR3

e11

TR1

e9

EP

EP4

e4

EP3

e3

EP2

e2

EP1

e1

.58 .88 .73.52

CJ

CJ3

e7

CJ2

e6

CJ1

e5

.75 .62.74

IC

IC2

e13

IC1

e12

IT

IT3

y3

IT2

y2

.78

z2

TR2

e10

.77.75

.43

IC3

e14

-.17

.27

.10

.10

.45.54.73

.89

IT1

y1

.50

Figure 2b. Hypothesized Model

External IT Capability (N=130)

*

*

+

* * * *

* *

**

*

* *

*

* *

*

* * *

EP: Employee Participation

CJ: Clearly Defined Jobs

IC: Internal Career Opportunities,

TR: Extensive Formal Training

IT: IT Usage

* p< 0.05, + p <0.10



23

TR

TR3

e11

TR1

e9

EP

EP4

e4

EP3

e3

EP2

e2

EP1

e1

.75 .79 .77.65

CJ

CJ3

e7

CJ2

e6

CJ1

e5

.80 .66.77

IC

IC2

e13

IC1

e12

IT

IT3

y3

IT2

y2

.89

z2

TR2

e10

.82.77

.41

IC3

e14

.12

.16

.27

.15

.62.64.72

.68

IT1

y1

.40

* * *

* * *

* * *

* * *

* * * *

*

EP: Employee Participation

CJ: Clearly Defined Jobs

IC: Internal Career Opportunities

TR: Extensive Formal Training

IT: IT Usage * p<0.05



24

Our results further show that for organizations with internal IT capability, employee

participation, clearly defined jobs and extensive formal training were significant in predicting IT

usage with clearly defined jobs having the largest effect. However for organizations that have

used external IT capability (External IT group), only internal career opportunities was significant

in predicting IT usage as hypothesized. Apparently, the presence of internal IT support

stimulates higher IT usage and has potential effects on some of the HRM practices. This results

support the finding that in-house IT operation is associated with higher levels of computer

systems utilization for small businesses [20]. The results also suggest that effective HRM

practices to motivate IT usage for organizations with internal IT capability may not be

appropriate to be used in organizations that rely on external IT capability. While the risk factors

of relying on outsourcing have been well elaborated in past studies [e.g. 17], very little has been

done to examine the effects they have on other organization resources (i.e. HRM practices). This

study provides an important first step in this direction to broaden our understanding on the

relationship between HRM practices and IT usage for firms with internal capability versus those

that rely on some external IT capability.

The IT outsourcing phenomena that become highly visible after Kodak outsourced its

data center operations in 1989 has grown rapidly over the last decade [38]. Our results

highlighted two important points related to outsourcing that deserved to be elaborated. First, an

important implication of our study is that organizations planning to outsource or have already

outsourced their IT capabilities should carefully review their tactics to make sure that employees

are not alienated with the IT operations and procedures. Such firms should hold regular

communication sessions between employees and IT personnel. Second, even though outsourcing



25

is expected to be a long-term arrangement, organizations should be prepared of any short-term

changes because it does not always to lead to long-term competitive advantages or favorable

outcomes [39, 42]. In fact, high chances of outsourcing failures are reported in many past studies

[e.g. 17]. Hence, it is not surprising to see firms with external IT capability cancelled such

arrangements and opted to "re-insource" their IT capability [29]. This indicates that the source of

the firm„s IT capability (internal or/and external) is subjected to change and as such HRM

practices should be flexible to cater for any changes.

LIMITATIONS AND FUTURE RESEARCH

When considering the generalizability of the present findings, potential limitations must

be noted. One issue was the cross-sectional design of the study which precluded our ability to

draw causal conclusions. Future longitudinal research is needed to track the variables over time

to avoid drawing invalid conclusions. Secondly, several items such as IT usage, extensive formal

training, and internal career opportunities are self-reported by the respondents. To reduce self-

reporting biases, objective measures of the variables (e.g., number of hours spent, system usage

logs) should be complemented with the self-reported measures. Lastly, we utilized a dated

dataset so as to uncover interesting phenomena during the 1990s where IT outsourcing was less

widespread and to capture substantial number of firms that rely on internal IT capability only.

Using such a dataset allows us to develop better understanding between HRM practices and

source of IT capability (especially internal IT capability). However, it has become abundantly

clear that IT outsourcing is not a transitory management fad since many firms outsourced or are

considering outsourcing significant IT activities. Evidently, IT outsourcing has become more

prevalent now as firms shifted to business strategies that focus on their core competences [38].

Future work should look into uncovering more recent IT outsourcing phenomena such as



26

examining the impact of different levels of external IT capability (i.e. basic IT operations, data

processing operations) on the external IT group.

Source of dataset

The dataset used in the study is from the 1996-1997 National Organizations Survey.

Kalleberg, Arne L., David Knoke, and Peter V. Marsden (1999). The 1996-1997 National Organizations Survey

[machine readable data file]. University of Minnesota [producer] 2001. Inter-university Consortium for Political and

Social Research (ICPSR) [distributor] 2001.

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Appendix.

Correlation among independent variables, N=130 External IT [above diagonal], N=454 Internal IT [below diagonal]

Correlation matrix for IT usage and independent variables, N=130 External IT

EP1 EP2 EP3 EP4 FM1 FM2 FM3 IC1 IC2 IC3 TR1 TR2 TR3 IT1 IT2 IT3

IT1 0.08 0.13 0.05 -0.09 0.07 0.06 0.02 0.03 0.18 * 0.12 0.21 ** 0.27 ** 0.19 * 1.00 0.29 * 0.35 *

IT2 0.12 0.06 0.02 -0.12 0.05 0.17 * 0.15 0.10 0.17 0.12 0.02 0.01 0.17 0.29 * 1.00 0.62 *

IT3 0.18 * 0.13 0.08 -0.10 0.12 0.23 * 0.20 * 0.19 * 0.21 * 0.21 * 0.15 0.07 0.30 ** 0.35 * 0.62 * 1.00

Correlation matrix for IT usage and independent variables, N=464 Internal IT

EP1 EP2 EP3 EP4 FM1 FM2 FM3 IC1 IC2 IC3 TR1 TR2 TR3 IT1 IT2 IT3

IT1 -0.09 * -0.08 -0.04 0.00 0.07 0.11 * 0.05 0.05 0.05 -0.03 0.16 ** 0.19 ** 0.26 ** 1.00 0.45 ** 0.37 **

IT2 -0.03 -0.06 -0.01 -0.03 0.16 * 0.20 * 0.12 * 0.03 0.10 * -0.01 0.07 0.03 0.14 ** 0.45 * 1.00 0.69 **

IT3 -0.05 -0.08 0.01 0.03 0.11 * 0.25 ** 0.10 * 0.03 0.07 -0.01 0.10 * 0.06 0.19 ** 0.37 * 0.69 * 1.00

** Correlation is significant at the 0.01 level * Correlation is significant at the 0.05 level

EP1 EP2 EP3 EP4 FM1 FM2 FM3 IC1 IC2 IC3 TR1 TR2 TR3

EP1 1.00 0.65 ** 0.51 ** 0.52 ** 0.09 0.16 0.23 ** 0.20 * 0.10 0.28 ** 0.01 0.03 0.04

EP2 0.64 ** 1.00 0.45 ** 0.60 ** 0.22 * 0.29 ** 0.19 * 0.15 0.09 0.13 0.00 0.10 0.00

EP3 0.39 ** 0.44 ** 1.00 0.57 ** 0.03 -0.03 0.00 -0.02 -0.02 -0.07 -0.10 -0.07 0.02

EP4 0.41 ** 0.51 ** 0.33 ** 1.00 -0.16 -0.20 * -0.13 -0.01 -0.04 -0.09 -0.04 0.02 0.13

FM1 0.17 ** 0.23 ** 0.13 * 0.15 ** 1.00 0.53 ** 0.62 ** 0.12 0.05 -0.03 0.21 * 0.05 -0.03

FM2 0.09 0.16 ** 0.11 * 0.12 * 0.46 ** 1.00 0.50 ** 0.15 0.20 * 0.22 * 0.10 0.01 -0.03

FM3 0.20 ** 0.31 ** 0.15 ** 0.19 ** 0.57 ** 0.46 ** 1.00 0.21 * 0.16 0.19 * 0.09 0.06 0.07

IC1 0.06 0.14 ** 0.11 * 0.11 * 0.00 0.04 0.12 * 1.00 0.46 ** 0.40 * 0.07 0.11 0.19 *

IC2 -0.06 0.06 0.05 0.12 * 0.00 0.09 -0.01 0.39 ** 1.00 0.44 ** 0.09 0.03 0.27 **

IC3 0.22 ** 0.29 ** 0.06 0.15 ** 0.11 * 0.08 0.13 * 0.26 ** 0.33 ** 1.00 0.00 0.08 0.10

TR1 -0.05 0.02 0.06 0.01 0.09 * 0.11 * 0.07 0.05 0.02 -0.05 1.00 0.63 ** 0.33 **

TR2 -0.05 0.00 0.05 -0.02 0.10 * 0.13 * 0.13 * 0.06 0.08 0.03 0.58 * 1.00 0.32 **

TR3 -0.13 * -0.09 * -0.03 -0.01 0.05 0.05 0.08 -0.04 0.13 * -0.03 0.33 ** 0.33 ** 1.00

7 effects of hrm practices on it usage

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