7 effects of hrm practices on it usage
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Lee, C. S. and Lee. C. H. (2010) Effects of HRM Practices on IT Usage. Journal of Computer
Information Systems, 50(2), 83-94.
1
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].
Lee, C. S. and Lee. C. H. (2010) Effects of HRM Practices on IT Usage. Journal of Computer
Information Systems, 50(2), 83-94.
2
Effects of HRM Practices on IT Usage
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
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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Effects of HRM Practices on IT Usage
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.
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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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
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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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
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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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
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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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,
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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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
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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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
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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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
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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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.
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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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
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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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
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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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
Lee, C. S. and Lee. C. H. (2010) Effects of HRM Practices on IT Usage. Journal of Computer
Information Systems, 50(2), 83-94.
15
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
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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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
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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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
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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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
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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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
Lee, C. S. and Lee. C. H. (2010) Effects of HRM Practices on IT Usage. Journal of Computer
Information Systems, 50(2), 83-94.
20
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
Lee, C. S. and Lee. C. H. (2010) Effects of HRM Practices on IT Usage. Journal of Computer
Information Systems, 50(2), 83-94.
21
IT usage will be more strongly associated for organizations
with only internal IT capability than for organizations with
external IT capability.
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
external IT capability.
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)
Lee, C. S. and Lee. C. H. (2010) Effects of HRM Practices on IT Usage. Journal of Computer
Information Systems, 50(2), 83-94.
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
Lee, C. S. and Lee. C. H. (2010) Effects of HRM Practices on IT Usage. Journal of Computer
Information Systems, 50(2), 83-94.
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
Lee, C. S. and Lee. C. H. (2010) Effects of HRM Practices on IT Usage. Journal of Computer
Information Systems, 50(2), 83-94.
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
Lee, C. S. and Lee. C. H. (2010) Effects of HRM Practices on IT Usage. Journal of Computer
Information Systems, 50(2), 83-94.
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
Lee, C. S. and Lee. C. H. (2010) Effects of HRM Practices on IT Usage. Journal of Computer
Information Systems, 50(2), 83-94.
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