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A meta-analytical study on the association of human resource management practices with financial, market and operational performance

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Purpose: This article draws on the meta-analysis technique to systematically analyse and compare the association of human resource management (HRM) practices with financial, market and operational performance. Design/methodology/approach: An exhaustive search of HRM-performance link resulted in a final sample (k) of 24 independent studies. For this purpose, Comprehensive Meta-Analysis (Version 3.0) software was used. Heterogeneity of the studies was determined using Q-statistic with a p-value, I2, T2 and Tau. As the degree of heterogeneity was very high, random effects model was selected to estimate the mean of effects. Lastly, publication bias was studied using graphical and statistical methods. Findings/results: The results revealed the average correlational (r) association of HRM practices with financial performance, market performance and operational performance as 0.305, 0.434 and 0.311, respectively. More specifically, HRM practices have the strongest association with market performance. Practical implications: The results statistically quantify the association between HRM practices and organisational performance measures for developing desired knowledge, skills and abilities to generate higher and improved performance. The results of this study provide HR managers with evidence that right investment in human resources does significantly contribute to the bottom line; they should make better and higher allocation of the resources for HRM. Originality/value: To the best of our knowledge, this study is the first to meta-analytically examine the varying association of HRM with three distinct organisational performance measures.
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South African Journal of Business Management
ISSN: (Online) 2078-5976, (Print) 2078-5585
Page 1 of 9 Original Research
hp://www.sajbm.org Open Access
Authors:
Sukhpreet Kaur1
Gurvinder Kaur1
Aliaons:
1School of Humanies and
Social Sciences, Thapar
Instute of Engineering and
Technology, Paala, India
Corresponding author:
Sukhpreet Kaur,
sukhpreetkaur358@gmail.com
Dates:
Received: 20 Apr. 2020
Accepted: 03 Dec. 2020
Published: 17 Feb. 2021
How to cite this arcle:
Kaur, S., & Kaur, G. (2021).
A meta-analycal study on
the associaon of human
resource management
pracces with nancial,
market and operaonal
performance. South African
Journal of Business
Management, 52(1), a2070.
hps://doi.org/10.4102/
sajbm.v52i1.2070
Copyright:
© 2021. The Authors.
Licensee: AOSIS. This work
is licensed under the
Creave Commons
Aribuon License. Introducon
Starting in the 1990s, the first studies statistically analysed the linkage between human
resource management (HRM) practices and organisation performance (Arthur, 1994; Becker
& Gerhart, 1996; Becker & Huselid, 1998; Huselid, 1995; MacDuffie, 1995; Welbourne &
Andrews, 1996). An increasing number of HRM scholars have attempted to establish that
human resource practices (HRPs) lead to better organisation performance (Guest, 2011;
Katou & Budhwar, 2007; Singh, 2004; Tzafrir, 2005). Another argument is that the so-called
high-performance work practices (HPWPs) in strategic HRM (SHRM) can improve
organisation performance by developing employees’ competencies, increasing their
knowledge and commitment (Appelbaum, Bailey, Berg, & Kalleberg, 2000). High-performance
work practices are a distinct but mutually related set of HRM policies and practices rather
than isolated individual HRM practices (Becker & Huselid, 1998; Huselid, 1995). Even though
the association is much discussed in the literature, there are certain issues that have remained
untouched so far.
Meta-analysis can be described as a set of statistical procedures designed to collect research
results across studies to estimate the relationship between variables in the population as a
whole (Glass, 1997). Researchers are increasingly using meta-analysis to aggregate the results of
empirical studies on key organisation outcomes, such as recruitment, selection, training and job
Purpose: This article draws on the meta-analysis technique to systematically analyse and
compare the association of human resource management (HRM) practices with financial,
market and operational performance.
Design/methodology/approach: An exhaustive search of HRM-performance link resulted in a
final sample (k) of 24 independent studies. For this purpose, Comprehensive Meta-Analysis
(Version 3.0) software was used. Heterogeneity of the studies was determined using Q-statistic
with a p-value, I2, T2 and Tau. As the degree of heterogeneity was very high, random effects
model was selected to estimate the mean of effects. Lastly, publication bias was studied using
graphical and statistical methods.
Findings/results: The results revealed the average correlational (r) association of HRM
practices with financial performance, market performance and operational performance as
0.305, 0.434 and 0.311, respectively. More specifically, HRM practices have the strongest
association with market performance.
Practical implications: The results statistically quantify the association between HRM
practices and organisational performance measures for developing desired knowledge,
skills and abilities to generate higher and improved performance. The results of this study
provide HR managers with evidence that right investment in human resources does
significantly contribute to the bottom line; they should make better and higher allocation of
the resources for HRM.
Originality/value: To the best of our knowledge, this study is the first to meta-analytically
examine the varying association of HRM with three distinct organisational performance
measures.
Keywords: HRM; meta-analysis; comprehensive meta-analysis software; firm performance;
financial performance; market performance; operational performance; HRM-firm
performance link.
A meta-analycal study on the associaon of human
resource management pracces with nancial,
market and operaonal performance
Read online:
Scan this QR
code with your
smart phone or
mobile device
to read online.
Page 2 of 9 Original Research
hp://www.sajbm.org Open Access
attitudes (Stone & Rosopa, 2017). In the words of Stone and
Rosopa (2017, p. 3), the following are the advantages of
using meta-analysis:
Results of meta-analysis can provide better estimates of
the relation in the population than single studies.
The precision and validity of estimates can be improved
as more data are used in a meta-analysis, and the
increased amount of data increases the statistical power
to detect an effect.
Inconsistencies in results across studies can be analysed,
and the bases for these differences can be analysed
(e.g. publication bias, differences in the representativeness
of samples).
Hypothesis testing can be applied on summary estimates.
Till date, meta-analysis on HRM and organisational
behaviour has been conducted on the following topics:
collective turnover (Hancock, Allen, & Soelberg, 2017), task
performance (Chiaburu, Oh, Wang, & Stoverink, 2017),
employee effectiveness (Mackay, Allen, & Landis, 2017),
performance ratings (Harari & Rudolp, 2017), recruitment
and job-choice (Kristof-Brown, Zimmerman, & Johnson,
2005) and longitudinal performance analysis (Saridakis, Lai,
& Cooper, 2017). Some recent meta-analytical studies on
HRM conveyed the results of its relationship with firm
performance across the public, private and semi-public
sector (e.g. Blom, Kruyen, Heijden, & Thiel, 2018). However,
studies highlighting meta-analytical mechanism by applying
objective measures for assessing the strength of HRM
practices in various types of firm performance are still scarce.
This study is an attempt to partially fill this gap. Given the
widespread use of meta-analysis in the field, the goal of this
study is to systematically analyse and compare the association
of HRM practices with financial, market and operational
performance using a meta-analytical approach. This article
aims at contributing to the debate on diverse characteristics
of HRM-performance link (Boselie, Dietz, & Boon, 2005;
Guest, Paauwe, & Wright, 2012; Paauwe, 2009).
Literature review
This section begins by highlighting the core issues
surrounding HRM-performance link. Literature review
is sub-divided under three perspectives: (1) financial
performance, (2) market performance and (3) operational
performance. It should be taken into consideration that there
are no hard and fast criteria to categorise the organisational
performance under these categories. However, three broad
perspectives have been undertaken to avoid any potential
overlap.
Human resource management-performance link
Barney (1991) argues that any firm can procure technology,
finance and information, whereas competitive advantage can
be achieved only through unique contribution of human
capital in the firm. The resource-based approach is directly
linked with employee’s skills and abilities and capabilities
which together encompass the firm’s pool of human capital
(Lado & Wilson, 1994). Pfeffer (1994) suggested 16 best
practices for explaining HRM-performance linkage. It is
reasoned that a greater use of these practices (training and
development, participation and employment, information
sharing and some others) would lead to improved
productivity and profitability, thus helping organisations in
achieving a competitive advantage (Darwish, Singh, &
Mohamed, 2013). According to previous empirical research,
the most common types of organisational performance
measures are (1) financial or accounting performance,
(2) market performance and (3) operational performance
(Brealey, Myers, & Marcus, 2001; Carton & Hofer, 2006;
Penman, 2001). Human resource practices influence
HR-based outcomes, followed by financial, market and
organisational outcomes. The rationale behind this
proposition is that HRPs have a direct impact on employee
behaviour, which increases job satisfaction, which in turn
helps to generate high financial and organisational outcomes
(Dyer & Reeves, 1995). Studies on HRM-performance link
suggest that HR practices enhance organisation performance
(Delaney & Huselid, 1996; Guerrero & Didier, 2004; Guest,
1997; Guest, Michie, Conway, & Sheehan, 2003; Lee, Lee, &
Wu, 2010; Lee, Phan, & Chan, 2005; Panayotopoulou,
Bourantas, & Papalexandris, 2003; Pfeffer, 1994; Richard &
Johnson, 2001; Sels et al., 2006b; Zhang & Li, 2009).
Human resource management and nancial performance
Considering the association between HRM and financial
performance measures, Venkatraman and Ramanujam
(1986) emphasised the use of simple outcome-based
indicators that are supposed to suggest the fulfilment of
the economic goals of the firm. High financial performance
has been associated with HRPs which may improve
employee behaviour and attitude towards strengthening
the competitive strategy of the firm (Hiltrop, 1996). High
correlations have been observed between HRPs and profits
amongst banks (Delery & Doty, 1996). Return on assets
(ROA), sales growth and valuation of stocks have been
linked with HR orientations and measured by effective
recruitment and selection of workers and employees (Lam &
White, 1998). Fitz-Enz (1997) identified three paradoxes
about best HRM practices: (1) complex business problems
have to be solved by employees and managers in the most
simplistic manner, (2) considering a visible programme in a
journal as best or general practice and (3) following the
learnings of past studies for future directions. For instance,
some HRPs may be related to financial performance whilst
others may be related to staff earning (Lau & Ngo, 2004).
According to Wright and McMahan (1992), the main
concern was whether or how firms should benefit from their
future source of profitability to achieve a firm competitive
advantage. Rogers and Wright (1998) examined 29 studies
that provided 80 effect sizes (i.e. described statistical
relationships between HRM practices and performance
measures) on the HRM-performance link surveying.
Amongst the studies surveyed by Rogers and Wright
(1998), a few had examined human resource outcomes,
whereas several had used accounting and financial market
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measures; the greatest number of effect sizes, however, was
seen for organisational outcomes. Hence, we hypothesise
that:
H1: HRM practices have strong association with financial
performance.
Human resource management and market performance
Data on market performance are most objective as they
comprise of numbers and can be easily obtained even if the
size of the investigation is large. On the contrary, whilst
viewing the subjective side of HRM-market performance
linkage, perceived market performance is utilised in cases
where obtaining objective data is uncertain. Specific market
performance indicators are market value, market share and
revenue (Byremo, 2015). Zhang and Li (2009) studied the
association between HPWPs and market performance in the
Chinese pharmaceutical industry. Human resource practices,
such as detailed job descriptions, performance appraisals,
training and development programmes and profit-sharing,
were found to be significantly associated with the company’s
perceived market results. Considering the criticism about
accounting-based performance measures, several authors
have proposed market-based performance measures (e.g.
McGuire, Schneeweis, & Hill, 1986). Various studies have
found a positive correlation between HRM practices and
market performance measures (Azmi, 2010; Behery, 2011;
Katou & Budhwar, 2010). Hence, we hypothesise that:
H2: HRM practices have strong association with market
performance.
Human resource management and operaonal
performance
Ahmad and Schroeder (2003) studied the effectiveness of
seven HRM practices suggested by Pfeffer (1998), focusing on
measuring the effects of these practices on operations.
The seven HRM practices include compensation/incentive
contingent on performance, selective hiring, use of teams and
decentralisation, employment security, status differences,
extensive training and sharing information. Operational
performance consists of cost, quality, flexibility, delivery and
organisational commitment. Their study overall supported
the relationship between the seven HRM practices and
operational performance. Chang and Chen (2002) evaluated
the relationship between HRM practices and firm performance
of Taiwanese high-tech firms. It was found that HRM practices,
such as teamwork, training and development, performance
appraisal, human resource planning and employee benefits,
have a significant effect on employee productivity. Human
resource planning and benefits are negatively related to
employee turnover. Huselid, Jackson and Schuler (1997)
investigated the effect of HRM practices on firm performance
of 293 US firms. They divided HRM effectiveness into two
types. The first type was HRM effectiveness, which included
recruitment and training, compensation, industrial/employee
relations, appraisal, selection tests and employee attitudes.
The second type was SHRM effectiveness that comprised
teamwork, employee participation and empowerment,
employee and manager communications and management
and executive development. Their study revealed that there
was a positive association between SHRM effectiveness and
firm performance. However, they found that technical HRM
effectiveness was not related to firm performance. MacDuffie
(1995) carried out research on 62 automotive assembly plants
to recognise the effect of HRM bundles on operational
performance and proposed that innovative HRM bundles
affected operational performance.
Three mediators through which HRM practices affect
organisational performance function are (1) increasing
employee’s knowledge, skill and abilities (KSAs), (2)
empowering employees to act and (3) motivating them
(Becker & Huselid, 1998). These mediators affect employee
discretionary effort, creativity and productivity (Becker,
Huselid, Pickus, & Spratt, 1997). As a result, operating
performance measures, such as job satisfaction and employee
turnover (Dyer & Reeves, 1995), transform into improved
accounting returns (Becker et al., 1997; Dyer & Reeves, 1995;
Huselid, 1995). According to Dyer and Reeves (1995), HRM
practices should affect operational performance such as
productivity and retention more than financial measures
such as accounting returns and growth. Despite the fact that
HRM practices affect both operational and financial set of
measures, operational performance measures are relatively
closer to behavioural developments employees are expected
to make. Improved financial results apart from HRM practices
are caused by a wide variety of factors, such as acquisition or
diversification activity (O’Shaughnessy & Flangan, 1998).
This results in more variability in accounting returns, market
returns and growth. Human resource management practices
indicate a smaller portion of the above variability (Combs,
Liu, Hall, & Ketchen, 2006). As a result, the relationship
between HRM practices and operational performance
measures like productivity and turnover should be stronger
as compared to financial performance measures. Hence, we
hypothesise that:
H3: HRM practices have strong association with operational
performance.
Method
Search strategy
To identify potential studies that investigate the statistical
association between HRM practices and organisational
performance, empirical studies were searched during April
and May 2019. To identify relevant studies, empirical
studies published from January 2000 to March 2019 were
searched. As all studies were considered as potentially
relevant, no limit was set on the search period. Databases
from EBSCO, Web of Science (WoS), Education Research
Information Center (ERIC) and Social Science Research
Network (SSRN) were used for this purpose. Search strings
included the following keywords: ‘HRM’, ‘Human
Resource’, ‘HR Practice’, ‘HR Policy’, ‘High Performance
Work Practices’, ‘HPWP’, ‘Personnel Practices’, ‘Personnel
Policies’, ‘Financial Performance’, ‘Market Performance’
and ‘Operational Performance’. In total, 9423 potentially
Page 4 of 9 Original Research
hp://www.sajbm.org Open Access
useful studies were identified using these keywords. We
have treated each association as independent. After applying
the inclusion criteria, 24 studies (Bae, Chen, Wan, Lawler &
Walumbwa, 2003; Beh & Loo, 2013; Björkman & Xiucheng,
2002; Chang & Huang, 2005; Chang & Xin, 2009; Chuang &
Liao, 2010; Darwish et al., 2013; Ferguson & Reio, 2010; Fey,
Björkman, and Pavlovskaya, 2000; Fu, Bosak, Flood & Ma,
2018; Gong,Green, Wu, Whitten & Medlin, 2006; Gurbuz &
Mert, 2011; Katou, 2017; Li, Qin, Jiang, Zhang, & Gao, 2015;
Ngo & Loi, 2008; Park, Mitsuhashi, & Björkman, 2003; Sels et
al., 2006; Singh, 2003; Singh 2004; Wan, Ong, & Kok, 2002;
Wei & Lau, 2008; Wei & Lau, 2010; Wright, Gardner, &
Moynihan, 2003; Zhang & Morris, 2014) were selected.
Figure 1 shows the search and selection criteria for relevant
studies. In addition, several papers (Arthur, 1994; Becker &
Gerhart, 1996; Becker & Huselid, 1998; Guest, Michie,
Sheehan, & Conway, 2000; Guest et al., 2012; Huselid, 1995;
Welbourne & Andrews, 2014) on the association between
HRM and organisational performance were also studied to
advance our knowledge about this relationship. However,
these papers were eventually not included in the meta-
analysis.
Inclusion criteria
Only those studies which met the following inclusion criteria
were included in our meta-analysis. Firstly, only studies
published in English were included. Secondly, only
journals/academic journals were searched for necessary
studies. Thirdly, only studies which provided correlations for
the association between HRM and organisation performance
were searched. However, no differentiation was made between
studies using self-rated or any other measure for understanding
this linkage. Finally, only studies that provide necessary
statistical information (sample size and correlation coefficient)
needed to perform the meta-analysis were included.
Meta-analysis technique
Hak, Van Rhee and Suurmond (2018) suggest meta-analysis
as a systematic technique for synthesising quantitative results
of different empirical studies regarding the effect of an
independent variable on a defined outcome. The meta-
analysis was done using Comprehensive Meta-Analysis
(Version 3.0) software developed by Biostat. Borenstein,
Cooper, Hedges and Valentine (2009) suggested that the
estimates of correlation parameter p are mere sample
correlation coefficient, r. The variance of r is approximately
=
vr
n
(1 )
1
r
22
[Eqn 1]
where n is the sample size, as shown in Equation 1.
Generally, synthesis on the correlation coefficient is avoided
because the variance strongly depends on the correlation.
When studies report correlation values, the values of the
correlation are themselves used as effect size. The correlation
values are converted into Fisher’s z scale, and all the analyses
are done using converted values (see Eqn 2).
The transformation from correlation to Fisher’s z is
Fisher’s z = 0.5 × Log +
1Correlation
1Correlation [Eqn 2]
Another key aspect is to determine the degree of homogeneity
of the studies included in the meta-analysis. It helps in
determining the statistical model to be used (Mills, 2009).
Population estimates are better for effect sizes based on larger
samples than those based on smaller samples (Lipsey &
Wilson, 2001). According to Hak et al. (2018), heterogeneity of
the studies is determined by the following: Q-statistic with a
p-value, I2, T2 and Tau. The Q-statistic (also known as
Cochran’s Q) gives the measure of variation around the
average but not heterogeneity. It is estimated to study
whether the variability across effect sizes is greater than
expected from sampling error alone. I2 is a relative measure
for the proportion observed variance that suggests real
differences in the effect size. If I2 is low, the degree of
homogeneity is high amongst the selected studies. However,
higher I2 suggests that selected studies cannot be considered
as belonging to the same population. Value of I2 statistic with
0% implies no heterogeneity, 50% implies moderate
heterogeneity and 75% or more implies high heterogeneity
(Higgins, Thompson, Deeks, & Altman, 2003). As I2 is more
than 95% in the studies used for meta-analysis here, it cannot
be considered as studies belonging to the same population.
Together, T2 and Tau are measures of dispersion of true effect
sizes between studies in terms of the effect size. Tau is an
FIGURE 1: Idencaon of relevant studies. EBSCO, Elton B. Stephens Company; WoS, Web of Science; ERIC, Educaon Research Informaon Center; SSRN, Social Science
Research Network.
Total number of studies
found: 9423
EBSCO: 1091
WoS: 6777
ERIC: 1494
SSRN: 61
Studies subjected
to tle and abstract
screening: 7212
Duplicates found
and removed: 2211
Studies removed aer the tle
and abstract screening: 6909
Studies finalised aer the full-text screening: 24
Studies whose
full-text were
retrieved: 303
Page 5 of 9 Original Research
hp://www.sajbm.org Open Access
estimate of the standard deviation of the distribution of true
effect sizes, whereas T2 is an estimate of the variance of true
effect sizes (Hak et al., 2018).
When the mean effect size is homogenous (i.e. nonsignificant
Q-statistic), it indicates that the dispersion of the effect sizes
around the mean is less than or equal to the sampling error.
Under this condition, the fixed effects model is used.
However, when the effect size is heterogeneous (i.e. significant
Q-statistic), the random effects model is used (Shelby &
Vaske, 2008). Fixed effects model assumes that there is one
true size which is shared by the studies included for meta-
analysis, whereas random effects model assumes that the
true effect size varies from study to study.
According to Berkeljon and Baldwin (2014), random effects
models make statistical inferences about the population of
studies which are not included in the meta-analysis. After
observing the degree of homogeneity, random effects model
was selected to estimate the mean of effects. It avoids the
underestimation of weights of a small study or overestimation
of the weight of a large study (Borenstein et al., 2009).
Lastly, the set of studies selected for meta-analysis is likely to
be biased in many ways. Publication bias refers to the
tendency of taking studies with significant studies. The field
of publication bias has been broader than estimated.
Following are some forms of publication bias categories:
reporting bias, language bias, database bias, prestige bias and
redundant publication bias (Rothstein, Sutton, & Borenstein,
2005). The main purpose of analysing publication bias is to
analyse the potential publication bias in selected studies
(Hak et al., 2018). Funnel plot is one of the most common
mechanisms for showing the relationship between study size
and effect size. Apart from funnel plot, there are certain
statistical analysis techniques which reveal publication bias
in meta-analytical investigation: (1) classic fail-safe N, (2)
Begg and Mazumdar rank correlation, (3) Duval and
Tweedie’s trim and fill method and (4) Egger’s regression
intercept (Borenstein, Hedges, Higgins, & Rothstein, 2011).
Results
Weighted average association between HRM practices and
organisational performance measures is estimated with the
random effects model in Table 1. To judge the magnitude of
product-moment correlation coefficient effect size,
correlation effect size values are considered small if less
than or equal to 0.10, medium, if equal to 0.25 and large, if
greater than or equal to 0.40 (Lipsey & Wilson, 2001).
Answering to H1, 17 559 respondents and 12 independent
studies gave an average correlation of 0.305 (95% confidence
interval [CI] = 0.175 to 0.424). Visual inspection revealed
that the funnel plot is relatively symmetric (see Figure 2). As
the funnel plot is largely subjective, various statistical tests
have been performed. The classic fail-safe N indicated that
2037 missing studies were needed to bring the p-value
above the α level (α= 0.05). Following Duval and Tweedie’s
trim and fill method, no missing studies were located to the
left of the mean. However, two missing studies were found
to the right of the mean. This changed the point estimate
from 0.315 to 0.364 (95% CI = 0.177 to 0.452 to 95% CI = 0.194
to 0.535). Egger’s regression test showed that the intercept
varied from zero (B0 = 3.749; 95% CI = 2.983 to 10.483;
p(one-tailed) = 0.122; p [two-tailed] = 0.243), thus indicating
asymmetry in the included studies. The average correlation
for association between HRM practices and financial
performance (0.305) is lower than the average correlation
for association between HRM practices and market
performance (0.434). However, it is also lower than the
average correlation for association between HRM practices
and operational performance (0.311). Therefore, H1 is
rejected.
Answering to H2, 5745 respondents and 16 independent
studies provided an average correlation of 0.434 (95%
CI = 0.173–0.638). Visual inspection revealed that the funnel
plot is considerably asymmetric. The direction of the effect
is towards the right, thereby creating a gap on left, where
the nonsignificant studies would have been if they had been
located (see Figure 3). The classic fail-safe N suggested that
3075 missing studies were needed to raise the p-value above
the arbitrary α level (α = 0.05). Duval and Tweedie’s trim
and fill method showed no missing studies to the left of the
mean. However, two missing studies were found to the
right of the mean. This shifted the point estimate from 0.465
TABLE 1: Meta-analysis results.
Hypothesis N k r z 95% CI
(under random
eects model)
Qwithin I2TT2p
H1: Financial performance 17 559 12 0.305 4.483 0.175–0.424 609.538 98.195 0.234 0.055 < 0.05
H2: Market performance 5745 16 0.434 3.139 0.173–0.638 1626.352 99.078 0.587 0.344 < 0.05
H3: Operaonal performance 7346 60.311 2.030 0.011–0.559 415.542 98.797 0.380 0.145 < 0.05
N, total sample size; k, number of studies in meta-analysis; r, average weighted correlaon coecient; 95% CI, lower and upper limits of 95% condence interval.
FIGURE 2: Funnel plot depicng publicaon bias for the associaon between
human resource management and nancial performance.
–2.0 –1.5 –1.0 –0.5 0.0 0.5 1.01.5 2.0
0.00
0.05
0.10
0.15
0.20
Standard error
Fisher’s Z
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to 0.543 (95% CI = 0.175 to 0.756 to 95% CI = 0.222 to 0.863).
Egger’s regression test showed that the intercept varied
from zero (B0 = 7.283; 95% CI = 3.522 to 18.089; p [one-
tailed] = 0.085; p [two-tailed] = 0.170), thus signifying
asymmetry in the selected studies. The average correlation
for the association between HRM practices and market
performance (0.434) was higher than the average correlation
for the association between HRM practices and financial
performance (0.305). It was also higher than the average
correlation for the association between HRM practices and
operational performance (0.311). Therefore, H2 is fully
supported.
Answering to H3, 7346 respondents and six independent
studies gave an average correlation of 0.311 (95% CI =
0.011–0.559). The funnel plot revealed studies as almost equal
distribution around the mean (see Figure 4). The classic fail-
safe N suggested that 1001 missing studies were needed to
bring the p-value above the α level (α = 0.05). Duval and
Tweedie’s trim and fill method found two missing studies to
the left of the mean. However, no missing studies were found
to the right of the mean. This shifted the point estimate from
0.321 to 0.124 (95% CI = 0.011 to 0.638 to 95% CI = 0.254 to
0.503). Egger’s regression test showed that the intercept
varied from zero (B0 = 1.716; 95% CI = 15.449 to 18.881;
p [one-tailed] = 0.398; p [two-tailed] = 0.795), thus signifying
asymmetry in the selected studies. The average correlation
for the association between HRM practices and operational
performance (0.311) was higher than the average correlation
for the association between HRM practices and financial
performance (0.305). However, it was lower than the average
correlation for the association between HRM practices and
market performance (0.434). Therefore, H3 is partially
supported. H1 and H3 predict that HRM practices have a
moderately strong association with financial performance
(r = 0.35) and with operational performance (r = 0.311),
respectively. H2 predicts that HRM practices have a strong
impact market performance (r = 0.434). Most importantly,
the difference is significant (p < 0.05), suggesting that the
effect of HRM practices on financial, market and operational
performance measures is statistically significant. Our findings
show that HRM practices positively affect organisational
performance measures and thus largely influence market
performance.
Discussion
The goal of this meta-analysis was to analyse and compare
the association between HRM practices and financial,
market and operational performance. To the best of our
knowledge, this work is first to meta-analytically examine
the varying association of HRM with three organisational
performance measures, thereby contributing to the debates
on the importance of context for HRM-performance linkage
(Paauwe, 2009; Vermeeren, 2014). In contrast to what we
expected, there seems only a small difference between
the association of HRM with financial and operational
performance measures. As all relevant studies were
included for initial search, there is a variety of issues like
goal ambiguity, personnel constraints and cultural context
in which those studies were conducted.
Our study does not specify the HRM practices which affect
the organisational performance; it simply studies the
correlation between HRM practices and various performance
measures. Traditionally, financial performance measures
were the first choice of academicians for analysing the effect
of HRM practices on organisational performance (MacDuffie
& Kochan, 1995; Uysal, 2008). This is evident as more than
55% studies of the sample population are on HRM-
organisational financial performance linkage. However,
with the changing business scenario, the emphasis has
shifted to market performance. Market performance matches
the external business environment with important financial
areas in the organisation, thus providing a roadmap for an
efficient utilisation of the corporate resources (Çetin & Oğuz,
2010). This change is strongly supported by statistical results
as the highest correlation (r = 0.434) is observed between
HRM-market performance. The findings of the study extend
the applicability of SHRM and work as an evidence for the
adoption of HRM practices for improving market performance.
The results support a universal context for the applicability
of HRM practices in improving firm performance, especially
market performance relationship (Guest, 1997). There are
many ways to understand the association of HRM practices
on firm performance, however adopting meta-analytical
procedure provides robust estimate by aggregating
information leading to an accurate statistical power. Each
FIGURE 3: Funnel plot depicng publicaon bias for the associaon between
human resource management and market performance.
–2.0 –1.5 –1.0 –0.5 0.0 0.5 1.01.5 2.0
0.00
0.05
0.10
0.15
0.20
Standard error
Fisher’s Z
FIGURE 4: Funnel plot depicng publicaon bias for the associaon between
human resource management and operaonal performance.
–2.0 –1.5 –1.0 –0.5 0.0 0.5 1.01.5
0.00
2.0
0.05
0.10
0.15
0.20
Standard error
Fisher’s Z
Page 7 of 9 Original Research
hp://www.sajbm.org Open Access
organisation must review its strategies, goals, objectives and
operations because changes in HRM policies and practices
influence organisational performance. In addition to the
main results, this meta-analysis has two noteworthy
findings. First, the results, at least statistically, quantify the
association between HRM practices and organisational
performance measures for developing desired knowledge,
skills and abilities to generate higher and improved
performance. Second, the results of this study provide HR
managers with evidence that right investment in human
resources does significantly contribute to the bottom line;
they should make better and higher allocation of the
resources for HRM.
Conclusion
In addition to the main results, this meta-analysis has two
noteworthy findings. Firstly, the results, at least statistically,
quantify the association between HRM practices and
organisational performance measures for developing desired
knowledge, skills and abilities to generate higher and
improved performance. Secondly, the results of this study
provide HR managers with evidence that right investment in
human resources does significantly contribute to the bottom
line; they should make better and higher allocation of the
resources for HRM.
Similar to other studies, this study has certain limitations
which must be acknowledged. The search for the studies to be
included for the current meta-analysis was restricted to
academic journals/journals. Including grey literature might
have offered more resources. A majority of the studies which
measured the association between HRM practices and
organisational performance used the same rate source. This
could lead to common method bias which could overstate the
correlational values. Future studies are encouraged to explain
the choice of HRM practices considered in the individual
studies as inclusion criteria. This will improve the reliability of
results. Moderator analysis was not performed as it would
further result in relatively fewer studies for the meta-analysis.
On lines of this research, future researchers are expected to
examine and compare this association on sectoral context.
Although not explicitly stated in the inclusion criteria, all
studies selected for meta-analysis happen to be cross-sectional.
This further limits our conclusions. Researchers are
encouraged to include longitudinal study designs as well for
high-quality research. Also, it would be worthwhile to adopt a
qualitative or case study approach for the association between
HRM practices and various organisational performance
measures. This study would thus be supplemented with
meta-synthesis and/or realist synthesis. In conclusion, this
literature review helps in statistically highlighting the
association between HRM practices and organisational
performance. It has been noted that:
[T]he behaviour of large and complex aggregates… is not to be
understood in terms of a simple extrapolation of the properties
of a few … instead, at each level of complexity entirely new
properties appear. (Anderson, 1972, p. 393)
Similarly, this study suggests that for different performance
measures, HR managers should take a different approach.
The aim of every organisation should be to efficiently deal
with the complexities which come in the way of improving
organisational performance.
Acknowledgements
Compeng interests
The authors declare that they have no financial or personal
relationships that may have inappropriately influenced them
in writing this research article.
Authors’ contribuons
S.K. and G.K. contributed equally to this research article.
Funding informaon
This research received no specific grant from any funding
agency in the public, commercial or not-for-profit sectors.
Ethical consideraon
This article followed all ethical standards for research without
direct contact with human or animal subjects.
Data availability
Data sharing is not applicable to this article as no new data
were created or analysed in this study.
Disclaimer
The views and opinions expressed in this article are those of
the authors and do not necessarily reflect the official policy or
position of any affiliated agency of the authors.
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