Vertical fit of HR practices and organizational culture: a case of a large-sized Korean conglomerate
| Date | 07 August 2017 |
| Published date | 07 August 2017 |
| DOI | https://doi.org/10.1108/EBHRM-05-2015-0017 |
| Pages | 122-138 |
| Author | Hwanwoo Lee,Joon Hyung Park,Shing-Chung Ngan,T. Siva Tian |
| Subject Matter | HR & organizational behaviour,Global HRM |
Vertical fit of HR practices and
organizational culture: a case of a
large-sized Korean conglomerate
Hwanwoo Lee
Department of Organizational Behavior and Human Resource Management,
China Europe International Business School, Shanghai, China
Joon Hyung Park
Department of International Business and Management,
Nottingham University Business School China, Ningbo, China
Shing-Chung Ngan
Department of Systems Engineering and Engineering Management,
City University of Hong Kong, Hong Kong, China, and
T. Siva Tian
Department of Psychology, University of Houston, Houston, Texas, USA
Abstract
Purpose –The purpose of this paper is to contribute to the human resources (HR) literature by using
exploratory network analysis (ENA), a data-driven technique. This technique was employed to discoverhow
the perceived effectiveness of HR practices interrelate with employee perceptions on organizational cultural
factors to enhance organizational commitment.
Design/methodology/approach –The authors used data from 1,459 employees of a large South Korean
conglomerate and studied how individual HR practices could be enhanced by specific organizational cultural
factors. The data were analyzed using ENA, which is an inductive approach.
Findings –The authors found that organizational commitment is associated with the positive perceptions of
employees on the effectiveness of HR practices, such as performance appraisal, training and development,
and compensation. Results show that when both HR practices and organizational cultural factors are
considered, they appear to influence organizational commitment independently.
Research limitations/implications –Data were collected from a large conglomerate. The authors were
limited by the use of the scales developed by a consulting firm. Therefore, readers should be cautious about
the generalizability of the findings.
Originality/value –The application of a data-driven technique (ENA) highlights the potentially fertile
methodological grounds for HR research. Literature on strategic HR management may benefit from inductive
approaches, wherein data serve as primary foundation for the design and development of new theories.
Keywords Organizational culture,Organizational commitment, Exploratory network analysis, HR systems
Paper type Research paper
1. Introduction
Researchers of SHRM are interested in achieving high levels of organizational performance by
employing human resource management (HRM) strategically (Delery and Doty, 1996; Lepak and
Shaw, 2008). One of the most central means for improving organizational performance in SHRM
frameworks is the concept of “horizontal fit,”or “bundling”HR practices, coupled with the notion
of “vertical fit.”HR bundles, which are often referred to as HR systems, are composed of an
array of synergistic and complementary HR practices (Dyer and Reeves, 1995). Frequently,
HR bundles are related to positive outcomes, such as increased motivation (Subramony, 2009),
low staff turnover (Batt, 2002), improved service performance (Chuang and Liao, 2010),
enhanced sales growth (Messersmith and Guthrie, 2010), and improved firm performance (Chow,
2012; Kim and Kang, 2013). Vertical fit aligns individual HR practices, or HR bundles, with
different levels of organizational factors, such as business strategies and organizational culture.
Evidence-based HRM: a Global
Forum for Empirical Scholarship
Vol. 5 No. 2, 2017
pp. 122-138
© Emerald PublishingLimited
2049-3983
DOI 10.1108/EBHRM-05-2015-0017
Received 23 May 2015
Revised 7 November 2015
11 March 2016
28 April 2016
Accepted 8 May 2016
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/2049-3983.htm
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Despite the valuable insights generated by prior research, several theoretical and
methodological voids have remained. First, limited studies have theorized about or
examined how HR systems and organizational cultural factors are intertwined. Many SHRM
scholars have focused on linking HR to business strategies (Lengnick-Hall and
Lengnick-Hall, 1988). Schuler and Jackson (1987) proposed that different strategy types
required different types of employee role behaviors, and that HR practices should be used to
ensure that those behaviors occur. Although vertical fit has been a dominant perspective in
the extant SHRM literature (Delery and Doty, 1996; Lepak and Shaw, 2008), a clear picture of
the detailed relations between these factors has yet to emerge. This omission is problematic
because the explanatory power of vertical fit remains questionable without suitable direct
examinations. We believe that clarifying the phenomena surrounding the bundling and
interrelatedness of individual HR practices and organizational cultural factors is an
important step toward the goal of using HRM strategically to achieve high levels of
organizational performance. Organizational commitment has held a prominent position in
the considerations of a variety of types of management literature, as committed employees
are critical for organizational performance and success (Mathieu and Zajac, 1990).
Therefore, our first goal has been to examine and ascertain whether individual HR practices
and cultural factors at organizations may be interrelated and influence organizational
commitment, as well as to determine what HR practices and organizational cultural factors
are most salient for developing high organizational commitment amongst employees.
Second, numerous types of HR systems can be generated because bundles are possible
combinations of HR practices. For example, high-performance work systems, which are one
of the most studied topics in the macro HRM literature, are merely one of many kinds of HR
bundles. Researchers have previously used reliability analysis (i.e. Cronbach’sα), factor
analysis, cluster analysis, regression analysis or sequential tree analysis (MacDuffie, 1995;
Guest et al., 2004) to identify appropriate sets of HR practices. However, inductive or
data-driven techniques are more suitable because the large number of factors that need to be
considered for vertical fit make identification highly complex for hypothesis-driven
techniques. This area is explained in detail in the next paragraph. As part of the current
study, we have introduced a new data-driven technique to identify the web of
interconnectedness amongst constructs (i.e. HR practices, organizational cultural factors
and organizational commitment). Specifically, we used exploratory network analysis (ENA)
to determine the path relations between the given HR practices and organizational cultural
factors (Ngan and Tsang, 2012).
ENA is a useful tool for researchers who intend to explore the connectivity relationships
between factors of interest in an inductive manner. For example, Ngan and Tsang (2012)
noted that, although structural equation modeling (SEM) is used commonly to analyze path
relations in given sets of theoretical constructs, this method has at least two disadvantages.
First, SEM is hypothesis-driven and confirmation-based. When using SEM, a researcher
must estimate a plausible path model for the constructs used in a study by relying on
existing domain knowledge. Therefore, the range of phenomena that need to be studied or
the type of research questions that should be asked are limited by the available background
theories. For example, given limited guidance from background theories, researchers may
find it difficult to specify a plausible path model if their research problems contain a large
number of constructs. Second, the issue of confirmation bias in SEM can be substantive.
Although the observed data may fit the conjectured model and pass a preset significance
threshold, the researcher will remain uncertain as to whether an alternative, untested path
model could yield a better fit for the data. ENA offers a promising approach for
circumventing these two limitations. As a data-driven technique, ENA uncovers the
connectivity relationships between given constructs both directly and algorithmically.
Therefore, using ENA, a researcher can expand the scope of a study beyond incremental
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