Financial Management
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HRFLEXIBILITY.pdf
HR S C I E N C E FORUM
HR flexibility: Precursors and the contingent impact on firm financial performance
Sean A. Way1 | Patrick M. Wright2 | J. Bruce Tracey3 | Jeremy F. Isnard4
1SAW Consulting, Lausanne, Switzerland
2University of South Carolina, Columbia,
South Carolina
3Cornell University, Ithaca, New York
4Independent Researcher, Bern, Switzerland
Correspondence
Sean A. Way, Monash University, Monash
Business School, Level 7, N Building, Sir John
Monash Drive, Caulfield, VIC 3145, Australia.
Email: [email protected]
Using data from 170 for-profit U.S. firms with 100 or more employees from 27 North American
Industry Classification System (NAICS) industry subsectors, we investigated firm-level precur-
sors of HR flexibility and industry-level boundary conditions of the HR flexibility—firm financial
performance relationship. The findings denote that a contingency illumination is warranted in
which consideration should be given to firm-level factors such as flexibility business strategy
and high-performance work systems, which may play a key role in engendering HR flexibility,
and external factors such as industry dynamism and growth, which may serve as boundary con-
ditions that influence the relevance and impact of HR flexibility. This study is an important
extension of extant HR flexibility research and adds clarity regarding the roles and relevance of
HR flexibility and the circumstances in which HR flexibility and/or its focal factors may aug-
ment (or diminish) firm competitiveness and performance.
KEYWORDS
environmental uncertainty, firm financial performance, hierarchical linear modeling/cross-
level moderational hierarchical linear models, HR flexibility, strategic HR
1 | INTRODUCTION
Developing ways of responding readily and effectively to changing
competitive circumstances is one of the most vexing challenges fac-
ing firm leaders (Bal & Jansen, 2016; Sanchez & Heene, 1997; Yu,
Cadeaux, & Luo, 2015). In many situations, firm leaders must sift
through a myriad of sometimes disparate information in their efforts
to develop strategies and processes that enhance their firm's overall
capacity, or flexibility, to readily and effectively respond to changing
competitive circumstances (Tracey, Way, & Tews, 2008). However,
the level of dynamism and growth in competitive outputs and pres-
sures diverge markedly across industries (see Chadwick, Way, Kerr, &
Thacker, 2013; Datta, Guthrie, & Wright, 2005). In light of such
industry-level divergences, and congruent with the contingency con-
tentions promoted by Way and colleagues (e.g., Tracey et al., 2008;
Way et al., 2015), Wright and Snell (1998), and others (e.g., Burns &
Stalker, 1961; Eisenhardt & Martin, 2000; Lawrence & Lorsch, 1967),
we contend that the utility of sustaining or heightening the overall
flexibility of a firm—and thus HR flexibility—depends on external fac-
tors such as industry dynamism and growth.
Several studies in the human resource management (HRM)
domain (e.g., Bal & Jansen, 2016; Beltrán-Martín, Roca-Puig, Escrig-
Tena, & Bou-Llusar, 2008; Bhattacharya, Gibson, & Doty, 2005;
Chang, Gong, Way, & Jia, 2013; Ketkar & Sett, 2009, 2010; Lepak,
Takeuchi, & Snell, 2003; Martínez-Sánchez, Vela-Jiménez, Pérez-
Pérez, & de-Luis-Carnicer, 2011) have integrated the concept of flexi-
bility to explain, in part, how firm leaders can manage volatility and
competitive pressures. Wright and colleagues (Way et al., 2015;
Wright & Snell, 1998) contend that HR flexibility reflects the extent
to which a firm's human resources (people) possess a variety of com-
petencies, and the firm's HR practices can effectively utilize those
people to be adaptive and facilitate the capacity of the firm to pursue
strategic alternatives in response to competitive changes. In addition,
Way and colleagues (Chang et al., 2013; Tracey et al., 2008; Way
et al., 2015) and others (e.g., Bhattacharya et al., 2005; Lengnick-Hall,
Lengnick-Hall, Andrade, & Drake, 2009) have described HR flexibility
as a firm-level capability that is instrumental in turbulent industries
by facilitating the capacity of firms to respond readily and effectively
to changing competitive circumstances. However, while HR flexibility
is postulated to be a valuable firm-level asset within turbulent com-
petitive settings, it is not assumed to have universalistic utility
(Tracey et al., 2008; Tuan, 2016; Way, 2005; Wu, 2011).
Subsequently, a central tenet in this current study is that HR
flexibility can be either a firm-level asset or liability, depending on the
DOI: 10.1002/hrm.21867
Hum Resour Manage. 2018;57:567–582. wileyonlinelibrary.com/journal/hrm © 2017 Wiley Periodicals, Inc. 567
circumstances. For instance, because investments in HR flexibility
may be costly, we contend that in industries with somewhat consis-
tent and predictable competitive outputs and demands (i.e., stable
industries), the costs of engendering HR flexibility are likely to out-
weigh its benefits (Wright & Ulrich, 2017, p. 55) and thus, in such
competitive settings (e.g., stable and/or low-growth industries), HR
flexibility is an unnecessary or slack resource that negatively affects
firm efficiency and financial performance (cf. Dyer & Shafer, 1999;
Way, 2005; Way et al., 2015). As such, whether HR flexibility is a
firm-level asset or liability is postulated to be contingent on the char-
acteristics of a firm's industry. Though, as noted by Way et al. (2015),
published studies that have examined the relationship between HR
flexibility and firm performance have explicitly or implicitly adopted a
universalistic view and have not investigated the external (industry-
level) boundary conditions that may affect the nature of the HR
flexibility—firm financial performance relationship.
In addition to the moderating effects that may stem from compet-
itive conditions, we concur with Way et al. (2015) regarding the need
for empirical inquiries to investigate the precursors of HR flexibility. It
is likely that there are internal (firm-level) factors that may be crucial
for engendering this potentially important firm-level capability. While
extant studies have provided important insights about the nature and
relevance of HR flexibility, there is still much to be learned about firm-
specific factors that may help engender and sustain HR flexibility.
Thus, the aims of the current study are to address these gaps and
develop a more detailed illustration of the roles and relevance of HR
flexibility as well as the circumstances in which HR flexibility and its
five focal factors may be firm-level assets or potentially liabilities.
From a theoretical standpoint, this current research effort should
afford new insights concerning the influence that internal factors
have on HR flexibility. In addition, by investigating external boundary
conditions under which HR flexibility may be a firm-level asset, or
potential liability, this current inquiry rejoins Chuang, Jackson, and
Jiang's (2016, p. 525) call for more empirical “analyses of contingen-
cies” and investigations of boundary conditions in HRM research.
Finally, from a practical standpoint the findings may allow firm
leaders to make more informed resource allocation decisions regard-
ing the roles and relevance of HR flexibility that may be needed to
improve their firm's competitive position and performance.
2 | LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK
2.1 | HR flexibility
Following Way and colleagues (Chang et al., 2013; Tracey et al.,
2008; Way, 2005; Way et al., 2015) and Wright and Snell (1998), we
delineate HR flexibility as a firm-level capability that represents the
extent to which a firm's human resources and HR practices enable
the firm to be responsive to changes in competitive outputs and pres-
sures and readily and effectively pursue strategic courses of action in
response to competitive changes. Based on Sanchez's (1995) and
Sanchez and Heene's (1997) conceptualizations of strategic flexibility
and Wright and Snell's (1998) delineation of flexibility in strategic
HRM, Way et al. (2015) developed and illustrated the construct valid-
ity of a five-factor HR flexibility measure. This measure's five factors
were: (a) resource flexibility in HR practices, (b) coordination flexibility
in HR practices, (c) resource flexibility in employee competencies,
(d) coordination flexibility in employee competencies, and
(e) coordination flexibility in contingent worker competencies.
2.1.1 | The five focal HR flexibility factors
The first focal factor, resource flexibility in HR practices, refers to the
versatility or general applicability of a firm's current HR practices (Way
et al., 2015, p. 1100). For example, this focal HR flexibility factor is evi-
dent when the HR practices used by a firm “enable the firm to acquire
employees who possess” or “enable the firm's employees to acquire” the
competencies required for the delivery of new products or services (see
Way et al., 2015, p. 1129). The second factor, coordination flexibility in
HR practices, refers to a firm's capacity to deploy “alternative HR prac-
tices readily and effectively” (Way et al., 2015, p. 1101). For example,
coordination flexibility in HR practices is evident when a firm can quickly
implement alternative HR practices (e.g., work structures and empower-
ment processes), which elicit its employees to perform work activities
that enable the firm to respond to a competitor's introduction of a new
product or service (cf. Tuan, 2016; Way, 2005; Way et al., 2015).
The third focal factor, resource flexibility in employee competen-
cies, refers to the extent to which a firm's employees can possess or
can readily acquire the needed competencies and are willing “to per-
form a variety of alternative work activities” (Way et al., 2015, p. 1101).
For example, resource flexibility in employee competencies is evident
when a firm's employees “can” and “will” do those alternative work
activities that would enable the firm to respond to new demands of cus-
tomers (Tuan, 2016) and/or “expand the range of products/services it
offers to its customers” (Way et al., 2015, p. 1101). The fourth factor,
coordination flexibility in employee competencies, refers to a firm's
capacity to acquire, deploy, and redeploy “standard employees who can
successfully perform current” and “a variety of alternative work
activities,” which enable the firm to meet its current strategic goals or
pursue strategic alternatives (e.g., product/service extensions and/or
innovations) in response to competitive changes (Way et al., 2015,
p. 1102). The fifth factor, coordination flexibility in contingent worker
competencies, refers to a firm's capacity to readily acquire, deploy, and
dismiss contingent workers to ensure that the firm has the appropriate
supply of labor to meet its current goals or pursue alternative courses
of action (e.g., product/service extensions and/or additional capacity) in
response to changing competitive circumstances (Bal & Jansen, 2016;
Tracey et al., 2008; Way, 2005).
2.2 | Internal precursors of firm HR flexibility and flexibilities in HR practices
A central dogma in HRM research (cf. Brueller, Carmeli, & Markman,
in press; Kim & Ployhart, 2014, in press; Way et al., 2015; Wright &
Snell, 1998) is that firm-level HRM processes and systems use are
proximal determinants of firm HR capital (the skill and behavioral rep-
ertoires held by a firm's workforce). Correspondingly, Wright and
Snell (1998, p. 760) asserted that firm flexibility-focused HR practices
are key proximal determinants of firm flexibilities in employee
568 WAY ET AL.
competencies. However, as highlighted by Way et al. (2015,
pp. 1100–1102, p. 1126, pp. 1128–1129), there is less clarity regard-
ing the precursors of firm HR flexibility, resource flexibility in HR
practices, and coordination flexibility in HR practices. Hence, in the
current study, we rejoin Way et al.’s (2015) call for empirical inquiries
to illuminate those internal (firm-level) factors that are crucial for
engendering overall firm HR flexibility and firm flexibilities in HR
practices.
Extant “contingency theory research” (Turner, Way, Hodari, &
Witteman, 2017, p. 34) and the notion of vertical alignment in strate-
gic HRM research (see Lengnick-Hall et al., 2009; Way & Johnson,
2005; Wright & McMahan, 1992) accentuate that firm competitive-
ness and effectiveness are dependent on the firm using control and
management processes/systems that support its business strategy.
Consistent with the conception of vertical alignment in published
strategic HRM research (Lengnick-Hall et al., 2009), Wright and Snell
(1998) proposed that the strategic orientation of a firm's mission,
goals, and choice regarding fit and flexibility are central determinants
of firm HR flexibility. In addition, Way (2005) contended (p. 14) and
presented empirical results that indicated (p. 59) that firm flexibility
business strategy use was an internal precursor of firm flexibilities
(resource and coordination flexibility) in HR practices. Thus, we posit
that firm flexibility business strategy use is an internal determining
factor of overall firm HR flexibility and firm flexibilities in HR
practices.
Although HR flexibility and high-performance work system
(HPWS) use are related constructs (Way et al., 2015,
pp. 1109–1112), scholars have delineated and empirical results have
presented empirical evidence which indicates that HPWS use is an
internal (firm-level) precursor of firm HR flexibility (e.g., Beltrán-Mar-
tín et al., 2008; Way et al., 2015). Similarly, Ketkar and Sett (2009,
p. 1026) found a positive association between firm “flexibility induc-
ing HR practices” use and firm flexibility in HR practices. On the
other hand, Way (2005) contended (p. 13) and presented empirical
results that indicated (p. 59) that HPWS use was an internal precur-
sor of firm flexibilities in HR practices. Thus, we posit that HPWS use
is an internal determining factor of overall firm HR flexibility and firm
flexibilities in HR practices.
As firms grow they tend to become progressively bureaucratized
(van der Weerdt, Verwaal, & Volberda, 2006; Wright & Snell, 1998)
and adopt more mechanistic, bureaucratic forms of control and man-
agement processes/systems (Chadwick et al., 2013; Turner et al.,
2017). Scholars have proposed that small firms are less bureaucratic
than larger firms, and as such, firm size may be negatively associated
with firm-level structural flexibility (van der Weerdt et al., 2006), as
well as the use of organic or flexibility-oriented management systems
(cf. Burns & Stalker, 1961; Chadwick et al., 2013; Chang et al., 2013).
In addition, prior empirical studies have found that firm size was neg-
atively related to firm flexibility (van der Weerdt et al., 2006) and
flexibilities in HR practices (Way, 2005). Thus, we posit that firm size
will be negatively associated with firm HR flexibility and firm flexibil-
ities in HR practices. Specifically:
Hypothesis 1: Firm flexibility business strategy and
HPWS use will be positively associated — and firm size
will be negatively associated—with overall firm HR
flexibility.
Hypothesis 2: Firm flexibility business strategy and
HPWS use will be positively associated — and firm size
will be negatively associated—with firm flexibilities
(resource and coordination flexibility) in HR practices.
2.3 | The contingent impact of HR flexibility on firm financial performance
Consistent with the prior work of Way and colleagues, Wright and
Snell (1998), and others (e.g., Lengnick-Hall et al., 2009), this study's
delineation of the HR flexibility construct highlights the competencies
and motivation of the firm's human resources, and the firm's internal
practices, processes, and routines, which may augment the firm's
capacity to readily pursue strategic alternatives and effectively
respond to competitive changes. For firms competing in turbulent or
high-growth industry subsectors, HR flexibility may have a positive
impact on firm financial performance because it provides firms with
human resources who “can” and “will” do the diverse set of alterna-
tive work activities that are required to readily pursue strategic alter-
natives (e.g., product/service extensions and/or innovations) and
effectively respond to changing competitive circumstances (Tracey
et al., 2008). As such, HR flexibility enables firms to achieve the pur-
pose of effectively adapting to competitive changes in a proactive
and/or reactive manner (cf. Brown & Eisenhardt, 1998; Way, 2005).
Although scholars (e.g., Chadwick et al., 2013; Chuang et al.,
2016) have highlighted the need for more empirical inquiries of con-
tingencies (boundary conditions) in HRM research, there is a dearth
of empirical research that has investigated the influence of external
factors on the HR flexibility—firm financial performance relationship.
However, a few HR flexibility studies have begun to consider the
influence of external factors. For instance, Martínez-Sánchez
et al. (2011, p. 715, p. 725) assessed the moderating effect of “per-
ceived environmental dynamism” on the relationships between five
“flexible HR practices” and firm innovativeness. In addition, Way
et al. (2015) found that firm HR flexibility was positively related to
firm financial performance among firms competing in turbulent high-
technology industries in China. However, the authors underscored
the need for future empirical inquiries to test “the proposition that
the nature of the HR flexibility—firm financial performance relation-
ship is contingent on [industry] dynamism” (Way et al., 2015,
p. 1122).
A common assertion in extant HRM research is that the value
of the HR capital created by HR practices differs based on the com-
petitive context within which firms compete (cf. Becker & Huselid,
2006; Chuang et al., 2016; Erhardt, Martin-Rios, & Way, 2009; Jack-
son & Schuler, 1995; Khatri, Gupta, & Varma, in press; Kim & Ploy-
hart, 2014, in press). For instance, Kim and Ployhart (in press)
asserted that industry dynamism and growth influenced the value of
selection practices on firm labor productivity and financial perfor-
mance. In addition, Kim and Ployhart (2014) contended and pre-
sented empirical evidence that showed that the task-related skills
created through internal training were more beneficial for firm
WAY ET AL. 569
performance when consumer demand was growing and high. How-
ever, when consumer demand was decreasing and low, selective
staffing played a more prominent role as a determinant of financial
performance. These findings amplify research (e.g., Chadwick et al.,
2013; Datta et al., 2005)1 that has shown that industry dynamism
and growth can moderate the influence of HRM system
(e.g., HPWS) use on firm labor productivity.
In the current study, we postulate that industry dynamism and
growth will influence the utility of HR flexibility such that the nature
of its impact on firm financial performance is contingent on these
external influences. Thus, consistent with HRM scholars' calls for fur-
ther empirical inquiries of contingencies in HRM research (Chuang
et al., 2016) and the proposed contingent impact of HR flexibility on
firm performance (Way et al., 2015), in this study, we delineate and
investigate the influence that industry dynamism and growth have on
the extent to which HR flexibility is needed and on the value of HR
flexibility on firm financial performance.
2.3.1 | Industry dynamism
Dynamic industries are typified by rapid changes and high variability
in competitive pressures (Chadwick et al., 2013). Scholars have
asserted that for firms that compete in highly dynamic industries, firm
competitiveness and performance depend on the capacity to address
threats and exploit opportunities that rapidly and often unexpectedly
arise in the outputs of competitors and consumer demands
(Chadwick et al., 2013; Larrañeta, Zahra, & Galán González, 2014).
That is, firms competing in high-dynamism industries face continuous
pressures to adjust to changes in the outputs of competitors and the
demands of customers, and thus, investing in resources that enhance
flexibility may enable firms to adapt to unanticipated contingencies
(Lecuona & Reitzig, 2014) and provide a buffer under conditions of
uncertainty (Nohria & Gulati, 1996).
Tracey et al. (2008) contended that in high-dynamism industries,
HR flexibility augments the capacity of firms to adapt to competitive
changes in a proactive and/or reactive manner, thereby increasing
the need for investments in HR flexibility. Similarly, Datta
et al. (2005, p. 138) suggested that the competencies required in
dynamic industries are more complex and varied, which may increase
the need for employees with “the capacity and willingness to deal
with complexity and change” (e.g., resource flexibility in employee
competencies), as well as HR practices that effectively utilize
employees (e.g., flexibilities in HR practices) and enable the firm to
respond to changing competitive pressures. Moreover, Martínez-
Sánchez et al. (2011) asserted that in high-dynamism industries there
is a greater need for employees with broad competencies
(e.g., resource flexibility in employee competencies) and the capacity
to quickly and effectively deploy employees and contingent workers
in response to competitive changes (e.g., coordination flexibility in
human resource [employee and contingent worker] competencies).
Consistent with the above assertions of Tracey et al. (2008), Datta
et al. (2005), and Martínez-Sánchez et al. (2011) and our delineation
of HR flexibility, we posit that within high-dynamism industries, that
there will be a positive relationship between HR flexibility and firm
financial performance.
Nevertheless, in the current study we advocate a contingency
view regarding the impact of HR flexibility on firm performance
(cf. Jackson & Schuler, 1995). Attaining and sustaining HR flexibility
requires substantive investments (cf. Way et al., 2015; Wright &
Ulrich, 2017).2 For instance, the cultivation of a workforce composed
of employees who possess broad, superior competencies (e.g., firm
resource flexibility in employee competencies) may require substan-
tial investments in employee hiring (selection) and training processes
(cf. Chang et al., 2013; Tracey et al., 2008; Tuan, 2016; Wright &
Snell, 1998). In addition, attaining and sustaining firm flexibilities in
HR practices and flexibilities in employee competencies (e.g., a work-
force composed of—and the capacity to acquire, deploy, and
redeploy—employees who “can” and “will” do those work activities
that enable the firm to meet its current goals and/or pursue strategic
alternatives in response changing competitive conditions) may require
the use of participative processes such as employee involvement in
decision making and voice (cf. Erhardt et al., 2009; Tracey et al.,
2008; Tuan, 2016; Way, 2005) and the proffering of employment sta-
bility to employees.
Hence, although we posit that HR flexibility is a firm-level asset
(beneficial) in high-dynamism industries, we contend that in industries
with more stable and predictable competitive pressures (low-dynamism
industries) that “HR flexibility is a slack resource, which is likely to be
rarely needed or of use, and thus, the expenditures required to attain
HR flexibility are not likely to be recoverable” and may diminish firm
efficiency and financial performance (Way et al., 2015, p. 1127). We
acknowledge that our above contention is markedly different from the
contention that HR flexibility will always be beneficial, but will have
greater utility in high-dynamism industries. However, the former con-
tention is consistent with Way and colleagues' (e.g., Chang et al., 2013;
Tracey et al., 2008; Way et al., 2015) and others' (e.g., Wright & Ulrich,
2017; Wu, 2011) conceptualizations of HR flexibility and contingency
postulations concerning its consequences.
For instance, a determining factor of the utility of resource flexi-
bility in employee competencies is the extent to which employees
are faced with inconsistent and unpredictable customer demands.
When customer demands are somewhat consistent and predictable,
resource flexibility in employee competencies is unnecessary because
variability in the employee competency requirements is low. Thus,
the expenditures incurred to foster resource flexibility in employee
competencies will not likely be recouped. This effect will reduce firm
efficiency and ultimately firm financial performance. Hence, we posit
that the HR flexibility—firm financial performance relationship will be
negative in stable and low-dynamism industries whereas, because HR
flexibility is better suited for firms competing in high-dynamism
industries, the relationship between HR flexibility and firm financial
performance will be positive (the nature of the relationship will be
reversed) in high-dynamism industries. Specifically:
Hypothesis 3: Industry dynamism will moderate the
relationship between HR flexibility and firm financial per-
formance, such that the influence of HR flexibility on
firm financial performance will be negative at low levels
of industry dynamism and positive at high levels of
industry dynamism.
570 WAY ET AL.
2.3.2 | Industry growth
Parallel to industry dynamism, industry growth can influence the
value of HR flexibility on firm financial performance. In low-growth
industries, competitive demands are generally stagnant or in decline,
equity markets are tight, and firms have difficulties accessing alterna-
tive sources of capital (Kim & Ployhart, 2014). As such, scholars have
suggested that in low-growth industries there is little need for firms
to make investments in and engender flexibility (cf. Dyer & Shafer,
1999; Wright & Ulrich, 2017; Wu, 2011). By comparison, in high-
growth industries firms have greater discretion to make investments
in slack resources such as HR flexibility (Shin & Konrad, 2014) and
more market opportunities and strategic options to expand their busi-
ness through high customer demand (Kim & Ployhart, in press). Fur-
thermore, in high-growth industries, “slack resources” such as HR
flexibility “can be put to productive service” by firms and thus invest-
ments in HR flexibility may enhance the financial performance of
firms by enabling them to pursue additional capacity or product/ser-
vice extensions (Kim & Ployhart, 2014, p. 364). Congruently, we con-
tend that industry growth can influence the extent to which HR
flexibility is needed (a strategic priority) and the value of HR flexibility
on firm financial performance.
For instance, when industry growth is high there is greater uncer-
tainty regarding the nature and number of human resources (standard
employees and/or contingent workers) that are needed to pursue
market opportunities and strategic options (e.g., additional capacity or
product/service extensions) in response to changing/growing con-
sumer demands. However, when industry growth is low there is
greater certainty regarding the required human resource levels and
their placement; thus, there is less need for firm flexibilities in human
resource competencies (e.g., resource flexibility in employee compe-
tencies and coordination flexibility in employee and contingent
worker competencies). Hence, in low-growth industries, HR flexibility
is unlikely to be put to productive service (i.e., not needed) by firms,
and thus, the expenditures required to promote HR flexibility are
unlikely to be recouped, which may reduce firm efficiency and ulti-
mately have a negative impact on firm financial performance (cf. Way
et al., 2015; Wu, 2011). Therefore, we posit that the HR flexibility—
firm financial performance relationship will be negative in stable and
low-growth industries, whereas the relationship will be positive (the
nature of the relationship will be reversed) in high-growth industries.
Specifically:
Hypothesis 4: Industry growth will moderate the relation-
ship between HR flexibility and firm financial performance,
such that the influence of HR flexibility on firm financial
performance will be negative at low levels of industry
growth and positive at high levels of industry growth.
3 | METHOD
3.1 | Sample and procedures
A multisource, multilevel data set was used to test this study's
hypotheses.3 Individual responses were obtained via computer-
assisted telephone interviews conducted by Customer Follow Up,
Inc. during the fourth quarter of 2003 from 273 senior HR managers
(Source 1) and 217 presidents, directors, and general managers
(Source 2: senior non-HR managers) from 292 for-profit U.S. firms
with 100 or more employees (41% response rate; Way et al., 2015,
p. 1107, p. 1129). Respondents' firms (n = 292) and nonrespondents'
firms (n = 716) did not significantly differ with respect to the prior
year's (2002) annual sales, number of employees, or industry sector
membership (Source 3: Dun & Bradstreet Million Dollar Database).
A listwise deletion of cases (firms) lacking a matched set of responses
from Source 1 and 2 respondents or missing HR flexibility or industry
data resulted in a final sample composed of responses obtained from
170 senior HR managers (Source 1) and 170 senior non-HR managers
(Source 2) from 170 for-profit U.S. firms with 100 or more employees
from 27 North American Industry Classification System (NAICS)
industry subsectors. The average number of employees per firm was
478 and 91% (n = 154) of the sample firms employed between
100 and 999 employees, 9% (n = 16) employed 1,000 or more
employees, 31% were unionized, and 53% were from the
manufacturing sector.
3.2 | Level 1 variables
As outlined below, to avoid common source/method concerns (Way,
Simons, Leroy, & Tuleja, in press), the current study's firm-level (level
1) variables were derived from data obtained from one of three inde-
pendent and distinct sources (also see Footnote 3).
3.2.1 | Firm HR flexibility
Using a subset of the data reported by Way et al. (2015, p. 1107)
obtained from senior HR manager respondents (Source 1), HR flexi-
bility was assessed with the Way et al. (2015, pp. 1128–1129) 21-
item, five-factor HR flexibility measure. Specifically, we assessed
resource flexibility in HR practices with Way et al.’s five-item RFHRP
scale (α = .81), coordination flexibility in HR practices with Way
et al.’s four-item CFHRP scale (α = .70), resource flexibility in
employee competencies with Way et al.’s four-item RFE scale
(α = .80), coordination flexibility in employee competencies with Way
et al.’s (p. 1129) four-item CFE scale (α = .84), and coordination flexi-
bility in contingent worker competencies with Way et al.’s four-item
CFCW scale (α = .82). Response options ranged from 1 (strongly dis-
agree) to 5 (strongly agree). The Cronbach's alpha coefficient for this
study's five-factor firm (level 1) HR flexibility variable was 0.72.4
While this firm HR flexibility measure was used as the dependent
variable (M = 3.73, SD = 0.53; n = 170 for-profit U.S. firms with
100 or more employees from 27 NAICS industry subsectors) in the
ordinary least squares (OLS) regression model (Table 2, Model 1) used
to assess Hypothesis 1 (concerning the internal precursors of HR
flexibility), two alternate HR flexibility variables—the HR flexibility
level 2 (industry subsector) mean-centered variable (HRFLX L2MC;
M = 0.00, SD = 0.45; n = 170 for-profit U.S. firms with 100 or more
employees from 27 NAICS industry subsectors) and the level 2 HR
flexibility variable5 (L2 HRFLX; M = 3.81, SD = 0.38; n = 27 NAICS
industry subsectors)—were included in the hierarchical linear model
WAY ET AL. 571
(see Table 3, Model 2) used to assess this current study's cross-level
moderation hypotheses (Hypotheses 3 and 4).
3.2.2 | Firm (level 1) precursors and control variables
Firm flexibility business strategy use, firm HPWS use, and firm size
are posited to be associated with overall firm HR flexibility and firm
flexibilities in HR practices. In addition, published empirical studies
have controlled for the potential influence of unionization
(e.g., Bhattacharya et al., 2005), firm age (e.g., Ketkar & Sett, 2010),
and firm capital intensity (e.g., Bhattacharya et al., 2005) on the rela-
tionship between HR flexibility and firm performance. Therefore,
measures of unionization, firm age, firm capital intensity, firm size,
firm flexibility business strategy use, and firm HPWS use were
included in the OLS regression models (see Table 2) used to assess
this study's hypotheses concerning the internal (firm-level) precursors
of firm HR flexibility (Hypothesis 1) and flexibilities in HR practices
(Hypothesis 2). These measures were also included as firm-level (level
1) control variables in the hierarchical linear model (see Table 3) used
to assess this study's cross-level moderation hypotheses (Hypotheses
3 and 4).
First, consistent with Shin and Konrad (2014), unionization was
assessed with a dummy variable; unionization was coded 1 if any of
the firm's employees were covered by a collective bargaining agree-
ment and 0 if not (Source 1). Second, following Way, Lepak, Fay, and
Thacker (2010), firm age was measured as the natural logarithm of
the number of years that the firm had been in business (Source 3).
Third, following Chadwick et al. (2013), firm capital intensity was
assessed with the following item (Source 2): “Compared to its closest
competitors, your firm's total investment in fixed capital stock
(machinery, buildings, etc.) is?” Response options ranged from 1 (well
below average) to 5 (well above average). Fourth, consistent with Wang
and Bansal (2012) firm size (log employees) and firm size (log sales)
were measured as the natural logarithm of a firm's prior year's (2002)
number of employees and annual sales, respectively (Source 3). Fifth,
six items from Parthasarthy and Sethi's (1993) flexibility-oriented
business strategy measure were used to assess flexibility business
strategy use (α = 0.78; Source 2); a sample item was, “Your firm's
business strategy emphasizes the importance of frequent new prod-
uct/service introductions.” Response options ranged from 1 (strongly
disagree) to 5 (strongly agree). Sixth, HPWS use was assessed with
Way et al.’s (2015, p. 1111) nine high-performance work practices
items (α = 0.73; Source 1); a sample item was, “For every employee
hired over the last two years, your firm's staffing process has gener-
ated a large pool of qualified candidates.” Response options ranged
from 1 (strongly disagree) to 5 (strongly agree).
3.2.3 | Firm financial performance
Firm financial performance was assessed with four items taken from
existing firm performance measures (e.g., Chang et al., 2013; Kerr,
Way, & Thacker, 2007; Ling, Zhao, & Baron, 2007; Miles, Covin, &
Heeley, 2000; Wang & Bansal, 2012). Each firm's senior non-HR
manager respondent (Source 2) was asked to indicate the extent to
which her/his firm's top management team was satisfied with her/his
firm's performance (satisfied that her/his firm was achieving itsT A B LE
1 D es cr ip ti ve
st at is ti cs
an d co
rr el at io ns
fo r th e fi rm
-l ev
el (le
ve l1
)v ar ia bl es
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l1 va
ri ab
le s
M SD
1 2
3 4
5 6
7 8
9 1 0
1 1
1 2
1 3
1 .
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fi na
nc ia lp
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0 .4 3
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2 .
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0 .3 1
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.0 4
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F ir m
ag e
3 .1 5
1 .0 4
–. 0 2
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4 .
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3 .4 8
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5 .
F ir m
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5 .7 6
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6 .
F ir m
si ze
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1 7 .6 6
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7 .
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H P W
S us e
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(f iv e fa ct o rs )
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R F H R P
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1 1 .
F ir m
C F H R P
3 .7 2
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.2 4 **
.2 8 **
.6 8 **
.5 0 **
1 2 .
F ir m
R F E
3 .3 7
0 .7 5
–. 0 4
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–. 0 7
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.0 5
–. 0 6
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.7 2 **
.5 4 **
.3 4 **
1 3 .
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C F E
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.4 4 **
.3 7 **
.4 4 **
1 4 .
F ir m
C F C W
3 .9 9
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–. 1 1
.0 7
–. 0 3
.3 1 **
–. 0 8
–. 1 4
–. 0 1
.0 8
.5 3 **
.2 1 **
.1 4
.1 7 *
.2 5 **
N ot es :M
is th e le ve
l1 (f ir m -l ev
el ) va ri ab
le m ea
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*p < .0 5 .*
*p < .0 1 .
572 WAY ET AL.
performance target) in relation to each of the following financial per-
formance criteria: (a) sales growth; (b) ability to fund business growth
from profits; (c) return on investment; 4) gross profit margin.6
Response options ranged from –2 (very dissatisfied) to 2 (very satis-
fied). The Cronbach's alpha coefficient for this four-item firm financial
performance scale was 0.81.
TABLE 3 HLM results for firm financial performance
Model 1 Model 2
β (SE) p β (SE) p
Intercept –5.28 (1.15) *** –4.01 (2.35) †
Level 1 Variables
Unionization –0.03 (0.12) –0.15 (0.13)
Firm age 0.00 (0.05) 0.00 (0.06)
Firm capital intensity –0.02 (0.06) –0.02 (0.06)
Firm size (log employees) –0.21 (0.10) * –0.21 (0.12) †
Firm size (log sales) 0.32 (0.08) *** 0.24 (0.10) *
Firm flexibility business strategy use 0.16 (0.08) † 0.24 (0.09) *
Firm HPWS use 0.20 (0.08) * 0.20 (0.08) *
HRFLX L2MC (HR flexibility level 2 mean-lefted) –0.13 (0.13) –0.51 (0.20) *
Level 2 Variables and Interaction Terms
L2 HRFLX (level 2 HR flexibility) 0.37 (0.53)
NAICS 22 (utilities) –1.91 (1.21)
NAICS 31–33 (manufacturing) –1.03 (0.65)
NAICS 42 (wholesale) –0.49 (0.44)
NAICS 48 (transportation) –0.79 (0.80)
NAICS 51 (information) –0.63 (0.58)
NAICS 52, 53 (finance) –0.85 (0.50)
NAICS 54 (services) –0.49 (0.38)
Industry labor intensity –2.76 (1.39) †
Industry dynamism 233.88 (156.52)
Industry growth –23.49 (43.32)
HRFLX L2MC × Industry dynamism 25.27 (9.96) *
L2 HRFLX × Industry dynamism –59.13 (41.55)
HRFLX L2MC × Industry growth 2.07 (3.69)
L2 HRFLX × Industry growth 6.43 (11.72)
Akaike information criterion 410.94 386.36
χ2-distribution — 23.44 (14) †
~R2 0.15 0.24
~ΔR2 — 0.09
Notes: β is the unstandardized coefficient. SE is the standard error. ~R2 or R_GLMM(m)2 represents the variance explained by fixed factors. ~ΔR2 for Model 2 is a comparison to the ~R2 for Model 1. †p < .10. *p < .05. **p < .01. ***p < .001.
TABLE 2 OLS regression results for firm HR flexibility and flexibilities in HR practices
Firm-Level (Level 1) Variables
Model 1 HR Flexibility
Model 2 RFHRP
Model 3 CFHRP
β (SE) p β (SE) p β (SE) p
Constant 3.97 (0.80) *** 3.12 (1.14) ** 4.81 (1.12) ***
Unionization 0.00 (0.08) 0.08 (0.12) 0.22 (0.12) †
Firm age 0.01 (0.04) 0.01 (0.05) 0.00 (0.05)
Firm capital intensity 0.02 (0.04) 0.00 (0.06) –0.05 (0.06)
Firm size (log employees) 0.06 (0.07) 0.06 (0.10) 0.13 (0.10)
Firm size (log sales) –0.10 (0.06) † –0.10 (0.08) –0.21 (0.08) **
Firm flexibility business strategy use 0.09 (0.06) 0.18 (0.08) * 0.23 (0.08) **
Firm HPWS use 0.22 (0.06) *** 0.36 (0.08) *** 0.30 (0.08) ***
F 3.83 *** 4.50 *** 5.12 ***
R2 0.14 0.16 0.18
Notes: β is the unstandardized coefficient. SE is the standard error. †p < .10. *p < .05. **p < .01. ***p < .001.
WAY ET AL. 573
3.3 | Level 2 variables
Each firm included in the current study's final sample was categorized
per the NAICS and then matched to the industry subsector
1999–2003 (five-year) annual gross output data (Source 4: Bureau of
Economic Analysis, U.S. Department of Commerce or BEA) that were
used to generate this study's objective measures of industry dyna-
mism and growth (n = 27 NAICS industry subsectors).
3.3.1 | Industry dynamism
Consistent with the procedures employed by Chadwick et al. (2013)
we regressed each industry subsector's annual gross output over five
years, 1999–2003, against time; industry dynamism was the standard
error of the resulting regression coefficient divided by the industry
subsector's average five-year (1999–2003) gross output (M = 0.014,
SD = 0.012, Min = 0.003, Max = 0.054; n = 27 NAICS industry
subsectors).
3.3.2 | Industry growth
Similar to prior HRM research (e.g., Chadwick et al., 2013; Guthrie &
Datta, 2008), industry growth was measured as the average percent-
age change in an industry subsector's annual gross output between
2000 and 2003 (M = 0.019, SD = 0.043, Min = –0.051, Max =
0.130; n = 27 NAICS industry subsectors).
3.3.3 | Industry control variables
Consistent with prior HRM studies (Way et al., 2010) we used
dummy variables to control for industry sector membership (Source
3): NAICS 22 (utilities), NAICS 31–33 (manufacturing), NAICS
42 (wholesale), NAICS 48 (transportation), NAICS 51 (information),
NAICS 52–53 (finance), NAICS 54 (services), and NAICS 62 (health
care; omitted category). Data obtained from the BEA (Source 4) were
used to generate this study's industry labor intensity control variable,
which was assessed as an industry subsector's average three-year
(2001–2003) ratio of annual compensation of employees to gross
output (M = 0.286, SD = 0.122, Min = 0.059, Max = 0.598; n = 27
NAICS industry subsectors).
4 | RESULTS
Table 1 presents descriptive statistics and correlations for the current
study's firm-level (level 1) variables (n = 170 for-profit U.S. firms with
100 or more employees from 27 NAICS industry subsectors). Consis-
tent with Hypothesis 1, firm flexibility business strategy (r = .18,
p < .05) and HPWS use (r = .31, p < .01) were positively correlated
with overall firm HR flexibility, and firm size (log sales) (r = –.14,
p < .08) was negatively correlated with overall firm HR flexibility. Fur-
thermore, as shown in Table 1, firm size (log sales) and firm HPWS
use were positively correlated with firm financial performance while
overall firm HR flexibility and its five focal factors were not signifi-
cantly correlated with firm financial performance.
4.1 | Precursors of firm HR flexibility and flexibilities in HR practices
Table 2 presents the OLS regression results for firm HR flexibility and
flexibilities in HR practices. Consistent with Hypothesis 1, firm HPWS
use (β = .22, p < .001) was positively related—and firm size (log sales)
(β = –.10, p < .08) was negatively related — to overall firm HR flexibil-
ity (see Table 2, Model 1); however, in contrast with Hypothesis
1, firm flexibility business strategy use and overall firm HR flexibility
were not significantly related. In support of Hypothesis 2, firm flexi-
bility business strategy (β = .18, p < .05) and HPWS use (β = .36,
p < .001) were positively related to firm resource flexibility in HR
practices (RFHRP; see Model 2) and firm flexibility business strategy
(β = .23, p < .01) and HPWS use (β = .30, p < .001) were positively
related—and firm size (log sales) (β = –.21, p < .01) was negatively
related—to firm coordination flexibility in HR practices (CFHRP; see
Model 3).
4.2 | The cross-level moderating effects of industry dynamism and growth
Hierarchical linear modeling (HLM)7 using R was employed to assess
this study's cross-level moderational hypotheses which concern the
influence that industry dynamism (Hypothesis 3) and industry growth
(Hypothesis 4) have on the nature of the HR flexibility—firm financial
performance relationship. Table 3 presents a summary of these HLM
results. In Model 2, firm size (log sales) (β = .24, p < .05), flexibility
business strategy use (β = .24, p < .05), and HPWS use (β = .20,
p < .05) were positively related to firm financial performance and the
HR flexibility level 2 (industry subsector-level) mean-centered vari-
able (HRFLX L2MC; β = −0.51, p < 0.05) was negatively related to
firm financial performance. Consistent with Hypothesis 3, the HRFLX
L2MC–industry dynamism interaction (β = 25.27, p < .05) was posi-
tively related to firm financial performance; however, in contrast with
Hypothesis 4, the HRFLX L2MC–industry growth interaction was not
related to firm financial performance. The HRFLX L2MC–industry
dynamism interaction is shown graphically in Figure 1. In support of
Hypothesis 3, which concerns the cross-level moderating effect of
industry dynamism on the relationship between HR flexibility and
firm financial performance, Figure 1 shows that the influence of
HRFLX L2MC on firm financial performance was negative at low
levels of industry dynamism (–1 SD) and positive (reversed) at high
levels of industry dynamism (+1 SD).8
To illuminate potential circumstances in which the focal HR flexi-
bility factors augmented or diminished firm financial performance,
auxiliary HLM analyses were conducted to investigate whether indus-
try dynamism and growth had cross-level moderating effects on the
relationships between each of the focal HR flexibility factors and firm
financial performance. Table 4 presents a summary of these auxiliary
HLM results. Except for coordination flexibility in HR practices, indus-
try dynamism had a similar antagonistic cross-level moderating effect
on the relationships between the focal HR flexibility factors and firm
financial performance (see Footnote 8). The RFHRP L2MC–industry
dynamism (Model 2), RFE L2MC–industry dynamism (Model 3), and
CFCW L2MC–industry dynamism (Model 5) interactions are shown
574 WAY ET AL.
graphically in Figures 2A, B, and C, respectively. These figures display
a consistent pattern of relationships about the antagonistic cross-
level moderating effect of industry dynamism on the relationships
between the HR flexibility factors and firm financial performance (see
Footnote 8). For example, Figure 2A shows that the influence of
RFHRP L2MC (the resource flexibility in HR practices level 2 mean-
centered variable) on firm financial performance was negative at low
levels of industry dynamism (–1 SD) and positive (reversed) at high
levels of industry dynamism (+1 SD).
Additionally, the auxiliary HLM results presented in Table 4,
Model 5, indicate that industry growth had an antagonistic cross-level
moderating effect on the relationship between coordination flexibility
in contingent worker competencies and firm financial performance
(see Footnote 8). As shown in Figure 3, the influence of CFCW
L2MC (the coordination flexibility in contingent worker competencies
level 2 mean-centered variable) on firm financial performance was
negative at low levels of industry growth (–1 SD) and positive
(reversed) at high levels of industry growth (+1 SD).
4.3 | Auxiliary OLS analyses
We contend that in low-dynamism industries, HR flexibility is an
unnecessary, or slack, resource that negatively influences firm labor
productivity (a proxy for firm efficiency). To test the above conten-
tion, the current study's sample (n = 170 for-profit U.S. firms with
100 or more employees from 27 NAICS industry subsectors) was first
split into two groups (subsets) based on industry dynamism, and aux-
iliary OLS regression analyses for firm labor productivity9 were con-
ducted. The appendix presents a summary of these auxiliary OLS
regression results. As shown in the appendix (consistent with the
above contention), within this study's subset of firms from low-
dynamism industry subsectors (n = 85), HR flexibility and firm labor
productivity were negatively related (β = –.33, p < .05), whereas
within this study's subset of firms from high-dynamism industry sub-
sectors (n = 85), HR flexibility and firm labor productivity were not
significantly related (β = .00, p > .97).
5 | DISCUSSION
Even a cursory review of the current popular business press will gen-
erate numerous articles that call for firms to be more adaptable and
flexible in order to respond readily and effectively to changing com-
petitive circumstances. However, some industries are characterized
by elevated levels of instability in competitive outputs and pressures,
while others are not. This study offers two substantive contributions
to the extant research literature and highlights two primary implica-
tions for managerial and executive internal resource allocation
decisions.
First, in contrast to universalistic explications, the results indicate
that HR flexibility may be a firm-level asset in certain circumstances
but a liability in others. Taken collectively, the results indicate that
the conceptual frameworks used in prior empirical inquiries, which
have implicitly or explicitly assumed that HR flexibility has a univer-
salistic positive impact on firm performance, may not be applicable.
Instead, a contingency illumination (cf. Jackson & Schuler, 1995)
appears to be warranted in which consideration should be given to
internal precursors such as firm flexibility business strategy and high-
performance work system use, which may play a crucial role in
engendering HR flexibility as well as external boundary conditions
such as industry dynamism and growth, which may influence the
extent to which HR flexibility is needed and the value of HR flexibility
(and/or its focal factors) on firm financial performance. This is an
important extension on the findings of published HR flexibility stud-
ies, and provides clarity concerning the process through which HR
flexibility may affect firm performance.
Second, this study's results underscore the need for future
empirical inquiries to integrate additional internal variables into
models that illuminate the HR flexibility—firm performance relation-
ship. In contrast to external (competitive) influences, which firm
leaders typically cannot directly control or manipulate, firm leaders
do have a great deal of control over internal factors such as firm flexi-
bility business strategy and high-performance work system (HPWS)
use. As such, this study's results underscore the need to identify fur-
ther internal precursors, particularly those that are within the direct
control of firm leaders, that may be essential for engendering HR
flexibility and the capacity (flexibility) to readily and effectively pursue
alternative courses of action in response to changing competitive
circumstances.
From a practical standpoint, this current study's results highlight
a key implication for managerial and executive decision making. Most
firms have finite or limited resources that can be allocated to support
various strategic and operational functions (Chadwick et al., 2013).
Given the positive association between firm HPWS use and firm
financial performance (see Table 1; Becker & Huselid, 1998) and the
nonsignificant association between HR flexibility and firm financial
performance (see Table 1), this study's results highlight the impor-
tance of developing a robust HPWS which aims to extend firm HR
capital and outcomes. Hence, “consistent with research on dynamic
capabilities (Eisenhardt & Martin, 2000) and HR flexibility (Way et al.,
2015)” as well as the prior work of Brueller et al. (in press, p. 16), this
study's findings connote that a hierarchical decision-making process
FIGURE 1 The HRFLX L2MC—Industry dynamism interaction
WAY ET AL. 575
TABLE 4 Auxiliary HLM results for firm financial performance
Model 1 Model 2 Model 3
β (SE) p β (SE) p β (SE) p
Intercept –5.23 (1.12) *** –4.98 (2.44) * –5.44 (2.44) *
Level 1 Variables
Unionization –0.05 (0.12) –0.13 (0.14) –0.18 (0.14)
Firm age 0.00 (0.05) 0.01 (0.06) 0.01 (0.06)
Firm capital intensity –0.00 (0.06) –0.01 (0.06) –0.04 (0.06)
Firm size (log employees) –0.23 (0.10) * –0.24 (0.12) † –0.24 (0.12) *
Firm size (log sales) 0.33 (0.08) *** 0.30 (0.11) ** 0.29 (0.11) **
Firm flexibility business strategy use 0.14 (0.08) † 0.20 (0.10) * 0.25 (0.10) *
Firm HPWS use 0.17 (0.08) * 0.15 (0.09) 0.14 (0.09)
RFHRP L2MC 0.08 (0.10) –0.11 (0.16) 0.08 (0.11)
CFHRP L2MC –0.01 (0.10) –0.04 (0.10) –0.03 (0.10)
RFE L2MC –0.06 (0.10) –0.09 (0.10) –0.31 (0.16) †
CFE L2MC –0.03 (0.09) –0.03 (0.09) –0.03 (0.10)
CFCW L2MC –0.11 (0.08) –0.12 (0.08) –0.15 (0.08) †
Level 2 Variables and Interaction Terms
L2 RFHRP 0.96 (0.81) 0.02 (0.43)
L2 CFHRP 0.49 (0.51) 0.64 (0.45)
L2 RFE –0.40 (0.48) 0.47 (0.60)
L2 CFE –0.52 (0.51) –0.43 (0.47)
L2 CFCW –0.17 (0.34) –0.16 (0.27)
NAICS 22 (utilities) –2.75 (2.01) –2.52 (1.47)
NAICS 31–33 (manufacturing) –0.68 (0.71) –0.45 (0.70)
NAICS 42 (wholesale) –0.34 (0.49) –0.12 (0.47)
NAICS 48 (transportation) –2.05 (1.55) –0.98 (0.90)
NAICS 51 (information) –0.28 (0.62) –0.30 (0.61)
NAICS 52, 53 (finance) –0.98 (0.59) –1.18 (0.62) †
NAICS 54 (services) –0.55 (0.42) –0.52 (0.43)
Industry labor intensity –2.74 (1.60) –3.00 (1.50) †
Industry dynamism 322.09 (237.95) 378.73 (188.33) †
Industry growth –12.67 (47.14) 22.15 (34.56)
RFHRP L2MC × Industry dynamism 19.16 (9.37) *
L2 RFHRP × Industry dynamism –81.71 (58.39)
RFHRP L2MC × Industry growth 0.69 (2.40)
L2 RFHRP × Industry growth 4.38 (11.84)
RFE L2MC × Industry dynamism 23.65 (9.59) *
L2 RFE × Industry dynamism –98.84 (50.12) †
RFE L2MC × Industry growth –0.82 (2.69)
L2 RFE × Industry growth –4.40 (8.97)
Akaike information criterion 386.40 401.40 394.67
χ2-distribution ----- 21.15 (14) † 27.87 (14) *
~R2 0.16 0.23 0.26
~ΔR2 ----- 0.07 0.10
Model 4 Model 5
β (SE) p β (SE) p
Intercept –5.01 (3.34) –6.90 (2.91) *
Level 1 Variables
Unionization –0.15 (0.15) –0.16 (0.14)
Firm age 0.00 (0.06) –0.01 (0.06)
Firm capital intensity –0.03 (0.06) –0.03 (0.06)
Firm size (log employees) –0.20 (0.12) –0.23 (0.12) †
(Continues)
576 WAY ET AL.
may be needed in which a firm's leaders first focus on developing a
robust HRM infrastructure (e.g., a HPWS that enhances firm HR capi-
tal and outcomes) before deliberating about whether HR flexibility
may or may not be needed.
A second practical implication, and an extension of the first, con-
cerns the use of industry (competitive) information. More specifically,
this study's findings highlight the importance of acquiring industry
subsector gross output and employment data10 (gross domestic
product-by-industry data, which can be obtained from the Bureau of
Economic Analysis, U.S. Department of Commerce) on an ongoing,
multiyear basis, and using these data to create indices (e.g., industry
dynamism and growth indices) to inform resource allocation
decisions, particularly those that may be dedicated to HR flexibility.
As such, this study underscores that industry information can be used
to create indices that enable firm leaders to more accurately evaluate
the level and nature of industry (competitive) changes, and the poten-
tial need for (utility of ) HR flexibility.
5.1 | Limitations and avenues for future research
The current study's data were collected in 2003 (see Footnote 3),
and it is conceivable that the nature of competition,11 particularly the
level of dynamism in industry subsectors, has changed dramatically.
While this may suggest that engendering and/or sustaining HR
TABLE 4 (Continued)
Model 4 Model 5
β (SE) p β (SE) p
Firm size (log sales) 0.26 (0.11) * 0.28 (0.11) *
Firm flexibility business strategy use 0.20 (0.10) * 0.24 (0.10) *
Firm HPWS use 0.20 (0.09) * 0.16 (0.10) †
RFHRP L2MC 0.09 (0.11) 0.10 (0.11)
CFHRP L2MC –0.07 (0.11) –0.12 (0.10)
RFE L2MC –0.03 (0.10) –0.02 (0.10)
CFE L2MC –0.12 (0.08) –0.38 (0.13) **
CFCW L2MC –0.15 (0.14) –0.06 (0.09)
Level 2 Variables and Interaction Terms
L2 RFHRP 0.04 (0.46) –0.45 (0.52)
L2 CFHRP –0.42 (0.46) 0.20 (0.63)
L2 RFE 0.35 (0.43) 0.10 (0.47)
L2 CFE –0.20 (0.30) 1.42 (0.91)
L2 CFCW 0.66 (0.86) –0.27 (0.42)
NAICS 22 (utilities) –3.88 (3.66) –1.32 (1.11)
NAICS 31–33 (manufacturing) –0.77 (0.82) –1.30 (0.86)
NAICS 42 (wholesale) –0.40 (0.54) –0.83 (0.57)
NAICS 48 (transportation) –2.04 (2.05) –1.71 (1.11)
NAICS 51 (information) –0.60 (0.71) –0.74 (0.65)
NAICS 52, 53 (finance) –1.05 (0.51) † –0.76 (0.50)
NAICS 54 (services) –0.50 (0.43) –1.20 (0.54) *
Industry labor intensity –2.47 (1.59) –2.32 (1.46)
Industry dynamism 394.59 (388.89) 394.13 (194.21) †
Industry growth –3.20 (46.83) –60.96 (35.11) †
CFE L2MC × Industry dynamism 10.22 (5.86) †
L2 CFE × Industry dynamism –95.58 (96.56)
CFE L2MC × Industry growth –0.77 (2.46)
L2 CFE × Industry growth 1.66 (11.40)
CFCW L2MC × Industry dynamism 21.12 (9.08) *
L2 CFCW × Industry dynamism –93.32 (47.56) †
CFCW L2MC × Industry growth 4.90 (2.47) *
L2 CFCW × Industry growth 15.16 (8.93) †
Akaike information criterion 404.27 396.71
χ2-distribution 18.27 (14) 25.83 (14) *
~R2 0.22 0.25
~ΔR2 0.06 0.09
Notes: β is the unstandardized coefficient. SE is the standard error. ~R2 or R_GLMM(m)2 represents the variance explained by fixed factors. ~ΔR2 for Models 2–5 are comparisons to the ~R2 for Model 1. †p < .10. *p < .05. **p < .01. ***p < .001.
WAY ET AL. 577
flexibility may be applicable across a larger set of firms now than
when the data were collected, we do not believe that this changes
the nature of our conclusions. This study's findings highlight the key
roles that firm flexibility business strategy and HPWS use can play in
engendering HR flexibility. While this study's objective industry dyna-
mism, growth, and labor intensity variables were not found to be sig-
nificantly associated with HR flexibility (results available upon
request from the first author), the requirements to comply with
employment and labor laws (cf. Frenkel & Lee, 2010) and industry
employment growth (see Footnote 10) may affect HR flexibility and
thus may be external precursors of overall firm HR flexibility. How-
ever, demonstrating causality requires longitudinal designs or the use
of precursor data obtained (e.g., at time 1) prior to when the HR flexi-
bility data are obtained (e.g., at time 2). Hence, we deem that a fruit-
ful and important avenue for future research would be to expand on
the current study's results and identify further internal (firm-level)
precursors as well as external (e.g., industry subsector-level) precur-
sors of overall firm HR flexibility using longitudinal and more
contemporary data.
This study's findings support the assertion that industry dyna-
mism is an external boundary condition of the HR flexibility—firm
financial performance relationship. However, firm size (cf. Chadwick
et al., 2013; van der Weerdt et al., 2006) and industry characteristics
such as complexity (cf. Lepak et al., 2003) and research and develop-
ment intensity (cf. Guthrie & Datta, 2008) may affect the utility of
HR flexibility. Thus, we encourage future studies to use longitudinal
and more contemporary data that are obtained from appropriate
independent sources to replicate and expand on this study's results
and identify internal and further external boundary conditions of the
HR flexibility—firm performance relationship as well as the circum-
stances in which HR flexibility and its five focal factors may be firm-
level assets or potentially liabilities.
Gerhart, Wright, and McMahan (2000) questioned the reliability
and validity of HRM research in which independent and dependent
variables were derived from subjective measures and a single respon-
dent/source. To circumvent this common source/common method
problem, the independent (HRFLX L2MC) and dependent variables
FIGURE 2A The RFHRP L2MC—Industry dynamism interaction
FIGURE 2B The RFE L2MC—Industry dynamism interaction
FIGURE 2C The CFCW L2MC—Industry dynamism interaction
FIGURE 3 The CFCW L2MC—Industry growth interaction
578 WAY ET AL.
(firm financial performance) used to test this study's cross-level mod-
eration hypotheses were derived from data obtained from two dis-
tinct and independent sources (see Footnote 3; Way et al., in
press).12 Accurate, objective firm-level financial data were not avail-
able in this current study.13 However, given that absolute scores on
firm financial performance criteria are affected by industry-related
factors and the number of different industry subsectors included in
this study's sample, directly comparing objective financial data could
be misleading (Kerr et al., 2007). Hence, subjective/quasi-perceptual
measures of firm financial performance are markedly appropriate
(useful) when firms from different industry subsectors are studied
simultaneously (Ketkar & Sett, 2009; Miles et al., 2000). In addition,
and consistent with prior studies (e.g., Ling et al., 2007), positive cor-
relations were found between this study's subjective four-item firm
financial performance dependent variable (Source 2) and objective
measures of firm performance (Source 3).14 Nevertheless, in light of
the challenges associated with the use of objective measures of firm
financial performance when studying firms from different industries
simultaneously, we encourage future studies to replicate this study's
results using longitudinal designs and both subjective and objective
measures of firm performance.
Finally, consistent with U.S. Census Bureau 2013 Statistics of
U.S. Businesses which show that in 2003,15 91.6% of U.S. firms with
100 or more employees employed fewer than 1,000 employees, 6.6%
employed between 1,000 and 4,999 employees, and 1.8% employed
5,000 or more employees, 90.6% of the 170 for-profit U.S. firms with
100 or more employees included in the current study's sample
employed fewer than 1,000 employees (Source 3; n = 154), 6.5%
employed between 1,000 and 4,999 employees (n = 11), and 2.9%
employed 5,000 or more employees (n = 5). As shown in Table 1, firm
size (log employees), which was assessed as the natural logarithm of
a firm's 2002 number of employees (Source 3; see Footnote 3), was
not significantly correlated with firm financial performance, HR flexi-
bility, or the five HR flexibility factors. However, given the small num-
ber of large firms included in this study's sample, we encourage
future studies to use samples that include a greater number of large
firms (firms with 1,000 or more employees) to replicate the current
study's results and to ensure that they are generalizable to large
firms.
5.2 | Conclusion
This study's results support the contention that HR flexibility can be
a firm-level asset in certain circumstances and a liability in others.
The current study affords a more detailed delineation of the roles
and relevance of HR flexibility and the circumstances in which HR
flexibility and its focal factors may augment (or diminish) firm finan-
cial performance. The findings underscore the need for a more con-
textualized, contingency approach to examining the HR flexibility–
firm performance relationship in future research. We hope that the
current study's findings promote future empirical studies that illumi-
nate the circumstances in which HR flexibility and its focal factors
can and should be used to enhance firm competitiveness and
performance.
ACKNOWLEDGMENTS
The authors thank Clint Chadwick, Song Chang, Charles H. Fay, Yap-
ing Gong, Mark A. Huselid, Douglas L. Kruse, Fred Oswald, and Jason
Shaw as well as Associate Editor Jake Messersmith and the journal's
two anonymous reviewers for their constructive feedback and sug-
gestions, which greatly improved the quality of this article.
NOTES
1 For example, Chadwick et al. (2013) found that (a) the high- investment HR system—small-firm labor productivity relationship was negative when industry dynamism was low while the relationship was not significant when industry dynamism was high and (b) the high- investment HR system—small-firm labor productivity relationship was negative when industry growth was low while the relationship was not significant when industry growth was high.
2 Auxiliary regression results (available upon request from the first author) indicate that (a) selectivity in staffing and comprehensive training were positively related to resource flexibility in HR practices and resource flexibility in employee competencies, (b) employee involvement in decision making was positively related to resource
flexibility in HR practices and resource and coordination flexibility in employee competencies, and (c) employee voice and proffering employment stability to employees were positively related to coordi- nation flexibility in HR practices.
3 Source 1 (firm senior HR managers), Source 2 (firm senior non-HR managers), and Source 3 (Dun & Bradstreet Million Dollar Database) data were obtained in 2003, and Source 4 (Bureau of Economic Anal- ysis, U.S. Department of Commerce [BEA]) data were collected for the appropriate five-year (1999–2003) and three-year (2001–2003) time periods, respectively. More specifically, Source 1 and Source 2 data were obtained via computer-assisted telephone interviews conducted by Customer Follow Up, Inc., during the fourth quarter of 2003. The firm-level annual sales, number of employees, age, and industry sector membership data procured from the Dun & Brad- street Million Dollar Database (Source 3) were for 2002. The objec- tive measures of industry dynamism and growth were generated from the industry subsector 1999–2003 (five-year) annual gross out- put data—and the industry labor intensity control variable was gener- ated from the industry subsector 2001–2003 (three-year) annual gross output and employee compensation data—obtained from the BEA (Source 4).
4 Amos 22 (IBM SPSS Amos 22, 2014) was used to conduct confir- matory factor analysis (CFA) and verify the factor structure of the Way et al. (2015) measure: The CFA of both the first-order and second-order HR flexibility measurement models demonstrated a good fit with the data (n = 170): χ2179 = 315.86, p < .001; standard- ized root mean square residual (SRMR) = 0.07, comparative fit index (CFI) = 0.91, incremental fit index (IFI) = 0.91 and χ2184 = 325.50, p < .001; SRMR = 0.07, CFI) = 0.91, IFI = 0.91, respectively.
5 We used “centered within context with reintroduction of the sub- tracted mean at level 2” analysis (Curran & Bauer, 2011; Kreft & de Leeuw, 1998, p. 110) to assess this study's cross-level moderation hypotheses (Hypotheses 3 and 4). Thus, we industry subsector group-centered the HR flexibility variable (HRFLX L2MC) to disaggre- gate the within-industry subsector group and between-industry sub- sector group variations. The industry subsector group means were reintroduced as a level 2 HR flexibility variable (L2 HRFLX) in order not to lose the between-industry subsector group variations in the hierarchical linear model (see Table 3, Model 2) used to assess this
study's cross-level moderation hypotheses (Curran & Bauer, 2011).
WAY ET AL. 579
The HRFLX L2MC—industry dynamism interaction, for example, assesses the true cross-level interaction between the within-group variance of HR flexibility and industry dynamism, by considering (assessing) it separately from the between-group variance of HR flexi- bility and industry dynamism interaction effect (Aguinis, Gottfred- son, & Culpepper, 2013).
6 The respondent (firm president, director, or general manager) was a knowledgeable informant regarding the actual performance targets set by her/his firm's top management team for her/his firm as well as the extent to which her/his firm's top management team was satis- fied that her/his firm was meeting its performance targets. Thus, respondents (Source 2) were knowledgeable informants regarding their respective firm's financial performance.
7 Variables were entered in two steps (Hofmann & Gavin, 1998): the level 1 control and the HRFLX L2MC variables were entered first (Table 3, Model 1), followed by (Table 3, Model 2) the level 2 HR flex- ibility (L2 HRFLX), control, dynamism, and growth variables as well as the four interaction terms (HRFLX L2MC × Industry dynamism; L2 HRFLX × Industry dynamism; HRFLX L2MC × Industry growth; L2 HRFLX × Industry growth).
8 An antagonistic, cross-level moderating effect ensues when higher levels of (increases in) the level 2 (e.g., industry subsector-level) mod- erator reverses the effect of the level 1 (e.g., firm-level) predictor on the level 1 outcome.
9 Consistent with prior HRM studies (e.g., Chadwick et al., 2013)
firm labor productivity was assessed as the natural logarithm of the ratio of the firm's 2002 annual sales to number of employees (Source 3; see Footnote 3).
10 Auxiliary HLM results (available upon request from the first author) indicate that industry employment growth (the average per- centage change in an industry subsector's annual number of full-time equivalent employees between 2000 and 2002) was negatively related to (a potential external precursor of ) overall firm HR flexibility.
11 In this study, industry dynamism was operationalized as the level of volatility in industry subsector gross output and industry growth was operationalized as the level of change (growth/reduction) in industry subsector gross output. Hence, this study's objective indus- try dynamism and growth measures do not reflect the specific nature of the change in output that may occur within an industry, for exam-
ple, the introduction (and recall) of a new smartphone.
12 Similarly, the firm flexibility business strategy use (Source 2) and firm size (Source 3) independent variables and the firm HR flexibility and firm flexibilities in HR practices (Source 1) dependent variables used to test Hypotheses 1 and 2 (precursors of firm HR flexibility and firm flexibilities in HR practices) were derived from data obtained from three distinct and independent sources (see Footnote 3).
13 Accurate, objective firm-level financial data were not available for most of this study's sampled firms because only 34 of the sampled firms were publicly traded and thus required by law to publish such data (Way et al., 2015).
14 Consistent with the findings of Dess and Robinson (1984) and Ling et al. (2007), positive correlations were found between the cur- rent study's subjective four-item firm financial performance depen- dent variable (Source 2) and 1) an objective measure of firm sales performance (r = .27, p < .001; n = 170); assessed as the natural log- arithm of the firm's 2002 annual sales (Source 3; see Footnote 3) and 2) an objective measure of firm labor productivity (r = .30, p < .001; n = 170); assessed as the natural logarithm of the ratio of the firm's 2002 annual sales to number of employees (Source 3). These results provide evidence of the convergent validity of this study's subjective four-item firm financial performance dependent variable (cf. Ling et al., 2007).
15 http://www.census.gov/epcd/susb/2003/us/US–.HTM
ORCID
Sean A. Way http://orcid.org/0000-0003-1944-5256
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AUTHOR'S BIOGRAPHIES
SEAN A. WAY is an associate professor of human resource man-
agement at Monash University. He received his PhD in Industrial
Relations and Human Resources from Rutgers University. His
current research focuses on strategic HRM topics such as the
effects of HRM systems, HR flexibility, and support and internal
marketing climates on the performance and effectiveness of
organizations and their employees. His research has been pub-
lished in Journal of Management, Human Resource Management,
Journal of Business Ethics, Personnel Psychology, International Jour-
nal of Hospitality Management, Human Resource Management
Review, Cornell Hospitality Quarterly, and Journal of Small Business
and Entrepreneurship.
PATRICK M. WRIGHT is Thomas C. Vandiver Bicentennial Chair
in the Darla Moore School of Business at the University of South
Carolina and the founder and director of the Center for Executive
Succession. His research focuses on strategic human resource
management topics such as how firms use people as a source of
competitive advantage, the changing nature of the chief HR
WAY ET AL. 581
officer role, and C-suite succession. His research has been pub-
lished in Journal of Management, Personnel Psychology, Human
Resource Management Journal, Academy of Management Journal,
Human Resource Management, International Journal of Human
Resource Management, and Journal of Applied Psychology.
J. BRUCE TRACEY is a professor of management in the School
of Hotel Administration at Cornell University. His research has
focused on a wide range of strategic and operational-level human
resource management topics, including the roles and relevance of
HR flexibility, impact of training initiatives on individual and firm
performance, employee turnover, employment law, and leader-
ship. His work has been published in outlets such as Journal of
Applied Psychology, Journal of Management, Personnel Psychology,
Organizational Research Methods, Cornell Hospitality Quarterly, and
University of Pennsylvania Journal of Labor and Employment Law.
JEREMY F. ISNARD is an independent researcher. He received
his MSc in management from the NEOMA Business School in
Reims and his MAS in Economic Crime Investigation from the
University of Applied Sciences of Neuchâtel. His primary research
interests focus on terrorism financing and fraud investigation.
How to cite this article: Way SA, Wright PM, Tracey JB,
Isnard JF. HR flexibility: Precursors and the contingent impact
on firm financial performance. Hum Resour Manage.
2018;57:567–582. https://doi.org/10.1002/hrm.21867
APPENDIX
Auxiliary OLS results for firm labor productivity
Level 1 variables
Low-dynamism industry subsectors
High-dynamism industry subsectors
β (SE) p β (SE) p
Constant 13.37 (1.04) *** 12.46 (0.73) ***
Unionization 0.13 (0.18) 0.48 (0.14) ***
Firm age 0.04 (0.09) –0.12 (0.05) *
Firm size (log employees)
–0.14 (0.10) –0.03 (0.08)
Firm capital intensity
0.22 (0.09) * 0.05 (0.09)
Firm flexibility business strategy use
–0.29 (0.14) * –0.17 (0.09) †
Firm HPWS use 0.28 (0.13) * 0.04 (0.09)
Firm HR flexibility –0.33 (0.16) * 0.00 (0.13)
F 3.19 ** 3.23 **
R2 0.23 0.23
Notes: β is the unstandardized coefficient. SE is the standard error. The subset of firms from low-dynamism industry subsectors is composed of 85 U.S. firms with 100 or more employees from the sample's low- dynamism industry subsectors, and the subset of firms from high- dynamism industry subsectors is composed of 85 U.S. firms with 100 or more employees from the sample's high-dynamism industry subsectors. †p < .10. *p < .05. **p < .01. ***p < .001.
582 WAY ET AL.
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- HR flexibility: Precursors and the contingent impact on firm financial performance
- 1 INTRODUCTION
- 2 LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK
- 2.1 HR flexibility
- 2.1.1 The five focal HR flexibility factors
- 2.2 Internal precursors of firm HR flexibility and flexibilities in HR practices
- 2.3 The contingent impact of HR flexibility on firm financial performance
- 2.3.1 Industry dynamism
- 2.3.2 Industry growth
- 3 METHOD
- 3.1 Sample and procedures
- 3.2 Level 1 variables
- 3.2.1 Firm HR flexibility
- 3.2.2 Firm (level 1) precursors and control variables
- 3.2.3 Firm financial performance
- 3.3 Level 2 variables
- 3.3.1 Industry dynamism
- 3.3.2 Industry growth
- 3.3.3 Industry control variables
- 4 RESULTS
- 4.1 Precursors of firm HR flexibility and flexibilities in HR practices
- 4.2 The cross-level moderating effects of industry dynamism and growth
- 4.3 Auxiliary OLS analyses
- 5 DISCUSSION
- 5.1 Limitations and avenues for future research
- 5.2 Conclusion
- REFERENCES
