Production Planning & Control

The Management of Operations

ISSN: 0953-7287 (Print) 1366-5871 (Online) Journal homepage: http://www.tandfonline.com/loi/tppc20

Modelling the impact of performance

management practices on firm performance:
interaction with human resource management
practices

Andrey Pavlov, Matteo Mura, Monica Franco-Santos & Mike Bourne

To cite this article: Andrey Pavlov, Matteo Mura, Monica Franco-Santos & Mike Bourne (2017)
Modelling the impact of performance management practices on firm performance: interaction
with human resource management practices, Production Planning & Control, 28:5, 431-443, DOI:
10.1080/09537287.2017.1302614

To link to this article: http://dx.doi.org/10.1080/09537287.2017.1302614

Published online: 23 Apr 2017.

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Download by: [Eastern Michigan University] Date: 24 April 2017, At: 05:42
Production Planning & Control, 2017
VOL. 28, NO. 5, 431–443
http://dx.doi.org/10.1080/09537287.2017.1302614

Modelling the impact of performance management practices on firm performance:

interaction with human resource management practices
Andrey Pavlova, Matteo Murab, Monica Franco-Santosa and Mike Bournea
a
School of Management, Cranfield University, Cranfield, UK; bDepartment of Management, University of Bologna, Bologna, Italy

ABSTRACT ARTICLE HISTORY

The paper draws on resource orchestration theory to investigate whether and how performance Received 11 August 2015
management (PM) practices interact with human resource management (HRM) practices in organisations Accepted 28 October 2016
as well as how this interaction affects firm performance. The proposed theoretical model was tested
KEYWORDS
through a survey of 192 UK firms using Partial Least Squares approach for structural equations modelling. Performance management;
The findings show that the effect of PM practices on firm performance is better explained when the performance measurement;
interaction between these practices and other organisational practices is considered. In particular, we human resource
examine the extent to which the interaction between PM practices and commitment-based HRM practices management; survey; partial
affects performance. We find that when HRM practices and PM practices are misaligned, their effect on least squares
performance can be negative. This is the first paper in the PM literature that establishes the relationship
between PM and HRM practices in organisations and demonstrates the effect of this relationship on firm
performance.

1. Introduction personnel decisions (Schuler, Fulkerson, and Dowling 1991). For

example, it is now common for firms to cascade down organisa-
This paper responds to an increasing number of calls in the
tional performance measures to the individual level and make
operations management (OM), and more specifically, perfor-
individual rewards and training needs, which are both considered
mance management (PM) literature for understanding the
to be HRM practices, contingent on the achievement of specific
role of human resource management (HRM) in the task of
performance results.
managing firm performance (e.g. Leffakis and Dwyer 2014)
To date the PM literature has been silent about how or why PM
and for incorporating it into the existing models of the effects
practices interact with HRM practices and whether these inter-
of PM systems on performance (e.g. Bourne et al. 2013; Busby
actions are likely to influence firm performance. Drawing on the
and Williamson 2000; De Toni and Tonchia 2001; Vagneur and
ideas of resource orchestration theory (Sirmon et al. 2007, 2011)
Peiperl 2000). Research in PM has seen a number of attempts
as well as previous HRM research suggesting that HRM practices
to observe (Askim 2004), conceptualise (Pavlov and Bourne
influence performance through their influence on the social struc-
2011) and quantify (Davis and Albright 2004) the effects of PM
ture of the firm (e.g. Chadwick et al. 2015; Collins and Smith 2006),
on performance. However, as the discussions in the field moved
we address this gap in our knowledge arguing that managers
from the design and evaluation of PM frameworks to the issues
combine these practices as they believe that together these prac-
of implementation, maintenance and collaborative use (Bourne
tices help them to better organise, synchronise and support – i.e.
2005; Folan and Browne 2005), it became apparent that the rela-
‘orchestrate’ – their human resources, improving the firm’s social
tionship between PM and firm performance was more complex
capital and overall performance.
than previously thought (Franco-Santos, Lucianetti, and Bourne
Heeding these considerations, we set the task of not sim-
2012).
ply testing the presence of the separate effects of PM and HRM
The effect of PM practices on firm performance is difficult
practices on firm performance, but also of conceptualising and
to untangle because PM practices do not operate in isolation
measuring their interaction testing its combined impact on firm
(De Toni and Tonchia 2001). PM practices interact with multiple
performance. In other words, we hypothesised that, although we
organisational practices. They share ‘inputs’ with other practices
would see independent effects of PM and HRM on firm perfor-
(e.g. goal selection, data collection) and produce ‘outputs’ for
mance, we would also be able to distinguish and assess the effect
other practices (e.g. performance information) (Kaydos 1999).
of their interaction. This in turn would allow us to show empirically
Specifically, the relationship between PM practices and HRM prac-
the importance of analysing PM effects in conjunction with HRM
tices is likely to be of high significance as outputs of PM practices
practices, as the literature above intimated.
are more often than not used by managers to influence and guide

CONTACT  Mike Bourne  m.bourne@cranfield.ac.uk

© 2017 Informa UK Limited, trading as Taylor & Francis Group
432   A. PAVLOV ET AL.

More formally, our research was guided by the following practices have been found to affect performance through their
research questions: effects on critical organisational areas such as team goal achieve-
ment (Scott and Tiessen 1999), project delivery (Davila 2000),
(a) Do PM practices and HRM practices have an effect on firm
customer satisfaction (Hyvonen 2007) and managerial results
performance in their own right?
(Hall 2008). PM practices have also been shown to contribute to
(b) Does the interaction of PM practices and HRM practices
performance by stimulating decision-making and continuous
have an effect on firm performance?
improvement (Nudurupati and Bititci 2005), generating strate-
Following a review of the relevant literature, these research gic alignment (Chenhall 2005) and organisational learning and
questions were translated into five hypotheses, which were innovation (Henri 2006). These firm capabilities are likely to trans-
subsequently tested by means of a mailed survey and analysed late into higher levels of company non-financial performance in
using a structural equation modelling approach. The results show terms of quality of products and services offered, delivery time,
that both PM practices and HRM practices influence firm perfor- flexibility in volume and product mix and customer satisfaction
mance, although several of these effects are indirect. More inter- (Chenhall 2005; de Leeuw and van den Berg 2011; Malina and
estingly, our results show that firm performance is affected by Selto 2001), which in turn are likely to lead to higher firm financial
the interaction between PM practices and two key HRM practices performance.
(recruitment and reward practices), thus demonstrating that PM The specific underlying mechanisms that explain why PM prac-
initiatives do indeed rely on the support of HRM practices for tices influence firm performance have not always been explicit
generating organisational performance. in the literature (Franco-Santos, Lucianetti, and Bourne 2012).
The rest of the paper is structured in the following way. The However, drawing on resource orchestration theory (Sirmon
next section develops and refines the argument into a set of for- et al. 2007, 2011), the recent work of Koufteros, Verghese, and
mal hypotheses. The subsequent section operationalises the key Lucianetti (2014) is starting to shed some light on this issue.
constructs and describes our approach to data collection and Koufteros, Verghese, and Lucianetti (2014) suggest that PM prac-
analysis. This is followed by the summary of our findings and a tices influence firm performance because they provide managers
detailed discussion of the effects we observed. The paper closes with the information they require to take action and organise
with a brief conclusion, revisiting our findings in light of the recent their work and the firm resources to meet strategic demands.
research in PM and outlining several avenues for further research. Managers at all levels must coordinate and organise themselves
and the strategic resources at their disposal to improve the com-
petitive advantage of their firm (Sirmon et al. 2011). PM practices
2.  Literature review and hypothesis development
facilitate the coordination of managerial action and help firms to
2.1.  PM practices and firm performance structure, bundle and leverage their resources, orienting employ-
ees towards achieving firms’ goals and objectives (Chadwick et al.
PM practices have been described as the formal practices ‘by
2015; Koufteros, Verghese, and Lucianetti 2014).
which the company manages its performance in line with its
We thus expect that PM practices will positively affect firm
corporate and functional strategies and objectives’ (Bititci,
performance. However, considering the evidence and theoretical
Carrie, and McDevitt 1997, 524). Such practices mainly include
underpinnings presented above, the direct effect may be more
the selection of strategic goals, the use of performance meas-
readily observable when firm performance is understood as
ures and targets, as well as the periodic review and evaluation
non-financial performance, which is more likely to be influenced
of measured performance (Bititci, Carrie, and McDevitt 1997;
by managers’ decisions and actions:
Buckingham and Coffman 1999; Ferreira and Otley 2009; Lebas
Hypothesis H1: PM practices positively affect firm non-financial
1995; Otley 1999). These practices are used for communicating
performance.
direction, providing feedback on current performance, influenc-
ing behaviour and stimulating improvement action (Bourne and
Bourne 2011). In this sense, PM practices closely parallel what
2.2.  Commitment-based HRM practices and firm
Bititci et al. (2011) call ‘managerial processes’. The latter, how-
performance
ever, are a broad and generic set, while PM practices have per-
formance improvement as their distinct aim. The role of HRM in generating firm performance is well-
The link between PM practices and firm performance has been recognised amongst firms in all sectors (Wright, Gardner, and
widely debated over the last decades (De Geuser, Mooraj, and Moynihan 2003). HRM practices have been classified into
Oyon 2009; Evans 2004; Hoque 2004; Hoque and James 2000; transaction-based and commitment-based practices (Arthur
van der Stede, Chow, and Lin 2006). Studies examining the direct 1992). Commitment-based HRM practices emphasise mutual
relationship between PM practices and firm financial performance employee–employer relationships focused on long-term
have produced inconclusive and sometimes contradictory results, exchange, while transaction-based HRM practices emphasise
with some work providing evidence of a positive effect of PM individual short-term exchange employee–employer
on financial performance (Davis and Albright 2004; DeBusk and relationships. Recent reviews of the field of HRM suggest that
Crabtree 2006; Ittner and Larcker 1998), and other suggesting the commitment-based practices are more likely to lead to higher
opposite (Braam and Nijssen 2004; Griffith and Neely 2009; Ittner, firm performance, as these practices create an environment
Larcker, and Randall 2003). conducive to higher productivity; in contrast, transactional-
Some scholars suggest that PM practices contribute to firm based practices are perceived to limit the potential of HRM as a
financial performance but they do so indirectly. For instance, PM driving force behind firm performance (Arthur 1992; Batt 2002;
 PRODUCTION PLANNING & CONTROL   433

Collins and Smith 2006; Huselid, Jackson, and Schuler 1997; that express employees’ views of how they interact with one
McClean and Collins 2011; Youndt et al. 1996). Based on the another while carrying out tasks for their firm’ (547). By focusing
work of Arthur (1992), Tsui et al. (1997), and Collins and Smith employees’ motivation on organisational rather than individual
(2006, 544) suggest that outcomes and by stimulating teamwork and long-term growth
the exact individual HRM practices that create a commitment-based rather than short-term performance, commitment-based HRM
environment differ across companies and studies, but they generally practices are likely to contribute to an organisational social cli-
include a combination of employee selection practices that focus mate that stimulates employees’ cooperation, trust and oppor-
on creating internal labor markets and assessing fit to the com-
tunity to share knowledge. These elements motivate individuals
pany rather than on specific job requirements; compensation prac-
tices that focus employee motivation on group and organizational to act in the best interest of the organisation, rather than in their
performance indicators; and training programs and performance own interest (e.g. Collins and Smith 2006; Rousseau 1995; Tsui
appraisals that emphasize long-term growth, team building, and the et al. 1995).
development of firm-specific knowledge. Organisational social climate is expected to be positively
Drawing on resource orchestration theory (Sirmon et al. 2007, related to the firm’s non-financial performance. This is supported
2011), HRM scholars argue that commitment-based HRM practices by a number of reasons (Collins and Smith 2006). Firstly, a cli-
become critical for firms ‘by increasing the knowledge, skills, and mate of cooperation limits competition between employees and
abilities of employees, by increasing their motivation, and by increases their predisposition to share critical information with
organising work to give employees the best opportunity to help one another. This, in turn, positively affects firm performance by
further the accomplishment of their firms’ goals’ (Chadwick et al. increasing the exchange of valuable and unique information.
2015, 362). In other words, commitment-based HRM practices help Similarly, a climate of commitment generates common under-
managers to orchestrate the firm’s human resources in a way that standing through which individuals with disparate experience,
builds the support for the firm’s competitive position (Chadwick knowledge and backgrounds can exchange and integrate new
et al. 2013, 2015). Commitment-based HRM practices thus favour ideas. Finally, opportunity to share knowledge is widely seen as
long-term investments in human resources, whose immediate an essential aspect for increasing interaction and information
payoff affects non-financial performance before it is reflected in exchange between individuals (Mayer, Davis, and Schoorman
financial performance indicators. Consequently, based on these 1995; Nahapiet and Ghoshal 1998). Knowledge sharing helps an
ideas, we expect to find that the use of commitment-based HRM organisation promote the exchange of valuable ideas between
practices will be related to firm performance, in particular, the core knowledge workers that, in turn, will lead to greater inno-
non-financial dimensions of performance. We thus propose the vation and firm growth.
following hypotheses: Summarising the above discussion, organisational social cli-
Hypothesis H2: Commitment-based HRM practices positively affect mate would lead to a greater quality of products and services, and
firm non-financial performance. to a greater satisfaction of customers, it would also increase the
Specifically: innovation capability of the organisation, and help the company
to attract and retain essential employees. As these aspects con-
Hypothesis H2a: Commitment-based recruitment practices positively
affect firm non-financial performance.
tribute to the firm’s non-financial performance, we expect that:
Hypothesis H3: The relationship between commitment-based HRM
Hypothesis H2b: Commitment-based reward practices positively practices and non-financial performance is mediated by a firm’s
affect firm non-financial performance. social climate for trust, cooperation and knowledge sharing.
Hypothesis H2c: Commitment-based training practices positively Hypothesis H3a: The relationship between commitment-based
affect firm non-financial performance. recruitment practices and non-financial performance is mediated by
Although commitment-based HRM practices have been a firm’s social climate for trust, cooperation and knowledge sharing.
argued to influence organisational performance, HRM research- Hypothesis H3b: The relationship between commitment-based
ers have pointed out that this relationship is underpinned by a reward practices and non-financial performance is mediated by a
number of intermediate mechanisms, whose nature and dynam- firm’s social climate for trust, cooperation and knowledge sharing.
ics are worth investigating in order to better understand how Hypothesis H3c: The relationship between commitment-based train-
HRM affects performance. In particular, researchers have argued ing practices and non-financial performance is mediated by a firm’s
that commitment-based HRM practices affect performance social climate for trust, cooperation and knowledge sharing.
through their influence on the organisation’s social climate for
trust, cooperation and opportunity to share knowledge (Bowen
2.3.  Interaction between PM practices and HRM practices
and Ostroff 2004; Collins and Clark 2003; Collins and Smith 2006).
In fact, Collins and Smith (2006) have shown that the only way The crucial importance of HRM for effective PM and manage-
commitment-based HRM practices affect performance is through ment control has been known for a very long time (Hofstede
the mediation effect of social climate, implying that it is critical 1978). Research suggests that people management may be
for this construct to be considered in research investigating the amongst the most significant factors contributing to the success
relationship between commitment-based HRM practices and of PM systems (Bourne et al. 2003; Kennerley and Neely 2003).
performance. The wider management literature has likewise highlighted that
Following Ashkanasy, Wilderom, and Peterson (2000), Smith, the type of HRM practices that organisations use are a key fac-
Collins, and Clark (2005), and Collins and Smith (2006) define tor for the success of operations management and vice versa
social climate as ‘the collective set of norms, values, and beliefs (Boudreau et al. 2003).
434   A. PAVLOV ET AL.

Resource orchestration theory (Sirmon et al. 2007, 2011) retain essential employees is positively related to its financial
requires coordinated management of resources towards compet- performance (e.g. Becker and Huselid 1992; Shaw, Gupta, and
itive advantage and therefore suggests that the interaction of PM Delery 2005). Secondly, the development of high quality products
and HRM practices would be critical for managers. This is because and services that meet the needs of customers has traditionally
the alignment of these practices enables the managers better been found a key driver of financial success (e.g. Lynn, Chang,
to synchronise their actions, thus improving the orchestration and Buzzell 1983; Zeithaml 2000). Thirdly, customer-related
of resources and enhancing their firms’ organisational capabili- performance has also been suggested as a leading contributor
ties and performance (Chadwick et al. 2015; Koufteros, Verghese, to financial returns. Specifically, customer satisfaction and loyalty
and Lucianetti 2014). PM practices provide managers with the have been found to lead to market share growth, which is in turn
information (e.g. goals, performance measurement results) to related to firm financial performance (Choi and Eboch 1998; Ou
make ‘critical adjustments to their resources and mobilise req- et al. 2010; Yeung 2008). Hence, we expect that firm non-financial
uisite resources as conditions change’ (Koufteros, Verghese, and performance assessed in terms of its ability to attract and retain
Lucianetti 2014, 314). People are arguably firms’ most important essential employees, develop high quality products and services,
resource (Amabile and Kramer 2011) so the ability of managers to and satisfy its customers would be positively related to firms’
use the information extracted from their PM practices to structure, financial success. This relationship is simplified and formally
bundle and leverage people’s skills, knowledge and motivation stated here as:
through the firms HRM practices becomes crucial for firm success. H5: Firm non-financial performance positively affects firm financial
Based on these arguments, we propose that: performance
Hypothesis H4: The interaction between PM practices and commit- The theoretical framework of our research is summarised in
ment-based HRM practices positively affects firm non-financial Figure 1.
performance.
In particular:
3.  Data and method
Hypothesis H4a: The interaction between PM practices and
commitment-based recruitment practices positively affects firm non- 3.1.  Sample and data gathering process
financial performance.
To test the proposed hypotheses, data were collected through
Hypothesis H4b: The interaction between PM practices and
a structured questionnaire. The target population consisted of
commitment-based reward practices positively affects firm non-
financial performance. 1200 UK-based firms in the range of 13 different primary UK
SIC codes. The questionnaire was sent to the members of the
Hypothesis H4c: The interaction between PM practices and top management team. Before starting our survey, we piloted
commitment-based training practices positively affects firm non-
financial performance.
our questionnaire with a reduced set of respondents, including
scholars and HR managers attending our university executive
education courses. We mailed our questionnaire to the pilot par-
2.4.  Firm non-financial and firm financial performance ticipants and asked them to complete it and review its content
Non-financial performance has been shown to be a leading for clarity and understandability purposes. In total 25 respond-
indicator of financial performance (Bititci, Firat, and Garengo ents provided feedback for the questionnaire improvement.
2013; Ittner and Larcker 1998). This link between non-financial Based on our pilot, some redundant or ambiguous items were
and financial performance is central to all contemporary per- eliminated or modified. No new items were added.
formance measurement and management frameworks, from The survey implementation followed four steps: pre-
Balanced Scorecards (Kaplan and Norton 1992) to Strategy Maps notification, initial mailing, first follow-up and second follow-up.
(Kaplan and Norton 2000), to performance drivers (Olve, Roy, To generate early interest, the first step was to invite respondents
and Wetter 1999) and the Performance Prism (Neely et al. 2002). through an introductory letter, phone call or e-mail. Then the
When discussing the link between non-financial and financial questionnaire was sent via e-mail or post. The first follow-up
performance, both practitioners (e.g. Lingle and Schiemann 1996; consisted of a postcard or an e-mail reminder sent to every
Rucci, Kirn, and Quinn 1998) and scholars (e.g. Johnston and respondent, while the second follow-up was a phone call and a
Michel 2008) emphasise three key non-financial performance replacement questionnaire sent to those who had not answered.
dimensions, which are likely to influence the financial A total of 192 firms participated in the survey, giving a response
performance of a firm. These are: the attraction and retention rate of 16%, which is similar to the 15–25% range reported in
of people with the attitudes, capabilities and behaviour that recent studies (Baines and Langfield-Smith 2003; Lee, Lee, and
suit the mission and goals of the firm; the development of good Pennings 2001). Most of the companies were medium-sized (45%),
quality products or services; and the satisfaction of customer 23% were large companies, while 28% were small organisations.
expectations. Firstly, being able to hire from the best pool of Most respondents were from the HR function (67%), followed
potential employees is critical for an organisation as this helps by operations (10%) and strategy (5%) departments. The sample
to improve the quality of its workforce and facilitates the delivery statistics are listed in Table 1.
of its mission and the achievement of its goals. In addition, having
attracted and selected talented people, it is important to be able 3.2. Measures
to retain those that are of greater value to the organisation. HRM
research has provided substantial evidence in support of this As detailed below, measures were drawn from existing instru-
argument showing that an organisation’s ability to attract and ments derived from a literature review in the areas of interest
 PRODUCTION PLANNING & CONTROL   435

Figure 1. Theoretical model and hypotheses.

Table 1. Sample characteristics.

Company size (FTE) N (%) Industry (UK SIC) N (%) Respondent functional area N (%)
Small (1–49) 54 (28) Real estate, renting and business administration 54 (28) HR 119 (67)
Medium (50–249) 86 (45) Manufacturing 52 (27) Operations 17 (10)
Large (250–4999) 44 (23) Wholesale retail trade, repair of motor vehicles, motorcycle 26 (14) Other 15 (8)
Very large (>5000) 7 (4) Construction 18 (9) Strategy 9 (5)
Other community, social and personal service 13 (7) Finance/accounting 7 (4)
Financial intermediation 8 (4) Marketing and sales 6 (3)
Education 6 (3) Admin 5 (3)
Transport, storage and communication 4 (2)
Health and social work 4 (2)
Hotels and restaurants 3 (2)
Agriculture, hunting and forestry 1 (1)
Electricity, gas and water supply 1 (1)
Total 191 (100) Total 190 (100) Total 178 (100)

across PM and HRM. All measures used a seven-point Likert scale Table 2. Descriptive statistics and reliability coefficients.
(1 = ‘strongly disagree’ and 7 = ‘strongly agree’). The question- No. of Cronbach Composite
naire was divided into sections examining PM practices, HRM Construct items Mean SD alpha reliability AVE
practices, organisational social climate and firm performance. Financial perf. 3 5.121 1.283 0.810 0.887 0.724
Non-financial 4 5.569 0.864 0.808 0.874 0.634
perf.
3.2.1.  Performance management practices PM practices 5 5.659 1.043 0.889 0.919 0.693
As noted earlier, the literature generally includes as key PM Recruitment 3 5.862 1.053 0.609 0.787 0.556
practices the selection of goals, the use of performance meas- Rewards 2 4.325 1.500 0.567 0.822 0.697
Training 3 4.431 1.391 0.664 0.810 0.591
ures and targets, as well as the periodic review and evaluation Social climate 5 5.169 1.234 0.903 0.939 0.837
of measured performance (Bititci, Carrie, and McDevitt 1997;
Buckingham and Coffman 1999; Ferreira and Otley 2009; Lebas
1995; Otley 1999). Therefore, based on this literature we devel-
oped a five-item scale given in the Appendix 1. The measure- considered commitment-based varied across studies. However,
ment model resulted in one factor with high reliability as shown as suggested by Collins and Smith (2006) most researchers now
in Tables 2 and 3. agree that there are three key sets of practices associated with
how organisations ‘recruit and select’, ‘reward’ and ‘train and
3.2.2.  Commitment-based HRM practices develop’ their people that create a high commitment-based
These practices were conceptually introduced by Arthur’s (1992) environment. Thus, we measure commitment-based HRM
research and for a number of years the particular practices practices using Collins and Smith’s (2006) scales given in the
436   A. PAVLOV ET AL.

Table 3. Factor loadings. and r = .24, p < .05, respectively), lending strong external validity
Non-­ to the subjective financial performance measure.
Financial financial PM Recruit- Social
perf. perf. practices ment Rewards Training climate 3.2.6.  Control variables
FP1 0.826 0.379 0.265 0.118 0.063 0.071 0.225 Previous studies suggested that specific contingencies such
FP2 0.896 0.434 0.207 0.067 0.151 0.021 0.132
FP3 0.829 0.339 0.163 −0.001 0.033 0.005 0.084 as organisational size and industry type may have an effect on
NFP1 0.420 0.752 0.377 0.257 0.240 0.243 0.292 our endogenous variable (e.g. Huselid, Jackson, and Schuler
NFP2 0.401 0.814 0.484 0.272 0.356 0.355 0.503 1997; Malina and Selto 2001). Therefore, the following control
NFP3 0.317 0.818 0.591 0.363 0.387 0.356 0.478
NFP4 0.315 0.799 0.460 0.278 0.263 0.363 0.357 variables were included in our model: organisational size (nat-
PM1 0.217 0.519 0.862 0.451 0.403 0.510 0.575 ural logarithm of number of employees), industry sector (UK
PM2 0.241 0.510 0.776 0.454 0.286 0.321 0.438 SIC code), manufacturing vs. service (a dummy variable coded
PM3 0.192 0.459 0.839 0.467 0.250 0.429 0.424
PM4 0.201 0.539 0.845 0.390 0.334 0.563 0.660 1 for manufacturing and 0 for service). Additionally, since most
PM5 0.186 0.491 0.839 0.319 0.281 0.517 0.635 of the respondents were from the HR function, we included
RC1 0.151 0.214 0.350 0.730 0.118 0.097 0.182 the function of the respondent as a control variable (HR, oper-
RC2 0.048 0.392 0.387 0.870 0.244 0.283 0.317
RC3 −0.018 0.161 0.405 0.615 0.238 0.268 0.251 ations, strategy, finance, marketing, sales, ICT – all measured as
RW1 0.127 0.356 0.314 0.158 0.848 0.338 0.283 dummies).
RW2 0.041 0.307 0.314 0.304 0.822 0.309 0.290
TR1 0.050 0.407 0.527 0.184 0.290 0.868 0.515
TR2 −0.018 0.175 0.275 0.297 0.222 0.658 0.276 3.3.  Analytical procedure
TR3 0.036 0.321 0.447 0.269 0.381 0.765 0.358
SC1 0.239 0.564 0.630 0.321 0.345 0.471 0.934 Due to the fact that data were collected from individual
SC2 0.144 0.469 0.608 0.304 0.305 0.492 0.927
SC3 0.080 0.381 0.572 0.325 0.287 0.468 0.884 respondents in a cross-sectional study, the potential for com-
mon method variance (CMV) was a concern (Spector 2006).
Consequently, the external validity of the financial performance
Appendix 1. The measurement model resulted in three factors measure was checked, as highlighted in the previous section.
with high reliability as presented in Tables 2 and 3. CMV was assessed after the data were collected using Harman’s
one-factor test (Podsakoff and Organ 1986). In this test, all the
3.2.3.  Social climate principal constructs are entered into a principal components
We measure social climate for trust, cooperation and knowledge factor analysis. Evidence of common method bias exists when
sharing using four items extracted from the work of Collins and a single factor emerges from the analysis or when one general
Smith (2006), Mayer, Davis, and Schoorman (1995), and Tsui et al. factor accounts for the majority of the covariance in the inter-
(1997). These items are presented in the Appendix 1. Our social dependent and dependent variables (Pavlou and Gefen 2005,
climate scale demonstrated high reliability and all its items p. 388). The fact that each of the principal constructs explained
loaded up into one single factor as shown in Tables 2 and 3. roughly equal variance suggests the data did not indicate sub-
stantial common method bias.
3.2.4.  Firm non-financial performance The proposed model was tested using the Partial Least
The measure for firm non-financial performance was assessed Squares approach for structural equations modelling (PLS-
by adapting a seven-point Likert scale developed by Delaney SEM) (Wold 1985). PLS-SEM has been employed in numerous
and Huselid (1996) and adopted in previous PM and OM studies OM studies (i.e. Braunscheidel and Suresh 2009; Jeffers 2009;
(Chenhall 2005; Vereecke and Muylle 2006; Villena, Revilla, and Rosenzweig 2009), as well as in the marketing field (i.e. Anderson
Choi 2011). The five-item scale asked respondents to compare and Swaminathan 2011; Cotte and Wood 2004) and in informa-
the performance of their company to that of their competitors tion systems research (Liang et al. 2007; Pavlou and Gefen 2005).
over the last three years in terms of quality of products and ser- PLS-SEM is more appropriate than LISREL when models are com-
vices, development of new products or services, attraction and plex and there is low theoretical information on the relationships
retention of essential employees and customer satisfaction. amongst the constructs (Fornell and Bookstein 1982). PLS-SEM
has also been shown to provide higher statistical power than
3.2.5.  Firm financial performance covariance-based SEM (e.g. LISREL) when dealing with samples
A perceptual measure adapted from Delaney and Huselid (1996) of small or moderate size (Reinartz, Haenlein, and Henseler
was used to assess firm financial performance. This four-item 2009). Moreover, PLS-SEM does not make assumptions about
measure asked respondents to compare, on a seven-point Likert data distributions to estimate model parameters, observation
scale, the performance of their company to that of competi- independence or variable metrics (Bass et al. 2003). PLS-SEM
tors over the last three years in terms of turnover, profitability, generates estimates of standardised regression coefficients (beta
growth in sales and market share. Additionally, an external valid- values) for the model’s paths, which are then used to measure
ity check was conducted for the financial performance measure. relationships amongst latent variables. PLS-SEM also generates
For each company, data on Return on Assets, Turnover and Profit factor loadings for measurement items, which are interpretable
Margin were extracted from the FAME database1 for the financial similarly to loadings generated by principal component factor
year that preceded the administration of the survey. Two of these analysis. Therefore, PLS-SEM was used in order to effectively
relative measures of financial performance (ROA and Turnover) manage the high number of variables in the model in relation
were significantly correlated with aggregated measures of sub- to the moderate sample size, and the low theoretical support
jective performance as rated by senior managers (r = .21, p < .05 in the identification of the causal relations amongst the model
 PRODUCTION PLANNING & CONTROL   437

constructs. This study employed the SmartPLS software version shows that indicators loaded much higher on their hypothe-
2.0 (Ringle, Wende, and Will 2005). To assess the statistical sig- sised factor than on other factors (own loadings are higher than
nificance of the path coefficients, which are standardised betas, cross-loadings).
a bootstrap re-sampling procedure (500 sub-samples were ran- In order to assess discriminant validity of the constructs using
domly generated) was performed (Chin 1998). Following Hulland the PLS-SEM approach, it is necessary that the square root of each
(1999) the model was analysed in two steps: first, the measure- factor’s AVE is larger than its correlations with other factors (Chin
ment model was assessed and the reliability, convergent and 1998; Gefen and Straub 2005; Straub, Boudreau, and Gefen 2004).
discriminant validity of the model constructs were evaluated; As evident from Table 4, the square root of all the AVEs was larger
secondly, the structural model was evaluated by examining the than all other cross-correlations; therefore, discriminant validity
size and significance of the path coefficients and the R2 values was confirmed.
of the dependent variables.
4.2.  Structural model
4. Results
Figure 2 and Table 5 contain the standardised PLS-SEM path
4.1.  Measurement model coefficients and t-values. The control variables used in this study
do not show significant relations and are therefore not reported.
The Cronbach’s alpha, the composite reliability and the average
The model explains roughly 21% of the variance in firm finan-
variance extracted (AVE) were calculated to assess the reliabil-
cial performance, 47% of the variance in firm non-financial per-
ity of the constructs. All these coefficients were higher than the
formance and 33% of the variance in social climate.
recommended thresholds (i.e. Cronbach α  >  0.60, composite
reliability > 0.70, and AVE > 0.50) thus supporting the reliability
4.2.1.  Main effects
of all the measures adopted (Hair et al. 2009, 2014; Nunally and
Hypotheses 1 and 2 expect PM practices and commitment-
Bernstein 1994). The descriptive statistics and reliability coeffi-
based HRM practices to affect firm non-financial performance
cients are provided in Table 2.
directly. The results show that PM practices positively and
The convergent validity of the scales was supported, with
significantly affect firm non-financial performance (β  =  0.374,
loadings higher than 0.60 and highly statistically significant
p < 0.001), thus providing support for Hypothesis 1. Hypothesis 2,
(p < 0.001). Moreover, an analysis of factor loadings (see Table 3)
however, received only partial support. The three commitment-
based HRM practices studied were positively related to firm
Table 4. Inter-construct correlation. non-financial performance; however, only commitment-based
1 2 3 4 5 6 7 reward practices positively and significantly affect firm non-
1. Financial 0.851 financial performance (β  =  0.142, p  <  0.05), thus supporting
perf. Hypothesis 2b. Commitment-based recruitment and training
2. Non-financial 0.455 0.796 practices do not significantly affect firm financial performance
perf.
3. PM practices 0.250 0.607 0.833 (β  =  0.086, p  >  0.05 and β  =  0.031, p  >  0.05, respectively);
4. Recruitment 0.075 0.370 0.500 0.746 therefore, Hypotheses 2a and 2c are not supported.
5. Rewards 0.103 0.398 0.376 0.274 0.835
6. Training 0.038 0.416 0.565 0.301 0.387 0.769
7. Social climate 0.174 0.521 0.660 0.345 0.343 0.521 0.915 4.2.2.  Mediation effects
Considering Hypothesis 3, the results of the PLS-SEM analysis
Note: N = 192. On the diagonal the square root of the AVE.
show that all three HRM practices positively and significantly

Figure 2. Results of the structural model.

438   A. PAVLOV ET AL.

Table 5. Path coefficients and t-values.

Hypothesis Description of path Path coefficient t-Value

H1 PM practices → Non-financial performance 0.374 4.457***
H2a Recruitment → Non-financial performance 0.086 1.524
H3a Recruitment → Social climate 0.185 2.518*
H2b Rewards → Non-financial performance 0.142 2.231*
H3b Rewards → Social climate 0.132 2.189*
H2c Training → Non-financial performance 0.031 0.686
H3c Training → Social climate 0.414 6.281***
H3a,b,c Social climate → Non-financial performance 0.181 2.269*
H5 Non-financial performance → Financial performance 0.454 7.228***
H4a PM × Recruitment → Non-financial performance 0.184 2.313*
H4b PM × Rewards → Non-financial performance −0.170 2.656**
H4c PM × Training → Non-financial performance −0.107 1.448
*p < 0.05; **p < 0.01; ***p < 0.001.

Figure 3. Interaction between PM practices and commitment-based HRM practices.

affect social climate (recruitment: β = 0.185, p < 0.05; rewards: We find that the interaction of PM practices with commitment-
β = 0.132, p < 0.05; training: β = 0.414, p < 0.001) and social cli- based recruitment practices has a positive and significant effect
mate positively and significantly affects firm non-financial per- on firm non-financial performance (β  =  0.184, p  <  0.05). Thus,
formance (β = 0.181, p < 0.05). These results provide support for Hypothesis 4a is supported. Figure 3(a) contains the plot of the
Hypotheses 3a, b and c. interaction effect. The impact of PM practices on performance
In order to test whether organisational social climate mediates is stronger when the level of commitment-based recruitment
the relationship between HRM practices and firm non-financial practices is also high. Moreover, the slope of the high recruitment
performance, we also followed the procedure suggested by regression line is steeper than the slope of the low recruitment
Preacher and Hayes (2004, 2008). The bootstrap analysis suggested line. This suggests that in the environment characterised by
the mean indirect effects of the three HRM practices on non- high level of PM practices, the interaction effect is even more
financial performance to be positive and significant (a × b = 0.109 pronounced, and the strength of commitment-based recruitment
for recruitment; a × b = 0.108 for rewards; a × b = 0.258 for training), practices becomes even more important, as strong recruitment
with a 95% confidence interval excluding zero (0.022–0.213 for practices have an even greater impact on the relationship
recruitment; 0.031–0.199 for rewards; 0.171–0.353 for training). between PM practices and non-financial performance than weak
Therefore, these results provide support for the mediating effect recruitment practices do.
of social climate between HRM practices and firm non-financial The interaction between PM practices and commitment-based
performance. reward practices influences non-financial performance but not
in the way we predicted, as this relationship is negative and
4.2.3.  Interaction effects significant (β = −0.170, p < 0.05). Therefore, Hypothesis 4b is not
Hypothesis 4 posits that the interaction between PM and HRM supported. Figure 3(b) contains the plot of the interaction effect.
practices will be positively related to firm non-financial perfor- While the effect of high commitment-based reward practices on
mance. Our data show that the results of this interaction are the relationship between PM and non-financial performance
more complex than expected. remains essentially unchanged regardless of how strong PM
 PRODUCTION PLANNING & CONTROL   439

Table 6. Summary of the results. HRM practices on firm performance. Our findings show that PM
Relationships discovered practices indeed directly affect firm non-financial performance,
1. PM practices positively affect firm non-financial performance thus confirming previous studies in this area (Ittner, Larcker, and
2. Commitment-based reward practices positively affect firm non-financial
performance
Randall 2003; Malina and Selto 2001). The results also confirm
3. Commitment-based recruitment practices positively affect organisational previous research documenting the independent effect of HRM
social climate practices on firm performance. However, our study shows that
4. Commitment-based reward practices positively affect organisational social
climate
the effect of these practices is partially mediated by the organ-
5. Commitment-based training practices positively affect organisational social isational social climate. Specifically, our results show that social
climate climate fully mediates the relationship between recruitment
6. Organisational social climate positively affects firm non-financial perfor-
mance
and training practices and firm non-financial performance and
7. Organisational social climate mediates the effect of all three commit- partially mediates the effect of rewards on performance. Thus,
ment-based HRM practices on firm non-financial performance our findings both support and extend previous studies in this
8. The interaction between PM practices and commitment-based recruitment
practices positively affects firm non-financial performance
area (Batt 2002; Bowen and Ostroff 2004; Collins and Clarke 2003;
9. The interaction between PM practices and commitment-based reward prac- Collins and Smith 2006; Huselid, Jackson, and Schuler 1997) and
tices negatively affects firm non-financial performance suggest that commitment-based HRM practices on their own
do not directly contribute to generate firm performance; rather,
they foster the development of an organisational social climate
practices are, the effect of low commitment-based rewards based on employees’ trust, cooperation and opportunity to share
changes significantly as the strength of PM practices increases. knowledge that in turn positively influence firm non-financial
Finally, the interaction between PM practices and commitment- performance.
based training practices seems to influence negatively firm non- As far as the central hypothesis of the study is concerned, our
financial performance, but this relationship is not statistically results show that the interaction of PM practices and HRM prac-
significant (β = −0.107, p > 0.05). Therefore, Hypothesis 4c is not tices produces mixed effects on firm non-financial performance.
supported. Specifically, the interaction between PM practices and commit-
Overall, these findings suggest that the interaction of PM ment-based recruitment practices does have a positive effect on
practices and two of the three commitment-based HRM practices firm non-financial performance, while the interactions of PM prac-
affects firm non-financial performance; however, these effects tices with the other commitment-based HRM practices studied,
are mixed. While high level of commitment-based recruitment for example commitment-based reward practices, may actually
practices contributes to strengthening the positive relationship have a negative effect. The observed positive effect is consistent
between PM practices and firm non-financial performance, high with Collins and Smith (2006), who found that employee recruit-
level of commitment-based reward practices seems to do the ment and selection practices needed to be focused on the internal
opposite in our data-set. Commitment-based training practices fit of the employee to the company to engender high commit-
display no significant interaction effects. ment. The negative effects, however, need further explanation.
The negative effect may be observed where the HRM prac-
4.2.4.  Firm non-financial and financial performance tices have been designed on a premise that is different from
Finally, Hypothesis H5 posits that firm non-financial performance that of the PM practices. For example, while PM practices are
positively affects firm financial performance. The results of this usually aimed at aligning processes across levels and functions
study provide support for this hypothesis as firm non-financial and are therefore cross-functional, reward practices may focus
performance positively and significantly influences firm financial on incentivising individual behaviours and hence may be only
performance (β = 0.454, p < 0.001). A summary of the findings is effective at the functional or departmental level. This discrepancy
presented in Table 6. may contribute to explaining the negative effect of the PM–HRM
interaction on non-financial performance. The reward practices
5. Discussion and the PM system would then negatively affect one another, as
they are not built to be mutually supportive in delivering firm
As previous research suggests, PM and HRM practices may non-financial performance; rather, they might be based on dif-
affect organisational performance on their own. However, build- ferent aims. Theoretically, this explanation is consistent with the
ing on the ideas of resource orchestration theory (Sirmon et al. logic of resource orchestration theory, which posits that a positive
2007, 2011) and the work of Koufteros, Verghese, and Lucianetti effect of managerial actions on performance requires not only
(2014), we argue that their interaction provides a more complete that such actions be taken, but that they should be synchronised
picture of how resources (in particular, human resources) are (Koufteros, Verghese, and Lucianetti 2014). Empirically, this is very
orchestrated within organisations and suggest a better explana- similar to the results obtained by Leffakis and Dwyer (2014), who
tion of how firm performance is generated. Generally, the results studied the joint effect of HRM systems and mass customisation
of this study support this argument, as two of the three HRM practices on performance and found that this effect was negative
practices we considered were found to affect the relationship when the logic of the HRM systems contradicted that of the mass
between PM and firm non-financial performance. However, the customisation approach.
observed effects are quite complex and would therefore benefit In our study, commitment-based reward practices were pre-
from further discussion. dominantly measured in terms of the level of employees’ salary
Firstly, the results of this study answer our first research ques- and in terms of perceived equity. It is often the case that manag-
tion regarding the independent effects of PM practices and ers prone to using common PM practices (e.g. high reliance on
440   A. PAVLOV ET AL.

performance measures and targets) are also inclined to use incen- PM does indeed need HRM in order to generate organisational
tive pay practices, which are considered to be transaction-based performance. Secondly, the study is based on a robust model,
HRM practices rather than commitment-based practices (Ittner, which made it possible to observe the effect of PM and HRM prac-
Larcker, and Meyer 2003). Transaction-based reward practices tices and their interaction on both non-financial and financial
emphasise individual short-term exchange relationships, neg- performance. Finally, the model developed in this study made it
atively affecting mutual trust, collaboration and knowledge possible to surface many nuances in the examined relationships,
sharing, which in turn may negatively affect performance (Tsui such as the mediation effect of social climate and the complex
et al. 1995). Therefore, it could happen that a high reliance on PM nature of the effect of PM-HRM interaction on firm performance.
practices may be more likely to interact with (or indeed lead to) There are, however, some limitations. Firstly, although our sam-
the use of transactional-based reward practices that are in conflict ple was quite large, we had only one respondent per organisation.
with HRM practices that aim to generate a high commitment envi- Consequently, we could not triangulate our data by means of
ronment. The co-existence of conflicting HRM practices within intraclass correlation or other measures (e.g. Rwg) that would allow
organisations may, in turn, have a negative effect on performance, us to assess the inter-rater agreement from multiple respond-
which could explain our results. Unsurprisingly, having conflicting ents and increase the validity of our findings. Secondly, the fact
practices within an organisation appears to be detrimental for that all firms in the sample were based in the U.K. may limit the
performance. Therefore, aligning the PM system and the HRM opportunity to extend our findings to other geographical areas,
system seems to be essential if the most is to be made from the especially to those that operate in a significantly different busi-
investment in these two sets of practices. ness environment (e.g. China or Far East). Finally, the cross-sec-
In general, viewing the results of this study through the lens tional nature of the study assumes that the PM and HRM practices
of resource orchestration theory highlighted the importance of studied are stable across firms, industries, and over time (Bowen
focusing not only on individual practices but also on their inter- and Wiersema 1999). Yet, these practices may have clear firm-,
action. In doing this, it made a step towards explaining the joint industry- and time-specific components which may be influenc-
effect of HRM and PM practices on firm performance. ing our results, which means that any claims about the causality of
Finally, this study was also able to demonstrate a strong and the found relationships must remain tentative – a common issue
highly significant relationship between the non-financial and the with studies of this kind. Future studies could employ longitudinal
financial measures of organisational performance – something datasets, where independent and dependent variables are col-
that often eluded researchers in the past (e.g. Ittner, Larcker, and lected using time lags that can better control for causality in the
Randall 2003). The strength of this relationship made it possible hypothesised relationship amongst model constructs.
for this study to trace with confidence the effects of organisational The relationships explored in this study suggest several ave-
practices and their interaction to firm financial performance. nues for future research. Firstly, it remains to be seen whether
commitment orientation in HRM practices is indeed the factor
that determines the sign of their interaction with PM practices.
6. Conclusions
In other words, can HRM practices that are not in Collins and
The aim of this study was to determine if PM practices and Smith’s (2006) definition strictly commitment-based also support
commitment-based HRM practices have an effect on firm per- PM initiatives in producing an effect on performance? Secondly,
formance in their own right, and if the interaction of those two the alignment between PM and HRM initiatives as a determi-
sets of practices have an effect on firm performance. Overall, nant of their joint effect on organisational performance needs
the results show that PM practices and HRM practices have to be investigated further – for instance, which dimensions of
independent effects on firm non-financial performance, with these practices overlap and what is the nature of the alignment
the effect of HRM practices being mediated by the an organi- that generates their joint effect on performance? Finally, a firm’s
sational social climate for trust, collaboration and knowledge experience with PM and HRM practices may also play a role in
sharing. The findings show that the interaction between recruit- the relationship we observed. Therefore, future studies may
ment practices and PM practices is positively related to firm examine the moderating role of experience with PM and HRM
non-financial and ultimately financial performance. However, practices in the relationship between PM and firm performance
the interaction of PM with other commitment-based HRM prac- or perhaps assess a three-way interaction effect of PM practices,
tices has a negative effect on performance, possibly due to the HRM practices and the experience with using them on firm
misalignment between PM and HRM. performance.
This study is important for several reasons. Firstly, it responded
to multiple calls in prior research for investigating the role of HRM
Note
in supporting PM efforts. While earlier work noted the seeming
importance of HRM for the success of PM initiatives, these obser- 1. 
FAME is a database that contains published financial information on
vations remained largely speculative. This study, however, built companies in the U.K. and Ireland taken from statutory returns. It
has information on 3.4 million companies, 2.6 million of which are
on the resource orchestration theory to suggest that managers in a detailed format, so it includes not only information on publicly
might indeed use both sets of practices in managing the firm’s quoted companies but also on private companies as well.
performance and was subsequently able to demonstrate empiri-
cally the presence of the interaction effect between PM practices
and two of the three commitment-based HRM practices. In so Disclosure statement
doing, this study provided some support to the argument that No potential conflict of interest was reported by the authors.
 PRODUCTION PLANNING & CONTROL   441

Notes on contributors Batt, R. 2002. “Managing Customer Services: Human Resource Practices, Quit
Rates, and Sales Growth.” Academy of Management Journal 45: 587–597.
Andrey Pavlov, PhD, is a senior lecturer in Business Becker, B. E., and M. A. Huselid. 1992. “Direct Estimates of SD and the
Performance Management at Cranfield School of Implications for Utility Analysis.” Journal of Applied Psychology 77: 227–
Management. His main interests lie in the areas of organ- 233.
isational performance and strategic change. His research Bititci, U., F. Ackermann, A. Ates, J. D. Davies, S. Gibb, J. MacBryde,
examines the impact of performance measurement on D. Mackay, et al. 2011. “Managerial Processes: An Operations Management
people’s behaviour and explores alternatives to top- Perspective towards Dynamic Capabilities.” Production Planning & Control
down hierarchical control in organisations. At Cranfield, 22 (2): 157–173.
Andrey teaches across the entire range of graduate and Bititci, U. F., A. S. Carrie, and L. McDevitt. 1997. “Integrated Performance
executive education programmes. Prior to switching to a Measurement Systems: A Development Guide.” International Journal of
career in academia, Andrey worked in Moscow, Russia, as a financial analyst, Operations & Production Management 17 (5): 522–534.
assisting executive teams in the pharmaceuticals and chemicals industries. Bititci, U., S. U. O. Firat, and P. Garengo. 2013. “How to Compare Performances
Matteo Mura, PhD, is an associate professor at the of Firms Operating in Different Sectors?” Production Planning & Control
Department of Management of the University of Bologna 24 (12): 1032–1049.
and a visiting Research Fellow at the Centre for Business Boudreau, J., W. Hopp, J. O. McClain, and L. J. Thomas. 2003. “On the Interface
Performance of the Cranfield School of Management. His between Operations and Human Resources Management.” Manufacturing
research interests are in the field of performance meas- & Service Operations Management 5 (3): 179–202.
urement and management, with a special focus on intan- Bourne, M. 2005. “Researching Performance Measurement System
gible assets, intellectual capital, organisational learning Implementation: The Dynamics of Success and Failure.” Production
and corporate social responsibility. He is currently explor- Planning & Control 16 (2): 101–113.
ing how performance measurement and knowledge Bourne, M., and P. A. Bourne. 2011. The Handbook of Corporate Performance
management systems can be effectively used to generate both efficiency Management. London: Wiley.
and innovation in organisations. Bourne, M., A. Neely, J. Mills, and K. Platts. 2003. “Why Some Performance
Measurement Initiatives Fail: Lessons from the Change Management
Monica Franco-Santos, PhD, is a senior lecturer at Literature.” International Journal of Business Performance Management
Cranfield School of Management. Her research broadly 5 (2/3): 245.
focuses on organisational governance systems. In par- Bourne, M., A. Pavlov, M. Franco-Santos, L. Lucianetti, and M. Mura. 2013.
ticular, her work examines the design, implementation, “Generating Organizational Performance: The Contributing Effects
and use of performance measures, targets and rewards of Performance Measurement and Human Resource Management
as well as their impact on people’s behaviour and deci- Practices.” International Journal of Operations & Production Management
sion making. Monica is part of the teaching faculty of 33 (11/12): 1599–1622.
Cranfield’s MBAs, MScs, PhD, DBA and various executive Bowen, D. E., and C. Ostroff. 2004. “Understanding HRM-firm Performance
education programmes. She is a visiting professor in Linkages: The Role of the ‘Strength’ of the HRM System.” Academy of
ESADE (Barcelona) and an occasional lecturer in a number of European aca- Management Review 29: 203–221.
demic institutions. Prior to joining Cranfield, Monica worked as a senior con- Bowen, H. P., and M. F. Wiersema. 1999. “Matching Method to Paradigm in
sultant for a leading global management consultancy. Strategy Research: Limitations of Cross-sectional Analysis and Some
Mike Bourne, PhD, is Professor of Business Performance Methodological Alternatives.” Strategic Management Journal 20: 625–636.
and Director of the Centre for Business Performance at Braam, G. J. M., and E. J. Nijssen. 2004. “Performance Effects of Using the
Cranfield University. He also is the Chair of the Balanced Scorecard: A Note on the Dutch Experience.” Long Range
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has spent the last 20 years helping senior management Braunscheidel, M. J., and N. C. Suresh. 2009. “The Organizational Antecedents
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straddle the fields of strategy and performance, focusing Buckingham, M., and C. Coffman. 1999. First Break All the Rules: What the
on strategy implementation, the management of risk World’s Greatest Managers Do Differently. London: Simon & Schuster.
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of Corporate Performance Management. Measurement in Non-production Activity: The Case of Engineering
Design.” International Journal of Operations and Production Management
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the Efficacy of Covariance-based and Variance-Based SEM.” International We asked respondents to compare, on a seven-point Likert scale, their com-
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Scales Items
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Performance in Manufacturing.” Journal of Operations Management 27: Financial FP1 Turnover
462–478. performance FP2 Profitability
Rousseau, D. M. 1995. Psychological Contracts in Organizations: Understanding FP3 Market share
FP4 Growth in sales*
Written and Unwritten Agreements. Thousand Oaks, CA: Sage.
Non-financial NFP1 Quality of products and services
Rucci, A. J., S. P. Kirn, and R. T. Quinn. 1998. “The Employee-Customer Profit performance NFP2 Ability to attract essential employees
Chain at Sears.” Harvard Business Review Jan/Feb 76: 82–97. NFP3 Ability to retain essential employees
Schuler, R. S., J. R. Fulkerson, and P. J. Dowling. 1991. “Strategic Performance NFP4 Satisfaction of customers/clients
Measurement and Management in Multinational Corporations.” Human NFP5 Development of new products, services or programmes*
Resource Management 30 (3): 365–392.
Scott, T. W., and P. Tiessen. 1999. “Performance Measurement and Managerial We asked respondents to indicate, on a seven-point Likert scale, the degree
Teams.” Accounting, Organizations and Society 24 (3): 263–285. to which they agreed or disagreed with the following statements.
Shaw, J. D., N. Gupta, and J. E. Delery. 2005. “Alternative Conceptualizations
of the Relationship between Voluntary Turnover and Organizational Scales Items
Performance.” Academy of Management Journal 48 (1): 50–68. Performance PM1 People know what is expected of them at work
Sirmon, D. G., M. A. Hitt, and R. D. Ireland. 2007. “Managing Firm Resources manage- PM2 People have the materials and equipment needed to do
in Dynamic Environments to Create Value: Looking inside the Black Box.” ment their job right
PM3 People have clear targets to achieve
Academy of Management Review 32 (1): 273–292.
PM4 People receive regular feedback on their performance
Sirmon, D. G., M. A. Hitt, R. D. Ireland, and B. A. Gilbert. 2011. “Resource PM5 People know how their performance at work contributes
Orchestration to Create Competitive Advantage: Breadth, Depth, and Life to the company’s goal
Cycle Effects.” Journal of Management 37: 1390–1412. Recruitment RC1 We select employees based on an overall fit to the com-
Smith, K. G., C. J. Collins, and K. D. Clark. 2005. “Existing Knowledge, pany
Knowledge Creation Capability, and the Rate of New Product Introduction RC2 Our selection system focuses on the potential of the candi-
in High-technology Firms.” Academy of Management Journal 48: 346–357. date to learn and grow with the company
Spector, P. E. 2006. “Method Variance in Organizational Research.” RC3 We ensure that all employees are made aware of internal
Organizational Research Methods 9: 221–232. promotion opportunities
RC4 Internal candidates are given consideration over external
van der Stede, W., C. Chow, and T. Lin. 2006. “Strategy, Choice of Performance
candidates for job openings*
Measures, and Performance.” Behavioral Research in Accounting 18 (1): Rewards RW1 Salaries for employees are higher than those of our
185. competitors
Straub, D., M. C. Boudreau, and D. Gefen. 2004. “Validation Guidelines for IS RW2 Rewards are designed to ensure equity with peers
Positivist Research.” Communications Association of Information Systems RW3 Employee bonuses or inventive plans are based primarily
14: 380–426. on the performance of the company*
Tsui, A., J. L. Pearce, L. W. Porter, and A. M. Tripoli. 1997. “Alternative RW4 Shares of stocks are available to all employees through
Approaches to the Employee-Organization Relationship: Does specific plans*
Investment in Employees Pay Off?” Academy of Management Journal 40 Training TR1 We provide training focused on team-building and team-
work skills
(5): 1089–1121.
TR2 We use job rotation to expand the skills of employees
Tsui, A. S., J. L. Pearce, L. W. Porter, and J. P. Hite. 1995. “Choice of Employee- TR3 We have a mentoring system to help develop our employ-
Organization Relationship: Influence of External and Internal ees
Organizational Factors.” In Research in Personnel and Human Resource TR4 We provide multiple career path opportunities for employ-
Management, edited by G. R. Ferris, 117–151. Greenwich, CT: JAI Press. ees to move across functional areas of the company*
Vagneur, K., and M. Peiperl. 2000. “Reconsidering Performance Evaluative TR5 We offer an induction programme that introduces employ-
Style.” Accounting, Organizations and Society 25: 511–525. ees to the history and objectives of the company*
Vereecke, A., and S. Muylle. 2006. “Performance Improvement through Social SC1 There is high level of cooperation between employees and
Supply Chain Collaboration in Europe.” International Journal of Operations climate teams in this company
SC2 There is high level of knowledge sharing between employ-
and Production Management 26 (11): 1176–1198.
ees in this company
Villena, V. H., E. Revilla, and T. Y. Choi. 2011. “The Dark Side of Buyer–Supplier SC3 Employees here are willing to sacrifice their self-interests
Relationships: A Social Capital Perspective.” Journal of Operations for the benefit of the team
Management 29 (6): 561–576. SC4 In this company is important to maintain harmony within
Wold, H. 1985. “Partial Least Squares.” In Encyclopedia of Statistical Sciences. teams and amongst employees*
6 vols., edited by S. Kotz and N. L. Johnson, 581–591. New York: Wiley.
*Item dropped after the measurement model validity and reliability tests.