On the soft side of open
innovation: the role of human
resource practices, organizational
learning culture and knowledge
sharing
M. Muzamil Naqshbandi1,* , Sheik Meeran2
Adrian Wilkinson3,4
and
1
School of Business & Economics, University of Brunei Darussalam, Jalan Tugku Link, BE1410,
Brunei Darussalam. muzamil.naqshbandi@ubd.edu.bn virkul@gmail.com
2
School of Management, University of Bath, Claverton Down, Bath, BA2 7AY, UK. s.meeran@bath.
ac.uk
3
Department of Employment Relations and Human Resources, Nathan Campus, Griffith University, 170
Kessels Road, Nathan, QLD 4111, Australia.
4
Sheffield University Management School, The University of Sheffield, Conduit Road, Sheffield, S10
1FL, UK. adrian.wilkinson@griffith.edu.au
This paper examines the role of HR practices in shaping inbound open innovation outcomes
through the lens of the competency, motivation and opportunity (CMO) model. The paper
investigates the aforementioned associations by considering the intervening roles of organizational learning culture and knowledge sharing. The data were collected from managers
working in different sectors in the United Kingdom. The findings reveal positive linkages
between the different types of HR practices studied and establish the mediating mechanisms of organizational learning culture and knowledge sharing. This study contributes to
the current body of knowledge by developing and empirically testing an integrated model
of open innovation that is at the intersection of four broad areas –­strategic HRM, organizational learning, knowledge management, and innovation management. By identifying processes that can improve the odds of success in the open innovation paradigm, this study can
assist managers in designing more effective strategies.
1. Introduction
M
any organizations work in an open environment characterized by porous organizational
boundaries and consequently adopt an open approach toward innovation to improve their innovative
© 2022 RADMA and John Wiley & Sons Ltd.
performance (West and Gallagher, 2006). As a result,
academic researchers have paid increasing attention
to firms operating in the open innovation paradigm
(Candi et al., 2018). Factors such as the role of
management (Engelsberger et al., 2022), leadership
(Ahmed et al., 2018), organizational culture (Herzog
279
and Leker, 2010; Kratzer et al., 2017; Naqshbandi
and Kamel, 2017), ‘innovation communication’
(Enkel et al., 2017), individuals’ affective responses
to tensions (Stefan et al., 2022), managerial cognition and cognitive dissonance (Bhimani et al., 2022)
have been shown to affect open innovation.
We also have studies published in the domain of
innovation and human resource management, providing some evidence of the influence of HR practices on
organizations’ innovation outcomes (c.f. James, 2002;
Laursen, 2002; Engelsberger et al., 2022; Remneland
Wikhamn et al., 2022). Focusing on the theme of
human capital, R&D Management has explored the
enlisting of internal scientists to work with the innovation intermediary (Sieg et al., 2010), the crucial
managerial roles for open innovation collaborations
(Ollila and Yström, 2017), and the role of CEO characteristics in facilitating open innovation in SMEs
(Ahn et al., 2017). However, few studies have specifically examined the role of HR practices in facilitating open innovation (Palumbo et al., 2022). Of
the studies which touch on HR issues, Engelsberger
et al. (2022) examined the role of job design,
employee selection, training, performance, and
compensation in open innovation in multinational
companies in the United States, while Natalicchio et
al. (2018) explored the effect of acquiring externally
developed knowledge on innovation performance in
the Italian manufacturing sector focusing on recruitment and training. Closely, Peris-­Ortiz et al. (2018)
studied open innovation in knowledge-­based companies in Spain, France, and Portugal looking at the
roles of leadership, worker qualifications, employee
autonomy and engagement but did not provide
detailed insights into these HR-­related variables and
their operationalization.
These studies all emphasize the role of human
resource management in open innovation. However,
while these studies link human resource management with open innovation in general, their approach
is broad and not theoretically driven. Furthermore,
little is known about the impact of or the role played
by specific HR practices in facilitating an organization’s inbound open innovation performance, hence
the value of this study. Drawing on the Competency–­
Motivation–­Opportunity (CMO) model, we consider
the effectiveness of three bundles of HRM practices
in improving inbound open innovation outcomes and
examine the intervening mechanisms in these associations. To the best of our knowledge, this has not
been done.
The CMO model, also known as the Abilities,
Motivations and Opportunities (AMO) framework,
traces its roots to Vroom (1964) who focused on two
determinants of individual performance: ability and
280
R&D Management 53, 2, 2023
motivation, and Blumberg and Pringle (1982) who
added opportunity as the third determinant of individual performance (Ujma and Ingram, 2019). In
its current form, the AMO framework was proposed
by Bailey (1993) and it has since been developed
to examine how targeted HR practices and bundles
may elevate employee performance (Appelbaum
et al., 2000; Jiang et al., 2013). The CMO model
has become widely adopted in management (Van
Beurden et al., 2021; Rincon-­
Roldan and Lopez-­
Cabrales, 2021), including HRM (Salas-­Vallina et
al., 2021; El-­Kassar et al., 2022) as it captures the
viewpoint of both the industrial psychologists (who
focus on the function of hiring and training) and social
psychologists (who see motivation as necessary to
ensure performance) (MacInnis and Jaworski, 1989)
and can be used to explore how HR practices might
activate these variables.
In addition to shedding light on the role of HR
practices in affecting inbound open innovation outcomes, we also study the mechanisms involved by
focusing on two key intervening variables: knowledge sharing and organizational learning culture.
An organizational culture that supports the generation, development, learning, and utilization of new
and innovative ideas –­termed an organizational
learning culture –­can be crucial for innovation,
and such a culture can be promoted by the use of
appropriate HR practices (Boxall and Purcell, 2000;
Beugelsdijk, 2008). Extant research provides evidence about the influence of HR systems on firm
performance through the mediating link of employee
behaviors (Park et al., 2003). In this study, we see
knowledge sharing as a behavioral factor which acts
as a mediator between HR practices and inbound
open innovation (Scarbrough, 2003).
Using primary data collected from managers working in organizations operating in diverse
industries in the United Kingdom, this study
reveals positive linkages between the different
types of HR practices and explains these linkages via the intervening mechanisms of organizational learning culture and knowledge sharing.
Therefore, the study contributes to the open innovation literature by using the CMO framework to
understand the mechanisms of how HR practices,
along with organizational learning culture and
knowledge sharing, affect inbound open innovation (Beugelsdijk, 2008). Our study contributes
by marrying disparate literature into an integrated
framework, providing interdisciplinary perspectives. Past studies (e.g., Rodriguez Perez and
Ordóñez de Pablos, 2003) have used such an integrative approach in knowledge management and
human resource management research. We focus
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M. Muzamil Naqshbandi, Sheik Meeran and Adrian Wilkinson
on a firm’s internal resources that can be leveraged
to enhance inbound open innovation outcomes by
formulating HR practices that enhance employee
competencies, motivation, and opportunities. From
the lens of practice, this study offers practitioners
insights to enhance inbound open innovation outcomes. The rest of the paper unfolds as follows:
a literature review is provided, followed by the
research methodology, findings of the study, their
discussion, and implications.
2. Theoretical background and
hypotheses
Open innovation is ‘the use of purposive inflows
and outflows of knowledge to accelerate internal
innovation, and expand the markets for external use of innovation, respectively’ (Chesbrough
et al., 2006, p. 1). It typically comprises inbound
and outbound dimensions. The inbound dimension
implies the use of discoveries that others make
and involves opening up to and establishing relationships with external entities. The aim remains
to access the external entities’ competencies and
rely on purposive knowledge inflows to enhance
innovation performance. The outbound dimension
emphasizes the commercialisation of technologies
developed by a firm and not relying only on internal paths to market (Vanhaverbeke, 2006). Thus
outbound open innovation focuses on the purposive outflows of knowledge and allows for firms to
search for external players with better fitting business models for the technologies they develop.
In a fully open setting, a firm can adopt both
dimensions of open innovation to maximize value
creation (Chesbrough and Crowther, 2006). In most
cases, however, inbound open innovation is more
widespread than outbound open innovation (Enkel
et al., 2009). Parida et al. (2011) attribute this to the
fact that starting with open innovation practices in
an exploitation mode (outbound open innovation)
is more challenging. In contrast, after first engaging in exploration (inbound open innovation), firms
find it more feasible later to externally exploit and
commercialize their knowledge resources and
technologies. This points to an element of path
dependence, where exploration precedes exploitation. Given this, this research focuses on inbound
open innovation as it focuses on a firm’s core new
product technologies, which can be central to its
viability and sustainable advantage (Sisodiya
et al., 2013). Furthermore, given the diversity of
firms that form the sample of this study, a spotlight on inbound open innovation activities is
© 2022 RADMA and John Wiley & Sons Ltd.
appropriate as most firms usually embark on their
open innovation journey focusing more on inbound
than outbound open innovation activities and processes (Bianchi et al., 2011).
The current literature points to a research gap
in our understanding of the human side of open
innovation. As noted above, several studies have
explored the human (or soft) dimension of open
innovation. These include Podmetina et al. (2013),
who focused on the impact of HR initiatives such
as learning and training practices, human capital
value, and motivation on open innovation activities
in Russian firms; Petroni et al. (2012), who examined the changes in the HR policies and procedures
used to manage the scientific staff in developing
open innovation practices; Salampasis et al. (2015)
who posited that human resources management
can affect the adoption of open innovation; and,
Bhandari (2021) who noted that the collaborative
human resource management practices (e.g., networking events, mentoring programs, teamwork-­
based training, and performance management
system) can help employees develop an open innovation mindset.
While we lack a theoretical lens to examine the
role of HR practices in open innovation, we also
know from the extant research that open innovation does not always deliver benefits (Chaudhary
et al., 2022). Several organizations are reported
to have failed or faced trouble reaping the desired
benefits from open innovation adoption, including prominent organizations such as LEGO and
Boeing (Lindegaard, 2013). We thus adopt the theoretical lens of the CMO model to fill the research
gap and investigate how the practitioners of open
innovation can use HR practices to avert open innovation failures and enhance open innovation outcomes. In brief, the CMO framework incorporates:
Competencies, the importance and the level of the
skills held by employees influence the bundle of
practices that is likely to motivate them and the
extent to which an organization is likely to offer
and invest in those practices. The focus is on individual abilities, although most organizations are
likely to tailor their practice bundles to a workforce
group. Motivations, the bundle of practices that
should motivate high performance. Motivations
are individualized and influenced by the nature of
the work undertaken. Most organizations are likely
to offer bundles that differ between groups to an
individual level. So HR practice bundles to motivate knowledge employees may differ from those
targeted at employees doing task-­
based work.
Opportunities offered to workers to participate,
develop and progress are likely to be influenced by
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On the soft side of open innovation
the strategic value and uniqueness of their human
and social capital. For example, greater opportunities may be offered to those occupying more critical positions or those seen as having more talent
and potential (see Marchington et al., 2020).
In the next section, we build on the extant literature and propose a research framework positing that
the different types of HR practices can positively
improve inbound open innovation outcomes. The
research framework also considers the intervening
role of organizational learning culture and knowledge sharing in HR practices-­inbound open innovation association. We propose three hypotheses: the
first (divided into three sub-­hypotheses) theorizes
the impact of the competency-­
, motivation-­and
opportunity-­
enhancing HR practices on inbound
open innovation. The second and the third hypotheses theorize the mediating mechanisms of organizational learning culture and knowledge sharing in the
link between HR practices and inbound open innovation. Figure 1 below presents these hypotheses in the
form of a research framework.
2.1. Competency-­enhancing HR practices
and inbound open innovation
Open innovation helps organizations build a competitive edge by leveraging knowledge available inside
and outside the organizational boundaries, generating increased knowledge and synergies (Tan and
Nasurdin, 2011; Donate and de Pablo, 2015). Human
capital and its components (such as skills, knowledge, education, experience, and managerial roles),
play a critical role in a firm’s external engagements
(Albats et al., 2020) and thus it is important to pay
attention to the human side of inter-­firm engagements
for collaborative innovation (Bertello et al., 2022).
Employees tend to generate novel ideas and display
innovation-­oriented behaviors when firms focus on
their innovative capabilities during selection and hiring (Brockbank, 1999), and hence Scarbrough (2003)
argues that HR practices focused on employee competencies are essential for innovation.
Competency-­
enhancing HR practices include
those that are related to the recruitment of a qualified
and highly competitive workforce, selection, staffing and training of employees. Gardner et al. (2011)
note that such practices directly influence employees’ type and level of knowledge, skills, competencies, and abilities by either bringing new skills into
the organization or by developing and upgrading
the competencies of the existing workforce (see
also Brockbank, 1999; Laursen and Foss, 2003).
Employee training results in functional benefits and
helps the workforce familiarize themselves with the
desirable attitudes and values necessary for innovation and organizational success (de Araújo Burcharth
et al., 2014). In its broader sense, this process is typically facilitated by exposing the employees to several
knowledge sources and inculcating a sense of openness in acquiring innovative capabilities (Hurley and
Hult, 1998).
Recent research (c.f. Natalicchio et al., 2018) has
shown that HR practices help employees seek and
acquire knowledge from different knowledge sources
and implement it to enhance innovation outcomes.
These arguments are even more significant for open
innovation as it focusses on the inflows of ideas and
knowledge into an organization. However, as open
innovation can be seen as radical and may run counter
to traditional organisationally based approaches to
innovation, (competency-­
enhancing) HR practices
and strategies can help signal a new approach and
hence can favorably affect the capabilities of a firm
in exploiting external sources of knowledge (Jiang
et al., 2012) and thus promote inbound open innovation. This leads us to our first hypothesis:
H1a
Competency-­
enhancing HR practices are
positively associated with inbound open innovation.
2.2. Motivation-­enhancing HR practices
and inbound open innovation
Along with ability, skills, and talent, organizations
require the motivation of human capital to innovate
and exploit new opportunities (Scarbrough, 2003).
The motivation of the human capital involved in
the innovation process can be seen as a micro-­
foundational aspect (Locatelli et al., 2021) that is
Figure 1. Research framework.
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M. Muzamil Naqshbandi, Sheik Meeran and Adrian Wilkinson
critical to the outcomes of collaborative engagements
(Oliveira et al., 2021). In this context, motivation-­
enhancing HR practices are practices that are specially designed to get workers involved in creative
thinking and innovation (Laursen and Foss, 2003).
These practices may include training, regular performance feedback, improving employee participation,
tangible or intangible incentives, and performance-­
based pay (Gardner et al., 2011).
Motivated employees can feel empowered to
discover and utilize new ideas and make decisions
that can lead to better acquisition and utilization
of knowledge. Because of this, organizations often
design incentives and reward systems to increase
employee motivation. In turn, ‘felt obligation’
should facilitate employees working for the betterment of the organization, sharing knowledge
with others, generating new ideas, exploiting them
and exhibiting behaviors that improve innovation results (de Araújo Burcharth et al., 2014). As
noted, open innovation is a different way of operating; hence, it is important to use HR practices to
shift the employee mindset. In short, motivation-­
enhancing HR practices can create a workforce
better positioned to acquire and utilize ideas and
knowledge, promoting inbound open innovation.
This leads to our second hypothesis:
H1b
Motivation-­
enhancing HR practices are
­positively associated with inbound open innovation.
2.3. Opportunity-­enhancing HR practices
and inbound open innovation
The opportunity-­
enhancing HR practices encourage employees to display behaviors that can foster
innovation. Such practices may include establishing platforms for information sharing and providing opportunities for active employee participation
in decision-­
making to empower them (Gardner
et al., 2011). As open innovation is a more radical
approach, it is up to organizations to establish new
structures to provide opportunities for employees to
gain experiences and confidence with new ways of
working where the employees can interact and collaborate with knowledge experts to acquire new ideas
and knowledge.
Innovation occurs when the information is
shared among organizational members, providing opportunities to generate new shared insights
(Škerlavaj et al., 2010). Opportunity-­enhancing HR
practices allow employees to stay abreast of the current knowledge, which can help them create value
for the organization (Jiang et al., 2012). Delery
and Doty (1996) identified HR practices (e.g.,
© 2022 RADMA and John Wiley & Sons Ltd.
internal hiring, formal training, reward systems,
secondments etc.) that can enhance opportunities
for employees and expose them to other environments and ideas (see also Laursen and Foss, 2003;
Lau and Ngo, 2004; Chuang et al., 2016). These
HR practices can enable knowledge inflows and
outflows (Ardito and Petruzzelli, 2017) and positively impact inbound open innovation. Inbound
open innovation typically enhances organizational
innovativeness when an organization enriches its
knowledge base by benefiting from the discoveries
of others and by collaborating with outside knowledge sources (Bogers et al., 2018). Therefore, it
could be argued that the opportunity-­
enhancing
HR practices can create prospects for employees
to contribute effectively in acquiring and utilizing
new ideas, thereby promoting inbound open innovation. This leads us to the third hypothesis of this
study:
H1c
Opportunity-­
enhancing HR practices are
positively associated with inbound open innovation.
2.4. The mediating role of organizational
learning culture in HR practices-­
inbound open innovation
relationship
Organizational learning refers to the process of
creating, retaining, and transferring knowledge
within an organization. In an organization characterized by a learning culture, the organizational
members share common norms and agree that
learning is valuable to achieving creative outcomes
and fulfilling organizational goals (Bates and
Khasawneh, 2005; Naqshbandi and Tabche, 2018).
In such organizations, employee voice and questioning are considered vital so that employees
function with an attitude of continuous investigation (Aagaard, 2017), and the organizations support inquiry, risk-­
taking, and experimentation
(Stjernholm Madsen and Ulhøi, 2005). This also
prepares the employees for better acquisition and
utilization of learning (Claver et al., 1998), which
in turn opens the way for the exchange of information and ideas and becomes a critical facilitator of
innovation (Naqshbandi et al., 2015).
HR practices can help organizations develop a
culture that allows the flow and integration of ideas
to produce innovative knowledge (Chi et al., 2009).
HR practices may create opportunities for members
to come close and interact with each other by assigning tasks in teams so that ideas can be shared, thus
developing an environment of learning conducive
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On the soft side of open innovation
to innovation (Claver et al., 1998). We see an intervening role for organizational learning culture in the
HR practices-­open innovation relationship. So HR
practices can affect organizational learning culture
in organizations which in turn can enhance inbound
open innovation outcomes. This leads us to the following hypothesis:
H2 Organizational learning culture mediates the
relationship between HR practices and inbound
open innovation.
2.5. The mediating role of knowledge
sharing in HR practices-­inbound open
innovation relationship
Knowledge is a vital intangible resource for any
organization, while sharing is the process in which
a resource is given by one party and received by
another. Knowledge sharing thus refers to the giving and receiving of information framed within a
context (Sharratt and Usoro, 2003). Knowledge is
the property of an individual mind, and the process
of knowledge transfer is realized by acquiring and
disseminating knowledge (Lincoln et al., 1998).
Knowledge sharing offers several benefits to an
organization. Knowledge sharing can create opportunities to increase a firm’s ability to generate solutions and enhance efficiency (Lin, 2007) and boost
innovation (Scarbrough, 2003). Knowledge-­sharing
activities contribute to an organization’s efforts to
explore, exploit and utilize new knowledge resources
(Grant, 1996). Knowledge sharing and interactions
with others can enable managers to identify appropriate knowledge sources and adopt innovative solutions in different contexts (Crupi et al., 2021). As
the open innovation model involves the exploration,
acquisition, and exploitation of knowledge resources
available internally and/or externally, the role of
knowledge sharing and knowledge exchange assumes
paramount importance (Tan and Nasurdin, 2011).
HR practices enable a firm to develop employee
competence –­by investing in human resources, providing more resources for experimentation, allowing and rewarding occasional failure –­and hence
increasing the likelihood of enhanced innovation
outcomes (Schuler and Jackson, 1987). Practices
such as job rotation, job enrichment, and job
enlargement increase the likelihood of knowledge
exchanges among organizational members, while
job secondments facilitate learning across organizations. HR practices can also be designed to promote
teamwork by providing employees with a platform
(such as a digital bulletin board and other intranet
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R&D Management 53, 2, 2023
and internet-­based tools) for idea-­sharing. Similarly,
organizations can also develop other variants of
cross-­organizational platforms to encourage knowledge exchanges (Kang et al., 2007).
Employees who get more opportunities to share
information are more likely to participate in innovative activities which influence the inflows and
outflows of knowledge in a firm (Naqshbandi and
Jasimuddin, 2018) and are thus likely to facilitate
inbound open innovation. In short, HR practices
promote knowledge sharing among organizational
members, facilitating the acquisition, internalization,
and utilization of knowledge from various sources,
and supporting inbound open innovation through the
intervening role of knowledge sharing. Hence, we
propose the following hypothesis:
H3 Knowledge sharing mediates the relationship
between HR practices and inbound open innovation.
3. Methods and analysis
3.1. Sample and procedures
This study’s hypotheses were tested using the data collected from the managers of organizations operating in
the United Kingdom. The data were collected through
two different sources. Firstly, we drew on a list of HR
managers provided by the University of Bath School
of Management external relations and marketing team
(henceforth ERM team). This list was drawn on the
premise that these managers oversee the HR function,
though they may not have the designation ‘HR manager’; they could be general managers, training managers, etc. The list provided by the ERM Team helped
us identify 400 suitable candidates, the ones who were
responsible for the HR function in their organizations.
These managers were contacted, and their help was
sought in data collection. If the survey recipients were
not in an appropriate position to respond to the survey,
they were asked to pass it to their relevant colleagues.
Using this channel, we were able to get 160 responses.
However, upon scrutiny, only 51 of these responses
were found to be complete, thus necessitating a second round of data collection. The second round of data
collection was conducted using ‘Qualtrics online panels’. Using this second channel, we were able to collect
151 valid responses. After cleaning the data of outliers,
disengaged responses, and incomplete answers, both
data sets were merged, resulting in 202 responses. It is
pertinent to note that the same instrument was used to
collect data through both channels.
Because we aimed at generalizability, organizations operating in different industries were
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M. Muzamil Naqshbandi, Sheik Meeran and Adrian Wilkinson
approached. Therefore, 25.2% of respondents
belonged to the IT industry, 4.0%, to Telecom,
7.4% to Engineering, 3.0% to Education and
development, 16.8% to Health care, 8.9% to
Hospitality, 19.8% to Auto and Manufacturing,
1.5% to Financial Service, 7.9% to Trust, 3.0% to
Public sector, and 2.5% to private sector industries.
42.6% of respondents were at the top, 54.5% at the
middle, and 2.0% at the lower management positions, whereas 1% of respondents belonged to non-­
managerial positions. And 5.4% of respondents had
worked for the current organizations for less than
1 year, 10.4% for 1–­2 years, 24.3% for 3–­5 years,
21.3% for 6–­10 years and 38.6% for over 10 years.
Most of the surveyed firms were established in their
industries. And 1.5% of the firms had been operating for less than a year, 1.5% for 1–­2 years, 8.9%
for 3–­5 years, 11.4% for 6–­10 years and about three-­
quarters of the surveyed firms had been operating
for over 10 years. And 56.4% firms surveyed for this
study had an R&D department, whereas the rest of
the 43.6% of firms had no R&D department. Almost
half of the firms (47%) operated locally, while
22.8% and 30.2% of firms operated regionally and
globally, respectively. Most surveyed firms were
privately owned (56.4%), while 25.7% were publically owned, 9.4% by the government, 3.5% had
foreign ownership, and 5.0% had mixed ownership.
[competency-­enhancing HR practices]; ‘The company offers a variety of incentives (e.g., gain sharing, stock option, etc.) to attract and retain top
talent’ [motivation-­enhancing HR practices]; ‘My
organization often arranges events for knowledge
exchange’ (e.g., seminars, visits by outside experts,
etc.) [opportunity-­
enhancing HR practices]. The
focal variable of this study, inbound open innovation, was measured using a 6-­item scale developed
and validated by Sisodiya et al. (2013). We further
assessed and confirmed the validity and reliability
of these measures in this study (see Table 2). The
six items of this scale capture the different aspects
of inbound open innovation, such as an organization scanning the external environment for inputs,
relying on external sources to complement its R&D,
seeking out technologies and patents from external entities and purchasing externally developed
intellectual property. Example items include: ‘My
organization actively seeks out external sources of
knowledge and technology (e.g., research groups,
universities, suppliers, customers, competitors, etc.)
when developing new products.’ and ‘My organization often brings in externally developed knowledge
and technology to use in conjunction with our own
R&D.’ To measure organizational learning culture,
we used Marsick and Watkins (2003)’s 7-­item scale.
Example items are: ‘In my organization, teams/
groups revise their thinking as a result of group
discussions or information collected’, and ‘In my
3.2. Measurements
organization, leaders continually look for opportunities to learn’. Knowledge sharing was measured
This research explores the linkages between open
using a 13-­
item scale (Wang and Wang, 2012).
innovation and the three types of human resource
practices (competency-­
enhancing, motivation-­ Example items are: ‘People in my organization freenhancing, and opportunity-­
enhancing HR pracquently collect reports and official documents from
tices) via the mediating roles of organizational
others in their work’, and ‘People in my organizalearning culture and knowledge sharing. As these
tion frequently share existing reports and official
variables have been studied in the past (though
documents with members of my organization’. All
not in the same combination as in this study), we
the items were anchored on a 5-­point Likert scale
adopted validated and reliable measurements from
ranging from Strongly Agree to Strongly Disagree.
past research. Accordingly, human resource pracThe measurement items for all the variables are
tices were measured using 31 items: competency-­ provided in Appendix A.
enhancing (15 items), motivation-­
enhancing (10
items), and opportunity-­enhancing (6 items) HR
3.3. Non-­response bias
practices (Chuang et al., 2016). Example items
are: ‘The company invests considerable time and
Aware of the detrimental impact of non-­response
resources in training for knowledge workers’
bias on the validity of findings, particularly as the
Table 1. Results of confirmatory factor analyses
Measurement models
χ2
df
χ2/df
CFI
RMSEA
HRP-­OLC-­KS-­INOI
(4-­factor model)
HRP-­OLC-­KS-­INOI
(1-­factor model)
657.16
416
1.58
.92
.062
1250.54
334
3.74
.88
.11
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On the soft side of open innovation
Table 2. Reliability and validity of measures
Construct
HR practices
Competency-­enhancing
HRP
Motivation-­enhancing HR
Practices
Opportunity-­enhancing
HR Practices
Organizational learning
culture
Knowledge sharing
Inbound open innovation
Items
Factor loadings
CR
AVE
Sqr. AVE
Cronbach’s
alpha
CEHR8
CEHR11
CEHR12
CEHR13
CEHR14
MEHR6
MEHR7
MEHR9
MEHR10
OEHR3
OEHR4
OEHR5
OEHR6
OLC1
OLC4
OLC5
KS2
KS3
KS4
KS7
KS8
KS9
KS10
KS11
KS12
KS13
INOI3
INOI4
INOI5
INOI6
0.690
0.677
0.775
0.725
0.651
0.533
0.660
0.861
0.892
0.706
0.688
0.831
0.811
0.685
0.766
0.774
0.703
0.705
0.786
0.832
0.870
0.722
0.809
0.699
0.811
0.702
0.689
0.776
0.756
0.728
0.846
0.581
0.762
0.848
0.831
0.500
0.705
0.847
0.827
0.544
0.738
0.853
0.853
0.664
0.815
0.756
0.724
0.569
.754
0.931
0.934
0.589
0.767
0.812
data were collected using two channels, we compared the two subsets of data: one obtained from the
contacts in the list provided by the ERM Team and
the other using Qualtrics survey panel. We ensured
the absence of common respondents between the
two data sets by verifying their unique respondent
ID captured by the Qualtrics Questionnaire platform used in both groups of respondents. To be
sure, we statistically compared the means of the
responses from the two respondent groups for all
the variables in the survey. At a 5% significance
level, 56 of the variables had a non-­
significant
difference, hence taken as there is no difference
between the responses of these two groups proving the absence of any systemic non-­response bias.
Then in the second stage, we took the first and the
last 46 respondents and compared the means of
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R&D Management 53, 2, 2023
their responses. We found again, except for a small
number of variables, that the means are not significantly different, confirming there is no systematic
non-­response bias in the survey.
3.4. Common method bias
Like non-­response bias, the presence of common
method bias (CMB) can invalidate the findings of
a study. Therefore, we took several measures from
the survey design to the analysis stages to ensure
CMB does not occur. We used psychological separators and interspersed the survey items belonging
to different variables. At the analysis stage, we used
statistical techniques such as Harman’s single factor
test to rule out the presence of CMB. The results of
this test showed that it is unlikely for the findings of
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M. Muzamil Naqshbandi, Sheik Meeran and Adrian Wilkinson
this study to be affected by CMB because the variance explained by a single factor was found to be less
than the threshold of 50%. However, because of the
criticism of Harman’s single factor test (Podsakoff
et al., 2003), we also performed the Common Latent
Factor test in AMOS™, which also ruled out the
presence of CMB.
3.5. Control variables
Prior research (e.g., West et al., 2006) has considered differences regarding open innovation between
firms within an industry and between industries or
sectors. Other research (c.f. Naqshbandi et al., 2016)
has highlighted differences across types of firm ownership. Considering the effects that demographic
factors can have on the study’s findings, we conducted several one-­way ANOVA tests to compare
mean differences across variables for inbound open
innovation. We did not find any significant mean
differences for open innovation across: industries
(F = .744, P > .05); firm ownership types (F = 1.008,
P > .05); firm age (F = .861, P > .05); and firm market orientation (F = .371, P > .05). T-­test for mean
differences for open innovation across firms that had
a formal R&D department (M = 3.54, SD = .82) and
those that did not have one (M = 2.87, SD = .79)
revealed significant mean differences; t (200) = 5.88,
P = .00. Hence we controlled for this variable in further analyses.
3.6. Exploratory factor analysis (EFA),
convergent and discriminant validity
This study used existing research to identify indicator variables. Therefore, the researchers were
aware of the indicator variables that needed to be
measured for each latent variable used in this study.
However, we started with an Exploratory Factor
Analysis (EFA) to make an initial verification
of the model structure. We checked the suitability of the data for performing EFA (using KMO,
Bartlett’s Test of Sphericity, and correlation coefficients between variables). Then EFA was conducted. EFA provides an initial idea on whether
different measured items belong to certain underlying dimensions or not, that is, the dimensionality
of measured variables. The results of EFA initially
showed some low and cross-­loadings. After an iterative process in which items that lead to an unclear
factor structure were removed, an acceptable factor structure was obtained. Next, we took a confirmatory route and subjected the model obtained in
the EFA to confirmatory factor analysis (CFA) in
AMOS™ v21. The primary aim in this step was to
© 2022 RADMA and John Wiley & Sons Ltd.
confirm whether the observed/measured indicators
measured the constructs/latent variables accurately
or not that is, whether different measured variables do measure the latent variables/underlying
dimensions correctly. We formed a four-­factor and
a single-­factor model and assessed the fit of each
model.
Table 1 above shows the model-­fit indices for the
four-­factor and the single-­factor model. The four-­
factor model resulted in a better model fit than the
single-­factor model. The overall fit of the four-­factor
model is tested using ‘Chi-­square/df’. This metric
has a value of 1.58 (<acceptable value of 2), indicating a good model fit to the data. The Comparative
Fit Index (CFI) of 0.92 also shows a good fit (Hair
et al., 2010). The RMSEA of 0.062 is acceptable,
although slightly higher than the limit set at 0.06
(Hair et al., 2010). Given the above acceptable model
fit metrics, we retain the four-­factor model and use it
for further analyses.
Before proceeding with further analyses,
Composite reliability (CR) was calculated for each
latent variable; CR for each latent variable is found
to be >0.70, while factor loadings of all the items
are above the cutoff value of 0.5, indicating that
the constructs are reliable. These metrics demonstrate the unidimensionality and reliability of the
measures used in this study (Hair et al., 2010).
Further, we examined the convergent (convergence
between similar constructs) and discriminant validity (discrimination between dissimilar constructs)
of the constructs. Table 2 shows these results in
detail and provides evidence for both validity measures. Since Composite Reliability (CR) is found
to be equal to or greater than 0.7 and the Average
Variance Extracted (AVE) was 0.5 or greater for all
the related variables, convergent validity is established (Hair et al., 2010). We used the Fornell and
Larcker (1981) method to assess the discriminant
validity. All the inter-­
construct squared correlation values of the latent variables are less than the
square root of AVE, thus indicating discriminant
validity (Fornell and Larcker, 1981).
3.7. Hypothesis testing
We used Multiple Linear Regression, executed
in SPSS 21™, to test the direct hypotheses of this
study. Multiple Linear Regression is an appropriate
technique to test the direct hypotheses and show
variance in the outcome variable as explained by the
change in predictor variables. The results demonstrate that the competency-­enhancing HR practices
(β = .50, P < .00), the motivation-­enhancing HR practices (β = .52, P < .00) and the opportunity-­enhancing
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On the soft side of open innovation
HR practices (β = .55, P < .00) all relate positively
to inbound open innovation (see Table 3). Hence,
hypotheses H1a, H1b, and H1c are supported.
The last two hypotheses of this study relate to the
mediational roles of organizational learning culture
and knowledge sharing in the association between
HR practices and inbound open innovation. In testing these intervening roles of organizational learning
culture and knowledge sharing, we avoided using
the traditional Baron and Kenny (1986) approach
due to its several limitations (Hayes, 2009). Instead,
a contemporary and robust approach –­using the
Process Macro for SPSS developed by Preacher
and Hayes (2008) ̶ was used. This macro applies the
bootstrapping technique producing 5000 resamples
and bias-­corrected confidence intervals. As Table 4
shows, first, the direct effect of HR practices on
inbound open innovation using the bootstrapping
technique was computed (β = .51, P < .000). Next, the
lower and upper limit confidence intervals (LLCI and
ULCI, respectively) were obtained for the indirect
effect of HR practices on inbound open innovation
through organizational learning culture (LLCI and
ULCI values are 0.0071 and 0.2190 respectively).
As the lower and the upper confidence intervals do
not include zero between them, it can be inferred
that the indirect effect of HR practices on inbound
open innovation is significant, indicating that organizational learning culture mediates the relationship
between HR practices and inbound open innovation.
This provides support for H2.
We repeated the above steps to test the mediating
role of knowledge sharing. The lower (LLCI) and
upper limit confidence intervals (ULCI) obtained
for the indirect effect of HR practices on inbound
open innovation through knowledge sharing are
0.0359 (LLCI) and 0.2129 (ULCI). In this case as
well, the lower and the upper confidence intervals
do not include zero between them, hence it can be
inferred that the indirect effect of HR practices on
inbound open innovation is significant, indicating
that knowledge sharing plays an intervening role in
the relationship between HR practices and inbound
open innovation. This provides support for H3.
4. Discussion of findings
Notwithstanding myriads of studies on open innovation, the ‘what’ and the ‘how’ questions regarding the relationship between HR practices and open
innovation have received scant attention, leaving the
phenomena under-­theorized. We, therefore, explored
the impact of the competency-­enhancing, motivation-­
enhancing and opportunity-­enhancing HR practices
on open innovation (Chuang et al., 2016). The current
body of literature (e.g., Lau and Ngo, 2004; Fındıklı
et al., 2015; Aagaard, 2017) has primarily focused on
the relationship between HR practices and (closed)
innovation. However, this study is distinct in that we
examine HR practices and innovation in the open
innovation paradigm, which is essential given the
inherent differences between the closed and the open
innovation paradigms (Chesbrough, 2003). In trying
to understand these associations and the underlying
mechanisms more closely, we also examined the
Table 3. Results of multiple regression analysis
Relationship
Unstandardized β
SE
t
P
(H1a) INOI ← CEHR
(H1b) INOI ← MEHR
(H1c) INOI ← OEHR
.50
.52
.55
.063
.049
.050
8.02
10.66
11.00
.000
.000
.000
CEHR = Competency Enhancing HR practices; MEHR = Motivation Enhancing HR; OEHR = Opportunity Enhancing HR Practices;
INOI = Inbound Open Innovation.
Table 4. Organizational learning culture and knowledge sharing as mediators between HR practices and inbound open
innovation
Direct effect of HR practices on inbound open innovation
Effect
SE
t
0.51
0.076
6.67
P
.0000
LLCI
0.3578
ULCI
0.6583
Bootstrap results for indirect effect of HR practices on inbound open innovation
Percentile bootstrap 95% confidence interval
Effect
Estimate
Boot SE
Lower
Upper
0.11
0.053
0.0071
0.2190
(H2) INOI ← OLC ← HRP
0.12
0.044
0.0359
0.2129
(H3) INOI ← KS ← HRP
HRP = HR practices; OLC = organizational learning culture, and INOI = Inbound Open Innovation; SE, standard error; 5000 bootstrap
samples.
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M. Muzamil Naqshbandi, Sheik Meeran and Adrian Wilkinson
intervening roles of organizational learning culture
and knowledge sharing.
The findings of this study show that HR practices
play a significant role in facilitating open innovation.
The HR practices enhance the skills and abilities of
employees and increase their motivation to promote
innovative activities. HR practices unlock opportunities by creating favorable situations that promote
knowledge acquisition, utilization, and commercialization. Albats et al. (2020) noted that specific human
capital and managers’ individual-­level characteristics
affect an organization’s relational capability and strategic partnerships. As such, the firms that implement
HR practices such as those related to training and
development focus on the capacity development of
their employees, which enables them to acquire and
utilize new knowledge and technology for innovative
purposes. Also, firms that pay attention to improving the motivation of their employees –­by rewarding
them and creating opportunities for them to participate in knowledge-­based innovative activities across
the organizational context –­also secure better open
innovation outcomes. In general, this supports the
findings in varied contexts, which show how HR
practices enhance firms’ innovation performance
through effective knowledge flows (Chowhan, 2016;
Chuang et al., 2016). In view of the latest research
highlighting the dark aspects of open innovation and
indicating hidden tolls of operating in the open innovation paradigm (Stefan et al., 2022), these insights
can benefit organizations by alleviating the potential
costs of opening up.
This study further shows that the three sets of HR
practices (i.e., competency-­
enhancing, motivation-­
enhancing, and opportunity-­enhancing HR practices)
play a role in developing an organizational learning culture in firms. These practices can contribute
to social norms in firms that motivate members to
develop and adopt new ideas by creating opportunities to interact and communicate with each other. This
finding is consistent with LePine et al. (2000), who
suggested that firms focus on developing an environment for employees in which they are provided with
training and development to promote knowledge
creation and adoption. Thus, competency-­enhancing
practices help build a skilled workforce, facilitating the employees to gain and utilize new knowledge. Similarly, motivation-­
enhancing practices
develop shared norms of learning through incentives
and rewards (Chen and Huang, 2009), while the
opportunity-­enhancing practices create a culture that
increases the likelihood of learning among members
through different procedures and designs that promote communication among them and those outside
the organization for idea exchange. A learning culture
© 2022 RADMA and John Wiley & Sons Ltd.
is promoted by the systems within organizations that
motivate members to acquire and implement new
knowledge, providing them the opportunity to carry
out knowledge-­based activities. Implementing these
HR practices facilitates inbound open innovation
by promoting an organizational culture that embeds
learning and promotes knowledge acquisition, internalization, and application. In short, these three sets
of HR practices help develop a learning culture in
firms that promotes inbound open innovation (c.f.
Lau and Ngo, 2004).
We also reveal the intervening role of knowledge
sharing in explaining the effect of HR practices on
open innovation. Connecting knowledge at intra-­and
inter-­organizational levels can accrue to organizations several benefits, including competitive advantage (Bertello et al., 2022). The results suggest that
the three sets of HR practices examined in this study
promote knowledge sharing among organizational
members. Thus, competency-­
enhancing practices
help develop employees’ skills and abilities that
enable them to communicate with each other and
share their ideas, helping firms manage the inflows
and outflows of knowledge. Moreover, firms can
improve open innovation outcomes by motivating
employees through intrinsic and extrinsic rewards
and creating opportunities through work designs
and knowledge-­sharing communities (Behrens and
Patzelt, 2018). The exchanges can be further enhanced
when employees are motivated through appropriate
rewards, incentives, and opportunities to share their
knowledge in the open innovation context. (Camelo-­
Ordaz et al., 2011; Chuang et al., 2016). Therefore,
as the effective exchanges of knowledge and ideas
among members result in knowledge inflows and
outflows, knowledge sharing affects open innovation positively (Svetlik et al., 2007; Camelo-­Ordaz
et al., 2011).
In conclusion, the findings of this study show
that HR practices directed toward enhancing
employee competencies, motivation, and opportunities encourage members to participate in
knowledge-­based activities that promote inbound
open innovation. The associations between these
HR practices and open innovation are explained
through the intervening mechanisms of organizational learning culture and knowledge sharing. As
anticipated, HR practices help develop a culture
conducive to learning and adopting new knowledge
and ideas, which in turn helps enhance open innovation outcomes. Furthermore, these HR practices
enhance inbound open innovation by promoting
knowledge-­sharing among employees, which helps
the members acquire, source, utilize, and commercialize knowledge. These findings add to our current
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On the soft side of open innovation
understanding concerning the role of HR practices,
organizational learning culture and knowledge
sharing in the open innovation paradigm.
4.1. Implications for theory and practice
Firms are increasingly adopting open innovation to
remain competitive in the current perplexing market conditions (Reed et al., 2012). This study developed and empirically tested an integrated model of
competency-­motivation-­opportunity HR practices,
organizational learning culture, knowledge sharing,
and open innovation. By drawing on a comprehensive framework, the study builds on literature in four
broad research areas –­strategic HRM, organizational
culture, knowledge management, and innovation
management. A review of the extant literature indicates that studies have rarely developed models based
at the intersection of these four broad areas (Lau and
Ngo, 2004; Chen and Huang, 2009). Hence, this
study bridges a critical research gap in the literature
by working across boundaries and providing an interdisciplinary perspective. Integrating disparate kinds
of literature into a model explaining the variance in
open innovation outcomes and empirically testing
this model using a rich primary dataset valuably
adds nuance to the open innovation conversation. As
open innovation research trends on the firmament
of management research (Lopez-­Vega et al., 2016),
developing a comprehensive model that clarifies the
untapped HR practices-­open innovation association
offers novel insights. Explaining the roles of organizational learning culture and knowledge sharing
in the relationships described above further deepens
our understanding and clarifies the interplay of these
variables in enhancing open innovation outcomes.
From a practitioner’s perspective, operating in
an environment characterized by porous boundaries, managers must identify appropriate processes
and strategies to improve the odds of success in the
open innovation paradigm (Hameed et al., 2021).
While past studies have provided insights into the
HR practices for firms to remain innovative (Ruël
et al., 2014), this study provides empirical evidence
on the specific role of different HR practices (CMO)
and other variables in enhancing inbound open innovation outcomes. These insights can assist managers
in designing their HR and innovation strategies while
envisioning better open innovation performance.
By implementing competency-­, motivation-­and
opportunity-­
enhancing HR practices, managers
can develop employee skills and motivate them to
acquire and utilize new knowledge and technology to
promote open innovation. Tailoring the HR practices
to be complementary to inbound open innovation
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R&D Management 53, 2, 2023
activities could involve paying keen attention to
recruiting talent and adopting appropriate assessment
tools. Organizations may also organize appropriate
internal and external training programs for employees to develop their competencies. One-­size-­fits-­all
training programs may not yield the best results
since HR practices, particularly those targeted at
enhancing motivation and opportunity, may differ
across employee types. Hence the HR department’s
role in designing and implementing the training programs must take a bespoke approach. Similarly, to
enhance employee motivation for open innovation
activities, a nuanced approach is called for during
performance feedback and the design of incentive
systems (Gardner et al., 2011). Further, HR practices
(e.g., secondments) can enhance opportunities for
employees and expose them to diverse work environments and ideas, affecting inbound open innovation
favorably.
At the same time, organizations can develop an
organizational culture favorable for knowledge creation and learning, one that fosters a learning climate
to encourage employee interactions for knowledge
exchange (Naqshbandi and Kamel, 2017). Relatedly,
since the study highlights the vital role of knowledge
sharing in the success of open innovation, the practitioners can create opportunities for the employees
to interact. For instance, digital technologies have
become increasingly valuable and profoundly impact
how individuals and organizations innovate (Enkel et
al., 2020). The managers may thus consider developing and/or investing in online and offline platforms
to facilitate knowledge exchanges among employees.
4.2. Limitations and future research
This study has some limitations. First, the cross-­
sectional design of data collection makes us cautious about the causal inferences for the tested
associations. While many open innovation studies
have used cross-­sectional data, future studies could
replicate the same model using a longitudinal
design to claim causality with greater confidence.
Second, even though this study considers two
potential mediators that explain the direct associations, it is suggested to identify possible interactional factors that could strengthen or weaken the
effect of HR practices on open innovation. Further,
we focused on inbound open innovation. While this
per se is not a limitation, examining the other facet
of open innovation, that is, outbound open innovation in future research, may be particularly revealing. We further recommend that future research
identify other possible mediators in HR practices-­
open innovation associations, such as the nature
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M. Muzamil Naqshbandi, Sheik Meeran and Adrian Wilkinson
of human capital (Nieves and Quintana, 2018) or
knowledge management capabilities (Chen and
Huang, 2009). This study does not consider the
separate effects of tacit and explicit knowledge on
the linkages studied, which could be an interesting future research area. Further, although the data
for this study were collected from diverse sectors
in the United Kingdom, the generalizability of the
findings should be carefully considered. Future
studies can replicate the same model in other industries and national settings.
Acknowledgments
The authors would like to acknowledge and appreciate the reviewer comments and the advice and support of the Associate Editor and the Editor of R&D
Management during the review process. Errors, if
any, remain ours.
Ethics statement
Not applicable.
Data availability statement
Data available on reasonable request from the
authors.
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M. Muzamil Naqshbandi is a faculty member at the
School of Business & Economics, University of Brunei
Darussalam. Previously, he worked at the University
of Dubai in the UAE and the University of Malaya
in Malaysia. Before joining academia, he worked in
diverse industries such as outsourcing, non-­banking
financial services, and media. Dr. Naqshbandi is
the Associate Editor of the International Journal of
Productivity and Performance Management (Emerald
Publishing), Senior Associate Editor of FIIB
Business Review (Sage Publishing) and Associate
Editor of Organizational Psychology (speciality
section of Frontiers in Psychology and Frontiers in
Communication). He serves on the editorial boards
of several prestigious international journals such as
Journal of Knowledge Management, Management
Decision, European Journal of Innovation
Management, and Leadership & Organization
Development. His current research is focused on
open innovation, leadership and knowledge management. Dr. Naqshbandi has won several international
research grants totaling more than $150,000. He
actively presents his work in international fora and
contributes on the program committees and advisory boards of several international conferences. His
recent work has appeared in leading journals such
as International Business Review, Technological
Forecasting & Social Change, Production Planning
& Control, Industrial Management & Data Systems
among others. Dr. Naqshbandi is reachable at:
virkul@gmail.com or muzamil.naqshbandi@ubd.
edu.bn.
Sheik Meeran, a Fellow of Higher Education
Academy of UK, completed his undergraduate degree with special Honors and University first rank
in Engineering in Madurai University, India. After
graduation, he worked in the industry for nearly
10 years, mostly in production management. He then
continued with his Masters and Doctoral degrees
© 2022 RADMA and John Wiley & Sons Ltd.
in Cranfield, UK in the domain of Manufacturing
Management. Since then, he has been teaching and
is currently an Associate Professor at the University
of Bath, UK. In addition to successfully supervising fourteen PhD/Eng D candidates, he has published widely in international journals of repute
such as Journal of Product Innovation Management,
International Journal of Production Research,
Computer-­
Aided Design, International Journal of
Forecasting. He has co-­authored a book entitled ‘New
Product forecasting entitled: Judgmental, Statistical
and Combination Methods’. In addition to obtaining research funding from different sources totaling
nearly £500,000 during his career in universities in
the UK, Dr. Meeran has been doing consultancy to
various international and UK firms.
Adrian Wilkinson is Professor at Griffith University,
Australia. Prior to his 2006 appointment, Adrian
worked at Loughborough University in the UK where
he was a Professor of Human Resource Management
from 1998, and Director of Research for the Business
School. Adrian has also worked at the Manchester
School of Management at the University of
Manchester Institute of Science and Technology. He
is also a Visiting Professor at Sheffield University.
Adrian has authored/co-­authored/edited thirty books
and over 200 articles in academic journals. He is
an Academician (Fellow) of the Academy of Social
Sciences in the UK and a Fellow of the Academy of
Social Sciences in Australia.
APPENDIX A
Measures
Human resource practices (Chuang et al. 2016)
• The selection of knowledge workers emphasizes
their overall fit to the company (personality, values, etc.).
• The selection of knowledge workers focuses on
their potential to learn and grow.
• If an employee has good technical skills, his/her
interpersonal skills are NOT very important.
• When new employees are being selected for my
organization, their teamwork ability is weighted
heavily in the decision.
• When new employees are being selected for my
organization, their adaptability to the environment
and self-­
adjustment is weighted heavily in the
decision.
• When new employees are being selected for my
organization, their interpersonal relationships
within the company are weighted heavily in the
decision.
• When new employees are being selected for my
organization, their interpersonal relationships with
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On the soft side of open innovation
people outside the company (e.g., suppliers, customers, other professionals) are weighted heavily
in the decision.
• The company invests considerable time and resources in training for knowledge workers.
• The company provides an orientation program for
new knowledge workers to learn the history, culture, and values of the company.
• The company has a mentoring program (individually or as a group) aimed at employee development.
• The company uses mentoring assignments as a
way to encourage employees to learn from each
other.
• The company provides training that improves my
organization’s employees’ ability to learn from
each other.
• The company provides training to improve the interpersonal skills of employees in my organization.
• The company provides training to help my organization’s employees develop and update their technological know-­how.
• The employees in my organization have attended
training designed to improve their working skills.
• The company’s performance management practices emphasize individual improvement and
development.
• Performance appraisals are based on input from
multiple sources (coworkers, supervisors, clients,
etc.).
• Internal candidates take priority over external candidates for knowledge job openings.
• Employees’ pay and rewards are closely linked to
the organization’s overall performance.
• Knowledge workers’ bonuses or incentive plans
are based primarily on the performance of the
company.
• On average the pay level of our knowledge workers is higher than that of our competitors.
• The company offers a variety of incentives (e.g.,
gain sharing, stock option, etc.) to attract and retain top talent.
• The company provides many benefits for knowledge workers to continually learn new knowledge
(e.g., paying tuition costs, supporting attendance
of conferences or other learning events, etc.).
• The company recognizes and rewards employees
who come up with the best new ideas.
• The company rewards employees for sharing new
information and knowledge.
• The company uses job rotation for knowledge
workers to gain experience by moving them across
different functional areas or divisions.
• Members of my organization are evaluated on their
interpersonal relationships with other co-­workers
outside the team.
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• My organization often arranges events for knowledge exchange (e.g., seminars, visits by outside
experts, etc.).
• The company sponsors various social events to encourage contact and relationship building among
employees.
• The company actively encourages knowledge
workers to participate in ‘knowledge communities’ (a bunch of people who have similar interests
communicate and exchange information by using
discussion board, forum, listserv, etc.).
• The company invests considerable time and resources in building and operating communities of
practice (e.g., providing technical support, budgets, rewards, etc.).
Organizational learning culture (Marsick
and Watkins 2003)
• In my organization, people are rewarded for
learning.
• In my organization, people spend time building
trust with each other
• In my organization, teams/groups revise their
thinking as a result of group discussions or information collected
• My organization makes its lessons learned available to all employees.
• My organization recognizes people for taking
initiative.
• My organization works together with the outside
community to meet mutual needs.
• In my organization, leaders continually look for
opportunities to learn.
Inbound open innovation (Sisodiya
wet al. 2013)
• My organization constantly scans the external environment for inputs such as technology, information, ideas, knowledge, etc.
• My organization actively seeks out external
sources of knowledge and technology (e.g., research groups, universities, suppliers, customers,
competitors, etc.) when developing new products.
• My organization believes it is good to use external
sources (e. g., research groups, universities, suppliers, customers, competitors, etc.) to complement
its own R&D.
• My organization often brings in externally developed knowledge and technology to use in conjunction with our own R&D.
• My organization seeks out technologies and patents
from other firms, research groups, or universities.
• My organization purchases external intellectual
property to use in our own R&D.
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M. Muzamil Naqshbandi, Sheik Meeran and Adrian Wilkinson
Knowledge sharing (Wang and Wang 2012)
• People in my organization frequently share existing reports and official documents with members
of my organization.
• People in my organization frequently share reports
and official documents that they prepare by themselves with members of my organization.
• People in my organization frequently collect reports
and official documents from others in their work.
• People in my organization are frequently encouraged by knowledge sharing mechanisms.
• People in my organization are frequently offered a
variety of training and development programs.
• People in my organization are facilitated by IT systems invested for knowledge sharing.
© 2022 RADMA and John Wiley & Sons Ltd.
• People in my organization frequently share knowledge based on their experience.
• People in my organization frequently collect
knowledge from others based on their experience.
• People in my organization frequently share knowledge of know-­where or know-­whom with others.
• People in my organization frequently collect
knowledge of know-­where or know-­whom with
others.
• People in my organization frequently share knowledge based on their expertise.
• People in my organization frequently collect
knowledge from others based on their expertise.
• People in my organization will share lessons from
past failures when they feel necessary.
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On the soft side of open innovation