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The influence of human resourcemanagement on knowledge sharing andinnovation in Spain: the mediating roleof affective commitmentCarmen Camelo-Ordaz a , Joaquín García-Cruz b , Elena Sousa-Ginel b & Ramón Valle-Cabrera ba Universidad de Cádiz, Glorieta Carlos Cano, Cádiz, Spainb Universidad Pablo de Olavide, Ctra. de Utrera Km. 1, Sevilla,SpainPublished online: 19 Apr 2011.

To cite this article: Carmen Camelo-Ordaz , Joaquín García-Cruz , Elena Sousa-Ginel & Ramón Valle-Cabrera (2011) The influence of human resource management on knowledge sharing and innovationin Spain: the mediating role of affective commitment, The International Journal of Human ResourceManagement, 22:07, 1442-1463, DOI: 10.1080/09585192.2011.561960

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The influence of human resource management on knowledge sharingand innovation in Spain: the mediating role of affective commitment

Carmen Camelo-Ordaza, Joaquın Garcıa-Cruzb, Elena Sousa-Ginelb* and

Ramon Valle-Cabrerab

aUniversidad de Cadiz, Glorieta Carlos Cano, Cadiz, Spain; bUniversidad Pablo de Olavide, Ctra.de Utrera Km. 1, Sevilla, Spain

The aim of this paper is to explain and to test empirically how human resourcemanagement (HRM) practices contribute to knowledge sharing and innovation throughemployees’ affective commitment. Results show that HRM practices do not influenceknowledge sharing in a direct way, but they do have a positive effect when affectivecommitment mediates the relationship. We also find a positive relationship betweenknowledge sharing and innovation performance. That is, HRM practices contribute toknowledge creation and innovation through the generation of the affective commitmentnecessary for employees to be willing to share their knowledge. The relationshipsidentified have been tested by applying structural equation models to a sample of 87R&D departments of Spanish innovative companies.

Keywords: affective commitment; human resource management practices; innovation;knowledge management

Introduction

Previous studies have highlighted the importance of knowledge sharing for new

knowledge creation and innovation (Kogut and Zander 1992; Nahapiet and Ghoshal 1998;

Nonaka, Toyama and Nagata 2000; Wu and Cavusgil 2006). Knowledge sharing is a

process that enables the knowledge held by individuals and by groups to be transferred to

the organizational level, where it can be applied to the development of new products,

services and processes (Van den Hooff and Ridder 2004).

Within the knowledge-sharing process, social and human factors play a key role

(Nonaka and Takeuchi 1995). However, despite this importance, the knowledge

management (KM) literature has made only limited use of human resource management

(HRM) concepts and frameworks (Hislop 2003). Recent studies stress the need to advance

jointly in KM and HRM, through building a bridge between both literatures, so that

attention is focused on the individuals themselves and on the impact of HRM practices on

KM practices and innovation (Yahya and Goh 2002; Scholl, Konig, Meyer and Heisig

2004; Oltra 2005; Svetlik and Stavrou-Costea 2007).

This paper is framed within this line of research. Thus, according to Nonaka and

Takeuchi (1995), Storey and Quintas (2001) and Hislop (2003), we propose that the

success of any KM and innovation initiative requires that employees be willing to share

their knowledge and expertise within the organization. However, people usually have a

natural reluctance to share what they know, therefore, organizations do not constitute

ISSN 0958-5192 print/ISSN 1466-4399 online

q 2011 Taylor & Francis

DOI: 10.1080/09585192.2011.561960

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*Corresponding author. Email: esougin@upo.es

The International Journal of Human Resource Management,

Vol. 22, No. 7, April 2011, 1442–1463

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harmonious settings in which knowledge creation and innovation processes take place

smoothly (Scarbrough and Carter 2000; Morris 2001; Hislop 2003; Thompson and Heron

2005). Hence, answers are needed to the question of what the organizations can do to

increase their employees’ willingness to share their knowledge.

Within the field of KM, several studies have illustrated the important role that

organizational managers can play, by applying particular HRM practices in the creation of

an appropriate context in which knowledge-sharing processes take place (Nonaka and

Takeuchi 1995; Nonaka et al. 2000; Cabrera and Cabrera 2005; Cabrera, Collins and

Salgado 2006; Collins and Smith 2006; Chang, Yeh and Yeh 2007). Nevertheless, in order

for people to share what they know, they have to want to do so; for this willingness to exist

people need to feel identified with, and involved in, the organization and its goals, that is,

they need to be committed. Therefore, it is not enough that the management create a

suitable climate that encourages employees to share their knowledge, but it is also

necessary to cultivate employees’ commitment to the organization and its goals (Nonaka

and Takeuchi 1995; Kim and Mauborgne 1998; Nonaka et al. 2000; Takeuchi 2001;

Hislop 2003; Thompson and Heron 2005).

However, although the importance of commitment and HRM has been argued

thoroughly from the theoretical point of view, there have been relatively few researchers

analysing how these two issues jointly affect knowledge-creation processes and,

consequently, innovation (Hislop 2003). Thus, in order to contribute to filling this gap in

the literature, the aim of this paper is to explain, and to test empirically, how particular

HRM practices contribute to knowledge creation and innovation through the generation of

the necessary commitment for employees to be willing to share their knowledge.

By establishing and achieving this aim, we intend to contribute to the literature in three

specific ways. First, our approach incorporates the people perspective into the analysis of

KM processes, which is an issue that has received little attention in the literature (Yahya

and Goh 2002; Scholl et al. 2004; Oltra 2005; Svetlik and Stavrou-Costea 2007). Second,

the study allows us to highlight the mechanisms by which HRM practices contribute to

organizational performance and, in particular, to innovation, which has been one of the

main concerns of the strategic HRM literature (Smith, Collins and Clark 2005; Collins

and Smith 2006; Selvarajan, Ramamoorthy, Flood, MacCurtain and Liu 2007). Finally,

we identify affective commitment as the key element that makes it possible to integrate

KM and HRM (Hislop 2003).

In order to achieve the proposed objective, this paper is structured in five sections.

Following this introduction, we formulate the corresponding hypotheses to explain how

HRM relates to KM and innovation, through affective commitment. The third and fourth

sections are devoted to method and results analysis, respectively. Finally, conclusions,

limitations and future research lines are presented.

Literature review and hypotheses

Knowledge sharing and innovation

It is widely accepted that a firm’s innovation capability is closely linked to its ability to

manage,maintain and create knowledge (Cohen andLevinthal 1990;Madhavan andGrover

1998; Nahapiet and Ghoshal 1998; Smith et al. 2005; Subramaniam and Youndt 2005).

However, organizations cannot create knowledge without individuals, who therefore play a

critical role in knowledge-creation and innovation processes (Nonaka and Takeuchi 1995;

Madhavan and Grover 1998; Ipe 2003; Subramaniam and Youndt 2005). According to

Nonaka and Takeuchi (1995), knowledge creation and innovation should be understood as a

The International Journal of Human Resource Management 1443

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process by which the knowledge held by individuals is enlarged and internalized as a part of

organizational knowledge. The idea underlying this statement is that the knowledge

possessed by individuals must be transferred to the levels of the group and the organization

as a whole, so that it can be applied, giving rise to innovation (Ipe 2003). In other words,

individual knowledge provides the company with the raw material necessary for the

creation of new knowledge and innovation (Brachos, Kostopulos, Sodersquist and

Prastacos 2007). However, unless this knowledge is shared with other individuals and

groups within the organization, it will remain in the domain of the individual and will have

little or no impact on the performance or the innovation capability of the company (Nonaka

and Takeuchi 1995; Ipe 2003; Subramaniam and Youndt 2005).

The literature defines knowledge sharing as the act of placing knowledge possessed by

an individual at the disposition of others within the organization, in such a way that it can

be absorbed and utilized by them. The use of the term ‘sharing’ implies some conscious

action on the part of the individual who possesses the knowledge, and that the sender does

not relinquish ownership of the knowledge but that joint ownership results between the

sender and the recipient (Ipe 2003). It is important to distinguish knowledge sharing from

other concepts such as reporting or transmitting, which are frequently used as synonyms.

Thus, Davenport and Prusak (1998) emphasized that knowledge sharing is a voluntary act,

which distinguishes it from reporting. According to these authors, reporting implies the

exchange of information on the basis of a series of routines or structured formats, while

knowledge sharing is a conscious and voluntary act whereby an individual participates in

the exchange of knowledge even though there is no compulsion to do so. For their part,

Van den Hooff and Van Weenen (2004) stated that sharing implies both giving and

receiving knowledge; therefore, it covers both the transmission and the absorption,

allowing the individual to build new knowledge on the basis of that possessed by others

(Van de Ven 1986; Ipe 2003). Thus, knowledge sharing allows combining previously

unconnected ideas, views, facts and information, which constitutes the basis for the

creation of new knowledge and for innovation (Cohen and Levinthal 1990; Kogut and

Zander 1992; Nahapiet and Ghoshal 1998; Brachos et al. 2007).

The relevance of knowledge sharing for innovation has been theoretically argued in

several studies. Cohen and Levinthal (1990) considered that the interaction among

individuals who possess different knowledge improves the organization’s ability to

innovate. Boland and Tensaki (1995) stateed that the innovation capability of the

organization is the result of the interaction among individuals who possess different kinds

of knowledge. Similarly, several authors argue that knowledge sharing among employees

constitutes a fundamental step in the process of organizational knowledge creation, in such

a way that if it is not effectively performed, it can constitute a serious barrier to the

development of this process, and as a consequence, to innovation effectiveness (Ipe 2003;

Chang et al. 2007).

Recent empirical studies also support the relationship between knowledge sharing and

innovation. Thus, Seidler-de Alwis and Hartmann (2008) found that those organizations that

promote knowledge-sharing processes are more successful in innovation. Swan, Bresnen,

Newell and Maxine (2007), in their study of the factors that affect innovation in the

biomedicine sector found a positive relationship between knowledge sharing and innovation

projects. Finally, Brachos et al. (2007) concluded that when the necessary factors for

motivating individuals to share and transfer knowledge are present, innovation improves.

All these arguments are evidence that knowledge sharing among individuals and

groups within the organization is a critical process for the creation of new knowledge and

for innovation, as formulated in our first hypothesis.

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Hypothesis 1: The extent to which knowledge is shared among organizational members

is positively related to the company’s innovation performance.

The relationship between HRM and KM: affective commitment

In the previous section, we argued that it is important that individuals share their knowledge

for successful innovation (Nonaka 1994; Shih, Tsai, Wu and Lu 2006). However, the

identification of the factors that motivate individuals to share their knowledge is a topic that

has not received much attention in the literature (Chang et al. 2007).

Within KM, the efforts made to identify the appropriate mechanisms for encouraging

knowledge sharing have been centred on the technical aspects of the process, such as the

development and implementation of databases and KM tools and techniques. In fact, much

of this literature is currently classified under the information technologies and systems

field (Robertson and O’Malley-Hammersley 2000). However, Johannessen, Olaisen and

Olsen (1999) stated that it is an error to think that by investing in new technologies, the

behaviours of sharing and creating new knowledge are going to occur naturally; on the

contrary, they suggested that the impetus must be placed on the employees themselves, not

on information systems. Equally, Robertson and O’Malley-Hammersley (2000) found that

the excessive attention paid to the technological dimension has resulted in a failure to take

into account the central role played by individuals and HRM in the processes of

knowledge sharing and innovation. Along this line, several authors suggest that in order to

encourage individuals to share their knowledge, it is necessary to change the way in which

employment relationships are managed (Hibbard and Carrillo 1998; Robertson and

O’Malley-Hammersley 2000; Thompson and Heron 2005). These authors argue that

traditional HRM practices are not wholly appropriate for promoting the creativity and

autonomy needed for knowledge creation and innovation. There is, therefore, a need to

find alternative approaches in the management of human resources in order to motivate

people to share what they know and to participate actively in knowledge creation (Tampoe

1993; Casse 1994; Robertson and O’Malley-Hammersley 2000; Oltra 2005; Cabrera and

Cabrera 2005; Collins and Smith 2006).

High-involvement HRM practices and knowledge sharing

In the literature, recent studies have tried to identify those HRM practices that are most

effective in encouraging employees to share knowledge (Cabrera and Cabrera 2005;

Collins and Smith 2006; Cabrera et al. 2006). The HRM literature offers two basic

perspectives that an organization can adopt to manage relationships with its employees.

On the one hand, the transaction-based perspective, which involves the application of

HRM practices that foster individual short-term exchange relationships between

employees and the organization, represents the traditional perspective. On the other

hand, high-involvement HRM practices, which emphasize mutual long-term exchange

relationships, represent a more innovative perspective (Arthur 1992; Tsui, Pearce, Porter

and Hite 1995; Agarwala 2003; Collins and Smith 2006).

A growing body of evidence suggests that high-involvement practices are more

positively related to organizational performance than are transaction-based practices

(Arthur 1992; Tsui et al. 1995; Agarwala 2003; Smith et al. 2005; Collins and Smith 2006;

Selvarajan et al. 2007). The exact individual practices that create a high-involvement

perspective may differ from one study to another, but they generally include a combination

of the following: creation of growth opportunities for employees through internal labour

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markets; recruitment and selection based more on the fit between external candidates and

the company than on the specific requirements of the job; appraisal and reward systems

based on team or organization performance; compensation practices designed to promote

knowledge sharing among team members; training and development practices that

emphasize employees’ long-term growth, team building and the development of firm-

specific knowledge, etc. (Arthur 1992; Delaney and Huselid 1996; Delery and Doty 1996;

Youndt, Snell, Dean and Lepak 1996; Tsui, Pearce, Porter and Tripoli 1997; Collins and

Smith 2006; Pare and Tremblai 2007).

Regarding knowledge-sharing processes, in the literature we find studies showing

evidence that the application, whether individually or jointly, of high-involvement

HRM practices may encourage employees to share what they know. Thus, Chang et al.

(2007) demonstrated that a reward system that values collective efforts and cooperative

behaviours among members of different functional areas may be an effective mechanism

for fostering knowledge creation and innovation. According to these authors, because the

creation of knowledge is an activity that requires collective efforts, team-based rewards

are an especially suitable practice for encouraging knowledge-sharing and knowledge-

exchange processes. On the other hand, Cabrera and Cabrera (2005) and Cabrera et al.

(2006) found a positive relationship between HRM practices orientated towards teamwork

and identification with the company, and the employees’ willingness to share knowledge.

Collins and Smith (2006) also concluded that high-involvement HRM practices contribute

to the creation of an organizational social climate where employees are willing to share

their knowledge.

All these arguments support the idea that high-involvement HRM practices directly

encourage knowledge-sharing behaviours. However, some authors suggest that these

practices may not strengthen these behaviours directly; therefore, they may be ineffective

for this purpose because of the inherent characteristics of individual knowledge (Kim and

Mauborgne 1998; Child and Rodrigues 2001; Cabrera and Cabrera 2005). Individual

knowledge is a valuable resource that may confer status and power on the person who

possesses it; hence, sharing this knowledge is often perceived by the individual as a loss of

influence within the organization (Storey and Barnett 2000; Hislop 2003; Willem and

Scarbrough 2006). The potential value of individual knowledge generates tension between

employees and the organization over who owns and who controls their knowledge. For this

reason, any attempt on the part of the management to manage and control it, or to make

people share it, is likely to produce an internal conflict in the employee (Storey and Barnett

2000). Therefore, the questions that now arise are as follows. How can organizations

resolve this conflict? What are the mechanisms by which high-involvement HRM

practices influence the extent to which knowledge is shared in the organization?

Affective commitment and knowledge sharing

A recent body of research suggests that people’s tendency to share knowledge depends not

only on the HRM practices devised and applied by the management but also on

psychological factors, such as affective commitment (Hislop 2003; Cabrera and Cabrera

2005; Thompson and Heron 2005, 2006; Chang et al. 2007). Therefore, in line with these

authors, we consider it necessary to analyse the role played by affective commitment in

knowledge-sharing processes.

Organizational commitment is a concept derived from social psychology and

sociology, evolving for more than five decades in the field of organizational behaviour.

As Meyer and Allen (1991, 1997) state that commitment is the reason that justifies

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the decision of an employee to remain loyal to the organization in which he/she works.

These authors distinguish three factors that explain this behaviour of remaining in the

organization, which give rise to the three forms of commitment that comprise the

tri-dimensional model: affective (wish), continuance (need) and normative (obligation)

commitment. In the case of the affective dimension, the factor that explains why the

employee remains in the organization is the personal attachment and identification with

the organization’s goals and values. Thus, employees with strong affective commitment

stay loyal to their company because they want to. However, employees with continuance

or normative commitment remain with the organization because they need to or because

they feel they ought to, respectively.

Of these three forms of organizational commitment, in this study, we focus on the

affective, because that is considered to exert the strongest influence on general attitudes

and behaviours at work, driving employees to overcome their natural resistance to

knowledge sharing (Allen and Meyer 1996; Meyer, Stanley, Hercovitch and Topolnytsky

2002; Hislop 2003). There are two reasons supporting this statement. First, knowledge

sharing is an activity equivalent to an extra-role behaviour, to the extent that it involves a

discretionary effort, which goes beyond the employee’s contractual obligations and

without any counterbalance explicitly established for it (Kim andMauborgne 1997; Hislop

2003; Cabrera and Cabrera 2005; Tagliaventi and Mattarelli 2006). An extensive literature

supports a positive relationship between affective commitment and extra-role behaviour;

furthermore, some authors consider this to be the only form of commitment that leads an

employee to display this kind of behaviour (Schaubroeck and Ganster 1991; Organ and

Ryan 1995; Robison and Morrison 1995; Coyle-Shapiro and Kessler 2000; Hislop 2003).

Thus, several studies maintain that when the connection that links individuals and groups

with the organization is affective commitment, employees are willing to perform an extra

discretionary effort that, by aligning their interests with those of the organization, drives

them to provide higher levels of organizational citizenship behaviour and knowledge

sharing (O’Reilly and Chatman 1986; Meyer and Allen 1997; Coff and Rousseau 2000;

Storey and Quintas 2001). Second, the repository of organizational knowledge gives rise

to what is known as a public good social dilemma (Connolly and Thorn 1990; Connolly,

Thorn and Heminger 1992). This dilemma arises when the joint contributions made by

some members of the community are available to all the members, regardless of individual

contributions. The reward structure of a public good encourages opportunistic behaviour,

in such a way that employees will tend not to contribute to the provision of the shared

knowledge and at the same time will try to free-ride on the contributions of others. If that

strategy dominates, the public good does not get provided for. The literature on social

dilemmas has shown that the likelihood of an individual not displaying opportunistic

behaviour and so contributing to the provision of a public good depends on his/her sense of

group or organization identification, that is, on his/her affective commitment (Cabrera and

Cabrera 2005).

These arguments are reflected in many studies that stress the importance of

commitment in KM processes. Thus, Takeuchi (2001) considers that the personal

commitment of the employees and their identification with the company and its goals are

crucial for knowledge-creation processes. According to Alvesson (2000), the companies

that are successful in knowledge creation and appropriation are those that are capable of

generating high levels of employee commitment to the organization. Van der Bij, Song

and Weggeman (2003) stated that commitment is translated into greater involvement with

the organization’s aims, into a continuous flow of communication, and into higher levels

of social interaction, having a positive effect on the individual tendency to share

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knowledge. Similarly, recent empirical studies have provided evidence about the role of

organizational commitment as a key predictor of employees’ knowledge-sharing

behaviour (Van den Hooff and Ridder 2004; Van den Hooff and Van Weenen 2004;

Cabrera and Cabrera 2005; Song, Van der Bij and Weggeman 2006; Lin 2007).

On the basis of all these arguments, it seems clear that affective commitment to the

organization and its goals is a necessary condition for employees to be willing to share

their knowledge. Consequently, in order to encourage this behaviour, an organization must

seek ways to develop high levels of affective commitment in its employees.

High-involvement HRM practices, affective commitment and knowledge sharing

In previous sections of this paper, it has been argued that high-involvement HRM practices

promote knowledge-sharing behaviours in employees. Equally, we have shown that

affective commitment constitutes a necessary condition for employees to be willing to

share what they know. We propose that these arguments are closely related, such that

affective commitment is the mechanism through which high-involvement HRM practices

influence the extent to which knowledge is shared in the organization.

Following the argument in the strategic HRM literature (Arthur 1992; Tsui et al. 1997;

Collins and Smith 2006), it can be considered that high-involvement HRM practices do not

exert their influence directly on the extent to which knowledge is shared but rather that they

contribute to the generation of the commitment necessary for employees to be willing to

share what they know. Through long-term investments in employees, high-involvement

HRM practices signal to employees that they represent a major source of competitive

advantage for the company, leading in turn to a greater sense of organizational attachment

and membership, that is, greater affective commitment (Fiorito, Bozeman and Youndt

1997; Pare and Tremblai 2007). Equally, high-involvement practices are likely to be

perceived as a concrete signal of the company’s support, trust and commitment towards its

employees. This fosters the establishment of a mutually reinforcing high-investment

employer–employee relationship that motivates employees to make greater contributions

to the organizational goals, by displaying extra discretionary efforts, such as organizational

citizenship behaviour and knowledge sharing (Eisenberger, Huntington, Hutchison and

Sowa 1986; Guzzo and Noonan 1994; Collins and Smith 2006; Pare and Tremblai 2007).

Studies can be found in the literature that show empirically the role of commitment as a

mechanism through which variables related to HRM influence organizational performance,

thus providing evidence of the mediating role of commitment (Agarwala 2003). Benkhoff

(1997) conducted a study in which commitment was shown as the connection between

HRM, on the one hand; and employee satisfaction, the intention to remainwith the company

and the organizational performance, on the other. Yeung and Berman (1997) demonstrate

that the impact of HRM practices on organizational performance was more marked when

these practices generated commitment. Froma study of technology-intensive organizations,

Thompson and Heron (2006) concluded that affective commitment plays a critical role by

mediating the relationship between psychological contract fulfilment and knowledge-

sharing behaviour, which in turn strongly relates to innovation performance.

In short, we propose that high-involvement HRM practices have a positive influence

on employees’ willingness to share knowledge, to the extent that these practices are

capable of generating affective commitment of the employees (Agarwala 2003; Hislop

2003; Thompson and Heron 2005). Therefore, affective commitment emerges as the

central variable that makes it possible to integrate HRM and KM (Hislop 2003). On the

basis of these arguments, we formulate our second research hypothesis.

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Hypothesis 2: Affective commitment mediates between high-involvement HRM

practices and the extent to which knowledge is shared among

organizational members.

Figure 1 illustrates the two hypotheses put forward.

Method

Sample and procedures

Three criteria were adopted for selecting the companies of the target population: (1) they

should belong to innovative industries; (2) they should possess an R&D department or

equivalent and (3) they should have more than 50 employees.

Regarding the first selection criterion, it is shown in the literature that knowledge-

sharing processes are especially important for those companies that need to innovate in

order to maintain and enhance their competitive advantage (Thompson and Heron 2005,

2006; Lin 2007; Huang, Davison and Gu 2008). For this reason, it was decided to include

in the population companies belonging to the five Spanish industries classified as more

innovative according to the Survey on Technological Innovation of Companies conducted

by the Spanish National Institute of Statistics (INE). These industries are the chemical

industries; involving manufacture of mechanical machinery, equipment and material;

manufacture of electrical machinery and material; manufacture of electronic material and

equipment; and manufacture of motorized vehicles (CNAE classifications 24, 29, 31, 32

and 34, respectively). This selection criterion avoids possible bias that could result from

the consideration of only one industry (Thompson and Heron 2005, 2006; Brachos et al.

2007; Lin 2007; Hsu and Wang 2008; Saenz, Aramburu and Rivera 2009) and, at the same

time, provides homogeneity, because all the companies included in the population are

characterized by being intensive in knowledge and innovation, therefore, they are

companies in which the topic of study of the research is especially relevant (Saenz

et al. 2009).

According to the second selection criterion, the companies to be included in the

population are those that have an R&D department. The reason for this decision is that the

R&D department is the organizational area that assumes the highest responsibility for

Affectivecommitment

HRM PracticesInnovation

performanceKnowledge

sharingH1

H2

Figure 1. The influence of HRM practices on KM and innovation through employees’ affectivecommitment.

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knowledge-creation processes, and, therefore, it is the area in which knowledge-sharing

behaviours acquire the most importance (Thompson and Heron 2005, 2006). Moreover,

the creation of organizational knowledge that gives rise to new products and/or services

requires the exchange and combination of knowledge from different organizational areas,

such as marketing, production and finance (Kogut and Zander 1992; Nonaka and Takeuchi

1995; Collins and Smith 2006). For these reasons, in this research, we analyse the extent to

which the members of the R&D department share knowledge with employees belonging to

other departments.

Finally, only companies with more than 50 employees are considered in this study,

in order to focus on firms that are most likely to have formally established HRM systems

(Huselid 1995; Collins and Smith 2006).

To identify the companies with more than 50 employees and belonging to the five

Spanish industries characterized as more innovative, the DUNS 50,000 database 2004

version was used. An initial population of 942 companies was obtained by this procedure.

With the aim of ascertaining the existence of an R&D department or similar, the profile of

each of these companies was examined using secondary data obtained from web pages and

specialist journals. Telephone calls were made in those cases for which secondary data

were not available. The total number of companies that claimed to have an R&D

department or similar was 619; therefore, this was taken as the final and definitive

population to analyse.

To obtain the information, a questionnaire was devised based on a review of the existing

literature. A panel of 12 academic experts was created to edit and improve the items

included in the questionnaire; they reviewed each of the proposed questions in detail so that

respondents could understand them clearly. Their suggestions were then incorporated in the

final version of the questionnaire. This was sent to the director/manager of the R&D

department of each company; this executive is the person who has the most complete

and comprehensive information on the functioning of this department, and is, therefore,

the most appropriate person to evaluate the variables of relevance for this study (Snow and

Hrebiniak 1980; Huber and Power 1985).

A methodology of first contacting the company, then sending the questionnaire, and

then having one or more follow-ups (Cycyota and Harrison 2002) was carried out for the

survey, which took place in the last quarter of 2005. Thus, once the unit in which the

innovation activity was undertaken in each company had been identified, the person

responsible was contacted by telephone, the nature and objectives of the study were

presented, and they were notified of the sending of the questionnaire. Periodic reminders

were given to those companies that had not yet completed and returned the questionnaire;

on the termination of the fieldwork, a total of 87 valid questionnaires had been received,

representing a response rate of 14.05%.This response rate is considered acceptable, because

it is similar to that obtained in other studies on knowledge and innovation conducted in the

Spanish context (Aragon-Sanchez and Sanchez-Marın 2005; Perez, Montes and Vazquez

2005; Camelo-Ordaz, Perez-Luno and Sousa-Ginel 2009; Lopez-Cabrales, Perez-Luno and

Valle-Cabrera 2009; Saenz et al. 2009).

Table 1 summarizes the information about the empirical setting.

Two tests were carried out to check the non-response bias. With the first test, the aim

was to analyse whether there were significant differences, with respect to the activity,

between the companies in the sample that responded to the survey and those that did not.

For this, a contingency table was used to which a Chi-squared test was applied; from the

result of this (x 2 ¼ 2.782, p ¼ 0.734 . 0.05), it can be stated that there are no significant

differences between the companies in the two groups. The second test consisted of

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applying a t-test of equality of means for independent samples, regarding the number of

employees. Again, from the results obtained (t ¼ 20.763, p ¼ 0.446 . 0.05), it is clear

that there are no significant differences between the companies in the two groups.

Therefore, we conclude that there is not a problem of non-response bias in our data

because of industry and company size.

The problem of common method bias was tested using the Harman one-factor test

(Scott and Bruce 1994; Konrad and Linnehan 1995; Simonin 2004). This test consisted of

performing an exploratory factor analysis of principal components using all the items of

the questionnaire. We obtained several factors with eigenvalues greater than one that

explained 75.728% of the total variance. Because the first factor did not explain more than

half of the total variance (25.068%), we concluded that there does not seem to be a

problem of common method bias (Podsakoff and Organ 1986).

Measures

In this study, items used to operationalize the constructs were mainly adapted from

previous studies. All constructs were measured using multiple items. A list of items for

each scale is presented in Appendix A. The measurement approach for each theoretical

construct in the model is described briefly below.

Dependent variable

Innovation performance

After reviewing the scales reported in the literature for measuring innovation performance

(Booz, Allen and Hamilton 1982; OECD/Eurostat 1997; Avlonitis, Papastathopoulou and

Gounaris 2001; Darroch and Jardine 2002; Darroch 2003; Alegre, Lapiedra and Chiva

2006), a measure was devised on the basis of obtaining responses on a Likert scale of 7

points (1 ¼ less than competitors; 7 ¼ more than competitors).

Independent variables

High-involvement HRM practices

The method for the measurement of HRM practices was taken from the work of Lepak and

Snell (2002). The responses were obtained on a Likert scale of 7 points (1 ¼ almost never,

and 7 ¼ almost always).

Table 1. Technical data sheet on the empirical investigation.

Population 619 Spanish companies with more than50 employees, belonging to the fivemost innovative industries, according to thesurvey of the INE

Scope National (Spain)Sample 87 companiesResponse rate 14.05%Sampling error ^9.94%Confidence level 95.5%Information collection instrument Electronic questionnaire

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Organizational affective commitment

The items used for this variable are those proposed by Meyer and Allen (1991, 1997)

measured on a Likert scale of 1–7 (1 ¼ totally disagree, 7 ¼ totally agree). On the

assumption that the manager makes decisions relative to how reality is perceived, we have

adapted the scale to apply to the perception that the manager responsible for R&D has

regarding the level of commitment shown by employees.

Knowledge sharing

To assess the extent to which R&D employees share knowledge with employees from

other organizational areas, we based our approach on the measurement by Cummings

(2004), utilizing a Likert scale of 7 points (1 ¼ almost never, 7 ¼ almost always).

Data analysis

For the statistical treatment of the data, we utilize structural equation models (SEM),

following the two-step method (Anderson and Gerbing 1988). Thus, we first develop the

measurement model based on confirmatory factor analysis (CFA), and from this, second,

we build the structural model.

There are two reasons that justify the application of SEM to analyse the data. In the

first place, utilizing indicators for measuring latent variables implies assuming errors in the

measurements. In consequence, we need to ascertain the degree to which these indicators

are valid and reliable. In the second place, given that we want to analyse simultaneous

relationships between latent variables, we need to confirm the extent to which the causal

relationships specified in the model proposed are consistent with the data available

(Bollen 1989).

With respect to the reliability and validity of the scales, in addition to the content

validity supported by the literature review conducted, we carry out dimensionality and

reliability analyses, and convergent and discriminant validity analysis. All these analyses

are performed using the EQS software (Bentler 1995).

The dimensionality analysis is performed through the CFA applied individually to

each construct. From the fit of these models, we devise the measurement model that

enables us to carry out the corresponding analysis of reliability and validity of the scales.

This model presents a satisfactory fit, as can be inferred from reading the goodness-of-fit

indexes (x 2 ¼ 148.2741, df ¼ 129, p ¼ 0.11782; BB–NFI ¼ 0.786; BB–NNFI ¼ 0.958;

CFI ¼ 0.964; GFI ¼ 0.828; AGFI ¼ 0.772; RMSEA ¼ 0.042) because all of them are

within the limits customarily accepted as valid (Mueller 1996). At the same time, we

confirm that all the standardized factor loadings are significant and higher than 0.7 (Hair,

Anderson, Totham and Black 1999). From Table 2, it can be stated that the scales are

reliable, and that convergent and discriminant validity exist.

The composite reliability (CR) confirms the reliability of the scales, because in all

cases, it is greater than 0.7. The convergent validity is confirmed by the average variance

extracted (AVE). The AVE values appear in the diagonal of the table, and in all cases, are

greater than or equal to 0.5. Finally, following Fornell and Larcker’s (1981) procedure,

we can state that there is divergent validity by confirming that the AVE is greater than the

square of the correlations existing between each pair of factors. Furthermore, we verified

that none of the confidence intervals established for the correlations have a value of one

(Bagozzi 1995).

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Results

Hypotheses were tested by estimating the corresponding covariance structure models.

Applying these models, it is relatively simple to test H1. This consists of checking the

significance of the parameter that estimates the relationship between the variables that

define the hypothesis. However, it is more complex to test H2, because this involves

studying a mediating effect, which means checking that the conditions of mediation are

met (Baron and Kenny 1986) and explaining whether this mediation is full or partial.

For this, starting from the measurement model, we present in Table 3 the correlations

existing between the different factors and in the diagonal of the matrix, the CR associated

with each factor. In this way, we ensure that these results are applicable to the structural

model (Aryee, Budhwar and Chen 2002).

From the results obtained in the table of correlations, it can be stated that the three

conditions necessary for mediation to exist are met. In other words, the correlations among

HRM practices–knowledge sharing, HRM practices–affective commitment, and affective

commitment–knowledge sharing are significant. To confirm whether this mediation

relationship is full or partial, it is necessary to develop two structural models, one of partial

mediation, the other of full mediation, and check which one presents the better fit.

We start from the theoretical model proposed in Figure 1, which implies partial

mediation. Figure 2 shows the estimated parameters, with their statistical significance

in parentheses. Goodness-of-fit indexes (x 2 ¼ 148.5595, df ¼ 131, p ¼ 0.13994;

BB–NFI ¼ 0.786; BB–NNFI ¼ 0.962; CFI ¼ 0.968; GFI ¼ 0.828; AGFI ¼ 0.775;

RMSEA ¼ 0.04) show that the model presents a satisfactory fit (Mueller 1996).

From a reading of the significance of the estimated parameters, in Figure 2, it can be seen

that the relationship between HRM practices and knowledge sharing is not significant,

although the correlation between these variables (Table 3) is significant ( p # 0.01). The

other two relationships (HRM practices–affective commitment and affective commit-

ment–knowledge sharing) are significant ( p # 0.001 and 0.05, respectively), supporting

Table 2. CR, AVE and squared correlations between factors.

CR 0.873643 0.859958 0.85545 0.810061

AVE F1 F2 F3 F4

F1. HRM practices 0.53652F2. Affective commitment 0.370881 0.607793F3. Knowledge sharing 0.182329 0.219024 0.599093F4. Innovation 0.010609 0.000441 0.045796 0.517466

Note: CR (shown in the first row of the matrix); AVE (shown in the bold diagonal of the matrix); the rest of thenumbers show the squared correlations between factors.

Table 3. Mean (m), standard deviation (sd), correlations between the factors, CR.

M SD F1 F2 F3 F4

F1. HRM practices 4.99 1.06 0.873643F2. Affective commitment 4.62 0.89 0.609** 0.859958F3. Knowledge sharing 3.94 1.23 0.427** 0.468** 0.85545F4. Innovation 4.50 0.99 0.103 0.021 0.214*** 0.810061

Note: ***p # 0.1; **p # 0.01; *p # 0.05.

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the result of the correlation analysis. A first interpretation of these results leads us to think

that the mediating effect exerted by commitment is full, not partial.

To corroborate this interpretation, we consider the structural model of full mediation,

eliminating the direct relationship between HRM practices and knowledge sharing (Figure

3). The goodness-of-fit indexes of this model of full mediation (x 2 ¼ 150.7189, df ¼ 132,

p ¼ 0.12667; BB–NFI ¼ 0.783; BB–NNFI ¼ 0.960; CFI ¼ 0.965; GFI ¼ 0.825;

AGFI ¼ 0.774; RMSEA ¼ 0.041) show that this model also presents a satisfactory fit

(Mueller 1996).

To ascertain which of the two models presents the better fit, we apply a Chi-squared

test, using the sbdiff.exe software developed by Satorra and Bentler (2001). From the

result obtained (x 2(1) ¼ 2.3465, p ¼ 0.125568), it can be stated that there are no significant

differences between the models. In consequence, we can state that the mediating effect of

affective commitment is not partial but full. That is, HRM practices are related to

knowledge sharing through affective commitment.

Considering the results obtained with this full mediation model (Figure 3), we

conclude that H1, which postulates a direct and positive relationship between knowledge

sharing and innovation, is supported ( p # 0.1). H2 postulates a mediating effect of

employees’ affective commitment between high-involvement HRM practices and

knowledge sharing, is supported (t ¼ 2.924; p # 0.01).

Affectivecommitment

HRM practicesInnovation

performanceKnowledge

sharing

0.608***(t =4.34)

0.228(t =1.461)

0.327*(t =2.202)

H10.209†

(t = 1.687)

H2

Figure 2. Results of partial mediation model. Note: †p # 0.1 (t $ 1.645); *p # 0.05 (t $ 1.960);

**p # 0.01 (t $ 2.576); ***p # 0.001 (t $ 3.291).

Affectivecommitment

HRM practicesInnovation

performanceKnowledge

sharing

0.619**(t = 4.449) 0.483**

(t = 3.811)

H10.207†

(t = 1.669)H2

Figure 3. Results of full mediation model. Note: †p # 0.1 (t $ 1.645); *p # 0.05 (t $ 1.960);

**p # 0.01 (t $ 2.576); ***p # 0.001 (t $ 3.291).

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Discussion and conclusions

With the purpose of building a link between HRM and KM, the focus of this study is on

analysing the role played by individual employees and the company’s HRM practices in

KM processes. More specifically, the aim of this paper has been to explain and to test

empirically how high-involvement HRM practices contribute to knowledge creation and

innovation, through the generation of the affective commitment necessary for employees

to be willing to share their knowledge.

One of the relevant findings of this research is the confirmation that the extent to which

the members of the R&D department share knowledge with employees belonging to other

organizational areas constitutes a key process for the innovative performance of the

company. This result supports the argument that, in order for knowledge to be created and

exploited, it needs to be shared, and it is this knowledge-sharing process that leads to the

generation of new ideas, processes and products, that is, to innovation (Nonaka, Toyama

and Konno 2001; Lin 2007).

Within this process, the employees of the R&D department play a critical role, because

they can provide the company with a competitive advantage through the effective

generation, deployment, transfer and integration of knowledge (Liao 2008; Ortın and

Santamarıa 2009). R&D employees represent a highly qualified and knowledge-intensive

occupational group, which constitutes the main source of creativity and innovation of the

firm. As such, they can be considered the core employees of the companies that compete in

innovative industries (Dosi 1982; Thompson and Heron 2005, 2006; Benson and Brown

2007). However, as Thamhain (2003) stated, the key challenge for companies is not the

generation of innovative ideas at the R&D stage, but the effective transfer of those

ideas from the discovery stage to the market. This process requires the establishment of

inter-functional networks, in which R&D employees are willing both to give and to

receive knowledge from the other business areas (Sundgren, Dimenas, Gustafsson and

Selart 2005), which constitutes a key success factor for the development of market-

orientated innovation (Debruyne et al. 2002).

It is important to note that, although this constitutes a theoretical argument widely

supported and shared in the KM literature, it is only recently that researchers have begun to

analyse empirically the effects of knowledge sharing on the innovation performance of

companies, reaching conclusions similar to those of this research (Collins and Smith 2006;

Brachos et al. 2007; Liao, Fei and Chen 2007; Lin 2007; Swan et al. 2007; Liao 2008;

Saenz et al. 2009). In particular, the study of Saenz et al. (2009) analyses these processes

in companies belonging to industrial sectors and with high levels of R&D expenditure,

concluding that knowledge sharing is a key factor to take into account for improving

companies’ innovative capability. Furthermore, the research of Liao (2008) provides

evidence of how important it is for the innovative capability of the company that the

members of the R&D department share their knowledge.

Our second contribution comes from analysing the role played by high-involvement

HRM practices and affective commitment in encouraging knowledge-sharing behaviours.

The results show that high-involvement HRM practices do not directly influence the extent

to which R&D employees share knowledge, but rather that these practices contribute to the

generation of the affective commitment necessary for these employees to be willing to

overcome their natural resistance to share what they know.

Thus, from this study, it can be inferred that practices such as the development of team-

work skills, and the application of appraisal and reward systems based on teamperformance,

promote the transfer and sharing of knowledge, not directly as stated by some authors

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(Helleloid and Simonin 1994; Lepak, Takeuchi and Snell 2003), but indirectly, through the

positive effect of these practices on the level of affective commitment. Thus, the results

obtained seem to suggest that affective commitment sets the tone of the employment

relationship (Konovsky and Pugh 1994; Thompson and Heron 2005). In other words,

if high-involvement HRM practices are not capable of generating the involvement and

identification of the employees with the organizational values and goals, they are likely to

have less impact on knowledge-creation and innovation processes.

We conclude that, through the application of high-involvement HRM practices,

managers will be able to influence the willingness of R&D employees to share knowledge,

but only if they can make these practices work to generate strong and stable employment

relationships based on mutual affective commitment. This form of commitment

constitutes the primary mechanism by which relational capital is fostered and encouraged

in organizations (Nahapiet and Ghoshal 1998; Leana and Van Buren 1999; Thompson and

Heron 2005; Subramaniam and Youndt 2005).

Another important contribution underlying this research is that it has tried to

incorporate the people perspective in the study of knowledge-creation and innovation

processes. In this sense, the literature on KM recognizes the key role played by social and

human factors in knowledge-creation processes (Foss 2007). However, it is only recently

that researchers have begun to put emphasis on the need to establish linkage between

HRM and KM (Yahya and Goh 2002; Hislop 2003; Scholl et al. 2004; Oltra 2005; Svetlik

and Stavrou-Costea 2007). Likewise, there are still very few empirical studies that have

dealt with these questions, and some of those have focused on analysing the appropriate

mechanisms to get employees to develop innovative behaviours (Thompson and Heron

2006; Lin 2007). However, from our point of view, knowledge creation and innovation

should be understood as a collective effort. The real challenge that researchers in this field

face is that of ascertaining the mechanisms through which knowledge is transferred from

the individual to the organizational level, giving rise to innovation.

A final issue of this research we would like to emphasize is that by limiting our focus to

innovation-intensive companies, we analysed the role played by employees and by the

HRM in a sample in which knowledge-sharing behaviours are extremely important for the

firm’s survival (Collins and Smith 2006; Saenz et al. 2009). By focusing on R&D

employees, we have been able to analyse more accurately the effect of high-involvement

HRM practices on the attitudes and knowledge-sharing behaviours of this highly qualified

occupational group. Most previous studies considering these issues have used

heterogeneous occupational samples. However, recent research has shown that this

design can lead to error because the HRM strategies applied by companies, and their

consequences for employees’ attitudes and behaviours, can differ from one occupational

group to another (Tsui et al. 1997; Lepak and Snell 1999, 2002; Thompson and Heron

2005; Lin 2007). In particular, the paper of Lepak and Snell (2002) demonstrates that

organizations apply different HRM configurations based on the value and uniqueness of

the human capital of each occupational group. The study by Ortın and Santamarıa (2009)

also provides evidence that companies adapt their HRM practices to the idiosyncratic

needs of R&D employees. Finally, Benson and Brown (2007) present evidence that the

HRM practices that generate high levels of commitment in knowledge workers can be

detrimental to the commitment of other groups of employees in the same organization.

Therefore, the principal challenge that companies face is to develop and apply HRM

policies that recognize the differences among different occupational groups, but without

creating conflict among them that could put at risk the knowledge-sharing behaviours

among different areas.

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Despite the contributions stated, this research presents some limitations that should

be dealt with in future studies. First, because the analysis was carried out in the Spanish

national context, it cannot provide the basis for the generalization of the findings. However,

we think that having consideredmedium size, and large companies in innovative industries,

gives greater validity to the study regarding the generalization of the findings. Companies

in innovative industries usually compete in international contexts and, although it is true

that the national culture influences individuals’ behaviour, internationalization plays an

important role in the homogenization of processes and behaviours in companies. However,

this limitation leads us to consider the need to use cross-national samples in future studies.

A second limitation of this research is related to the size of the sample analysed. The

findings should be verified in a larger sample in order to draw conclusions that are more

generalized. Nevertheless, studies with purposes similar to this research have, equally,

utilized relatively small samples. (Camelo-Ordaz et al. 2009; Lopez-Cabrales et al. 2009;

Saenz et al. 2009). The principal reason lies in the difficulty of obtaining information from

companies for this kind of analysis. As a result of this difficulty, many researchers have

carried out case studies, or have conducted research with MBA students extrapolating their

results to the business world (Lin and Chen 2006; Cho, Li and Su 2007; Sondergaard and

Harmsen 2007; Huang et al. 2008; Wolfe and Loraas 2008). On the other hand, we should

also mention that the size of the sample has not constituted a problem for the application of

SEM. We are aware that authors such as Bearden, Sharma and Teel (1982), Bone, Sharma

and Shimp (1989), and Hair et al. (1999) recommend applying SEM on larger samples.

However, if the model to be studied is not very complex and presents a good fit, the use of

this technique is perfectly applicable. Thus, studies can be found like that of Lopez-

Cabrales et al. (2009), which apply SEM to samples similar to ours.

Finally, given that the data are cross-sectional, we believe that it would be desirable to

conduct longitudinal studies to explore and understand in greater depth the causal

relationships among the variables that are relevant in this research.

Acknowledgements

Financial support from the Spanish MEC Project SEC2006-15105 and from the Junta de AndalucıaProject SEJ-02478 is gratefully acknowledged. The authors also wish to thank Harry Sapienza, theeditor and the anonymous reviewers for providing valuable feedback on earlier drafts of this paper.

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Appendix A. Measures

HRM practices (Lepak and Snell 2002)On average, how often are the following practices applied to the management of R&D employees?Firm emphasizes promotion from withinPerformance appraisals include developmental feedbackSelection process assesses the ability to collaborate and work in a teamTraining activities focus on team building and interpersonal relationsAppraisals are based on team performanceAppraisals focus on employees’ ability to work with others

Affective commitment (Meyer and Allen 1991, 1997)Please rate your agreement with the following affirmations regarding R&D employees.Employees would be very happy to spend the rest of their career with this organizationEmployees really feel as if this organization’s problems were their own problemsEmployees are not emotionally attached to this organization (þ )This organization has great personal meaning for our employees

Knowledge sharing (Cummings 2004)On average, how often do R&D members share each type of knowledge with members of otherareas?General overview (e.g., goals, milestones estimates or responsibilities)Specific requirements (e.g., numerical projections, market forecast or order request)Analytical techniques (e.g., statistical tools, detailed methods or testing procedures)Progress report (e.g., status updates, resources problems or personnel evaluations)

Innovation (Booz et al. 1982; OECD/Eurostat 1997; Avlonitis et al. 2001; Darroch and Jardine 2002;Darroch 2003; Alegre et al. 2006)Please rate the situation of your company compared with competitors regarding the following issues.Introduction to the market of technologically new products developed by the company (totally orpartially)Development of new products line/rangeFrequency of renewal of old products by others with significant changesProduct innovation performed by the company

Note: Items marked with (þ ) are formulated in reverse terms.

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