76473523

HOW TO BRIDGE THE GAP BETWEEN RESEARCH KNOWLEDGE AND ACTION? EXPLORING THE ENABLERS OF KNOWLEDGE BROKERS’ ACTIVITIES

Hajer Hammami, Laval University, Québec City, Canada

Nabil Amara, Laval University, Québec City, Canada Réjean Landry, Laval University, Québec City, Canada

ABSTRACT

In the health care system, the issue of knowledge transfer is expanding rapidly and it has become a major concern for both researchers and decision-makers. However, field observations show that there is still a significant gap between available knowledge and how to apply it. Among the recommended strategies is the practice of knowledge brokering. It is performed by knowledge brokers whose role is to bridge this gap, to help turn research into policies, and to ensure their implementation. In this perspective, the conceptual framework proposed in this thesis represents an interesting avenue, not only to guide the actions of brokers, but also to support a broader reflection on their activities and the determinants associated with them, in order to help develop more effective knowledge transfer strategies in the decision-making process, and to harmonize the relationship between research and action. The data used in this research were collected with a questionnaire surveying a community of practice (CoP) of 301 knowledge brokers primarily engaged in professional activities at the Canadian Health Services Research Foundation (CHSRF). Keywords: Knowledge brokers, Organizational climate, Organizational culture, interaction social, MES, Healthcare 1. INTRODUCTION

In today's highly competitive business environment, the importance of knowledge is widely recognized as a critical resource for the competitive advantage of firms (Nonaka et. al., 2000). However, the main challenge facing most organizations is how to manage and use knowledge for better value creation. Indeed, knowledge is of limited value if it is not shared and transferred throughout an organization. Hence, implementing knowledge transfer activities that allow firms to effectively leverage their knowledge is a managerial concern. As the pace of global competition quickens, knowledge transfer has been proclaimed as one of the most complex and messy processes which go beyond the one-way push of information from researchers to decision-makers (Graham and al., 2006). To transfer knowledge so that it can be integrated, individuals must be able to access expertise in order to build on the work of others (Murray and O’Mahony, 2007). With regard to knowledge transfer from the sender to its receivers, managers must also consider the design of the channels over which knowledge is

to be provided (Thomas Hutzschenreuter and Julian Horstkotte, 2010). Furthermore, Carlile (2004) argued that while transferring knowledge is a simple information-processing act, and interpreting knowledge requires translation, the actual transformation of inputs is yet a more complex process because the actors involved may not share the same interests. Therefore, an important role in the knowledge transfer processes is assumed by intermediary actors, generally known as knowledge brokers, that can act as ‘‘bridges’’, positioned at the interface between the worlds of researchers and decision-makers. They are seen as the human force behind knowledge transfer, finding, assessing and interpreting evidence, facilitating interaction, and identifying emerging research questions (CHSRE, 2003; Ward and al., 2009). These actors are characterized by a high degree of communication and mediation skills (Dobbins and al., 2009), networks’ centrality (Giuliani and Bell, 2005), absorptive capacity (Cohen and Levinthal, 1990), and social capital (Boschma and Wal, 2007), which allow them to access a wide range of organizations, collect knowledge across researchers, and foster its circulation and sharing inside local and global networks. This

JOURNAL OF INTERNATIONAL BUSINESS AND ECONOMICS, Volume 12, Number 3, 2012 48

allows to suggest that the most successful knowledge brokers are those who have the capacity to bring together the art and science of influence in order to transform research into action. Nevertheless, despite the growing number of studies dealing with the topic of knowledge brokers, little attention has been paid to identify which factors may support their activities, in terms of actors’ capability to acquire, integrate, adapt, disseminate knowledge, and create links. Previous studies have proposed several definitions of knowledge brokers and identified their main role. However, these studies present two main gaps. The first one regards the activities of a knowledge broker: what are the activities of a knowledge broker that could help him to transform research into action? Secondly, these studies do not attempt to identify which factors can determine the realization of knowledge brokers’ activities. What are the determinants considered as enablers and which can influence directly and indirectly the realization of the brokers’ knowledge transfer activities? The present paper tries to fill these gaps. In particular, our general objective is double. Firstly, we wish to develop a dynamic process of knowledge transfer in which the brokers are at the center and achieve a series of activities in order to facilitate the bringing together of researchers and users. Secondly, we wish to build and test a model of determinants of knowledge transfer, taking into account the direct and indirect effects of the explanatory variables, with the aim of developing levers of actions and of better practices as regards knowledge transfer. 1.1. Contribution of the paper A knowledge broker, one of the popular emerging concepts for knowledge translation and exchange strategy, can promote interaction between researchers and users. However, little is known of the role which he can play, reducing the gap between knowledge and practice. In order to fill this lack in knowledge, our study contributes to the advancement of knowledge by focusing on intermediaries operating as brokers, whose aim is to bring parties together to effectively transfer useful knowledge for solving problems in decision-making (Lomas, 1997; Roy and Fortin, 2009; Dobbins et al., 2009).

Many studies have assigned specific activities to knowledge brokers to effectively transfer research results towards the practice environment (Howells, 2006; Landry et al., 2007b; Dobbins et al., 2009; Roy and Fortin, 2009). However, none of these studies have examined all the activities of knowledge brokers in a dynamic process of knowledge transfer. This paper aims to contribute to advance knowledge by considering that knowledge brokering involves a range of different activities that leads us to suggest that it thus means far more than simply moving knowledge; it also means transforming knowledge into action. Despite a large number of studies on the determinants of knowledge transfer in organizations (Wu et al., 2007; Landry et al., 2007a; Héliot and Riley, 2010), little research has concerned the study of the role played by the organizational and individual factors in the effectiveness of brokers’ knowledge transfer activities. Our study contributes to the advancement of knowledge by exploring the direct and indirect effects to explain the transfer of knowledge, taking into account the modeling of mediating variables such as organizational climate and social interaction. The paper is structured as follows. The second section describes the theoretical framework, introducing the notion of knowledge brokers, their activities of knowledge transfer and their explanatory factors. The third section explains the methodology of the research, including the description of the collected data, measuring instruments, and the analytical plan. Then, in section four, the results of the measurement and structural models are presented. Finally, implications, limitations, and further research are described in the conclusion. 2. REVIEW OF LITERATURE

2.1. Process of the knowledge brokers’ activities There are several views concerning knowledge transfer. Some researchers considered knowledge transfer as knowledge shared among people (Huber, 1991). While Szulanski (1996) focused on the relationship aspect of knowledge transfer by defining it as "dyadic exchanges of knowledge between a source and a recipient in which the identity of the recipient matters", others focused on the resulting changes to the recipient by seeing knowledge transfer as the


process through which one unit is affected by the experience of another (Argote and al.,, 2000). According to the knowledge-based view, certain authors studied knowledge transfer while insisting on the source of knowledge (the researcher) (Szulanski, 1996; Landry and al., 2007a). Others studied the mechanisms of practical the application of knowledge (practitioners) (Landry and al., 2003; Hemsley-Brown, 2004). However, a relatively recent literature has highlighted the absence of interfaces of communication between research and practice and, in particular, the absence of individuals able to cause the exchange between the two communities: researchers and experts (Huberman and Gather-Thurler, 1991; Roy and Fortin, 2009). In addition, even if communication and information technologies greatly facilitated the access of experts to the results of research, there remains an important gap between produced knowledge and that which is really used in practice. Particularly in the health sector, it is this difference between research and practice which would be at the base of the undervaluation of scientific knowledge during decision-making processes. For this reason, several authors have stipulated that a greater bringing together of researchers and decision-makers would be likely to increase the use of the evidence in the health sector (Lomas, 1997; Dobbins and al., 2009; Ward and al., 2009; Meyer, 2010). It is in such a context that the brokers of knowledge can play a crucial role in innovation and knowledge transfer useful for decision-making, and this while effectively contributing to build bridges, span boundaries, and otherwise facilitate the translation and adoption of ideas (Williams, 2002). A major contribution of this work would be to develop a process of knowledge transfer in which brokers are at the center, and achieve a series of activities in order to facilitate the bringing together of researchers and decision-makers. Acquisition For knowledge to be managed, it must first of all be captured or acquired in some useful form. Thus, the broker should be able to acquire knowledge from multiple sources: explicit (codified) and tacit (non codified) (Nonaka et al,

2000). This would make it flexible in the broker’s mind so that it can easily be applied to a variety of situations and transferred to practitioners. For Hagardon and Sutton (1997), the acquisition of new knowledge by brokers provides the organization with solutions or ideas for future applications. Integration As an initial recipient of knowledge generated by researchers, the broker must integrate this new knowledge to better transfer it to practitioners (Cillo, 2005; Lomas, 2007). For Hargadon (2003), the knowledge broker is considered a true integrator of knowledge, as he collects, combines, and tests the most promising ideas. In addition, the ultimate goal of a knowledge broker in this integration step is to propose methods and tools to facilitate the transfer of knowledge from researchers to practitioners (Dobbins et al., 2009). So, knowledge brokers must integrate their knowledge that is shared at the team level to realize its value. Integration work involves the selection, rejection, and synthesis of disparate ideas and contributions into a coherent whole. Adaptation The activity of the broker at this stage is to adjust the content of the results according to the characteristics of the target, so as to facilitate the use of knowledge. In this regard, many authors argue that the knowledge broker enjoys a strategic position to translate research into plain language, in an accessible manner, and to provide it to practitioners, using diffusion techniques (CHSRF, 2003; Dobbins et al., 2009; Roy and Fortin, 2009). For his part, Cillo (2005) found that through the activity of adaptation, knowledge brokers may reduce the cognitive distance that usually exists between the communities, using different languages and concepts. At this stage, brokers collect, adapt, and render timely knowledge accessible to decision-makers to make informed decisions (Roy and Fortin, 2009). Dissemination The literature reveals that the release of research results by knowledge brokers is based, first of all, on determining the appropriate audience and second, on adapting the message and its inherent means of communication (Huberman and Gather-Thurler, 1991; Kirst, 2000). This finding corroborates the comments of Hailey et al. (2008), underlining the


importance of knowledge brokers in the dissemination of research results in health services organizations. According to these authors, knowledge brokers can provide and use relevant information and disseminate it to target audiences, in order to better contribute to decision-making. Creation of links The literature shows that it is crucial to promote greater interactions between researchers and practitioners through the activities of knowledge brokers (Cillo, 2005; Roy and Fortin, 2009). The purpose of this activity, the creation of links, is to increase opportunities for communication mechanisms between researchers and practitioners, to enhance their impact on knowledge transfer (Dobbins et al., 2009). The shared knowledge, coming from brokers, is accessible and sustained through interactions with others actors (Lomas, 2007). As we can see, the role of brokers involves performing a range of different activities that leads us to suggest that knowledge brokering thus means far more than simply moving knowledge; it also means transforming knowledge into action. 2.2. Explanatory variables Organizational climate The construct of organizational climate has been the subject of thorough discussions concerning definition, content, theory, measurement, and analysis (Schneider, 1975; Denison, 1996; James and al., 2008). Different types of climates have been identified with a focus either on the organizational or the individual, i.e. organizational climate and psychological climate. These two aspects of climate are considered to be multidimensional phenomena that describe the nature of perception that employees have of their experiences within their organization (James et al., 2008). In our study, we will focus on the organizational climate which refers to employees’ shared perceptions of the types of behaviors and actions that are rewarded and supported by the organization’s policies, practices and procedures (Schneider, 1975). Researchers face a number of conceptual challenges in the measurement of organizational climate (Patterson et al., 2005). It has been argued that dimensions represent a useful method of measuring organizational climate. In

this paper we choose to adopt four global dimensions: autonomy, organizational support, cooperative interaction and innovation (see appendix 1). In our study, we decided to construct a generic concept of organizational climate by choosing to combine these four dimensions into one general index in the analysis. There are several studies that document the importance of organizational climate as a determinant of organizational outcomes (Vijayakumar, 2007; Gagnon and al., 2008; Sarros and al., 2008). They suggest that the existence of certain characteristics of the work environment may facilitate and encourage learning processes in terms of knowledge generation and knowledge sharing, as well as knowledge application (Vijayakumar, 2007; Sarros and al., 2008). Organizational climate, therefore, is thought to exert a strong impact on individual motivation to achieve work results (Gagnon et al., 2008). The organizational climate has also been found to influence knowledge and skills by increasing participation in activities. Indeed, the literature supports the premise that the organizational climate is an important determinant of knowledge transfer (Chen and Lin, 2004; Chen and Huang, 2007). In this context, our interest in the concept of organizational climate is mainly explained by its influence on the knowledge transfer activities performed by brokers. Hence, it is necessary to have a favourable organizational climate characterized by autonomy, support, collaboration, and innovation that stimulates brokers’ interactions and facilitates knowledge transfer. H1. A positive perception of organizational climate, characterized by autonomy, support, cooperation and innovation, has a direct effect on knowledge brokers’ activities. H2. A positive perception of organizational climate, characterized by autonomy, support, cooperation and innovation, has an indirect effect on knowledge brokers’ activities by the mediation of social interaction. Organizational culture The conceptualization of organizational climate represents a source of debate when compared with the concept of organizational culture. Consistent with recent studies, we regard climate and culture as distinctly identifiable elements within organizations. Organizational


culture is defined as « the set of values, beliefs and norms, meanings and practices shared by personnel in an organization » (Robbin, 2001), that guides their attitudes and their behaviours, and that also constitutes a pervasive context for everything they do and think in an organization (Mullins, 2005). In the listed literature, Organizational culture proved to be an important factor which makes it possible to influence the organizational climate and the attitudes of work (Glisson and James, 2002). In this direction, certain authors think that the organizational climate represents an important potential of mediation between organizational culture and organizational results (Aarons and Sawitzky, 2006). Thus, it would be judicious to check empirically if an organizational culture will make it possible to create a favourable climate so that the personal reports of the knowledge brokers can support the objectives to be reached, and in particular, to carry out their activities of knowledge transfer. In this article, we suggest that the organizational culture, by its specific beliefs and its values, will indirectly influence the transfer of knowledge by the mediation of the organizational climate. H3. The organizational culture has an indirect effect on the knowledge brokers’ activities by the mediation of the organizational climate. Social interaction Researchers of social capital have defined the concept in various ways, but there is some consensus that social capital means networks of relations (among people) through which certain phenomena, such as trust, norms of reciprocity or fast exchange of information, become possible. These phenomena, in turn, make, for example, collaboration and communication easier, and reduce the need for formal agreements and control (Auranen, 2007). Prior studies have recognized the importance of interpersonal social interaction for enabling knowledge management behavior among individuals (Chen and Huang, 2007). Landry et al. (2002) observed that firms’ willingness to innovate is explained by a structural dimension of social capital, which was measured by participation in business meetings, associations and networks, as well as the intensity of personal network ties between firms’ employees and outside actors.

Asymmetry of information between researchers and users of research arises when the users cannot precisely evaluate the applicability of the transferred research until they attempt to translate it into new or improved products or services (Landry, and al., 2007c). In a context of asymmetry, knowledge transfer is unlikely if researchers and users of research do not have frequent interactions. Thus, the knowledge broker, occupying a central position in the social network (Van Wijk, 2008), should forge links and increase the interaction between producers and users of research to bridge this information asymmetry and facilitate knowledge transfer activities. It thus follows that the bonds maintained between researchers and experts are one of the principal determinants of the transfer of knowledge (Landry and al., 2007a). This leads us to suggest that the social interaction of the brokers with the other actors will allow the former to facilitate the achievement of their knowledge transfer activities H4. The social interaction maintained between the brokers and their peers has a direct effect on their knowledge transfer activities. Type of organizations The organizational structure is frequently mentioned in the literature as a major determinant of the transfer of knowledge (Landry and al., 2007a; Chen and Huang, 2007) and it is treated differently depending on the authors. Several of them emphasize the modes of structure (organic versus mechanical), the size and the types of organization, like measurements of the organizational structure affecting the transfer of knowledge (Dickson, 2006; Landry and al., 2007a). In this article, we stress the various places of affiliation, in which the brokers evolve, as major determinants of knowledge transfer activities. H5. The types of organizations have a direct effect on the knowledge brokers’ activities. Cognitive capacity The literature on knowledge brokering can study the impact of the level of education on how knowledge intermediaries perform their activities. However, Landry et al., (2007b) have shown that individuals engaged in brokerage need to have the capacity to evaluate the collected information for its quality, relevance and applicability to a given problem. Specifically,


one can assume that the higher the level of education, the greater the capacity to absorb knowledge (Cohen and Levinthal, 1990). In turn, a greater capacity for absorption may facilitate the ability of brokers to transfer knowledge to provide practical solutions for members of their organization. H6. The cognitive capacity has a direct effect on the knowledge brokers’ activities. 3. RESEARCH METHODOLOGY 3.1. Data Studied population The population of this study consists of the members of the knowledge brokerage community of practices (CoP) under the initiative of the Canadian Health Services Research Foundation (CHSRF) in Canada. The purpose of this CoP is to share knowledge and expertise on knowledge brokerage, to focus on learning and furthering the practice of knowledge brokerage, to develop and share a collective repertoire of communal resources (including activities and means of participation), and to operate as an interdependent network defined by the collaborative efforts of the members. Since its inception in 2003, members of the CoP have participated in national or regional workshops, and have shared knowledge brokering resources (through forums and directories of experts) during these face-to-face activities as well as on

the virtual platform of the CoP supported by CHSRF (http://www.chsrf.ca/brokering/). This population was composed of 441 individuals in October 2005. We decided to exclude 12 individuals from the study who work for CHSRF in order to avoid response biases. The final population of the study was therefore made up of 429 individuals. Questionnaire development In this study, we used secondary data that were collected through a questionnaire survey conducted among a sample of knowledge brokers involved in the activities of the Canadian Health Services Research Foundation (CHSRF) and this was pre-tested by a survey firm. The authors of the survey (Landry et al., 2006) developed the questionnaire, drawing on theoretical and empirical work conducted in the field of study, namely brokerage and knowledge transfer. It is organized into three parts: the first deals with the knowledge brokers’ activities, including: the acquisition of new knowledge, integration of new knowledge, adaptation of research results, dissemination of research results, and linkages they have with potential users of research. The second part focuses on the organizational context in which knowledge brokers’ activities take place (i.e., organizational climate, organizational culture and organizational structure).

Figure 1: Hypothesized structural equation model

Organizational culture

Types of organizations

Cognitive capacity

Organizational climate

Social interaction

Knowledge brokers activities

H1

H2H3

H4

H5

H6


The final part was devoted to the socio-professional profile of knowledge brokers (gender, education level, status, experience, etc.). Most of the items in the questionnaire used 5-point Likert-type scales. The questionnaire on knowledge brokering in the health services organizations is considered to be the first empirical contribution to this field of study. To our knowledge, no such questionnaire has been used so far in this particular context in Canada. Data collection All individuals included in the population were contacted for an interview. The questionnaire and the 429 names composing the population were sent to a private survey firm using the CATI (computer-assisted telephone interviewing) technology, which allows for embedding data coding and data entry simultaneously within the data collection phase. The survey was administered by telephone between November 2005 and February 2006. Out of the 429 individuals, 17 respondents were found to be ineligible (i.e.. individuals not involved in knowledge brokering activities, or who had changed jobs and were no longer involved in professional activities related to knowledge brokering), and 63 respondents could not be reached after many telephone calls. In order to increase the response rate, 169 individuals were contacted by e-mail to inform them about the study, its objectives and its sponsors. A total of 39 individuals refused to participate in the study (after one recall). Finally, the survey generated 301 usable questionnaires for a net response rate of 74.69% (301/403). 3.2. Instruments and measurements Our conceptual framework includes four constructs measured by several items (activities of transfer of knowledge, organizational climate, organizational culture and social interaction) and two binary variables (types of organizations and cognitive capacity). The scales of measurements associated with the latent variables are of Likert type in 5 points. All these measurements are presented in their entirety in Appendix 2. Knowledge transfer activities In this study, the dependent variable was operationalized using six indexes relating to knowledge transfer activities. This measure of knowledge transfer includes 23 items that are

divided into six activities: acquisition of tacit knowledge, acquisition of explicit knowledge, integration of knowledge, adaptation of research results, dissemination of research results, and link creation. Appendix 2 lists all the survey items used to measure each factor. For each statement, a 5-point scale ranging from 1 (never) to 5 (very often) is used. The sum of the response scores for the six factors, which initially ranged from 5 to 25, was weighted in order to take into account “does not apply” answers. Thus, for each respondent, the sum of the score was divided by the number of applicable items. Even though the initial index has integer values from 1 to 5, once weighted, it can take on non-integer values. Organizational climate The measure of organizational climate includes 16 items that were divided into four dimensions: interactive cooperation, autonomy, organizational support and innovation. Appendix 2 lists all survey items used to measure each factor. For each statement, a 5-point scale ranging from 1 (never) to 5 (very often) was used. The construction of the four indexes associated with the dimensions of organizational climate was based on the same logic as the knowledge transfer activities. Organizational culture and social interaction In this study, the two other variables based on multiple-item scales, and included in the econometric model, are the organizational culture which was measured by only one index reflecting the frequency with which the brokers express their degree of agreement on 4 items, and the index measuring the intensity of social interaction reflecting the frequency of the contacts which the brokers maintain with several actors. Appendix 2 lists all survey items used to measure each factor. For each statement, a 5-point scale ranging from 1 (strongly disagree) to 5 (strongly agree) was used. Since all indexes were based on multiple-item scales, we conducted a Principal Components Factor Analysis (PCFA) with Varimax Rotation (PCFA) on the construct scales to assess their unidimensionality (Ahire and Devaray, 2001). Types of organizations and Cognitive capacity These two variables, types of organizations and broker’s cognitive assets, were measured with a series of binary variables defined in appendix 2. 3.3. Analytical plan The data collected were analyzed by statistical treatments, first, to validate the measurements


of the variables and, secondly, to establish these causal relationships to validate the research hypothesis. Next, a structural equation modeling (SEM) was conducted with the EQS program, assessing confirmatory measurement models (confirmatory factor analysis) and confirmatory structural models (structural equation) to test the dependency relationships between the variables. The structural equation modeling technique (SEM) was employed to assess the fit and to compare the hypothetical competing models between them. A popular SEM program, EQS 6.1 (Bentler, 2005), was used for modeling to test statistically, in a simultaneous analysis, the entire system of variables, and to then determine the extent to which it is consistent with data (Byrne, 2006). EQS 6.1 operates upon the normalized variance-covariance matrix derived from the raw database (Bentler, 1995). In our study, the maximum likelihood method was used as the estimation procedure because our data were normally distributed (Byrne, 2006). To evaluate the measurement and structural models, a mix of recommended fit index was used. Table 2 presents the limits of the validation of measurement which must respect certain thresholds. Therefore, the convergent validity, the degree to which multiple attempts to measure the same concept are in agreement, was evaluated by examining the factor loading within each construct, the composite reliability and the variance extracted (Hair et al., 1998). The recommended cut-off values should exceed 0.7 and 0.5 respectively. The significance level of the regression parameters for the relationship between the latent variables in the estimated models (path coefficients) was denoted by the critical ratio or t-value (t-value >1.96, p < .05). 4. RESULTS

4.1. Measurement model The reliability and validity of measurement for each construct associated with the activities of knowledge transfer, organizational climate, organizational culture and social interaction were tested by using an exploratory and confirmatory factor analysis based on the 301 samples collected from knowledge brokers.

Exploratory factor analysis An exploratory factor analysis was performed with SPSS 13.0. A principal component analysis with varimax rotation was used to examine measures. Factors with eigenvalue above 1.0 were extracted in each construct; these cumulatively explained over 56% of the total variance (see appendix 2). Items with low loadings on the intended factor or high cross-loadings on other factors were removed. The resulting scales were then evaluated for reliability using Cronbach’s α. All had an acceptable reliability (α > 0.65). Confirmatory factor analysis A confirmatory factor analysis was performed with EQS 6.1. The fit of the four measurement models, namely knowledge transfer activities, organizational climate, social interaction and organizational culture, were estimated by various index (see table 7). The ratio of to degrees-of freedom does not exceed 2.5 for the four measurement models, which was within the recommended value of 3. RMSEA showed the discrepancy between the proposed model and the population covariance matrix, ranging from 0.038 and 0.68, which was lower than the recommended cut-off of 0.08. All other index (CFI, NNFI) exceeded the commonly acceptance levels (0.90), demonstrating that the measurement models exhibited a good fit with the data. In addition, all composite reliabilities of the four constructs exceeded the recommended level (0.7). Table 1 summarizes the results of the fit index and the convergent validity of the measurement models. The measurement models exhibited a good level of model fit as well as evidence of convergent validity. 4.2. Structural model The structural model analysis was conducted to examine the hypothesized relationships among constructs. All indicators were fixed with the loadings and corresponding error coefficients obtained from the measurement model to avoid possible effects of measurement-structural interaction on parameter estimation (Bentler, 1995). The results from the structural model used to test the hypothesized research model are shown in figure 2.


Table 1: Fit index and convergent validity of the measurement models Constructs

Absolute fit index

Incremental fit index

Parsimonious fit Index

Convergent validity

RMSEA CFI NNFI dl / dl Composite reliabilities

( )

Validity

Knowledge transfer activities

372.1 0.049 0.935 0.926 224 1.66 0.932 0.75


214.5 0.065 0.937 0.923 99 2.16 0.925 0.71


4.296 0.064 0.995

0.984 2 2.14 0.871 0.80

Social interaction

4.830 0.028 0.998 0.994 4 1.21 0.753 0.82

To be considered adequate, the individual item reliability should be greater than 0.5 and/or a significant t-value should be observed for each indicator (Jöreskog and Sörbom, 1996). Table 2 summarizes the structural path between the indicators and constructs. The results indicate that all factor loadings exceed 0.5 and each indicator is significant at 0.05 levels. Table 2. Structural path between the indicators and constructs

Structural path Path coefficients

StandardizedPath

coefficients

t-values Significance

Kn

ow

led

ge

tran

sfer

ac

tivi

ties

AT K KTA 1 1,77 1,67 2,72 2,62 2,63

0.34 0,56 0,67 0,81 0,74 0,72

---- 5,01 5,08 5,20 5,14 5,02

---- Sig. Sig. Sig. Sig. Sig.

AE K KTA INTEG KTA ADAPT KTA DISSEM KTA CRELINK KTA

Org

aniz

atio

nal

cl

imat

e

INTCOOP OC 1 1.05 0.47 0.66

0,79 0,74 0,47 0,57

---- 11,19 6,12 8,28

---- Sig. Sig. Sig.

AUTON OC

ORGSUPP OC

INNOV OC

Org

aniz

atio

nal

cu

ltu

re

MISSION OCU 1 1.17 1.46 0.86

0,66 0,79 0,99 0,66

---- 11,72 11,62 8,71

---- Sig. Sig. Sig.

VISION OCU

OBJECT OCU

VALUES OCU

So

cial

in

terc

atio

n

SI1 SI 1 0.84 0.94 0.78 1.04

0,70 0,60 0,68 0,56 0,73

---- 8,81 10,16 7,23 11,78

---- Sig. Sig. Sig. Sig.

SI2 SI SI3 SI SI4 SI SI5 SI

KTA: knowledge transfer activities, ATK: acquisition of tacit knowledge, AEK: acquisition of explicit knowledge, INTEG: integration, ADAPT: adaptation, DISSEM: dissemination, CRELINK: creation of links, OC: organizational climate, INTECOOP: cooperative interaction, AUTON: autonomy, ORGSUPP: organizational support, INNOV: innovation, SI: social interaction, Sig: significant.


Table 3. Structural path between the exogenous and endogenous variables Structural path Path

coefficients

Standardized Path

coefficients

t-values signification

OCU OC 0,55 0,64 9,92 Sig. OC SI 0,45 0,40 4,31 Sig. OC KTA 0,66 0,66 2,34 Sig. SI KTA 0,20 0,63 4,60 Sig. ADM KTA -0,08 -0,14 -2,43 Sig. RECH KTA -0,06 -0,10 -2,00 Sig. FOND KTA -0,12 -0,15 -2,64 Sig. PRIV KTA 0,001 0,001 0,02 Non Sig. BACH KTA -0,11 -0,15 -2,44 Sig. MAST KTA 0,10 -0,20 -3,28 Sig.

OC 41,5 SI 15,8

KTA 64,8

Residulas OC 0,76 SI 0,92

KTA 0,59 KTA: knowledge transfer activities, OCU; organizational culture, OC: organizational climate, SI: social interaction, ADM: administrations of health, RECH: university or another research organization, FOND: non-profit foundation or a funding agency, PRIV: private firm, BACH: bachelor’s degree, MAST: master’s degree, Sig: significant.

Thus, we could proceed to examine the path coefficients of the structural model presented in table 3, the structural path between the exogenous and endogenous variables. This involved estimating the path coefficients and R² value. Path coefficients indicated the strengths of the relationships between the independent and dependent variables, whereas the R² value was a measure of the predictive power of a model for the dependent variables. As can be seen in fig. 2 and table 3, the relationship of organizational climate and knowledge transfer activities is divided into a direct path (path coefficient = 0.66, t = 2.34) and an indirect one through social interaction (path coefficient = 0.40, t = 4.31). Thus, social interaction was a mediator between organizational climate and knowledge transfer activities. These results attest to the direct and indirect relation between organizational climate and knowledge transfer activities by the intermediation of social interaction. As shown in fig. 2 and table 3, organizational climate significantly influences, on the one hand, social interaction, accounting for 15.8% of the variance and providing support for hypothesis H1; on the other hand, it had significant influence on knowledge transfer activities; it

accounted for 64.8% of variance and provided support for hypothesis H2. Hence, it is reasonable to conclude that organizational climate in terms of autonomy, support, cooperation and innovation, positively influences the effectiveness of brokers’ knowledge transfer activities. In the full model, there was no significant direct effect between organizational culture and knowledge transfer activities (path coefficient = 0.019, p = 0.152). However, the indirect effect through organizational climate was significant (path coefficient = 0.64, t = 9.92). Therefore, organizational climate was a mediator between organizational culture and knowledge transfer activities, which together explain 41.5% of the dependant variable’s variance, providing support for hypothesis H3. The relationship between social interaction and knowledge transfer activities was statistically significant (path coefficient = 0.63, t = 4.60). Additionally, the relationship between three of the four types of organization and knowledge transfer activities were statistically significant, respectively, for the administration of health (path coefficient = 0.-0.14, t = -2.43), for university and research organization (path coefficient = -0.10, t = -2.00), and for foundation


and funding agency (path coefficient = -0.15, t = -2.64). However, private firm had no significant effect on knowledge transfer activities (path coefficient = -0.001, t = -2.64). Finally, the level of education and knowledge transfer activities were statistically significant, respectively, for bachelor’s (path coefficient = -0.15, t = -2.44) and master’s (path coefficient = -0.20, t = -0.13). As hypothesized, knowledge transfer activities were significantly associated with social interaction, three of organization types and level of education. It explained 64.8% of the dependent variable’s variance. All paths had significant effects. Hypotheses H4, H5 and H6 were supported. The overall validity of the model results was evaluated with respect to goodness-of-fit index (see table 4). The GFI and AGFI exceeded the commonly acceptance levels (0.80). The chi-square/degrees of freedom ratio was 2.71, which was within the recommended value of 3. The RMSEA showed the discrepancy between the proposed model and the population covariance matrix to be 0.069, which was lower than the recommended cut-off of 0.08. All other index (NNFI, NFI, CFI) exceeded the commonly acceptance levels (0.90), demonstrating that the overall model exhibited a good fit with the data, and therefore, provided support to the overall validity of the hypothesized models and hypothesis testing results. 5. DISCUSSION AND CONCLUSION The structural equations modeling enabled us to identify major findings that are discussed in the following. The first findings show that organizational climate can be a major determinant to promote brokers’ knowledge transfer. In a second step, emphasis was placed on organizational climate as a significant predictor of brokers’ knowledge transfer activities. To do so, a better understanding of the relationship between organizational climate and brokers’ transfer of knowledge activities was first developed. Thus, our study aims to contribute to the advancement of knowledge by developing a better understanding of the scope of an organizational climate that fosters the ability of brokers to effectively conduct their activities of transfer knowledge, and this can be achieved through activities that focus on organizational support, interaction between staff, the

development of personal autonomy, and the establishment of an innovative climate. Our results showed that organizational climate can foster the skills and abilities of brokers to develop interactions and networks of links between researchers and decision-makers, which in turn has a direct influence on the achievement of knowledge transfer activities. Another finding emerging from the analysis highlights the importance of social interaction developed by knowledge brokers in achieving their knowledge transfer activities. As a result, networks and mechanisms of links allow greater exposure to two communities, researchers and decision-makers, which increase the level of knowledge transfer between them. A fourth finding that emerged from our analysis refers to the indirect influence of organizational culture on the achievement of knowledge transfer activities mediated by a favourable organizational climate. In other words, the organizational culture that relates to the common realities, symbols and rituals shared by members of an organization, including brokers, contributes significantly to shape the norms and expectations of their behaviour. This consequently influences the perceptions that knowledge brokers maintain with regard to their organizational climate. A fifth report to be noted is that relating to the direct effects that can be exerted by the types of organizations to which the brokers are attached, except for the private company, on the realization of knowledge transfer activities. Our results showed that the brokers affiliated to the services providing care are more willing to carry out their activities of transfer of knowledge than their counterparts who work in health administrations, universities and organizations of research or in foundations and organizations of financing. It is in such a place of practice, of the clinical type, that the knowledge brokers will be more suitable for carrying out their transfer of knowledge activities. A last observation indicates that the level of education, i.e. bachelor’s or master’s, is, in turn, significantly and negatively related to the achievement of knowledge transfer activities by brokers. These results highlight that brokers with Ph.D. have more ability to engage and to carry out their knowledge transfer activities.


Type of the organizations

ADM

RECHE

FOND

PRIVE

Cognitive capacity

BACH

MAST

Table 4 : Fit index of the structural model GFI AGFI NNFI NFI CFI RMSEA / dl

Index values

0.811 0.828 0.986 0.980 0.987 0.058 2.71

Recommended limits: GFI and AGFI 0.8; NFI and NNFI 0.90; CFI 0.90; RMSEA ≤ 0.08; / dl ≤ 3

Figure 2 : Results for the structural equation model



Social interaction

Knowledge transfer activities

Service delivery organization is the reference category.

Ph.D. is the reference category

0.64*

‐0.14*

‐0.10*

0.001

‐0.15*

‐0.15*

‐0.20*

0.019

0.40*0.66*

0.63*

Indicates significant relationship

Indicates non‐significant relationship


Above all, the findings of this paper should be considered as exploratory in an area where empirical studies are still scant. In particular, this exploratory study embodies two limits that need to be pointed out. The first limit is that the study did not examine the effect of organizational climate and its dimensions on each broker’s knowledge transfer activities separately. That would have yielded precise results and rigor regarding the relationship between organizational climate and brokers’ knowledge transfer activities. Second, this study focuses primarily on organizational attributes, including organizational climate, organizational culture, and social interaction and organizational structure, to explain the achievement of knowledge transfer activities. There are clearly other personal attributes that warrant discussion, such as motivation, experience and status. Our study was limited to studying only the educational level of the brokers to explain the realization of their knowledge transfer activities. Hence, the literature has revealed a plethora of individual and organizational determinants which should be considered for future research. Clearly, further studies should aim to better develop an integrated conceptual framework that will test direct and indirect links between a set of explanatory variables (organizational and individual) and the brokers’ knowledge transfer activities. REFERENCES : Aarons, G. A. and Sawitzky A. C., «

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AUTHOR PROFILES:

Hajer Hammami: Ph.D. candidate, Department of Management, Faculty of Business, Laval University, Québec City, QC, Canada,

Nabil Amara: Professor, Department of Management, Faculty of Business, Laval University, Québec City, QC, Canada,

Réjean Landry: Professor, Department of Management, Faculty of Business, Laval University, Québec City, QC, Canada

Appendix 1. Summarization of organizational climate dimensions and literature basis

Organizational climate dimensions

Definitions References

Cooperative interaction

When cooperative climate exists in companies, members of a group are more inclined to working together to share and develop tacit knowledge and try to promote each other’s performance and learning.

Janz and Prasarnphanich, (2003)

Autonomy

The degree to which the task provides substantial freedom, independence, and discretion in scheduling the work and in determining the procedures to be used in carrying it out.

Hackman and al., 1999 Chung-Jen (2007).

Organizational

support

This dimension refers to the various actions to encourage and motivate individuals as monetary rewards, promotion opportunities, moral support and resources.

Eisenberger and al., (2002)

Innovation

Innovative climate may allow subordinates to more fully engage in, and focus on, creative endeavors, instead of on external worries or concerns about how such behaviours will be viewed by the larger organization.

Bock et al. (2005); Jaw et Liu (2003).


Appendix 2. Definitions of exogenous and endogenous variables

Exogenous and endogenous variables

Operational definitions Eigen value

Explained Variance

Chronba- ch’s

Alpha

Brokers’ knowledge transfer activities Acquisition of tacit knowledge Acquisition of explicit knowledge Integration of new knowledge Adaptation of research results Dissemination of research results Creation of links

In conducting your day-to-day professional activities over the last twelve months, how frequently did you acquire studies and research reports: From other organizations From consultants From professional associations In conducting your day-to-day professional activities over the last twelve months, how frequently did you acquire studies and research reports: From professional magazines From bulletins and newsletters From Electronic Databases (PubMed, Science Direct, Proquest, etc.) Please indicate how frequently, in your day-to-day professional activities over the last twelve months, you have: Read research information, studies and research reports Understood research findings, studies and research reports Cited research information, studies and research reports to your colleagues Discussed research information, studies and research reports with colleagues Please indicate how frequently, in your day-to-day professional activities, over the last twelve months, you have: Presented research findings into non-technical language for

potential users in your organizational unit Prepared appealing reports for potential users in your

organizational unit (graphics, colour, humour, packaging) Prepared research syntheses and summaries on specific topics

for potential users in your organizational unit Discussed, with users in your organizational unit, implications of research results utilization Provided examples to people in your organizational unit on how to use research findings Please indicate how frequently, in your day-to-day professional activities, over the last twelve months, you have: Sent research findings, studies and research reports to target users in your organizational unit Organized meetings to discuss current research projects with

target users in your organizational unit Organized meetings to discuss preliminary results with target users in your organizational unit Discussed the implications of research results with target users in your organizational unit Please indicate how frequently, in your day-to-day professional activities over the last twelve months, you have: Facilitated the involvement of individuals from your organizational unit into research projects Facilitated the creation of research projects’ advisory committees Facilitated person-to-person contact between people in your organizational unit and researchers Organized seminars, meetings, conferences or other events to provide opportunities for exchanges between people in your organizational unit and researchers.

1.76

1.79

2.5

2.95

2.56

2.67

58.69

59.73

62.66

59.00

63.96

66.83

0.65

0.67

0.80

0.82

0.819

0.834


Organizational climate Cooperative interaction Autonomy Organizational support Innovation

To what extent does your organization invest resources in the following activities: To ensure effective communication channels so that priorities,

evidence and ideas are exchanged across all organizational units To promote linkages between people of your organization and researchers To promote partnerships involving people of your organization and researchers To encourage people in your organization to participate in scientific conferences Please indicate to what extent you agree with the following statements: in my organizational unit, individuals are encouraged to undertake, as they see best: Acquisition of research findings, studies and research reports Adaptation of research findings, studies and research reports Dissemination of research findings, studies and research reports Linkages development between researchers and decision-makers To what extent does your organization invest resources in the following activities: To provide training on how to better share knowledge To provide training on how to better use research findings in

your day-to-day professional activities To update databases to make sure that individuals in the

organization have access to the latest research findings, studies and research reports To prepare written documents such as lessons learned, training manuals, best work practices, etc. Please indicate to what extent you agree with the following statements: People in my organizational unit are encouraged to search for fresh, new ways to acquire, adapt, disseminate research findings, studies and reports People in my organizational unit are encouraged to come up with new ideas or recommendations on how to increase the acquisition, adaptation, dissemination of research findings, studies and reports People in my organizational unit are encouraged to put into

action new strategies or ideas to improve the acquisition, adaptation, dissemination of research findings, studies and reports

People in my organizational unit give high value to change and continuous quality improvement

2.79

2.57

2.65

2.60

69.88

64.43

66.42

73.77

0.85

0.81

0.82

0.88


Please indicate to what extent you agree with the following statements: My organization has: A clear mission statement regarding the acquisition, adaptation, dissemination of research findings, studies and reports, A clear vision regarding the type of research findings, studies and research reports it needs to achieve its organizational objectives, Clear objectives regarding the acquisition, adaptation, dissemination of research findings, studies and reports, Strong values promoting the acquisition, adaptation, dissemination of research findings,

3.554

71.08

0.90

Social interaction

Please indicate how frequently, in your day-to-day professional activities, over the last twelve months, you provided studies and research reports to people in the following types of organizations: Funding agency Federal Ministry Provincial Ministry University Other research organization

2.913

56.25

0.80


Appendix 2 (Suite). Definition of exogenous and endogenous variables

Cognitive capacity

Composed of three binary variables: BACH is a binary variable coded 1 if the broker has completed a bachelor’s degree and coded 0 otherwise; MAST is a binary variable coded 1 if the broker has completed a master’s degree and coded 0 otherwise; PhD is a binary variable coded 1 if the broker has completed a PhD and coded 0 otherwise. This last category was used as the reference category in the structural model.

Types of organizations

Composed of five binary variables: ADM is a binary variable coded 1 if the broker primarily carries out his professional activities in a Federal or Provincial ministry, or in a Regional health authority, and 0 otherwise; RESEAR is a binary variable coded 1 if the broker carries out his professional activities in a university or another research organization, and 0 otherwise; FUND is a binary variable coded 1 if the respondent carries out his professional activities in a Non-profit foundation or a Funding agency, and 0 otherwise; PRIVATE is a binary variable coded 1 if the respondent carries out his professional activities in a Private firm, and 0 otherwise; finally, SETTING is a binary variable coded 1 if the broker carries out his professional activities in a Hospital, long-term care facility or other service delivery organization, or in a Community organization, and 0 otherwise. This last category was used as the reference category in the regression models.


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