Talent And Knowledge Management Challenges Within Organisations

Working Paper

Scaringella, Laurent a* ; Chami Malaebb, Rola

Abstract

This article intends to develop a hypothetic-deductive study on self-perceptions of individuals’ talent requirement on the job regarding the environment, motivation and beliefs. This study determines major stakes of talent management with respects to Knowledge Management. This approach has been carried out under the effect of the economic paradigm change toward the knowledge-based economy (Schapiro & Varian, 1999) in the light of human changes (Cappellin, 2006), human capital (Carayannis, Ziemnowicz, & Spillan, 2007), and talent challenges requirements as scare resources (Helmstäder, 2006).

“Talent” is formally defined as the intellectual ability composed of three multiplicative and not additive variables: competence, commitment, and contribution, affecting the ways employees and organizations exchange knowledge (Ulrich, 2008).

Talent management excelled recently to govern talented individuals to enhancing the firms’ capabilities and to matching the employee’s human capital to firm-specific skills (Groysberg & Lee, 2009, Cappelli, 2008).

The article will discuss major stakes related to human resources holding job of high talent level from the lens of sociability, their behaviour regarding innovation (exploration, examination or exploitation), and their ability to absorb / learn from others, and the scope of their network in terms of distance / proximity that matters in managing knowledge workers. Furthermore, the article shed light on TM challenge in term of attraction, development and retention of talents. Several advantages are identified such as intended knowledge spillovers and key features of functional domain of knowledge (absorption, transfer and learning) (Vissers & Dankbaar, 2006a; (Vissers & Dankbaar, 2006b) Chen, 2004; (Nelson, 1982) (Gallupe, 2001)(Dankbaar, 2004). However, the paradigm shift from the industrial-based Economy to the Knowledge-based Economy is still to ponder some drawbacks such as inequality of talent distribution through exploration-examination-exploitation process (Cooke & De Laurentis, 2005; (Cooke, 2006) and inequality of wealth distribution (Carayannis, Ziemnowicz, & Spillan, 2007). Additionally, some challenges have been identified such as long distance knowledge dynamics (Cooke, 2006; Nooteboon, 2000) and “open innovation” (Chesbrough, 2003).

Thus, the article intends to measure how the self-estimation of talent’s requirement on the job might be related with the ability to handle customer needs, their added value (creation of new knowledge or use of existing knowledge) toward innovation, fair sharing of benefits under the control of potential unintended knowledge.

To address major stakes of talent management within organisation, authors have conducted a survey based on 932 respondents from Knowledge-based Economy in the cluster of Grenoble named “Minalogic” (Micro, nano and logiciel (software)).

Findings of this study from people who evaluate high-required level of talent in their jobs (PhJHT) are revealed in three angles: initially, they are more likely to be involved in the creation than in the use of knowledge that often leads to frustration of unfair distribution of wealth. Next, they are more likely to be able to absorb, transfer and learn within long distance and rich knowledge dynamics. Finally, they are more likely to be involved in the early stages of the Knowledge Value Chain, i.e. in the exploration rather than in the examination. Furthermore, the creation of new knowledge is not necessarily rewarded in the Knowledge Based Economy; probably due to long geographical distances, poor relational proximity or the length and heterogeneity of talent supply of the Knowledge Value Chain. Accordingly, the “exploration” tends to employ a workforce evaluating their talent’s added value as being higher than in the “examination”.

Keywords: Talent, talent development, competence, job requirement, knowledge-based economy, knowledge value chain, exploration, examination, exploitation, wealth distribution, geographical distance, open innovation, functional domain of knowledge, absorption, transfer, learning.

1. Introduction

The article aims to consider the economic paradigm change toward the knowledge-based economy (KBE) in the light of HR challenges. The shift of paradigm of economies of scale to the KBE considering economy of networks and collaborative effort (Schapiro & Varian, 1999). Researchers recently focus on KBE in order to observe transformations during the last ten years, and to identify sources of innovation in a world with no entry barriers.

This article will focus on the human side of this paradigm shift due to major impacts on the work of people facing new challenges. Social dimensions (People, culture) are creating “human capital” which is now in the centre of added value creation and lead to a competitive advantage (Carayannis, Ziemnowicz, & Spillan, 2007). It is not only as the restrictive sum of individual knowledge but also a greater entity which is not own by anyone (Helmstäder, 2006 inspired by Hayek, 1937). The KBE encompasses the concept of information society (high-tech industries, information and communication technologies, new economy and new technologies) and lead to “learning” where stakeholders have a central position. Human Resources Management became a strategic function because education investment through life career would increase productivity thanks to the identification of new needs of future markets (Cappellin, 2006).

Talent is considered as key concept since the world is becoming knowledge oriented and job’s requirements are becoming more demanding. Talent refer to the most effective leaders at all levels that can help the company to fulfil its aspirations toward performance (Michaels, Handfield-Jones, & Axelrod, 2001). Indeed a research by the Chartered Institution for Personnel and Development (CIPD) confirmed the way talent is defined is organizationally specific, highly influenced by the industry type, the nature of work and the intensity of dynamic knowledge. Consequently, talent is more likely to change overtime according to organizational priorities (CIPD, 2007)

This article provides an hypothetical-deductive outlook with a positivism philosophy to test potential linkages between the measurement of the involvement of people with customers, the measurement of creation or use of knowledge, the measurement of absorption, transfer and learning, the measurement of the geographical distance - proximity between stakeholders, the measurement of the degree of innovation, the measurement of the fairness of profit sharing between stakeholders, the sociability of people, the measurement of the involvement in the exploration, examination and exploitation step, the measurement of the degree of risk of imitation and unintended knowledge spillovers and the measurement of the required degree of talent required at job.

The relationship between the KBE and talent lead the research to highlight the importance of TM. Early contributions emerged from the McKinsey consultants who coined the term “the war to talent” and the conceptual and theoretical limits and foundations have gotten increased attention (Becker,Huselid, & Beatty, 2009; Boudreau & Ramstad, 2005; Cappelli, 2008; Collings & Mellahi, 2009;Schweyer, 2010) and there have been growing interest in the global environment (Tarique &Schuler, 2010; Scullion, Collings, & Caligiuri, 2010).

2. Talent

This part is of two sections: The definition of talent (2.1.), elements of talents (2.2.)

2.1. Definition of talentPrior determining if a job requires talent or not, several personal added values have been identified by(Cooke & De Laurentis, 2005): Talent, research and technique.

Talent, formally defined, is the innate ability, aptitude or faculty, especially when unspecified; above-average ability (Collins English Dictionary). The broad meaning of talent is the sum of intrinsic gifts, abilities, knowledge, skills, intelligence, attitude, character, and drive; as well as it refers to the most

effective leaders at all levels that can help the company fulfil its p1performance (Michaels, Handfield-Jones, & Axelrod, 2001). If talent is not nurtured it will fade away. Indeed, a research by the Chartered Institution for Personnel and development “CIPD” confirmed that the way talent is defined is entirely organizational specific, highly influenced by the type of industry and the nature of its work, dynamic and it is so likely to change overtime according to organizational priorities (CIPD, 2007). CIPD defined talent as “it consists of those individuals who can make a difference to organizational performance either through immediate contribution or in the longer term by demonstrating the highest level of potential.” Whereas Buckingham and Coffiman, in ‘First Break all the Rules’ clearly defined talent as “recurring patterns of thought, feeling, or behaviour and everyone has these” (Bukingham & Coffiman, 1999). Cooke and De Laurentis (2005) associate talent to the exploration. Indeed, talent would mostly occur during the exploration step, research during the exploration – to seek change in response to internal strategy or external constraints– while improving the efficiency, delivery, or profitability of the existing business model—the exploitation step.

2.2. Elements of Talent

Looking from an academic lens, Ulrich (2008) has set a formula for talent that can help to turn talent objectives into actions: Talent = Competence x Commitment x Contribution (Ulrich, 2008). The three terms are multiplicative, not additive. Ulrich affirmed that firms with high competence but low in commitment have talented employees who cannot get things done, oppositely, low competence high commitment, firms have less talented employees who can get things done rapidly. That is why he argues that these two (competence and commitment) are multiplicative and the third important element added to be the equation of talent is contribution (Ulrich, 1998). Employees may be competent and committed, but unless they are making a real contribution and finding meaning and purpose in their work, their interest diminishes and their talent wanes (Ulrich, 2008).

3. Job requiring talent and variables of Knowledge Management: The conceptual framework

This part is composed of four sections presenting the framework of talent within the knowledge processes.

3.1. Framework of talent within knowledge processesCooke (2005) developed the knowledge value chain (KVC) composed of three steps: Exploration (search, including research), examination (trialling, testing, standard setting or benchmarking) and exploitation (commercialisation of innovation, sale on market, or socially useful & used)

The exploration phase is often leading to pure creation of knowledge. The hypothesis stated that ‘people holding a job requiring a high level of talent’ (PhJHT) would be positively correlated with the involvement within the exploration. Talent development urges high performers who occupy key positions in the company to excel. Exploration is seeking change in response to internal strategy or external constraints. The extent to which hiring talented individuals can enhance a firm’s capability depends on whether the employee’ human capital is firm-specific or whether their sector has never been researched by the hiring firm in the past (Groysberg & Lee, 2009). Exploration requires a strong proximity and partnerships between sectors, such as aeronautics, defence, military, transport, care industries, chemistry or business service, leading to a composite knowledge mobilisation. Since regions are often driven by only one sector, we can also hypothesis that people working in the exploration requires rather long distance knowledge dynamics.

The examination phase is revealing tradability of knowledge features by trailing, testing and assessing standards. This stage is driven by research and technique rather than talent. Contrary to the exploration step, we hypothesis a negative correlation between the level of talent required in a job and the implication within the examination because other actors are involved.Exploitation includes production and marketing where economies of scale principle are sought (Brossard & Vicente, 2007). The exploitation is clearly focussing on the use of knowledge rather than creation of knowledge driven by technique. It is improving the efficiency, delivery, or profitability of the existing business model. It depends on the willing of analyzing a sector in which the firm has already

1a- Haas School of Business, University of California, Berkeley, 2220 Piedmont Avenue, Berkeley, CA 94720, USA- * Author for correspondence Tel.: 1-510-809-7634-Laurent_scaringella@haas.berkeley.edu (L. Scaringella), b-Modern University for Business and Science- Hamra, Wardieh Square- Lebanon. Rola.CHAMI@grenoble-em.com (R. Chami Malaeb)

established research (Groysberg & Lee, 2009).Similarly to the examination, PhJHT would not be reluctant to work in the exploitation because of low creation of knowledge interest.

The exploration phase is driven by radical innovation, the examination phase is an intermediary phase and the exploitation phase is driven by incremental innovation (Cooke, 2006). The KVC analysis often reveals a poor causal relationship between the exploration towards the exploitation stage.

3.2. Added value and socialisation of people holding a job requiring talentThe socialisation of knowledge worker, Contribution is in term of creation requires a fertile job environment where talent is identified to hold certain jobs, more especially in the creation of knowledge. Contribution occurs when people feel that their personal needs are met in their work (Ulrich, 2008). Creation and use ratio is very sector specific. Within the ICT sector, knowledge generation is rare, leaving a greater free space for knowledge use. Swindler and Arditi (1994) and (Vissers & Dankbaar, 2006a; 2006b) distinguished individual and collective knowledge.

Tuomi (2000) who demonstrated that transformation from data into information and then, from information into knowledge, stated that individual knowledge, is an individual minded process. Individuals could develop radical innovation, while now; the focus is made on improvement driven by groups (like Japanese’ Kaizen). It underlines a potential linkage between the added value (creation / use) of people and their degree of socialisation (individuals / groups). Knowledge process and the personal added value might influence the number of people being involved, i.e. the creation-based exploration step is rather individual and use of knowledge based examination-exploitation step is rather collective.

Talented employees work solely or in groups depending on their managerial skills and commitment (Groysberg, Sant, & Abrahams, 2008). Those who are more committed have a network and they show high level of leadership traits that can influence the group and more likely to perform in teamwork (Hackman 2002). On this basis, it would be relevant to measure the linkages between the level of required talent for a job and the socialisation of people to know if PhJHT work alone.

As a critical part of talent management, functional domain of knowledge (absorption, transfer and learning) mentoring require the transfer of explicit and tacit knowledge where the experienced mentor can provide a host of information and expertise and knowledge to the protégé (Mohram, 2005).

3.3. The effect of talent on international knowledge dynamics toward open innovationThrough KVC, it is important to focus on KM from tacit to explicit knowledge and from individual to groups. Functional domains of knowledge lead to appropriate knowledge governance and geographical distance of knowledge dynamics leads to innovation. The knowledge long-term phenomenon is highly dependent on the capability to absorb knowledge from a person A to a person B (Chen, 2004).

Optimum comprehension, understanding and absorption of knowledge inputs require skills, which can be provided from sources such as training, education degrees, coaching and social capital, and thus involve university and academic partner. Alavi & Leidner (2001) proposed a model of “knowledge cycle” from objectives to knowledge identification, knowledge development and acquisition, knowledge distribution, knowledge storage, knowledge utilization and knowledge evaluation while learning is added by Dankbaar (2004).

The fit with company specific skills, the adaptation of the person could be optimum (Groysberg, Sant, & Abrahams, 2008). Talent holding a great score for all five human capitals are more likely to perform well in all three functional domains of knowledge that is confronted with a positivism analysis. Geographical proximity/ distance are defined as the variance of the “geodesic distance” between stakeholders. Geographic proximity factor is criticized as an outdated advantage in the global economy due to new communication channel expansion. Indeed, short distance relationships are not sufficient to guarantee efficiency (Vicente & Suire, 2007).

“Open innovation” occurs in companies grasping knowledge in distant relationships (Bell labs, P&G, GE) (Cooke, 2006, (Chesbrough, 2003). Cognitive proximity consists of sharing capabilities and knowledge (technological, marketing and business) in a broad context (Nooteboon, 2000), and

depends on the degree of learning (Planque, 1991) Bathelt, Malmberg, & Maskell, 2004). We assume that this dependent variable of PhJHT is positively correlated with the geographical distance leading to innovation.

Talented people are creative and committed to their work; their natural outcome may generate new processes, new products, new markets, etc. Creative scientific and engineering talent is becoming the cornerstone of Innovation-based companies (Ruse & Jansen, 2008). This leads to hypothesize that PhJHT are positively correlated with the innovation.

In a market-oriented economy, consumers of knowledge-based products are in a central position, with “different attitude toward work, leisure, health, security, culture, preference for an urban living, etc…” (Cappellin, 2006).

It is worth to see if PhJHT incline developing knowledge dynamics with customers. If the correlation is positive, a connection is from the first actor working in the exploration to the customer located in the exploitation. Talented employees deliver results that may be related to financial, customer, and organization outcomes, but also maybe related to suppliers, and other external sources (Ulrich, Allen,Smallwood, Brockbank, & Younger, 2009). Sundbo (1997) points out that in market-oriented innovation the greater the cognitive proximity, the greater the innovation and the greater the risk is knowledge spillovers and potential unfair recognition perception (Brossard & Vicente, 2007). From this literature, it makes sense to hypothesis a positive relationship between the degrees of talent required for a given job and the ability to develop knowledge dynamics with customers.

3.4. Negative outcomes from knowledge dynamicsIn the KBE, drawbacks exist like unintended knowledge spillovers (Brossard & Vicente, 2007) worked. Within the ICT sector, accessibility and openness seem to be more significant during the exploration step and thus imply a greater risk. The risk is lower in the exploitation step where Intellectual Property Right (IPR) tends to be more frequent (Brossard & Vicente, 2007). As of Cooke & De Laurentis (2005) it is logical to hypothesize that PhJHT are more likely to work in the exploration and creation, to develop long distance knowledge dynamics and are thus more exposed to risk of unintended knowledge spillovers and imitation. On the contrary, people involved in the exploitation would rather use knowledge, protect innovation based-product with IPR and thus feel less exposed to risk of unintended knowledge spillovers and imitation.

Unintended knowledge spillovers have this negative impact because the number of stakeholders benefiting from the use of knowledge is greater than the number of stakeholders involved in the exploration step. Since the economy, innovation and knowledge dynamics are driven by financial, intellectual and other kind of interest, the distribution of wealth among stakeholders is a critical point where the value of net income, the working capital and costs are evaluated. According to the Lisbon strategy, the KBE aims to be profitable for all stakeholders and not to minority. This implies that PhJHT would in this be more likely to face risk of unintended knowledge spillovers.

4. Methodology

4.1. SampleThe survey is based on 932 respondents from the Grenoble cluster. 111 organisations have provided responses: 83 firms, 9 universities, 6 research centres and 13 public bodies. Global competitive cluster of Grenoble fosters research-led innovation in intelligent miniaturized products and solutions for industry. Indeed, the cluster has staked out a position as global leader in intelligent miniaturized solutions, a unique hybrid of micro and nano-technologies and embedded software from fundamental research to technology transfer. This scope revealed a limitation of this study.

The survey was administered via an email linked to an on-line questionnaire. 5000 people were contacted and the return rate was high (18.64%), with 932 responses. The survey was administrated to firms, research centres (25% of the response rate), universities (18%) and public bodies (6%). The ‘firms’ size mattered, as this might influence knowledge dynamics, they are categorized as small-sized (1-10 employees), medium-sized (10-500 employees), and international firms (>500 employees). In total, 111 organisations contributed to this survey.

The representativeness of the sample measured the mobilisation of 13 departments within organisations. Research and Development dominates (44%), as knowledge workers within the ICT sector are more likely to develop new products and to conduct research. 91.5% of the sample is represented by managers and highly intellectual jobs. Indeed, half of all respondents hold a Master’s degree and 37% have a doctorate. Only 13% have an education level beneath a Master’s degree.

4.2. Statistical analysisThis article puts forward a multiple regression analysis to predict the level of talent required at job, by identifying which concept is strongly affecting this variable, in order to drive governance and lead organisations toward best practices. To apply the regression procedure, these 9 concepts and 11 variables were included as independent variables.

Only 566 people provided complete responses. 11 variables, theoretically related with talent required for a given job, were selected table 1). 566 answers for 11 variables are enough to use the stepwise procedure. The above literature review draws certain relational linkages between the evaluations of talent required for a job and the 11 other independent variables described in Table 1.

Detection of serious violation was conducted in two stages: First, individual variables (both dependent and independent) and then, the overall model.

4.3. List of variablesThis list of: Talent, Buyer, Contribution, Functional domain, Geographical space, Innovation, Joint beneficiary, Mobilisation, Exploration, Examination, Exploitation, and X knowledge, is detailed in the Appendix A that shows these variables and the questions asked for analysis.

5. Analysis

The analysis from the data collection is composed of four sections detailed below. All tables of data analysis are shown in the appendixes at the end of this article.

5.1. Test of theoretical relations

It is necessary to detect non-significant variables in the prediction of the perceived level of talent required for a job. The statistical significance led the researchers to confirm, or not, theoretical relationships between certain variables and talent requirement perception (table 1). In the test of theoretical relationships, certain variables are not significant (>0.05). This explains the variance of the evaluation of talent requirement for a job. Consequently, the following variables have not been added, via a step-by-step addition process: Buyer (B), Innovation (I), Mobilisation (M), Exploitation (S3) and X knowledge (X) (Table 1). In the test of theoretical relationships, certain variables are significant (<0.05) and explain the variance of the evaluation of talent requirement.

5.2. Estimating the regression modelIt is possible to build a model, and to provide estimation of the fit of the overall model. The stepwise procedure is used for inclusion and removal in variate regression. The six following variables were introduced: Contribution to knowledge (C), Joint beneficiary of knowledge dynamics (J), Functional domain of knowledge (F), Geographical proximity/distance (G), Exploration (S1) and examination (S2).

The R2 is .095 for the sixth model with six variables. Adjusted R2 is very similar (.085). The R2 change of the last model is .008. The standard error of the estimate measures the accuracy of our predictions. This is the square root of the sum of the squared errors, divided by the degrees of freedom. There is a .956 standard deviation around the regression line (appendix 6).

The Variance Inflation Factor (VIF) is under 1.504 for all six variables, indicating that the multicollinearity between independent variables is limited, i.e. about 1.5%. This means that the real standard error of each independent variable is potentially 1.5 times greater than what the actual amount is. This lack of multicollinearity means that these six variables are indeed relatively independent from each other. This means that excluded variables have been deliberately deleted. By adding one, two, three, four, five and six variables into the model, the error is reduced respectively by 4.02%, 5.934%, 7.14%, 8.04%, 8.76% and 9.52% (appendix 8). This underlines the utility of adding

these six variables into the prediction. Consequently, by knowing the value of these six variables, the error is reduced by 9.52%. This reduction is statistically significant (.000).

The F ratio has been successively equal to 23.599, 17.758, 14.412, 12.256, 10.757 and 9.801, with a significance level of .000. This means that the last entered variable has 0% of chance of not being significant. Here, the study stopped adding other variables because their contribution would be marginal, i.e. the prediction power would not be improved significantly. It is, therefore, possible to reject the hypothesis that the reduction in error might have occurred by chance (because this would equated to 5% of the time).

Linearity is assessed by two different means, firstly, to look at the scatter plot. This graph represents a null plot, highlighting the linearity of equation. Secondly, to look at six-scatter plots of partial regression plots for each individual variate results no visible track of a nonlinear pattern to the residuals. The analysis of the scatter plot indicates homoscedasticity in the multivariate. To assess the independence of the residuals that no variable could affect the answer is confirmed

Finally, the P-P plot and a histogram show the regression of the perception of required talent for a given job, and at a histogram following a normal distribution, so the regression variate meets the assumed normality.

5.3. Interpreting the regression variateThe model estimation is now considered fully complete, and the regression variate is specified. The predictive equation considers six independent variables (Contribution to knowledge, Joint beneficiary of knowledge dynamics, Functional domain of knowledge, Geographical proximity/distance, Exploration and Examination).

The prediction equation of the perception of required talent at job is:T = 4.391 + (-.112) * C + .077 * J + .086 * F + .056 * G + .066 * S1 + (-.050) * S2

The standard error of the coefficient is equal to .029, .030, .037, .023, .023, and .023 for C, J, F, G, S 1

and S2, respectively. The interval of confidence for Contribution to knowledge goes from -.168 to -.056. The confidence interval ranges from .017 to .136 for Joint beneficiary of knowledge dynamics, from .013 to .158 for Functional domain of knowledge, from .012 to .101 for Geographical proximity/distance, from .021 to .110 for Exploration and from -.095 to -.005 for Examination.

The t-value is equal to -3.901 significant at .000 for Contribution to knowledge, 2.545 significant at .011 for Joint beneficiary of knowledge dynamics, 2.324 significant at .020 for Functional domain of knowledge, 2.492 significant at .013 for Geographical proximity/distance, 2.911 significant at .004 for Exploration, -2.161 significant at .031 for Examination. It is possible to affirm with confidence that the coefficient is not equal to zero. Consequently, C, J, F, G, S1 and S2 definitely predict the perception of talent required for a job. The F ratio of the last entered variable (9.801) determines the entrance and removal of variables.

5.4. Measuring the degree and impact of MulticollinearityIt is essential to check the impact of multicollinearity, as highly collinear variables can affect the regression. The VIF range is between 1.013 and 1.504. This measurement indicates that the level of multicollinearity is not strong enough to distort the regression variate. The t value of the last variable entered (Examination) is equal to -2.161 significant at .031, which will definitely improve the overall regression model predictive power. The validation of results and confirmatory regression models has been processed.

6. Learning from empirical study

The backbone of the regression is to obtain a better understanding concerning why people evaluated their job with a rather low or high degree of talent required. Such knowledge can help HR manager to consider talent estimation of people’s job. The variance explained is approximately 10%, and the expected error rate for any prediction is 95% confidence level. Validity analysis allows the study to provide a high level of assurance regarding the quality and accuracy of regression models, thus insuring a replicability of the model.

Similar results are obtained from three regression models (one full sample, one odd sample, and one even sample). There are six major variables influencing the perception of talent required for job: C, J, F, G, S1 and S2. A confirmatory regression model was processed to ensure that all variables included in the model are significant. If the contribution rather focuses on creation than on use of knowledge, if the sharing of benefits from knowledge dynamics is seen as unfair and unequally distributed, if functional domain of knowledge increase, if the geographical distance increase, if the implication within the exploration step increases, if the implication within the examination step decreases, the perception of required talent for a given job increases ceteris paribus.

6.1. Creation but frustrationAn increase of one point in the creation of knowledge (opposed to the use of knowledge) has a positive correlation with an increase in the perception of required talent for a given job of 11.2%. Even though the ICT is known for the domination of the use of knowledge, people working in the creation of new knowledge bits perceive their job with a greater level of talent requirement. This resonate with Huselid, Beatty, & Becker (2005) and Collings and Mellahi (2009) who emphasized the differentiation of positions that are more strategic, pivotal, and need higher level of talent. Matching the job-required competencies with the person who will occupy this job will affect the perception level (Huselid, Beatty,& Becker, 2005).

An increase of one point in the unfairness distribution of wealth (opposed to the fair sharing of benefits) has a positive correlation with an increase in the perception of required talent for a given job of 7.7%. Since knowledge dynamics are developed to pursue financial interest, people working in the creation of knowledge (exploration step) are often very far from the market where the entire return on investment is perceived. For someone having creating from scratch something, one could feel that people using ones knowledge take advantage of his talent without rewarding him at the right value. In regards to this perception, the Lisbon strategy still has a lot to do to insure a better repartition of outcomes in the KBE. It is a major challenge as emphasised (Antonelli, 2006a; 2006b) noticing that knowledge is very fragmented among numerous stakeholders where the origin is hard to be determined and even harder to be rewarded. Moreover, while the Employees value proposition specifies what employees will get from the firm when they meet expectations (Ulrich & Brockbank,2005)

Those two variables underline that the creation of new knowledge bits can be strongly linked to the geographical distance among stakeholders, a poor relational proximity or the length of the Knowledge Value Chain.

6.2. Knowledge absorption, transfer and learning at great geographical distance

An increase of one point in perceived level of absorption, transfer and learning ability (functional domain) is positively linked with the perception of required talent for a given job of 8.6%. Perceived learning capability is strongly related to the perceived talent required for a given job (Dankbaar, 2004; Vissers & Dankbaar, 2006a).

An increase of one point of the geographical distance between stakeholders is positively linked with the perception of required talent for a given job of 5.6%. To be in a job requiring talent, the jobholder should have a certain level of skills and competences required for this job. Managing talent consists on having the required competencies defined for this job. Many of the basic competencies are conceptual skills and generating new ideas and developing systems that challenge the status quo, takes risks, and encourages innovation (Berger & Berger, 2004). In other words, absorption, transfer and learning are conducted by knowledge PhJHT where international exposure, global networking and long distance knowledge dynamics are necessary to grasp scare knowledge bits toward “open innovation”.

6.3. Early involvement on the Knowledge Value Chain

An increase of one point of the involvement within the exploration step of the KVC is positively correlated with the perception of required talent for a given job of 6.6%. This empirical study confirmed that the job localised in the early stages of KVC are more likely to require a high level of talent. The prevailing, popular belief is that professionals’ expertise is portable because it is based on

standardized training, a common knowledge base, and shared approaches to problem solving (Woodruffe 1999).

On the contrary, an increase of one point of the involvement within the examination step of the Knowledge Value Chain is negatively correlated with the perception of required talent for a given job of 5%. The prediction of having a lower number of people evaluating the required level of talent required in their job is confirmed.

Creation of new knowledge bits is not necessarily rewarded in the KBE probably because of long geographical distances. the drawbacks of benefits in term of intended knowledge spillovers, functional domain of knowledge (absorption, transfer and learning) leading to grasp knowledge in particular location determined by people holding a job where talent, international networking and other competences are required in the KBE driven by “open innovation”.

7. New challenges

For the future, it can be a challenge to attract new talent in the entire Knowledge Value Chain, in order to absorb the gap between people creating and using knowledge. Talent has to be managed along with all the employee life cycle, from hire to retire (Schweyer, 2004). CIPD affirms that TM is "the systematic attraction, identification, development, engagement, retention and deployment of those individuals with high potential who are of particular value to an organization” (Tansley, Stewart,Turner, & Lynette, 2006). Many companies apply an inclusive approach of talent management where the practices involve the whole workforce, another approach emphasize either on talented people or on key positions. Holding a position that require innovation, mobilisation, transfer of knowledge, exploration, exploitation, contribution to knowledge or any relationship with the end-user requires that who is occupying those positions hold the talent required to run the job and perform effectively. Collings and Mellahi (2009) argue that “strategic TM focuses on those incumbents who are included in the organization’s pivotal talent pool and who occupy, or are being developed to occupy pivotal talent positions”. Collings and Mellahi (2009) emphasized the identification of key positions that can differentially contribute in sustainable competitive advantage of the organization (Huselid, Beatty, &Becker, 2005; Becker, Huselid, & Beatty, 2009; Whirlpool, 2007). Here the approach improves the theoretical development to differentiate TM as a decision science ( (Boudreau & Ramstad, 2005) and the traditional HR plans and strategies, and the starting point will be the position rather than talented individuals per se (Collings & Mellahi, 2009; Whirlpool, 2007). As Frank and his colleagues (2004) pointed out, the attraction and retention of knowledge workers are amongst the most difficult challenges facing firms and a critical area of emphasis for HR professionals. It is worth noting that when recruiting for critical jobs, the existing employees are more knowledgeable of the firm’s context that hires from outside the company. To capitalize on creativity, innovation and contribution, identifying positions requiring talent and talented people is a crucial second step after selecting employees. Existing Knowledge workers will participate in coaching and mentoring so that their knowledge will be transferred to those who could need it. Therefore, the talent required on the job will be supported by coaches and mentors. Knowledge workers as well will benefit from stretched assignments and rotation development programs that will excel and enhance their skills and consequently their talent (Morton, 2005). Development experiences allow more managers to be in linchpin positions due to more accurate bench strength analysis (Conger & Fulmer, 2003). Nevertheless, if development efforts were misallocated its purpose would be a waste of time and

Variables Entered Low talent requirement High talent requirementAdded value Use of knowledge Creation of knowledge

Distribution of wealth Fair perception Unfair perception

Functional domain of knowledge

Low absorption, transfer and learning ability

High absorption, transfer and learning ability

Distance Proximity knowledge dynamics

Distant knowledge dynamics

Knowledge Value Chain Examination step Exploration stepTable 2: Typology of the difference between the perception of job requiring a low level of talent and job requiring a high level of talent

money (Berger & Berger, 2004). Deploying talent in the right job will pay off. Companies that have high levels of development and retention practices are at risk from poaching (Ready, Hill, & Conger,2008). Therefore, if jobs are differentiated and they require talent, talented could be selected in better way and identifies with all talent management initiatives.

8. Conclusion

Findings of this study from people who evaluate high-required level of talent in their jobs are revealed in three angles: initially, they are more likely to be involved in the creation than in the use of knowledge that often leads to frustration of unfair distribution of wealth. Next, they are more likely to be able to absorb, transfer and learn within long distance and rich knowledge dynamics. Finally, they are more likely to be involved in the early stages of the Knowledge Value Chain, i.e. in the exploration rather than in the examination. Furthermore, the creation of new knowledge is not necessarily rewarded in the KBE; probably due to long geographical distances, poor relational proximity or the length and heterogeneity of talent supply of the Knowledge Value Chain. Accordingly, the “exploration” tends to employ a workforce evaluating their talent’s added value as being higher than in the “examination”.

Future research is worth conducting in this field, and urges to build better relationship between the fields of knowledge management and talent management.

Appendix A

VariablesScale type

Description Questions Authors

Talent T

7-point Likert

Measurement of the degree of talent at job

Evaluate the degree of talent required in your job. Talent: Intellectual ability to succeed in something1 Very weak - 7 Very strong

De Laurentis and Cooke, 2005; (Boudreau &Ramstad, 2005;Buckingham & Vosburgh,2001; Cappelli, 2008-a;Collings & Mellahi, 2009;Heinen & O'Neill, 2004 ;Huselid, Beatty, & Becker,2005; Lewis & Heckman,2006; Michaels, Handfield-Jones, & Axelrod, 2001;Ready & Conger, 2007;Ulrich, 2008)

Buyer B

7-point Likert

Measurement of the involvement of customers, clients and end-users

How do you evaluate the level of interaction between your organisation and following partners? Interactions with clients and end-users1 Very weak interaction - 7 Very strong interaction

Cappellin, 2006

Contribution C

7-point Semantic differential

Measurement your contribution in term of creation or use of knowledge

Your personal added-value is rather related to knowledge creation or existing knowledge use? Evaluate between 1 (rather creation of new knowledge) and 7 (rather use of existing knowledge), intermediate marks allow you to adjust your judgment.1 Rather creation of new knowledge - 7 Rather use of existing knowledge

Cooke, 2006a; Vissers and Dankbaar, 2006

Functional domain

F

7-point Likert

Measurement of the level of knowledge in term of absorption, transfer and learning

Evaluate the capability of absorption of your organisation. (Ex: Your organisation meets up with a pool of experts in nanotechnologies. Your organisation is able to listen carefully, to understand and to appropriate the content) Evaluate the capability of transfer of your organisation. (Ex: A research centre has developed a technology for your organisation. A transfer of knowledge occurs afterwards)Evaluate the capability of learning of your organisation. (Ex: Your organisation is capable do be in a continuous learning process)1 Very weak - 7 Very strong for all 3 items

Vissers and Dankbaar, 2006; Chen, 2004; Alavi and Leider, 2001; Nelson and Winter, 1982; Gallupe, 2001; Dankbaar, 2004

Geographical space

G

7-point Semantic differential

Measurement of the geographical distance - proximity between stakeholders

From a geographical perspective, most of interactions between your organisation and its partners are rather developed at proximity or rather at distance.1 Rather geographical proximity - 7 Rather geographical distance

Chesbrough, 2003; Cooke, 2006; Nooteboom, 2000

Innovation I

7-point Likert

Measurement of the degree of innovation (New production processes, New products, New materials, resources and technologies, New markets, New forms of organisations)

Please indicate the degree of innovation in the following domains within your organisationNew production processesNew productsNew materials, resources and technologiesNew marketsNew forms of organisations1 Very low degree of innovation - 7 Very strong degree of innovation for all 5 items

Schumpeter, 1934

Joint beneficiary

J

7-point Semantic differential

Measurement of the fairness of profit sharing between stakeholders

Is the distribution of profits equally shared between your organisation and its partners?1 Distribution of profits is fair - 7 Distribution of profits is unfair

Scherer, 1984; Carayannis, Ziemnowicz, and Spillan, 2007

Mobilisation M

7-point Semantic differential

Measurement of work networking to determine if people are working individually or in groups

According to you, knowledge within your organisation is generated by invidious or by groups? Mark between 1 (invidious only) and 7 (groups only), intermediate marks allow you to adjust your judgment.1 invidious only - 7 Groups of people only

De Laurentis and Cooke, 2005; Vissers and Dankbaar, 2006; Wunder, 2007; Veblen, 1964; Schumpeter, 1934 and 1942; Davis, 2003; Tuomi, 1998; Simon, 1991; Cunba and Cunba, 2000; Nelson and Winter, 1982

Exploration

S1

7-point Likert

Measurement of the involvement in the exploration step (studies, research)

Indicate your degree of involvement in exploration, examination and exploitation steps. Exploration step (studies, research)1 Very low involvement - 7 Very strong involvement

De Laurentis and Cooke, 2006; Cooke, 2005 and 2006a; (Groysberg & Lee,2009)

Examination

S2

7-point Likert

Measurement of the involvement in the examination step (experimental trial, testes, specification)

Indicate your degree of involvement in exploration, examination and exploitation steps. Examination step (experimental trial, testes, specification)1 Very low involvement - 7 Very strong involvement

De Laurentis and Cooke, 2006; Cooke, 2005 and 2006a; (Groysberg & Lee,2009)

Exploitation

S3

7-point Likert

Measurement of the involvement in the exploitation step (marketing of the innovation, sales on the market, utility of innovation and use of the product / service)

Indicate your degree of involvement in exploration, examination and exploitation steps. Exploitation step (marketing of the innovation, sales on the market, utility of innovation and use of the product / service)1 Very low involvement - 7 Very strong involvement

De Laurentis and Cooke, 2006; Cooke, 2005 and 2006a

X knowledge

X

7-point Likert

Measurement of the degree of risk of imitation and unintended knowledge spillovers

In the scope of your interaction with your partners, how to do evaluate following risks?Unintended knowledge spilloversImitation1 Very low risk - 7 Very high risk

De Laurentis and Cooke, 2009; Brossard and Vicente, 2007

Appendix 1: Partial regression plots for independent variables in the regression of job requiring talent (Full sample, enter mode)

Residual plots of job requiring talent against buyer Residual plots of job requiring

talent against contribution to knowledge

Residual plots of job requiring talent against functional domain of knowledge

Residual plots of job requiring talent against geographical proximity / distance

Residual plots of job requiring talent against innovation Residual plots of job requiring

talent against joint beneficiary

Residual plots of job requiring talent against mobilisation of knowledge

Residual plots of job requiring talent against exploration step of knowledge Residual plots of job requiring

talent against examination step of knowledge

Residual plots of job requiring talent against exploitation step of knowledge

Residual plots of job requiring talent against X knowledge

Tests of Normality

Kolmogorov-Smirnova Shapiro-Wilk

Statistic df Sig. Statistic df Sig.

RES_V1_1 .065 566 .000 .980 566 .000

a. Lilliefors Significance Correction

Appendix 2: Scatterplot of SRESID (Y) against DEPENDNT (X) to check linearity of job requiring talent (Full sample, enter mode)

Appendix 3: Normality test of job requiring talent (Full sample, enter mode)

Appendix 4: Histogram and normal curve of studentized residuals of job requiring talent (Full sample, enter mode)

Appendix 5: P-P Plot of job requiring talent (Full sample, enter mode)

Appendix 6: Model summary of the regression of job requiring talent (Full sample, stepwise mode)

Appendix 7: Coefficient table of the regression of job requiring talent (Full sample, stepwise mode)

Appendix 8: ANOVA table of job requiring talent (Full sample, stepwise mode)

ANOVAg

ModelSum of Squares df Mean Square F Sig.

1 Regression 22.683 1 22.683 23.599 .000a

Residual 542.103 564 .961

Total 564.786 5652 Regression 33.515 2 16.757 17.758 .000b

Residual 531.271 563 .944Total 564.786 565

3 Regression 40.347 3 13.449 14.412 .000c

Residual 524.439 562 .933Total 564.786 565

4 Regression 45.387 4 11.347 12.256 .000d

Residual 519.399 561 .926Total 564.786 565

5 Regression 49.492 5 9.898 10.757 .000e

Residual 515.295 560 .920Total 564.786 565

6 Regression 53.760 6 8.960 9.801 .000f

Residual 511.026 559 .914

Total 564.786 565

a. Predictors: (Constant), Contributionb. Predictors: (Constant), Contribution, Jointbeneficiaryc. Predictors: (Constant), Contribution, Jointbeneficiary, Functionaldomaind. Predictors: (Constant), Contribution, Jointbeneficiary, Functionaldomain, Geographicalspacee. Predictors: (Constant), Contribution, Jointbeneficiary, Functionaldomain, Geographicalspace, Explorationf. Predictors: (Constant), Contribution, Jointbeneficiary, Functionaldomain, Geographicalspace, Exploration, Examinationg. Dependent Variable: Talent

Appendix 9: Partial regression plots for independent variables in the regression of job requiring talent (Full sample, stepwise mode)

Residual plots of job requiring talent against contribution to knowledge

Residual plots of job requiring

talent against functional domain of knowledge

Residual plots of job requiring

talent against geographical proximity / distance

Residual plots of job requiring talent against joint beneficiary of knowledge dynamics

Residual plots of job requiring talent against exploration Residual plots of job requiring

talent against examination

Tests of Normality

Kolmogorov-Smirnova Shapiro-Wilk

Statistic df Sig. Statistic df Sig.

RES_V2_2 .070 586 .000 .982 586 .000

a. Lilliefors Significance Correction

Appendix 10: Scatterplot of SRESID (Y) against DEPENDNT (X) to check linearity of job requiring talent (Full sample, stepwise mode)

Appendix 11: Normality test of job requiring talent (Full sample, stepwise mode)

Appendix 12: P-P Plot of job requiring talent (Full sample, stepwise mode)

Appendix 13: Histogram and normal curve of studentized residuals of job requiring talent (Full sample, stepwise mode)

Appendix 14: Analysis of variance of job requiring talent in the confirmatory regression model (Full sample, enter mode)

ANOVAb

ModelSum of Squares df Mean Square F Sig.

1 Regression 59.905 11 5.446 5.976 .000a

Residual 504.881 554 .911

Total 564.786 565

a. Predictors: (Constant), Xknowledge, Exploration, Mobilisation, Geographicalspace, Jointbeneficiary, Exploitation, Contribution, Functionaldomain, Buyer, Innovation, Examinationb. Dependent Variable: Talent

Appendix 15: Model summary of job requiring talent in the confirmatory regression model (Full sample, enter mode)

Appendix 16: Variables entered into the confirmatory regression model of job requiring talent (Full sample, enter mode)

References:

Alavi, M., & Leidner, D. E. (2001). Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues. Management Information Systems Quarterly, 25(1), 107-136.

Antonelli, C. (2006a). The embedded generation of knowledge: contextual spillovers and localized appropriability. Eurodite.

Antonelli, C. (2006b). Models of Knowledge and Systems of Governance. Eurodite.

Armstrong, M. (2006). A Handbook of Human Resource Management Practice. London: Kogan Page.

Bathelt, H., Malmberg, A., & Maskell, P. (2004). Clusters and knowledge: local buzz, global pipelines and the process of knowledge creation, 28, p31–56.

Becker, B. E., Huselid, M. A., & Beatty, R. W. (2009). The differentiated Workforce: Transforming Talent into Strategic Impact. Harvard Business School Press.

Boudreau, J. W., & Ramstad, P. M. (2005). Talentship,Talent Segmentation, and Sustainability: A New HR Decision Science Paradigm for a New Strategy Definition. Human Resource Management , 44 (2), 129–136.

Brossard, O., & Vicente, J. (2007). Knowledge trends in the European ICT Sector, Knowledge value chain, proximities and networks. Eurodite.

Buckingham, M., & Vosburgh, R. M. (2001). The 21st Century Human Resources Function: It's the Talent, Stupid! Human Resource Planning , 24 (4), 17-23.

Cappelli, P. (2008-a). Talent Management for the Twenty-First Century. Harvard Business Review , 86 (3), 74-81.

Cappellin, R. (2006). Regional governance in the knowledge economy. Eurodite.

Carayannis, E. G., Ziemnowicz, C., & Spillan, J. E. (2007). Rediscovering Schumpeter: Conclusions and Lessons Learned for Public and Private Policy as Well as Practice . ( and C. Z. Elias G. Carayannis, Ed.). New York: Pelgrave Macmillan.

Cheese, P. (2008). Driving High Performance in the Talent-Powered Organization. Strategic HR Review , 7 (4), 25-31.

Cheese, P. (2008-b). Driving High Performance in the Talent-Powered Organization. Strategic HR Review , 7 (4), 25-31.

Chen, C.-J. (2004). The effects of knowledge attribute, alliance characteristics, and absorptive capacity on knowledge transfer performance, 34 (3), p311-21.

Chesbrough, H. W. (2003). Open Innovation: The new imperative for creating and profiting from technology.

Cohn, J., Katzenbach, J., & Vlak, G. (2008). Finding and Grooming Innovators . Harvard Business Review , 62-69.

Collings, D. G., & Mellahi, K. (2009). Strategic Talent management: A Review and Research Agenda. Human Resource Management Review .

Cooke, P. (2005). Markets and networks in the knowledge value chain. EU-FP6 EURODITE.

Cooke, P. (2006). Market & Networks in the knowledge value chain. Eurodite.

Cooke, P., & De Laurentis, P. (2005). Regional trajectories to the knowledge economy: a dynamic model. Centre for Advanced Studies, Cardiff University.

Dankbaar, B. (2004). Embeddedness, context, proximity and control, 12, p691-701.

Devenna, M. A., & al. (1984). A Framework for Strategic Human Resource Management. In C. Fombrun, Strategic Human Resource (pp. 33-45). Canada: John Wiley & Sons.

Farley, C. (2005). HR’s Role in Talent Management and Driving Business Results. Employment Realtions Today , 55-60.

Fitz-enz, J., & Davison, B. (2002). How to Measure Human Resources Management. McGraw Hill Professional.

Frank, F. D., & Taylor, C. R. (2004). Talent Management:Trends that Will Shape the Future. Human Resource Planning , 27 (1), 33-41.

Gagné, F. (1991). Toward a Differentiated Model of Giftedness and Talent. In N. Colangelo, & G. Davis, Handbook of Gifted Education (pp. 29-103). Boston: Allyn & Bacon.

Gallupe, B. (2001). Knowledge Management Systems: Surveying the Landscape, 3, p61-77.

Garrow, V., & Hirsh, W. (2008). Talent Management: Issues of Focus and Fit. Public Personnel Management , 37 (4), 389-404.

Groysberg, B., & Lee, L.-E. (2009). Hiring Stars and Their Colleagues: Exploration and Exploitation in Professional Service Firms. Organization Science , 20 (4), 740–758.

Hayek, F. A. (1937). Economics and Knowledge, 4, p33-54.

Helmstäder, E. (2006). About the economics of knowledge the macro-economic and institutional perspective. Eurodite.

Heinen, S., & O'Neill, C. (2004 ). Managing Talent to Maximize Performance. Employment Relations Today , 67-83.

Hughes, J. C., & Rog, E. (2008). Talent management:A Strategy for Improving Employee Recruitment,Retention and Engagement within hospitality organizations. International Journal of Contemporary Hospitality Management , 20 (7), 743-757.

Huselid, M. (1995). The impact of Human Resource Management on Turnover, Productivity, and Retention. Academy of Management Journal , 38 (3), 635-672.

Huselid, M., Beatty, R., & Becker, B. (2005). "A Players" or "A position" ? Harvard Business Review , 1-8.

Lawler III, E. (2009). Make Human Capital A Source of Competitive Advantage. Organizational Dynamics , 38 (1), 1–7.

Lewis, R. E., & Heckman, R. J. (2006). Talent management: A Critical Review. Human Resource Management Review , 139–154.

Michaels, E., Handfield-Jones, H., & Axelrod, B. (2001). The War for Talent. Boston, Massachusettts: Harvard Business School Press.

Morton, L. (2004). Integrated and Integrative Talent Management- A Strategic HR Framework. Research Report R-1345-04-RR (pp. 1-51). New York: The Conference Board.

Morton, L. (2005). Talent Management Value Imperatives: Strategies for Successful Execution. Research Report R-1360-05-RR (pp. 1-7). New York: The Conference Board.

Nelson, R. R. (1982). The role of knowledge in R&D efficiency, 97, p453-70.

Nooteboon, B. (2000). Learning by interaction: absorptive capacity, cognitive distance and governance, 4, p69-92.

Orr, B., & McVerry, B. (2007). Talent Management Challenge in the Oil and Gaz Industry. Natural Gaz and Electricity , 18-24.

Pfeffer, J. (2001). Fighting the War for Talent Is Hazardous to Your Organizational Health. Organizational Dynamics , 29 (4), 248-259.

Planque, B. (1991). Note sur la notion de réseau d’innovation : Réseaux contractuels et réseaux conventionnels. Revue d’Economie Régionale et Urbaine, 3(4), 295- 320.

Ready, D. A., & Conger, J. A. (2007). Make Your Company a Talent Factory. Harvard Business Review , 68- 77.

Ready, D., Hill, L., & Conger, J. (2008). Winning the Race of Talent in Emerging Markets. Harvard Business Review , 86 (11), 63-73.

Reilly, P. (2008). Identifying the Right Course for Talent Management. Public Personnel Management , 37 (4), 381-390.

Ruse, D. H., & Jansen, K. E. (2008). Stay in Front of the Talent Curve. Technology Management , 38-43.

Schapiro, H., & Varian, C. (1999). Innovation rules: A strategic guide to the Network Economy. Boston: Harvard Business School Press.

Spencer, L. M. (2004). How Competencies Create Economic Value. In L. Berger, & D. Berger, The Talent Management Handbook (pp. 64-84). McGraw Hill.

Storey, J., Wright, P. M., & Ulrich, D. (2009). The Routledge Companion to Strategic Human Resource Management. New York: Routledge.

Tansley, C., Stewart, J., Turner, P., & Lynette, H. (2006). Change agenda : Talent Management Understanding the Dimensions. UK: CIPD.

Tuomi, I. (2000). Data is more than knowledge: Implications of the reversed knowledge hierarchy for knowledge management and organizational memory, 16, p103–18.

Ulrich, D. (2008). Call for Talent: What is the Best Solutions? Leadership Excellence , 25 (5), 17-17.

Ulrich, D., & al, e. (2005). Conclusion: Reality, Impact, And Professionalism. Human Resource Management , Vol.44, 201-206.

Ulrich, D., & Brockbank, W. (2005). The HR Value Proposition. United State of America, Boston: Harvard Business School Publishing.

Vicente, J., & Suire, R. (2007). Informational cascades versus network externalities in locational choice: evidence of “ICT clusters” formation and stability. Regional Studies, 41(2), 173-184.

Vissers, G., & Dankbaar, B. (2006a). Knowledge dynamics in the firm, a review. Eurodite.

Vissers, G., & Dankbaar, B. (2006b). Part II: Knowledge dynamics. Eurodite.

Whirlpool, M. B. (2007). Analysing Critical Positions for Talent Needs. Organization Development Journal , 115-118.