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 talent
Prior 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; aboveaverage 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 p 1performance (Michaels, HandfieldJones, & 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 processes
Cooke (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
1
a- 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 talent
The 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 innovation
Through 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 dynamics
In 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. Sample
The 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 smallsized (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 analysis
This 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 variables
This 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 (S 3) 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 model
It 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 R 2 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 variate
The 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, S 1 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 Multicollinearity
It 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 frustration
An 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”.
Variables Entered
Low talent requirement
High talent requirement
Added value
Use of knowledge
Creation of knowledge
Distribution of wealth
Fair perception
Unfair perception
Functional domain of
knowledge
Distance
Low absorption, transfer and
learning ability
Proximity knowledge
dynamics
High absorption, transfer and
learning ability
Distant knowledge dynamics
Knowledge Value Chain
Examination step
Exploration step
Table 2: Typology of the difference between the perception of job
requiring a low level of talent and job requiring a high level of talent
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
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
Variables
Scal
e
Description
type
Questions
Authors
Talent
7poin Measurement
T t
of the degree
Like of talent at job
rt
Buyer
7poin
B t
Like
rt
Measurement
of
the
involvement
of customers,
clients
and
end-users
Contrib
ution
7poin
t
Sem
C
antic
diffe
renti
al
Measurement
your
contribution in
term
of
creation
or
use
of
knowledge
Functio
nal
domain
7poin
F t
Like
rt
Measurement
of the level of
knowledge in
term
of
absorption,
transfer and
learning
Geogra
phical
space
7poin
t
Sem
G
antic
diffe
renti
al
Measurement
of
the
geographical
distance
proximity
between
stakeholders
De Laurentis and Cooke,
2005;
(Boudreau
&
Ramstad,
2005;
Buckingham & Vosburgh,
Evaluate the degree of talent 2001; Cappelli, 2008-a;
required in your job. Talent: Collings & Mellahi, 2009;
Intellectual ability to succeed in Heinen & O'Neill, 2004 ;
something
Huselid, Beatty, & Becker,
1 Very weak - 7 Very strong
2005; Lewis & Heckman,
2006; Michaels, HandfieldJones, & Axelrod, 2001;
Ready & Conger, 2007;
Ulrich, 2008)
How do you evaluate the level of
interaction
between
your
organisation and following partners?
Interactions with clients and end- Cappellin, 2006
users
1 Very weak interaction - 7 Very
strong interaction
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 Cooke, 2006a; Vissers and
existing knowledge), intermediate Dankbaar, 2006
marks allow you to adjust your
judgment.
1 Rather creation of new knowledge
- 7 Rather use of existing knowledge
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)
Vissers and Dankbaar,
Evaluate the capability of transfer of
2006; Chen, 2004; Alavi
your organisation. (Ex: A research
and Leider, 2001; Nelson
centre has developed a technology
and Winter, 1982; Gallupe,
for your organisation. A transfer of
2001; Dankbaar, 2004
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
From a geographical perspective,
most of interactions between your
organisation and its partners are
Chesbrough, 2003; Cooke,
rather developed at proximity or
2006; Nooteboom, 2000
rather
at
distance.
1 Rather geographical proximity - 7
Rather geographical distance
Measurement
of the degree
of innovation
(New
production
7processes,
poin New
Innovati
I t
products,
on
Like New
rt
materials,
resources and
technologies,
New markets,
New forms of
organisations)
7poin Measurement
t
of the fairness
Joint
Sem of
profit
benefici J
antic sharing
ary
diffe between
renti stakeholders
al
Mobilis
ation
7poin
t
Sem
M
antic
diffe
renti
al
Measurement
of
work
networking to
determine if
people
are
working
individually or
in groups
Measurement
of
the
involvement
in
the
exploration
step (studies,
research)
Measurement
of
the
7involvement
poin in
the
Examin S
t
examination
ation
2
Like step
rt
(experimental
trial,
testes,
specification)
7poin
Explora S
t
tion
1
Like
rt
Please indicate the degree of
innovation in the following domains
within
your
organisation
New
production
processes
New
products
New materials, resources and
Schumpeter, 1934
technologies
New
markets
New
forms
of
organisations
1 Very low degree of innovation - 7
Very strong degree of innovation for
all 5 items
Is the distribution of profits equally
shared between your organisation Scherer,
1984;
and
its
partners? Carayannis, Ziemnowicz,
1 Distribution of profits is fair - 7 and Spillan, 2007
Distribution of profits is unfair
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
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)
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)
7poin
Exploita S
t
tion
3
Like
rt
X
knowle
dge
7poin
X t
Like
rt
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)
Measurement
of the degree
of
risk
of
imitation and
unintended
knowledge
spillovers
Indicate your degree of involvement
in exploration, examination and
exploitation steps. Exploitation step
De Laurentis and Cooke,
(marketing of the innovation, sales
2006; Cooke, 2005 and
on the market, utility of innovation
2006a
and use of the product / service)
1 Very low involvement - 7 Very
strong involvement
In the scope of your interaction with
your partners, how to do evaluate
De Laurentis and Cooke,
following
risks?
2009;
Brossard
and
Unintended knowledge spillovers
Vicente, 2007
Imitation
1 Very low risk - 7 Very high risk
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 innovation
Residual plots of job requiring talent
against geographical proximity /
distance
Residual plots of job requiring
talent against functional domain
of knowledge
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 exploitation step
of knowledge
Residual plots of job requiring
talent against exploration step
of knowledge
Residual plots of job requiring
talent against X knowledge
Residual plots of job requiring
talent against examination step
of knowledge
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)
Tests of Normality
Kolmogorov-Smirnova
Statistic
RES_V1_1
df
.065
Shapiro-Wilk
Sig.
566
.000
Statistic
.980
df
Sig.
566
.000
a. Lilliefors Significance Correction
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
Sum
Squares
Model
1
2
3
4
5
6
of
df
Mean Square
F
Sig.
23.599
.000a
17.758
.000b
14.412
.000c
12.256
.000d
10.757
.000e
9.801
.000f
Regression
22.683
1
22.683
Residual
542.103
564
.961
Total
564.786
565
Regression
33.515
2
16.757
Residual
531.271
563
.944
Total
564.786
565
Regression
40.347
3
13.449
Residual
524.439
562
.933
Total
564.786
565
Regression
45.387
4
11.347
Residual
519.399
561
.926
Total
564.786
565
Regression
49.492
5
9.898
Residual
515.295
560
.920
Total
564.786
565
Regression
53.760
6
8.960
Residual
511.026
559
.914
Total
564.786
565
a. Predictors: (Constant), Contribution
b. Predictors: (Constant), Contribution, Jointbeneficiary
c. Predictors: (Constant), Contribution, Jointbeneficiary, Functionaldomain
d.
Predictors:
(Constant),
Contribution,
Jointbeneficiary,
Functionaldomain,
Geographicalspace
e.
Predictors:
(Constant),
Contribution,
Jointbeneficiary,
Functionaldomain,
Geographicalspace, Exploration
f.
Predictors:
(Constant),
Contribution,
Jointbeneficiary,
Functionaldomain,
Geographicalspace, Exploration, Examination
g. 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 Residual plots of job requiring
Residual plots of job requiring
talent against contribution to
knowledge
Residual plots of job requiring
talent against joint beneficiary of
knowledge dynamics
talent
against
functional
domain of knowledge
Residual plots of job requiring
talent against exploration
talent
against
geographical
proximity / distance
Residual plots of job requiring
talent against examination
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)
Tests of Normality
Kolmogorov-Smirnova
Statistic
RES_V2_2
df
.070
a. Lilliefors Significance Correction
Shapiro-Wilk
Sig.
586
.000
Statistic
.982
df
Sig.
586
.000
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
Sum
Squares
Model
1
of
df
Mean Square
F
Sig.
5.976
.000a
Regression
59.905
11
5.446
Residual
504.881
554
.911
Total
564.786
565
a. Predictors: (Constant), Xknowledge, Exploration, Mobilisation, Geographicalspace,
Jointbeneficiary, Exploitation, Contribution, Functionaldomain, Buyer, Innovation,
Examination
b. 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)
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