Talent management and teamwork interaction: evidence in large Spanish companies.

Vivas-Lopez, Salvador

I. TALENT MANAGEMENT AND TEAMWORK INTERACTION: EVIDENCE IN LARGE SPANISH COMPANIES

Teamwork design should be carefully tackled, since flexible organizations increasingly rely on all kinds of teams as the axes of learning processes (i.e., knowledge creation) which are essential for organizational adaptation and renewal (Nonaka and Takeuchi, 1995). In a business world where intangible resources are the most valuable source of competitive advantage (Teece, 1998) organizations are increasingly aware of the importance of effectively managing knowledge-based assets. Intellectual property, corporate image and reputation, innovation skills, employee commitment and involvement, employee creativity, among others, constitute examples of intangible assets (intellectual capital) that rely of effective knowledge management (KM) for their successful development and optimization.

Whilst the terms 'learning' and 'knowledge' are obviously an intrinsic part of the concept of 'talent' (Vaiman and Vance, 2008; Whelan et al., 2010; Whelan and Carcary, 2011), the literature on talent management (TM) has not been so far robustly connected to mainstream academic research developments on OL and KM. Facing this gap as an opportunity, OL and KM challenges stimulate us to propose TM tackling the above mentioned OL-KM connections (Vivas-Lopez et al., 2011).

Therefore, TM can crucially help optimize organizational learning processes. In this sense, it is essential to recognize the strategic character of TM (Guthridge et al., 2008; Iles et al., 2010; Mellahi and Collings, 2010; Schuler et al., 2011; Scullion et al., 2010; Vaiman et al., 2012), especially in the context of the so-called 'knowledge-based economy' (Whelan et al., 2010). Considered by some authors as a set of human resource management (HRM) 'best practices' (Tichy et al., 1982), TM extends its scope further since it crucially links HRM and broader corporate strategy (Guthridge et al., 2008; Schuler et al., 2011). Certainly, TM tackles the relationship between talent and strategy, whereby talent is a valuable, scarce and often hard to imitate resource (Boudreau and Ramstad, 2005; Lewis and Heckman, 2006).

Notwithstanding the lack of consensus around the way to define TM and the existence of a broad of variety of approaches to the field (Iles et al., 2010; Lewis and Heckman, 2006; Preece et al., 2011; Tarique and Schuler, 2010), we find Collings and Mellahi's (2009: 309) thoughtful definition as especially useful in the context of our investigation: '[...] activities and processes that involve the systematic identification of key positions which differentially contribute to the organization's sustainable competitive advantage, the development of a talent pool of high potential and high performing incumbents to fill these roles, and the development of a differentiated human resource architecture to facilitate filling these positions with competent incumbents and to ensure their continued commitment to the organization'.

We think that this leads organizations' TM policies to pursue the ultimate aim of maximizing value created by talent, by means of organizational learning and improvement processes and also by developing knowledge assets (Vaiman and Vance, 2008). Successfully enhancing these dynamics requires the use of different types of organizational resources which are coordinated in diverse ways depending on the firm's strategy, its managers' strategic logic and also a number of firm's internal factors.

The aim of our paper is to study whether certain TM practices related to teamwork design and dynamics stimulate and develop learning (i.e., knowledge creation) processes within the organization across the different ontological levels (individual, group, and organizational-institutional). A model linking team-design based TM practices and OL is tested in our sample. Our empirical results emphasize the distinction between individual/group and institutional level of learning as the two pillars of knowledge creation processes (Akehurst et al., 2011). The results also highlight the role of team autonomy and creativity as crucial factors for successful KM, learning and talent creation.

The main contribution of this paper stems from a sample of large Spanish companies. Crucial aspects related to team design and dynamics are highly relevant for developing successful learning processes which, eventually, enhance the firm's competitive position. Consequently, work processes should be redesigned so that greater autonomy and creative freedom is given to teams.

This paper is structured as follows. After this introduction, the next section is devoted to deepening into the key concepts that, under our perspective, link TM, knowledge and learning with teamwork and team dynamics). The proposed model is presented in the following section, and the empirical methods and study results are explained in a subsequent section. The paper is closed with a final section devoted to a brief discussion and conclusion.

II. TM CONCEPTUAL BACKGROUND: LEARNING, KNOWLEDGE AND TEAMWORK

In a globalized business context intangible assets are essential as drivers for competitiveness (Teece, 1998). An organization's ability to develop dynamic capabilities is crucial in order to sustain successful innovation through OL (Vivas-Lopez, 2005; Alegre and Chiva, 2008). Hence, recent contributions to the TM literature (Collings and Mellahi, 2009; Farndale et al., 2010; Garavan, 2012; Iles et al., 2010; Mellahi and Collings, 2010; Preece et al., 2011; Scullion et al., 2010) provide relevant conceptual and operational support for better understanding the connections among (dynamic) capabilities, KM, OL and team management issues. However, explicit connections between TM and the other fields are scarce, and this fact shows an important research gap that needs to be addressed (Whelan and Carcary, 2011). This investigation aims at taking some first steps in such endeavor.

If management encourages continuous learning and the acquisition of new skills and knowledge, the organizational configuration and form of management will be essential in endowing the organization with more valuable knowledge assets, in both quality and quantity, than those possessed by its competitors. In order to do this, firms must be efficient in developing an organizational environment guidelines and processes aimed at securing, developing and retaining knowledge and talent. Policies and practices aimed at securing, developing and retaining knowledge and talent, labeled above as KM initiatives, are also core elements of TM (Whelan et al., 2010; Whelan and Carcary, 2011). The exercise of substituting the term 'knowledge' by 'talent' in the above expression would lead to define TM initiatives as policies and practices aimed at securing, developing and retaining talent. All in all, in a context whereby talent can be certainly regarded as the 'human catalyst' for knowledge, efforts for linking the fields of KM and TM are encouraged.

Certainly, teams are a crucial organizational element that acts as a nexus between the single individual and the whole organization, so a constant and on-going flow of comprehensive learning processes can be enhanced throughout the organization, from individuals, to groups, and up to the whole organization (Crossan et al., 1999; Bontis et al., 2002). There are many arguments and examples of situations that help reinforce the idea that effective teamwork and--more generally--team management are essential elements to take into account in order to foster KM and successful OL processes.

It is not just a matter of facing a simplistic 'individual vs. team based goals' dichotomy, but of tackling the challenge of being creative enough to find a way to make sound team dynamics a key component of performance management systems (e.g., by assessing knowledge sharing perceptions through 360 degree feedback, effective problem solving thanks to knowledge previously contributed by peers in databases, formally appraising senior employees through mentoring-based goals, etc.). All in all, emphasis must be made in the fact that all the above arguments and examples lead to the same conclusion: the crucial relevance of team related aspects as a key condition for developing successful KM and, eventually, enhancing learning processes throughout the organization.

Hence, explicit and clear TM, with a key emphasis on (re)designing knowledge-focused (project) teams (e.g., Newell et al., 2006) appear to be highly desirable to optimize learning across the different (ontological) learning levels (i.e. individual level, group level and organizational-institutional level). As a result, and consistent with the arguments presented in the previous paragraphs, team composition and dynamics are particularly relevant design variables to be included in TM (Pan et al., 2007). Regarding team composition, interdisciplinary views, creativity and systems thinking may be enhanced by a variety of complementary profiles of team members. Besides, trust among team members and strong, shared values--whilst allowing for a reasonable degree of change-enhancing disagreement--are usually considered positive elements (Argote et al., 2003; Levin and Cross, 2004). As for team dynamics, it is important that knowledge is effectively shared and transferred within teams, i.e., team bonding (Newell et al., 2004). However, the creation of isolated 'thought worlds' (Dougherty, 1992) needs to be avoided by all means. Any team needs to be well connected with other teams, and knowledge needs to be exchanged between teams; a collective organizational vision is hence developed, so that everybody works for the common organizational goals, i.e., team bridging (Newell et al., 2004).

III. MODEL and HYPOTHESES

The aim of our empirical study is to test whether TM initiatives--within the scope of broader contextual, managerial and organizational design conditioning factors--related to teamwork design and dynamics, stimulate and develop learning (i.e., knowledge creation) processes within the organization across the different ontological levels (individual, group, and organizational-institutional).

[FIGURE 1 OMITTED]

As shown in Figure 1, TM affects the processes of learning. Among the KM actions that comprise TM, we include in our model those particularly aimed at dealing with teamwork dynamics, namely team composition, team bonding and team bridging. Organizational learning is, then, the basic dependent variable (i.e., the amount of learning and knowledge creation that occurs in the organization), which is influenced by the independent variable, namely team-design based TM. In turn, OL is a construct that integrates the three ontological processes of learning and knowledge creation in firms: individual-level learning, group-level learning, and institution-level learning (also referred to as organizational learning in the literature, but we prefer to reserve such term for the broader processes that integrate the different levels).

Based on the above ideas, the following hypotheses are formulated:

H1: A positive and significant relationship exists between the organization's teamwork-design based TM initiatives and organizational knowledge creation.

H2: A positive and significant relationship exists between talent and knowledge assets creation at each organizational level and the other organizational levels.

IV. RESEARCH METHOD AND RESULTS

A. Data Gathering

The population used for this study was taken from the SABI database and the information therein provided on the population of large firms located in Spain. This criterion allows for an adequate sample size in statistical terms. From among the different quantitative criteria that can be considered in order to classify firms according to size, that of the fourth directive 78/660/EEC was chosen, in line with subsequent European Commission recommendations.

The basic data from the study are shown in Table 1 (above) and the technical datasheet is presented in Table 2 (below). We were not able to or not allowed to make contact with someone able to adequately answer the survey in 182 cases. 1283 contacts were eventually established (via e-mail or by telephone) of which 96 (7.5%) declared that they were unwilling to collaborate. Therefore, 1187 questionnaires were sent, 1078 via e-mail, which included a link to a webpage created for this purpose, and 109 where submitted by fax. By the end of the data gathering stage, 167 valid questionnaires had been received (134 via website and another 33 by fax), which implies a reasonable response rate, in this case 14.1% of the questionnaires sent out.

B. Variables and Data Analysis

In this study several multivariate statistical techniques were applied. An exploratory factor analysis was used to study the dimension of the measurement scales, with regard both to learning and to TM; a cluster analysis was applied in order to segment firms from the sample according to the level of learning; and a logistical regression model was used to analyze the influence of organizational design on the processes of knowledge creation (Hair et al., 1998).

The questionnaire applied included a group of items to evaluate the processes of learning in the firms of the sample. Another set of items was used to measure team-design based TM construct. Seven-point Likert scales were used for measuring all items of both dependent and independent variables. A sample of items employed to measure the OL construct is shown in Table 3 below.

The study of the OL construct is carried out through an exploratory factor analysis. The factors, or dimensions, necessary for representing the original data are drawn from a technical analysis of the main components. Those whose associated value was greater than 1 were chosen. Different rotations were carried out in order to clarify the meaning of the dimensions. The process ended with a varimax orthogonal rotation. This implied a considerable reduction of factors with a loss of an acceptable amount of information. The whole construct was reduced to just two factors, which explained 61.5% of the variability of the information.

Once the number of factors was established, the composition of the loading factors was studied in order to interpret their meaning. According to these analyses, a name was given to each dimension. The name and specific contents of the dimension are as follows:

* Dimension 1 (39.2% of the total variance): individual-group knowledge creation. This includes the aspects that correspond to learning developed by employees, as individuals and also collectively as group members.

* Dimension 2 (22.3% of the total variance): institutional (organizational) knowledge creation. This factor covers all the aspects related to learning developed throughout in the organization as such and thus formally institutionalized by management.

This analysis provides a partial acceptance of hypothesis 2, confirming a positive significant relationship between individual knowledge creation and knowledge creation in groups (both included in dimension 1). With regard to dimension 2, which deals with knowledge creation of an organizational-institutional nature, the statistical analysis hitherto carried out does not confirm a significant and positive relationship with the other two organizational levels, without taking into account the analysis of the influence of the team-design based TM.

Values given for firms in the sample for each dimension are measured via the average value from the items that make it up. Table 4 below contains the description of these two new variables. A segmentation of firms was carried out using these two variables. This grouping was done using a cluster analysis. The algorithm used for formulating the groups was the non-hierarchical K-average. This technique requires a pre-ordained number of clusters or segments. In this case, we opted for two groups.

Table 5 below shows details of the typologies found. It can be seen how the first cluster, or segment, composed of 47% of the firms analyzed, is defined by a less effective knowledge creation, i.e. those that make up segment 1 are firms where less learning occurs than those in segment 2. 53% of the firms analyzed make up segment 2.

On the other hand, team-design based TM was assessed through questionnaire including items assessed through a 7-point Likert scale (see Table 6 below). The study of the dimensions that make up the scale for TM was also done using an exploratory factorial analysis. Different rotations were carried out in order to characterize the meaning of the dimensions. The process ended with a varimax orthogonal rotation.

During the refining process of the model, items with similar factor loadings were eliminated, in order to avoid interference in the identification of the resulting dimensions. Five dimensions appeared as a result of the factor analysis, with a combined explained variance of 67.2% in the variability of the information.

The specific contents of each dimension are as follows:

* Dimension 1 (16.3% of the total variance): Employee participation in decision making.

* Dimension 2 (14.1% of the total variance): Job specialization within teams.

* Dimension 3 (13.8% of the total variance): Autonomous and creative team dynamics.

* Dimension 4 (13.2% of the total variance): Socialization within and across teams.

* Dimension 5 (9.8% of the total variance): Job formalization within teams.

We analyzed the effect of the organization's team-design based TM using a logistical regression model on the process of learning and knowledge creation. The dependent variable of the model 'Y' is the level of learning in firms drawn from the characterization resulting from the cluster analysis. It is a binary variable with a level of 1 associated with greater levels of learning and 0 for lower levels. The explanatory variables are the five dimensions that describe TM, and they are used as the basis for a logistic model (Greene, 2000).

As shown in Table 7 below, autonomous and creative team dynamics is a predictive factor of learning of an organizational-institutional nature. The p-value associated with the Wald contrast is less than 0.05. The value of the associated coefficient is positive, i.e., it has a positive effect on learning. Hence, it can be stated that the greater the intensity of the variable autonomous and creative team dynamics, the more capable the firm will be of creating knowledge. The other dimensions or variables of organizational design do not predict the creation of organizational knowledge. The associated p-value is greater than 0.05 and thus its effect on the variable knowledge creation is not significant. This outcome also leads to the partial acceptance of the proposal expressed in hypothesis 1, in the sense that the variable autonomous and creative team dynamics is the one that enables the existence of a positive correlation between individual-group knowledge creation and organizational-institutional knowledge creation.

Therefore, as a key result of our study, we emphasize the implication that the greater the effort by management to intensify autonomy and creativity of teams, the greater the organization's capacity to globally enhance learning processes throughout the organization, and therefore institutionalize, consolidate and distribute the knowledge that is developed by and among individuals, groups and communities.

V. DISCUSSION AND CONCLUSIONS

Consistent with prior research aimed at identifying contextual-policy factors that affect learning in organizations (e.g., Chiva et al., 2007; Fiol and Lyles, 1985; Goh and Richards, 1997; Jerez-Gomez et al., 2005), our empirical study reinforces this line of inquiry by deepening into details related to team dynamics as key elements of a sound KM strategy. The explicit use of learning-fostering organizational tools (i.e., TM) in general--and (project) teamwork design in particular--helps build an adequate context for organizational knowledge creation. There is a significant relationship between autonomous and creative team dynamics and individual-team learning processes, leading to partial acceptance of hypothesis 1. In turn, our study has also shown that the two key inter-related pillars of OL processes are individual-group learning and institutional learning, implying partial acceptance of hypothesis 2. A key conclusion of our study is that, among our sample of big Spanish firms, the most relevant team-related design aspects to be taken into account if OL is to be successful, revolve around building work processes involving teams where high degrees of autonomy and creativity are fostered.

As for managerial implications, managers are advised to pay great attention to the extent to which any (project) teams involved in knowledge-intensive activities are given enough autonomy and are actively encouraged to be highly creative. We would recommend that team leaders are people who have the crucial ability to discover 'hidden talent' among people who may not be in principle identified as members of the organizational 'talent pool'. Team leaders should then help realize such potential for the benefit of the team by means of allowing the team as great levels of autonomy as possible, and also encouraging creative problem solving and decision making.

These reflections fit well with recent developments on the links between HRM, KM, leadership, team management and/or project management (e.g., Newell et al., 2004 and 2006; Pan et al., 2007; Vaiman and Vance, 2008; Whelan et al., 2010), and thus lead to opening up promising directions for the practice of TM. For instance, it is often said that a key aim of TM is retaining talent, assuming that talent is 'possessed' by 'talented' individuals and efforts should be focused on preventing their departure from the organization. It may be hard sometimes to prevent talented employees from leaving the organization, so what is really important is that these employees' talent has been somehow distributed and has become embedded in the teams and processes with which a departing talented team member was involved (Calo, 2008).

Regarding research implications, these results are in line with prior research related to team autonomy and creativity (e.g., Chiva et al., 2007; Jerez-Gomez et al., 2005a), and also with other studies mainly related to the aspects that in our conceptual background where labeled as team composition (Pan et al., 2007) and team bonding (Newell et al., 2004). However, some questions remain open regarding the low predictive power of other apparently important factors, such as organization-wide socialization (Cabrera and Cabrera, 2002; Dougherty, 1992) and, more generally, processes and policies more related to team bridging (Newell et al., 2004). Further research may help explore and clarify these (and other) relevant questions. With this purpose, it seems wise and logical to construe Collings and Mellahi's (2009) definition of TM (the working definition used in our investigation, see introduction section) from as comprehensive a perspective as possible, so it crucially extends its reach so as to fully encompass--given the appropriate organizational context and goals--inclusive and social capital views of talent (Iles et al., 2010; Preece and Iles, 2009; Preece et al., 2011). Hence, the different views on TM need not be mutually exclusive, but complementary, depending on a huge diversity of organizational goals and priorities, contexts and contingencies (Baron and Kreps, 1999) and also idiosyncratic HRM architectures.

This study presents relevant outcomes at an initial stage, showing some limitations that need to be acknowledged. In this sense, although the response rate obtained was sufficient for conducting the analyses planned and obtaining meaningful results, it could be assessed as not impressive under more demanding requirements for statistical representatively. Besides, the analysis methods, although appropriate for testing our hypotheses, were not particularly sophisticated, having in mind the complexity of potential relationships among the multiple variables involved. It would be interesting to continue this line of research in the future with the incorporation of more complex methods and procedures that would allow overcome some of the current limitations.

The surprising results regarding with the low support to some of the aspects included in both hypotheses, may be good candidates for in-depth case studies that would help develop more detailed views and analyses of the organizational dynamics involved (we refer to the weak links shown between, on the one hand, individual and group learning level and, on the other, with institutional learning level; and we emphasize the interesting inquiry opportunities opened up by the surprisingly low importance that our study attributed to factors such as team bridging or a strong company-wide corporate's culture). Definitely, our study may provide interesting initial insights to deepen into these--and other--relevant inquiry challenges through a variety of (quantitative or qualitative) research methods.

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Salvador Vivas-Lopez

Department of Management, University of Valencia, Valencia, Spain

svivas@uv.es

Table 1
Basic research data

1.465 large firms
Firms with failed contact: 182
Firms contacted: 1.283 (100%)
Firms that were not willing to collaborate: 96 (7.5%)
Questionnaires sent: 1.187 (92.5%)
Firms that answered: 167 (14.1%)

Table 2
Technical datasheet of the empirical study

POPULATION AND FIELD 1.465 Spanish firms with more than 250
OF THE RESEARCH employees and a yearly turnover of more
 than 40 million euros

SAMPLE SIZE 167 firms

CONFIDENCE LEVEL 95,5%

SAMPLE ERROR 7%

SAMPLING PROCEDURE Convenience sampling

GEOGRAPHICAL FIELD Spain

SAMPLE UNIT Firm

TYPE OF QUESTIONNAIRE Structured questionnaire, sent to the CEO
 (responded by the Head of Quality Control or
 similar position in the absence or
 unavailability of the CEO).

Table 3
Items used for measuring organizational learning

People in our firm are capable of breaking with old conceptions
in order to see things in a new, different light.

People in our firm attempt to understand the way other colleagues
think and act.

New ideas and approaches to work are continually being tried out.

Employees tend to hoard knowledge as a source of power and are
unwilling to share it with colleagues (reversed scale).

Everyone's point of view is asked for in meetings.

In the firm, there are procedures for gathering proposals from
employees, assessing them, adding them and internally distributing
them.

Table 4
Descriptive statistics of the dimensions used for measuring
organizational learning

 INDIVIDUAL-GROUP INSTITUTIONAL
 LEARNING LEARNING

Average 4.4898 5.1708
Typical deviation 1.0739 1.2351
Minimum 2.0000 1.3300
Maximum 7.0000 7.0000
 25% of firms did not exceed 3.7500 4.3333
 50% of firms did not exceed 4.5000 5.3333
 75% of firms did not exceed 5.3750 6.0000

Table 5
Description of segments with regard to learning averages

 Segment

 1 2

Individual-group learning (average) 3.71 5.17
Institutional learning (average) 4.21 5.99

Table 6
Items used for measuring teamwork based TM

Team composition

The values and regulations of the organizations are considered
when hiring staff.

Project teams are made up of staff from different specialties.

Employee qualification makes direct supervision unnecessary.

Team bonding

Project teams possess their own collective objectives.

The collective outcomes of work teams are rewarded.

Project teams are self-organizing.

Team bridging

In training programs there are activities aimed at making staff
aware of the organization's values.

Project teams are a source of learning.

Non-managerial employees participate in strategic decisions.

Table 7
Estimation of the parameters of the logistic model

 [beta] Wald Degrees
 statistic of
 freedom

Employee participation -0.098 0.450 1
in decision making

Job specialization 0.284 2.130 1
within teams

Autonomous and creative 0.355 6.015 1
team dynamics

Socialization within 0.257 1.687 1
and across teams

Job formalization 0.057 0.146 1
within teams

Constant -3.558 6.969 1

 p-value Exp([beta])

Employee participation 0.502 0.906
in decision making

Job specialization 0.144 1.329
within teams

Autonomous and creative 0.014 1.427
team dynamics

Socialization within 0.194 1.293
and across teams

Job formalization 0.702 1.059
within teams

Constant 0.008 0.028