Relationship between team learning and team effectiveness

Katia Elizabeth Puente-Palacios (Department of Psychology, University of Brasília, Brasilia, Brazil)
Raquel Trinchão de Jesus Barouh (Superior Tribunal de Justica, Brasilia, Brazil)

Journal of Workplace Learning

ISSN: 1366-5626

Article publication date: 18 May 2021

Issue publication date: 28 July 2021

681

Abstract

Purpose

The purpose of this paper is two-fold: first, to demonstrate that learning occurs as a collective process in addition to traditional individual learning and second, to identify its antecedents and consequences at the team level.

Design/methodology/approach

Data were gathered using questionnaires answered by 356 participants organized in 90 teams. Quantitative analytic strategies were applied to verify if individual answers of team members were similar enough to compound team scores and to measure the predictive power of the proposed model.

Findings

Results showed that team learning is a collective phenomenon: intra-team differences were small and differences between teams were significant. Additional results demonstrated that team learning is predicted by team potency (34%) and, at the group level, explains 5% of the team’s satisfaction.

Practical implications

The findings of the present research suggest that organizational managers can improve the results of teams by supporting the development of social processes such as potency and learning.

Originality/value

Learning in organizations has received close attention in recent years. However, publications are focusing mostly on the individual learning that occurs in teams and organizations. The main contribution of this paper is to demonstrate what characterizes team learning as a collective process and which relations it maintains with other team processes.

Keywords

Citation

Puente-Palacios, K.E. and Barouh, R.T.d.J. (2021), "Relationship between team learning and team effectiveness", Journal of Workplace Learning, Vol. 33 No. 7, pp. 534-546. https://doi.org/10.1108/JWL-11-2020-0180

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited


1. Introduction

Learning is a relevant phenomenon and a longstanding special area of attention in the work context and has been traditionally studied from an individual perspective. However, at the end of the previous century, a change of level was seen when it came to be investigated as a collective phenomenon (meso level). This change was accompanied by inquiries that questioned whether work teams actually learn and if so, how they do this.

At the individual level, learning is understood as a powerful resource for workers, as it improves their employability. For teams and organizations, it is assumed as an advantage as it enhances their competitiveness and increases the probability of survival and growth (Noe et al., 2014). However, considering that individual learning is described as a process involving acquisition, retention, maintenance, generalization and transfer of new knowledge and skills, what is to be understood as team learning? Dimas et al. (2016) shed light on this question and clarify that team learning is not the sum of individuals’ learning but is a process resulting from the interaction and articulation of the members’ knowledge and cognition. It can also be described as the process by which team members transform and adopt new behavior patterns (Koeslag-Kreunen et al., 2018). Thus, team learning encompasses both the process and the result of transformation. This understanding raises an additional challenge for research, as Goodman and Dabbish (2011) warn as it is imperative that scholars of team learning consider the need to differentiate it from other collective processes that play the role of antecedents and consequences of learning. This challenge was confronted in this article, as we focused on studying the predictive power of potency regarding learning, while the consequent variable was team effectiveness, thus bringing a relevant contribution to the field.

As a starting point, it must be emphasized that team learning is not a natural process of maturation. It is a complex and specific phenomenon that has the potential to occur, as long as a set of conditions is available to the team (Koeslag-Kreunen et al., 2018). Thus, it is important to recognize that doubts remain about the role of team learning, for example: what individual or collective processes trigger it and what results can be expected? To respond to these questions, an empirical study was developed with the objective of demonstrating that learning arises from the articulation of individual attributes and is manifested as a collective phenomenon, as well as identifying the relationships that it maintains with antecedent and consequent variables at the team level.

2. Learning: a collective phenomenon

Despite the growing interest that team learning is raising, there is still a need for theoretical argumentation and empirical demonstration about the nature of this phenomenon when it is taken as a group characteristic or in this case, a team characteristic. In this research, we consider teams as social structures composed of three or more individuals who have a common goal, maintain relationships of interdependence, distribute roles and responsibilities among themselves and respond to work requirements defined by the organization to which they belong. The interaction between team members allows individual attributes such as knowledge or attitudes, to be communicated among the members, divided and shared. Then, through articulation processes, previously individual phenomena give way to the emergence of collective properties (Puente-Palacios et al., 2016).

The process of transforming individual attributes into collective phenomena, through social interaction that facilitates sharing, is described as emersion (Kostopoulos et al., 2013). The application of this comprehensive logic to team learning gains importance because this phenomenon refers to the process of obtaining information, sharing, refinement and combining of knowledge, through the interaction of team members (Hirst et al.,2009; Van der Vegt and Bunderson, 2005). Thus, learning at the collective level is based on the occurrence of processes of socialization, codification, dissemination and sharing of knowledge and information, making it possible for the group to recover the information later (Kostopoulos et al., 2013).

In presenting a comprehensive model of what team learning is, Decuyper et al. (2010) describe several theoretical proposals and conclude by arguing that team learning encompasses both the collective construction process, revealed in various behaviors embraced by the team, as well as the result of this collective construction manifested in the emergence of shared visions.

Thus, it is important to recognize that, in teams, the nature of their functioning has the potential to elicit the learning process (Savelsbergh et al., 2010). For example, when members ask questions, seek feedback, try out new ways of working, reflect together on results and discuss mistakes. These constitute forms of collective action that promote the occurrence of group learning, through a continuous process of reflection and action (Barouh and Puente-Palacios, 2015; Koeslag-Kreunen et al., 2018).

Understanding that collective learning is a social process of building shared cognitions, it is pertinent to argue that the emergence of shared knowledge packages resulting from interactions that occurred in the social scene can be taken as an indication that the collective phenomenon of learning occurred (Van den Bossche et al., 2006).

The emergence of these collective knowledge structures is made possible by the presence of the so-called team learning behaviors. These consist of patterns of interaction and discourse that enable mutual understanding and promote agreement among team members (Van den Bossche et al., 2011). The concept of mutual understanding involves the processes of construction and co-construction of meaning. The collective construction process refers to the interaction between team members and can be described as the active participation of the members that occurs when a member presents his/her conception of a problem and the rest of the group tries to understand and give meaning to the question posed. Co-construction refers to the modification of the original conception given by the team, resulting in new meanings that emerge through collaborative work.

The construction of agreement among team members also arises from the creation of a propitious environment for discussions and the expression of disagreements. These can be described as a beneficial disagreement or constructive conflict (Van den Bossche et al., 2011) that occurs when there is a rejection of an initial proposition, followed by arguments and clarifications negotiated by the members, which promote the identification of different possible meanings and which are followed by the construction of a collective vision resulting from the contributions of various members.

Focusing on the elements that facilitate the occurrence of team learning, scholars from this field point out that, by being a phenomenon constructed through interactions between members, it is a result of the processes experienced by the team (Edmondson, 1999). One of these processes is potency, which consists of a belief shared by team members about their ability to carry out assigned work successfully (Puente-Palacios et al., 2015). On the relation between potency and learning, researchers explain that they are complementary processes as both are phenomena that produce a social environment capable of stimulating team members to construct collectively sustained cognitive packages. Specifically, they argue that potency acts to the extent that members collectively believe that the team they comprise is able to use new learning to produce results for the group (Van den Bossche et al., 2006).

On the other hand, it is also argued that team learning behaviors affect performance (Van den Bossche et al., 2011), as, by appearing in the form of collective packages of shared knowledge, they allow team members to anticipate and execute their actions in an organized and more efficient manner (Kozlowski and Ilgen, 2006; Cooke et al., 2000). Thus, cognitive sharing facilitates the coordination and cooperation of the team members, promoting the emergence of similar interpretations about the demands of the context.

Among the consequences, satisfaction with the team is one of the more important ones. This attitude shows the extent to which individuals achieved their expectations from the experience of collective work and constitutes a legitimate indicator of team effectiveness. Team satisfaction develops through the social relations established in the workgroup and can legitimately be understood as a collective attribute to the extent that the members present similar levels of satisfaction.

A number of studies in this area (Mathieu et al., 2008; Reis and Puente-Palacios, 2019) demonstrate the pertinence of adopting satisfaction as evidence of success in the functioning of a team or team effectiveness criterion. It should also be pointed out that scholars of this field defend the link between learning behaviors and satisfaction, as presented in the learning model proposed by Van den Bossche et al. (2011). These authors also emphasize that team learning behaviors permit the development of shared cognitions that, in turn, further team effectiveness.

Thus, the members’ perception that the team is engaged in learning behaviors is associated, in the antecedent condition, with several attributes of the team. When the group has the conditions to generate shared knowledge, it can become more productive. In a similar way, in the presence of collective interpretations generated in the team learning process, a greater level of satisfaction can be observed.

From the theorizations as well as the empirical evidence brought about by the studies cited, we consider that the present research advances in relation to the findings of the field, as it proposes to demonstrate that the learning process can legitimately be treated as a collective attribute. With this evidence, we intend to identify the predictive power of potency and to measure the impact of team learning on team satisfaction. In addition to the objective described, we establish as research hypotheses:

H1.

Potency predicts individual perceptions of the learning behaviors of the team.

H2.

The perception of team learning behaviors (individual) is positively associated with collective cognitions (shared learning behaviors).

H3.

The perception of learning behaviors (individual) positively predicts team satisfaction.

H4.

Collective cognition (shared learning behaviors) add additional explanation to satisfaction, relative to that provided by the perception of learning behaviors (individual).

3. Method

3.1 Participants

The study was based on the answers collected from 394 undergraduate students of a Brazilian university, organized in 102 teams. Contact with these students took place in the classroom after obtaining the consent of the professor responsible for the course. As for the respondents, they were mostly male (59.9%), between 17 and 20 years of age (60.6%), 28.7% had work experience and of these, 21.6% did their work in a team.

The activities assigned to the teams consisted of collective problem-solving tasks, focusing on the content covered in the program of study. The tasks were performed over one academic semester and resulted in a collective grade assigned to the group at the end of the semester. Consistent with the definition adopted, the teams were composed of at least three members and had interdependent tasks and goal interdependence. Team members were constant throughout the semester.

3.2 Instruments

Information about potency was collected at the first point, approximately one month after the beginning of the work of the group, using a scale composed of 25 items organized into two factors, developed by Puente-Palacios et al. (2015). In this study, the structure with two factors explained around 48% of the variance. Factor 1, named Productive Performance, combined 17 items focusing on team members’ beliefs about the capability of the team to succeed in its tasks (eg. Members of my team believed that the team’s work will be recognized by their professors). Factor loadings were considered adequate (from 0.39 to 0.70; α = 0.91; item-total correlation = 0.59). Factor 2, named Social Relationship, condensed seven items focusing on team members’ beliefs about their capability to maintain positive relationships that support their work activities (eg. Members of my team believe that there is a good social relationship between team members; factor loadings between 0.51 and 0.85; α = 0.89; item-total correlation = 0.68). The test of the adequacy of the measure was carried out with the FACTOR program and the results obtained showed a good fit (CFI = 0.99; NNFI = 0.99; RMSEA = 0.02; RMSR = 0.04), according to criteria presented by Barrios-Fernandez et al. (2020).

The team learning behaviors were surveyed at a second point, about a month after collecting the potency data, using a scale containing nine items organized into a single factor (Barouh and Puente-Palacios, 2015). The measure focused on collective behaviors demonstrated by team members (e.g. We share all the relevant information and ideas we have). In this research, the measure explained approximately 49% of the variance and showed adequate psychometric properties (factor loadings between 0.54 and 0.75; α = 0.86; r item-total = 0.60). The model fit was considered appropriate (CFI = 0.99; NNFI = 0.99; RMSEA = 0.03; RMSR = 0.04).

Finally, one month after the previous collection, information about the criterion variable (satisfaction) was collected. The satisfaction with the team measure (Reis and Puente-Palacios, 2019) has five items answered on a five-point Likert scale and focuses on team members’ feelings about their team (eg. I have positive feelings about the way we work together on my team). The psychometric properties demonstrate that the measure is adequate for the sample and the single factor solution explained approximately 67.28% of the variance (factor loadings between 0.72 and 0.84; α = 0.88; r item-total = 0.71). The model fit was good, but one of the two residuals was poor (CFI = 0.98; NNFI = 0.96; RMSEA = 0.11; RMSR = 0.04). Analyzing the values of all indexes together, the use of this measure can be considered appropriate.

Regarding team collective learning behaviors (representing shared cognitions), these were operationalized by calculating the similarity of the responses given to the learning behaviors scale by the team members. More specifically, the mean value was calculated for the discrepancy among team member answers relative to the median of the group. Therefore, the interpretations must take into account that high similarity (shared cognitions) will be represented by the low discrepancy. The metric used to represent collective learning behaviors was the Average Deviation Index (ADMd).

3.3 Procedures

After obtaining authorization, the groups of students were informed about the content of the research, its scientific character, the confidentiality of the data, and therefore, all the ethical principles of COPE governing research with human beings were respected. Instruments were answered in printed form and each team was identified by a code given by the researcher, to maintain control and anonymity for the subsequent data collection events. Questionnaires were written in Portuguese and answered by team members at three points in time, seeking to reduce cross-sectional research bias. The application order was, first, potency, the antecedent variable. One month later, the team learning behaviors scale was applied and, around one month after that, team members answered the questionnaire on satisfaction, the criterion variable of the study.

3.4 Data analysis

The analysis procedures began with the verification of the adequacy of the measures for the sample. Next, evidence was sought to authorize the composition of team scores. To do so, intra- and inter-team variance analyzes were performed. Finally, after emersion procedures (generation of group scores), the predictive model was tested using regression analysis. The tools used for the analyzes were the Statistical Package for the Social Sciences (SPSS) and the FACTOR program.

4. Results

The initial number of respondents (991 individuals and 148 teams) was reduced for various reasons such as lack of identification code that allowed the respondent to be associated with the respective team, total response invariance, high level of missing data (50% of the questions) or even by the absence of the respondent on some of the data collection days (only those who answered the questionnaires on the three data collection days were kept). In total, 592 subjects were withdrawn, corresponding to 46 teams. After these exclusions, the sample on which the subsequent analyzes were done was composed of 394 subjects and 102 teams.

With this data sample, we first verified the psychometric properties of the measures used and, after that, we investigate the pertinence of composing group scores based on information collected at the individual level (emersion). To do so, we analyzed the magnitude of the similarity of team member responses to all phenomena focused on in this study (potency, learning behaviors and satisfaction) and the variance between groups.

The intragroup similarity was verified using the magnitude of mean deviations about the median or ADMd. In this analysis, a maximum discrepancy value is established, depending on the amplitude of the response scale (Burke and Dunlap, 2002), which in this case is five points for all measurements. The formula to be applied is c/6, where “c” represents the amplitude of the scale. Thus, the value 0.83 was identified as the maximum deviation tolerated in the responses of the team members so that the existence of shared visions that is evidence of a collective phenomenon could be defended. The use of this strategy in the case of studies presenting the emersion of constructs is defended by various scholars of group phenomena (Gamero et al., 2008; Iwai et al., 2019; Peñaroja et al., 2013).

The results revealed that some teams presented discrepancies in their answers, at a higher level than that established (0.83), in at least one of the variables of the model, and thus were eliminated from the database. Therefore, subsequent analyzes were run on a set of responses given by 356 subjects, organized into 90 teams. The mean ADMd values for each variable can be seen in Table 1. The lower these values, the greater the homogeneity of responses among the team members.

The second criterion adopted to verify the occurrence of emersion of the variables focused on in this study was variance analysis. ANOVA results showed that teams differed in the mean values of those variables (p <0.001). The last analysis performed was Intraclass Correlation Coefficient (ICC), which quantifies the variance attributable to the team level. The values obtained are similar to those of other studies reported in the literature of the organizational field (Bliese, 2000). All these criteria were carried out according to procedures adopted by scholars of the organizational psychology field (Coultas et al., 2014; Kostopoulos et al., 2013). The results obtained in this study can be seen in Table 1.

The identification of the intragroup agreement and between-group variability authorizes the composition of the group scores, condensing individual answers into 90 team scores. In addition, it should be noted that the value of the ADMd came to be used as an indicator of the collective (shared) cognitions. Therefore, it represents team learning behaviors. After these analyzes, a team-level database was generated and exploratory analyzes were carried out seeking evidence of normality, univariate and multivariate cases and absence of multicollinearity, as prerequisites to test the predictive modeling. The results showed that all the assumptions were respected by the data.

The first analysis runs aimed to identify the correlations between the study variables. The results from Table 2 show an association between potency (two factors), learning behaviors and satisfaction. Likewise, associations between shared cognitions regarding learning behaviors and satisfaction are observed. The negative sign in this last correlation is justified, as the collective learning behaviors of teams were operationalized by calculating the deviations (ADMd value). Thus, the greater the discrepancy (deviation), the lower the satisfaction.

Next, the predictive modeling was tested to verify the pertinence of the established hypotheses. H1 argues that potency predicts the perceptions of members about the occurrence of learning behaviors. The results showed that the predictors (two factors of potency) have an explanatory power of approximately 34% for the criterion variable. Therefore, support for H1 was found. Analyzing the independent contributions of the two factors of potency, it was verified that only factor 1 (Productive Performance) was associated with the perception of the team’s learning behaviors.

H2 predicts an association between the individual perceptions about team learning behaviors and the shared collective interpretations (collective cognitions) related to these behaviors. Following the theoretical logic that establishes that team learning is identified in the presence of shared visions in the team, collective cognitions were taken as evidence of the occurrence of those learning processes. Also, it must be noted that individual perceptions of team learning behaviors gave rise to two indicators:

  1. a score based on the arithmetic mean of individual perceptions; and

  2. a collective score showing the magnitude of team cognitions represented by the value of the ADMd score of each team.

Pearson’s correlation offered support for the hypothesis and, therefore, the association we proposed was corroborated (r = −0.54; p <0.001), as shown in Table 2. The results show that the more members perceive that their team exhibits collaborative learning behaviors, the greater the intensity of sharing of interpretations in this respect. The magnitude of the observed perception also reveals that these two phenomena (learning behaviors and collective cognitions) maintain a similarity of approximately 25% (of variance) and demonstrate that although both scores derive from the same measurement scale, they are differentiable phenomena.

H3 argues that the individual perceptions of team learning predict team satisfaction. The results found (Table 3) provide support for that relation, as individual perceptions were predictive of the satisfaction of team members, explaining 53% of the phenomenon. That is, the members’ perception of how much the team engages in actions that promote learning can, in itself, predict satisfaction.

Finally, H4 asserts an increase in explanatory power, derived from shared cognitions in relation to satisfaction, considering the percentage of prediction resulting from individual perceptions of learning (H3). The results obtained by running a hierarchical regression (Table 3) revealed that the collective interpretations explain an additional 5% of the variance of the phenomenon, beyond that already provided by the individual perceptions. This demonstrates that the development of collective cognitions, expressed here by similar interpretations built through a learning process, is responsible for predicting a specific proportion of team satisfaction. The implications of the findings reported here are discussed in the section that follows.

5. Discussion

The present study demonstrated the relevance of understanding learning as a meso level phenomenon, more specifically, as an attribute of teams. The results revealed that learning can legitimately be taken as a collective phenomenon, which showed theoretical alignment between this study and the propositions of scholars in this field of knowledge (Koeslag-Kreunen et al., 2018; Van den Bossche et al., 2011). The importance of this finding stems from the demonstrated fact that a process traditionally assumed as occurring at the individual level, learning, can be considered a collective process. Therefore, it can legitimately be stated that teams learn and this learning can be confirmed from the presence of behaviors that promote the capture, interpretation, refinement and archiving of the information for later use, depending on the needs of the team. This learning, when understood as a result, can also be confirmed in the emergence of cognitions shared by the team members.

The findings on the level of the phenomenon also elicit reflections on the fact that team learning involves not only cognitive but also social aspects. This is because learning, as a collective process, is enabled in a collaborative setting, in which certain beliefs about the interpersonal context will influence the display of member behaviors that favor the formation of collective knowledge, as Van den Bossche et al. (2006) explain. Thus, relationships built in the collective sphere have the power to stimulate the manifestation of learning behaviors.

Based on this understanding, the first hypothesis argued that potency has the power to predict individual perceptions of learning behaviors, which was partially corroborated. The results showed that only factor one, named Productive Performance has explanatory power. Thus, when members believe in the ability of their team to deliver quality work, they also affirm that in their teams there are manifestations of behaviors such as construction, co-construction of meanings and constructive conflict. In a similar vein, the research done by León-del-Barco et al. (2017) demonstrated an association between potency and cooperative learning in teams. Therefore, our findings are aligned with other empirical discoveries of this field and theorizing presented in proposed models (Ilgen et al., 2005) where associations between group processes are defended.

Next, H2 was tested and its results reveal that individual perceptions about the presence of learning behaviors in teams predict collective interpretations of these behaviors. Therefore, it is reasonable to state that the more the team engages in learning behaviors, the greater the similarity observed in the members’ interpretations of these behaviors. Those shared cognitions constitute evidence of the occurrence of the learning process in the team (Decuyper et al., 2010). That means that shared cognitions are demonstrations of the development of specific team knowledge about team behaviors and this knowledge becomes available for later access by the members. In concrete terms, the findings demonstrate that teams in which their members engage, participate and discuss collectively the problems they face are teams in which similar interpretations of those facts are constructed, which, in turn, reveal the occurrence of team learning. The importance of this finding lies also in the fact that it forms empirical evidence of the association between processes and results of team learning. A similar demonstration was made by Zhang and Wang (2020). In their research, the authors identified that team learning improved shared mental models, a specific type of shared cognition. In addition to that finding and taking a theoretical perspective, Kozlowski and Ilgen (2006) emphasized that team mental models are different from team learning, even though related. Therefore, we can conclude that the defended connection between these two phenomena is pertinent.

The association mentioned above may also be read as evidence that in the presence of active social interaction behaviors, collective cognitive structures are generated. This assertion is supported by the high intensity in the similarity of responses given by the team members (mean value of the ADMd), which did not allow differentiating one individual from another in the team but differentiated one team from another.

In relation to predicting satisfaction, it is important to highlight that this is one of the effective results most-studied in the team context, being explained by meso level and individual level variables (Mathieu et al., 2008; Reis and Puente-Palacios, 2019). Although the literature of the area indicates that this is a phenomenon at the micro-level, as it is about individuals’ perceptions about their team, the construct was operationalized as a collective score. Thus, it addresses the satisfaction regarding teamwork.

The third hypothesis specifies that individual perceptions about learning behaviors would be predictors of satisfaction with teamwork. The expected relation was corroborated by the observation of predictive power of 53% (p <0.05). This data reveals that individuals engaging in acts that facilitate learning leads to positive responses from members, suggesting that the workgroup is able to meet their needs. This result is relevant because it demonstrates that learning behaviors can generate other positive effects in the group, beyond the construction of the group knowledge itself.

Finally, the fourth hypothesis predicts that collective interpretations, taken as evidence of the occurrence of team learning (learning as a result), also predict satisfaction with the team, beyond the proportion predicted by individual perceptions. This hypothesis was corroborated by the fact that team learning brought a 5% increase in the explanation related to satisfaction. Considering that satisfaction is a criterion of team effectiveness, this finding is relevant as it is consistent with the results of studies in this field that reveal that shared cognitions are predictors of satisfaction with group processes (Park, 2008). On this subject, Salas et al. (2008) emphasize that shared cognitions really matter for team results. In a similar vein, the model of team learning behavior presented by Van den Bossche et al. (2011) establishes a clear connection between those behaviors, shared cognitions and team effectiveness. The association between learning and satisfaction is not defended at the team level only. Erdem et al. (2014) demonstrated that organizational learning is associated with job satisfaction also. These results, together with the results of our research, allow us to affirm that the connection between learning and satisfaction can be expected to occur at different organizational levels.

In a summarized analysis, it should be noted that the confirmation of the fourth hypothesis corresponds with the model of learning proposed by Van den Bossche et al. (2006). These authors postulate that the development of shared cognitions makes the teams more effective. Therefore, we verify through empirical research the pertinence of the associations among the variables present in that model.

6. Final considerations and implications

The model presented in this study contributes to the development of knowledge related to group processes by demonstrating that it is possible to transpose comprehension about the phenomenon of learning to the meso level. Thus, the results found assume theoretical and empirical relevance by offering evidence of the pertinence of adopting the concept of learning in a collective perspective, besides explaining how the process occurs, which products are expected and what are the impacts.

This study not only supports theoretical advances on the subject such as the recognition of work team learning, as it also offers some practical contributions or has practical implications. One example is the identification of behaviors that precede the emergence of this collective learning in organizations. Therefore, it is important that managers take care of establishing a work environment characterized by active communication standards in which workers can feel comfortable about expressing opinions, disagreeing and helping to refine the proposed work brought to their team. In these environments, member learning is highly likely to occur and resulting from it, greater satisfaction from collective work.

Another practical contribution relates to team effectiveness. In this study, member satisfaction with the team was adopted as a criterion of effectiveness, following the guidelines of area authors (Lacerenza et al., 2018; Reis and Puente-Palacios, 2019) and this was derived, at least partially, from the learning process. Thus, team managers should also pay attention to the levels of worker satisfaction with these performance units and remember that they result both from individual perceptions about learning behaviors and from shared cognitions about the presence of those behaviors.

Despite the theoretical and practical contributions previously mentioned, this study also has some limitations. It must be mentioned that the data were collected from students, in an academic context and not in real organizations. There is also fragility involved in the fact that only self-reporting measures were used. Nevertheless, this research demonstrates how promising the findings related to team learning are and it remains for researchers in the field to verify the stability of the findings presented here. Therefore, the demonstration of the occurrence of collective learning and its effects on the effective results of the team are not insignificant findings and confer value to the study now completed.

Values of the tests for intragroup homogeneity and intergroup variance

Scale/factor Mean ADMd SD ANOVA (F) ICC
Potency F1 – productive performance 0.45 0.17 2.17** 0.23
Potency F2 – aocial relationship 0.47 0.25 2.42** 0.27
Team learning behaviors 0.44 0.32 2.42** 0.27
Satisfaction with the team 0.49 0.25 2.75** 0.31
Note:

**p <0.001

Matrix of correlations between variables at the group level

Variables V.2 V.3 V.4 V.5
V.1 Potency F1 – productive performance 0.72** 0.57** −0.17 0.47**
V.2 Potency F2 – social relationship 0.48** −0.24* 0.41**
V.3 Perceptions of team learning behaviors −0.54** 0.73**
V.4 Collective interpretations of team learning behaviors −0.21*
V.5 Satisfaction with the team
Notes:

**p <0.01; *p <0.05

Multiple regression analysis – prediction of satisfaction with the team

Variables β SE B
Step 1
Constant 0.39
Individual perceptions – learning behaviors 0.73** 0.09
ΔR² 0.53**
Step 2
Constant 0.53
Individual perceptions – learning behaviors 0.67** 0.11
Collective interpretations – learning behaviors 0.26* 0.31
ΔR² 0.05**
R² 0.58
Notes:

*p < 0.01; **p < 0.001

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Acknowledgements

This research received partial financial support from the Post-Graduate Program in Social, Work and Organizational Psychology – PG-PSTO.

Corresponding author

Katia Elizabeth Puente-Palacios can be contacted at: kep.palacios@gmail.com

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