The performance of researchers in multidisciplinary research groups: does social capital matter?

Abstract

The aim of this article is to discuss the relationship between disciplinary diversity (multidisciplinarity) and the performance of researchers, exploring the moderating role of social capital. The article contributes to the literature explaining the internal processes of multidisciplinary research units and how they affect the scientific performance of researchers. Furthermore, the article explores the potential moderating role of social capital and how relational dynamics can mitigate the potential problems associated with multidisciplinarity. To test the hypotheses proposed, we performed a quantitative study based on a sample of 155 researchers in the field of academic management. Multiple regression analysis was used in the empirical analysis. The findings suggest that a positive relationship between researchers’ performance and multidisciplinarity exists (an inverted U-shaped relationship). Estimations also show that internal social capital moderates this curvilinear relationship, making it possible to achieve higher research performance at higher levels of multidisciplinarity.

Points for practitioners

• Research and development managers at supranational, national and university levels should consider promoting the formation of multidisciplinary research groups. Our results provide evidence that multidisciplinary research groups support higher research performance, at least to some extent.

• The research performance of multidisciplinary groups is assisted by the social capital of the research groups. Therefore, leaders of research groups should promote initiatives that allow collaboration and the exchange of ideas, knowledge and information among researchers, thus creating trust and increasing the internal social capital of the groups.

Keywords

diversity multidisciplinarity research researchers’ performance social capital

Introduction

In the organizational and management literature, there is a growing interest in determining whether groups composed of members who are heterogeneous with regard to relational-related and/or job-related attributes obtain better results than groups that are more homogeneous (Van Dijk and Van Engen, 2013). There are two opposing points of view that rely on social categorization and information/decision-making theories (Van Knippenberg et al., 2004) and that can lead to contradictory results. While the former viewpoint suggests that homogeneous groups should perform better with regard to mainly relational-related attributes, the latter suggests that diverse groups have higher performance in relation to job-related attributes (Van Knippenberg et al., 2004; Williams and O’Reilly, 1998).

In today’s knowledge-based societies, research is largely carried out by groups of researchers (Bozeman et al., 2013). Much scientific research is produced by collaborations among scientists from diverse scientific fields working together towards a common goal (Bozeman et al., 2013; Cummings and Kiesler, 2014). Previous studies have shown that scientific collaboration increases productivity and the impact of research (Bozeman et al., 2013; Lee and Bozeman, 2005), facilitating knowledge sharing and knowledge transfer (Inkpen and Tsang, 2005). In academic research, multidisciplinary groups perform better than less diverse disciplinary groups (Cummings and Kiesler, 2005), at least up to a certain level of multidisciplinarity. A key issue is to know how the performance of researchers is affected by belonging to a multidisciplinary research group – an issue on which empirical evidence is quite scarce.

The social capital (SC) literature suggests that the SC embedded in social networks influences the knowledge resources available to group members, thus affecting performance (Chung and Jackson, 2012; Nahapiet and Ghoshal, 1998). In the academic context, some studies emphasize the role of social networks in researcher productivity, focusing on SC dimensions (Gonzalez-Brambila, 2014; Gonzalez-Brambila et al., 2013). However, little is known about how the SC of research groups moderates the relationship between multidisciplinarity and researchers’ performance.

The purpose of this article is to provide an in-depth analysis of the relationship between the multidisciplinarity of the members of the research groups and the performance of the researchers. Additionally, it explores the role played by the SC of research groups in this relationship. To do this, the article proposes a model that determines the conditions under which multidisciplinary research groups lead to higher research performance. Specifically, the article suggests that SC moderates this relationship, allowing an increase in the positive effects and mitigating the potential problems of the disciplinary diversity of the members of the research groups.

The contributions of the article are twofold. First, it contributes to the disciplinary diversity and performance research literature by providing new evidence on the relationship between the multidisciplinarity of groups and researchers’ performance. In this sense, the article tries to shed light on how research groups can take advantage of their disciplinary diversity to improve their research performance standards. Second, it contributes to the SC literature by testing the moderating effect of SC in the performance–multidisciplinarity relationship. In addition, the article tries to contribute to the measurement of SC. This construct is usually measured in the academic context through drawing on social network techniques. The article proposes a different measure, applying a scale adapted from the management literature. This approach allows us to deepen the qualitative and behavioural dimensions of SC, which must also be considered to fully understand the internal dynamics within multidisciplinary research groups.

The article is organized as follows. The second section presents the theoretical background and the related empirical literature on which the proposed model is based. The third section describes the applied empirical methodology. It includes the data and the variables used, as well as the factorial analysis. The results are presented in the fourth section. The fifth section presents the main conclusions, limitations of the study and future lines of research.

The role of SC in the performance of multidisciplinary groups

Diversity in the academic research world is a concept that has been widely developed in the field of management (Liu and Xia, 2015). Despite its use, various definitions coexist and the factors considered for each definition may vary. Some of the traits considered include education and training, personality, gender and age, and so on (Van Dijk et al., 2012). In general terms, diversity refers to the different characteristics that define the members of a work group (Jackson et al., 2003; Van Knippenberg and Schippers, 2007).

The literature shows confronting perspectives regarding the effects of diversity in teams on performance (Guillaume et al., 2017; Van Knippenberg and Mell, 2016). Williams and O’Reilly (1998) support the idea of a positive influence of heterogeneity given the wider range of abilities, information and know-how to which the group would have access. Access to broader perspectives supports problem-solving and decision-making processes in diverse teams (Horwitz and Horwitz, 2007; Martín-Alcázar et al., 2011). Furthermore, recent research indicates that openness to diversity in teams could lead to improved performance (Lauring and Villesèche, 2017; Nielsen and Börjerson, 2019).

Studies based on social categorization and identity theories (Tajfel and Turner, 1986) and the similarity-attraction paradigm (Byrne, 1971) support the opposite idea. According to those theories, creating a highly diverse group would entail assuming the appearance of subgroups formed by people with similar traits, which would hinder the overall performance of the group (Van Dijk et al., 2012). Research has shown that people tend to identify and collaborate with people who share similar characteristics and values, which helps them to avoid discrepancies and possible disagreements as regards the development of ideas (Lungeanu and Contractor, 2015). In societies characterized by high collectivism, the stress on belonging leads to the identification of people as part of the in-group or out-group (Hofstede, 2011). Nevertheless, this reasoning also applies to the general behaviour of people. According to Janssen and Huang (2008), individuals tend to cooperate with other individuals who share their same social identity because they are considered part of the same group. In fact, it seems that difference with some individuals increases people’s perception of social identity and creates stronger bonds with in-group individuals. At the same time, according to Guillaume et al. (2012), such differentiation lowers group members’ trust and cooperation with those that are not part of their group. All in all, the results of the study by Randel and Jaussi (2003) suggest that the performances of the group and of the individual are influenced by social and personal identities.

Diversity regarding factors that define relationships, like race, age or gender, may have a negative effect on team performance due to their importance for social categorization. That is, these factors are the basis for social identity, and diverse social identities within a group may lead to feelings of distrust or to uncooperative behaviours, and hence impact on performance. In contrast, a diversity of traits related to work, such as education, affects team performance positively, possibly because these traits condition team processes concerned with creation, though this effect is highly complex. That is, a suitable integration and management of cognitive diversity seems to be necessary in reducing the negative impact on team performance (Williams and O’Reilly, 1998).

Research has focused on different factors for exploring the effects of cognitive diversity on performance. In particular, Chi et al. (2009) analysed tenure diversity and found a positive relationship between organizational tenure diversity and the innovation of the team. Similarly, Mitchell et al. (2017) found that cognitive diversity in research teams led to debate, which, in turn, led to innovation. In addition, Wang et al. (2016) found that cognitive diversity is related to team creativity and motivation. On the other hand, Tsai et al. (2014) suggested a positive quadratic relationship between knowledge heterogeneity and innovation. Studies of the effects of functional background yielded inconclusive results (Mello and Rentsch, 2015). Finally, team members’ educational level and education type received have opposite effects on performance (Cummings et al., 2013). While diversity as regards educational level has negative effects on performance, diversity as regards specialization is found to affect the performance of teams in a positive way.

Cognitive diversity as regards scholars’ disciplines allegedly enhances team performance (Salazar et al., 2012). Van Knippenberg at al. (2013) and Jackson et al. (2003) identify team diversity as regards specialization with team success. Zuo and Zhao (2018) point out that the multidisciplinarity of teams does not seem to enhance the number of collaborations; however, Cummings and Kiesler (2005) indicate that high interdisciplinarity in teams enhances performance outcomes regarding research, though also stress the importance of coordination. Porac et al.’s (2004) comparison between multidisciplinary teams and homogeneous teams on scientific collaboration indicates that high diversity regarding disciplines increases the amount of published research. Nevertheless, the wider social network available to diverse teams and their enhanced productivity may have some drawbacks, such as the problems resulting from a lack of cohesion, that is, conflict or cooperation and identification issues (Perry-Smith and Vincent, 2008).

The foregoing discussion suggests that multidisciplinarity could have a curvilinear rather than a linear effect on research performance. In other words, cognitive diversity would have a positive effect on knowledge creation and productivity; however, once the threshold is crossed, diversity would have a negative influence on productivity derived from conflicts and coordination costs (Cummings and Kiesler, 2005; Yong et al., 2014). Thus, the first hypothesis is as follows:

Hypothesis 1: There exists a positive and increasing relationship between the level of multidisciplinarity of academic groups and the researchers’ performance, with a maximum from which the effect is inverse.

The SC literature suggests that the structure and configuration of social networks affects the knowledge resources available to the members of the group, and therefore to their performance (Chung and Jackson, 2012). Nahapiet and Ghoshal (1998) introduced the SC concept in the management literature. Their definition significantly resembles that which was provided by Bourdieu, differentiating between three dimensions of SC: relational, cognitive and structural. These dimensions are interrelated and provide the basis for knowledge transfer, with trust and common viewpoints considered as necessary conditions for the correct development of these networks.

Literature on the relational dimension shows the paramount importance of factors such as trust for cooperation (Adler and Kwon, 2002; Choi, 2015). Therefore, relationships between individuals must comply with norms of behaviour based on reciprocity expectations, among other factors (Gonzalez-Brambila, 2014). The cognitive dimension deals with cognition and involves shared beliefs and codes that enable communication (language), and hence knowledge transfer, as well as the setting of common goals, thus enhancing collaboration and cooperation (Chow and Chan, 2008). In the same vein, when collaborating members have common goals, responsibility and integrity are more easily developed (Leana and Pil, 2006). The structural dimension draws on common ties and relationships that link the members of a group (Nahapiet and Ghoshal, 1998) and allow access to the resources offered by participants in the network. In contrast, the first two dimensions are more related to members’ ability as regards knowledge transfer (Andrews, 2010).

An alternative classification of the components of SC distinguishes between its internal and external dimensions, focusing on the structure of the network (Woolcock and Narayan, 2000). The internal (or bonding) SC comprises resources inherent to the relations among actors within the collective; while the external (or bridging) dimension refers to the external relations that actors maintain with other actors (Adler and Kwon, 2002). In this regard, Chung and Jackson (2012) suggest that both types of relationship have a positive influence on performance. Similarly, Oh et al. (2006) highlight that group effectiveness is maximized through optimal configurations of group closure, and horizontal and vertical inter- and intra-group bridging relationships. As Adler and Kwon (2002) point out, the definition of SC of Nahapiet and Ghoshal (1998) focuses on both internal and external linkages.

Despite the scarcity of research on SC related to the academy, this section provides an overview of the main contributions. Siadat et al. (2012) found that knowledge creation is positively influenced by SC and organizational culture. Steinmo (2015) analyses the role of SC on firm–university collaboration and identifies a positive influence of relative SC on the effectiveness of these collaborations. In addition, the author points out that shared goals exert a cohesive effect on members of the network and their collaboration. Gonzalez-Brambila (2014) indicates that the productivity of scientists increases if they are at the centre of their publishing network and according to the diversity of cognitive capital available to them, though this situation may vary depending on the researcher’s discipline. Regarding the output of the collaborations, relational ties explain the quality of the publications but not the impact of the studies, while interdisciplinary collaborations yield high-impact research outputs. The structural dimension is also explained by Gonzalez-Brambila et al. (2013), who suggest that citation impact is positively affected by structural holes. Similarly, citation impact and publications are also positively influenced by structural SC (Rodriguez and Gonzalez-Brambila, 2016). In the same vein, the citation impact of researchers is positively affected by average tie strength, efficiency and centrality (Abbasi et al., 2011) as it is indirectly affected by cognitive SC and author productivity (Li et al., 2013).

Through the SC of groups’ members, multidisciplinary research groups can access diverse information that could complement their skills and generate new ideas for research based on a greater scope of skills (Gonzalez-Brambila, 2014; Gonzalez-Brambila et al., 2013; Perry-Smith and Vincent, 2008). Nevertheless, the diversity of backgrounds may generate barriers for collaboration, such as misunderstandings due to differences in language, culture or viewpoints, as well as a lack of cooperation due to integration conflicts, with a potential negative influence on performance (Perry-Smith and Vincent, 2008). All these problems may be solved by constant interaction, which serves to establish solid ties based on trust. Therefore, higher identification and trust between collaborating members would increase knowledge sharing and cooperation in knowledge creation (Yu, 2018; García-Sánchez et al., 2019). In turn, the increased trust would facilitate problem solving.

Based on the foregoing, a conceptual model in which there is a relationship between researchers’ performance and the multidisciplinarity of research groups is established (see Figure 1). It is expected that researchers who belong to research groups with greater multidisciplinarity perform better than researchers who belong to groups with less diversity of disciplines. Although Lee et al. (2015) indicate that the size of the diverse team has a positive relationship with the probability of publishing high-impact studies, these positive effects may be limited. Research shows that positive effects of size on productivity from large multidisciplinary teams turn into negative ones after a certain threshold is crossed (Cummings et al., 2013; Lavie and Drodi, 2012).

Figure 1.

Conceptual model.

The literature suggests that a complete analysis of the effects of diversity on performance requires the use of additional variables regarding the unit conditions. Thus, direct models are not appropriate. The use of moderating variables includes variables related to the institution or the context (Van Dijk et al., 2012). Accordingly, this study proposes the use of SC as a variable that moderates the relationship between multidisciplinarity and researchers’ performance. Drawing on previous research, the second hypothesis is as follows:

Hypothesis 2: Social capital embedded in research groups positively moderates the relationship between multidisciplinarity and the scientific performance of researchers.

Empirical framework

Data

To test the hypotheses described earlier, we performed a quantitative study based on a sample of scholars in the field of management. The questionnaire was pretested through a sample of researchers belonging to the discipline, who received the questionnaire by email and provided feedback. To enhance the questionnaire, all the suggestions were added in the final version. As a first step, a self-response questionnaire was delivered to researchers attending the annual conference of the European Academy of Management (EURAM). We chose this congress because it is a benchmark in the field of management due to its international character and the high degree of diversity. The questionnaire was specifically designed to measure issues related to researchers’ SC, as well as some aspects of their respective scientific collaborations and some information related to university, country, research experience and so on. Responding researchers were informed about the purpose of the study. Although 155 identified questionnaires were obtained, 45 scholars in the initial sample had to be removed because they either could not be identified in Scopus or had no publication records. Therefore, the final number of usable questionnaires was 110.

In addition to the individual information extracted from the questionnaire, for scholars, we obtained information about their respective research networks in order to measure their multidisciplinarity levels. A scholar’s network is defined as the group formed by them and all their co-authors in Scopus-ranked publications.

Variables

The dependent variable in our model is Researcher performance. For evaluating research performance, there exist different measures, such as publications, citations and so on, that are used in empirical literature. In our study, we opted to use the Scopus indicator known as the h-index (Hirsch, 2005). Hirsch defines the h-index as follows: ‘a scientist has an h-index if h of his or her Np papers have at least h citations each, and the other (Np – h) papers have ≤ h citations each’ (Hirsch, 2005: 16569). This indicator has received criticism because it does not take into account the length of the scientific career, the effect of co-authorship or journal quality (Batista et al., 2006; Kelly and Jennions, 2006). However, the main advantage of this indicator, and the reason why it has been widely used in the academic context, is that it combines a measure of the quantity and impact of publications in a single indicator (Huang, 2012).

The independent variables included are the following:

Multidisciplinarity: to assess the degree of multidisciplinarity, we focused on the research group built by each focal scholar and all of their respective co-authors. We used Blau’s (1977) index of validity, which is a heterogeneity measure traditionally adopted by the literature on workforce diversity (Pitts, 2005) to operationalize multidisciplinarity. It is calculated as [1 – Σp_i²], where p is the proportion of researchers in the respective discipline and i is the number of disciplines represented in the academic research group. The multidisciplinarity index ranges from 0 to 1; values close to 1 indicate greater multidisciplinarity (Pitts, 2005). For each researcher, Scopus provides information on the number of articles published in each of the possible 27 subject areas, ordering them from highest to lowest frequency. Within a research group, in order to identify scholars’ and co-authors’ disciplinary affiliation, each researcher was ascribed to a research field through a variable built by concatenating the two Scopus areas in which they had published more. This artificial variable was created to ensure variance, considering that the study was focused on the management discipline. In total, 88 new disciplines were identified in the network.

SC: given the lack of specific instruments to measure SC in the academic context, we opted to develop our own scale, adapting the items proposed by Chow and Chan (2008). To assess the relational, cognitive and structural dimensions of SC, as proposed by Nahapiet and Ghoshal (1998), we designed a set of seven-point Likert-scale items, ranging from strongly disagree (1) to strongly agree (7) (see the questionnaire in Appendix 1, available as online Supplemental Material). The wording of the indicators was based on previous empirical research and was carefully pretested by a group of scholars in the field of management.

Control variables: to reduce potential omitted variable bias, a set of plausibly relevant control variables were included in the empirical analysis. Specifically, we controlled for the size of the research group (size) because of its potential effect on research productivity (Cummings et al., 2013). To identify research groups, we focused on the co-authorship network of each academic, considering publications in the 2000–2016 period. With this, we followed the output-based method proposed by Cohen (1991), which does not consider formal affiliation, but rather collaborations between members of the group. Additionally, we controlled for the years of research experience (experience) (Lee and Bozeman, 2005), with the objective of considering the duration of academics’ careers when assessing their respective h-indexes. To measure this variable, a specific question was included in the questionnaire.

Factorial analysis

With the exploratory analysis, we intended to confirm the validity and reliability of the SC scale and to verify the internal structure of the construct, checking whether the dimensions proposed in the previous literature are relevant in the specific field of academic research. Following the commonly used rules, eigenvalues near or greater than 1.0 were used to select the factors to be used in the analysis. To define each of the retained factors, we kept items with loads higher than 0.50 in a single factor. The adequacy of factor analysis in our data set was confirmed using Bartlett’s test of sphericity (significance value < 0.05) and the Kaiser–Meyer–Olkin measure of sampling adequacy (0.829 > 0.70). The value obtained for the Cronbach’s alpha coefficient for each of the factors extracted confirmed the reliability of the data collection, as well as inter-item correlations for the variables in the survey. Results for all these analyses are shown in Table 1 (available as online Supplemental Material).

From the exploratory factor analysis, three factors were obtained. Factor 1 was labelled internal SC and contained items related to relational and cognitive SC embedded in relations with colleagues in the same field of research. Factor 2 was labelled external SC and included items associated with cognitive and relational SC embedded in relations either with colleagues outside the field or with professionals. Factor 3 was labelled structural SC and is comprised of two items associated with the patterns of connections among research group members.

Observing the nature of the three factors extracted, we conclude that they support the expected categorization of SC into relational, cognitive and structural dimensions. However, they also highlight that SC flows through intra-group social bonds and bridging relationships. Therefore, the results of our factor analysis show that both classifications of SC overlap. In fact, the heterogeneity of research group members in relation to their main disciplines emphasizes the importance of bridging relationships for relational and cognitive SC resources (external SC). However, on the other hand, homogeneous subgroups (formed by researchers from the same discipline) can also be found within research groups, making close relationships predominant for relational and cognitive SC (internal SC). Figure 2 (available as online Supplemental Material) illustrates the factors, showing how they link with these categories.

Results

The extracted factors were introduced together with the rest of variables into multiple regression models as interaction terms, with the objective of testing the hypotheses established earlier. Table 2 (available as online Supplemental Material) shows the maximum and minimum values, means, standard deviations, and correlations of the variables introduced in further analyses. These figures show that the average size of research groups is eight members; the researchers have an average h-index of 4 and an average research experience of roughly 14 years. The average level of diversity in disciplinary categories within groups is 0.51, in a 0–1 scale.

Four models were estimated (see Table 3, available as online Supplemental Material). Model 1 shows the isolated effect of the two control variables (experience and size) on the dependent variable (h-index). The variable measuring scholars’ years of experience was significant and positively related to performance. This result could be explained by the definition of the h-index, which is an accumulative measure that tends to grow as the number of publications of the researchers increases.

To test the effect of multidisciplinarity on researchers’ performance, two models were estimated (Models 2 and 3). Model 2 shows that the linear term of the multidisciplinarity variable has the expected sign but is not significant. The result dramatically changes when the quadratic term of the multidisciplinarity variable (multidisciplinarity²) is introduced. As can be observed in Model 3, in this case, multidisciplinarity turns out to be significant, showing a positive effect on the scientific performance of researchers, as previous studies have also found (e.g. Cummings and Kiesler, 2005; Porac et al., 2004). The estimated coefficient β for the quadratic term (multidisciplinarity²) is negative but significant, confirming that the effects of this variable on researchers’ h-indexes can be depicted as an inverted U-shaped relationship. As the relation between multidisciplinarity and the h-index is non-linear, the effect on the h-index of a change in multidisciplinarity depends on the value of multidisciplinarity. The marginal effects are linearly decreasing, making the h-index grow to a point where the marginal effects begin to be negative. The results support Hypothesis 1, showing that the h-index is reduced when discipline diversity is increased over a certain level. This result is in line with previous research that found a curvilinear relationship between cognitive diversity and performance (De Saá-Pérez et al., 2015).

To test the second hypothesis, internal SC, external SC and structural SC (factors extracted) were introduced as moderators of the relationship between multidisciplinarity and researchers’ performance (Model 4). The estimation of the last regression model confirmed that only the interaction term measuring the moderating effect of internal SC was significant. Therefore, we find evidence that the relational and cognitive SC of a research group between members of the same field positively moderates the curvilinear relationship between multidisciplinarity and research performance, making the optimum reached with higher levels of disciplinary diversity. This result partially confirms Hypothesis 2: to mitigate the conflict and communicational problems that appear as multidisciplinarity increases, research groups should foster frequent and smooth interactions among members, building a collaborative climate that facilitates the exchange of ideas and knowledge sharing. According to the results of the regression analysis, this internal SC seems to be more relevant than the establishment of formal coordination mechanisms, or the development of external collaborations.

Conclusions and discussion

Diverse groups are becoming more plentiful in organizations, and universities are no exception. Research is increasingly carried out in large research groups and, in many cases, these groups are composed of researchers from different disciplines (Bozeman et al., 2013; Cummings and Kiesler, 2014). This multidisciplinarity is a response to the challenges of today’s societies, where integrated knowledge occupies a predominant position as a source of economic growth and productivity (OECD, 1996). Literature on research teams has highlighted the importance of group processes and the influence of group characteristics for research group productivity (Wang, 2016). A key element is to know how internal processes are developed within multidisciplinary research groups in a way that leads to the greater research results of researchers.

This article addresses this issue by analysing the multidisciplinarity–performance relationship, drawing on a sample of research groups within the field of management. The article finds that the relationship between multidisciplinarity and the performance of researchers is non-linear. That is, as disciplinary diversity increases, research results improve until multidisciplinarity reaches an optimum level, at which point the researchers’ performance decreases. This suggests that when a certain level of multidisciplinarity is reached, some difficulties related to the lack of cohesion arise, as well as conflict, cooperation and identification problems (Perry-Smith and Vincent, 2008). Thus, an additional interesting issue that exceeds the scope of this study would be to determine the level of multidisciplinarity at which the positive effects of disciplinary diversity begin to diminish.

The productivity of the researchers depends on diverse variables, including the size of the group, the experience of the researchers or the researchers’ SC, as previous literature has shown (Cummings et al., 2013; Rodriguez and Gonzalez-Brambila, 2016). The article proposes a conceptual model in which the effect of multidisciplinarity on the results of the researchers would be conditioned by the SC that the researchers bring to the group. In the article, SC is measured through a scale that tries to obtain deeper qualitative and behavioural information about this multidimensional construct. From the exploratory factorial analysis, three factors emerged, supporting the need to differentiate between the relational, cognitive and structural dimensions of SC. Additionally, the factor analysis provides new insights into the internal and external dimensions of SC, distinguishing between the relational and cognitive SC embedded in relations with colleagues within the same research field (internal SC), and the relational and cognitive SC located in relations outside the research field (external SC). This result indicates that multidisciplinary groups may be fragmented into different subgroups formed by researchers belonging to the same field, affecting internal dynamics and therefore collective performance.

As the empirical analysis has confirmed, not all the dimensions of SC help to manage internal dynamics within multidisciplinary research groups. The moderating effect seems to be limited to the relationships established between academics from the same discipline. The results are not significant in relation to contacts that are external to the field, which do not seem to affect internal dynamics. Similarly, the result obtained for the structural dimension of SC shows that the term of interaction is not significant. The mere organization of formal or informal meetings and other spaces for encounters among collaborators does not, by itself, appear to reduce potential task conflict or communication difficulties.

From the perspective of the managers of the research groups, it seems fair to recommend the implementation of human resource practices that attract collaboration from researchers whose knowledge and skills are better adapted to the needs of the group in order to increase the disciplinary diversity of the research group. Practices that facilitate collaboration should be encouraged, thus strengthening cooperation among group members, which is linked to trust and knowledge sharing. In this way, the performance of researchers could be increased and multidisciplinary research groups could achieve better outputs.

Finally, it should be noticed that the research design proposed in this article, based on a self-response survey to measure SC, has provided an interesting insight into the internal dynamics of multidisciplinary research groups; however, a deeper empirical analysis would be necessary to fully understand the structure of relationships between academics from different disciplines. Future research should be undertaken to overcome some limitations of the article. SC is measured through respondents’ perceptions of trust and cooperation within a research group; therefore, to capture a more complete picture of SC, surveys could be administered to all members of the research group. Likewise, the sample is based on researchers in the field of management; therefore, the study should be extended to other scientific fields to test if the internal dynamics of the research groups are field dependent. Since there may be an inverse causality between SC and the performance of researchers, the mechanisms that explain the effect of interaction could be conditioned by this fact. A deeper analysis of this influence should be carried out in future research.

The conclusions of our study invite future research that could extend and contrast the findings obtained here. Social network analysis could provide interesting tools for this purpose in order to describe and represent the social structures of different research fields. Therefore, it might be interesting to perform future analyses designed to compare both methodologies. This would allow a deeper understanding of the objective and subjective aspects of SC, showing which way to measure the construct is the more appropriate in each circumstance. Finally, diversity is a multifaceted construct (Tasheva and Hillman, 2019), so future research should also address how the disciplinary diversity of research groups interacts with individual-level diversity, as well as its impact on research results.

Supplemental Material

sj-pdf-1-ras-10.1177_0020852320919487 - Supplemental material for The performance of researchers in multidisciplinary research groups: does social capital matter?

Supplemental material, sj-pdf-1-ras-10.1177_0020852320919487 for The performance of researchers in multidisciplinary research groups: does social capital matter? by Fernando Martín-Alcázar, Marta Ruiz-Martínez and Gonzalo Sánchez-Gardey in International Review of Administrative Sciences

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study has benefited from financing from the Research Projects ECO2014-56580-R of the Spanish Ministry of Economy and Competitiveness, P12-SEJ-1810 and P12-ASEJ-1618 of the Andalusian Government (Spain), and PR2016-018 of the University of Cádiz.

ORCID iD

Marta Ruiz-Martínez

Supplemental material

Supplemental material for this article is available online.

Fernando Martín-Alcázar, PhD, is a Professor of Organization at University of Cádiz, Spain. He is currently leading a funded research project on influencing factors in the Human Resource Management strategy formulation process. His work has been published in international peer-reviewed journals such as the Journal of Intellectual Capital, International Journal of Manpower, British Journal of Management, the Journal of Business Research, the European Management Journal, the International Journal of Human Resource Management, the Journal of Organizational Behavior and Scientometrics.

Marta Ruiz Martínez, PhD, is a Lecturer in the Business Management Department of the University of Cádiz, Spain. Her research focuses on group diversity, with a particular interest in the disciplinary diversity of research groups and its relationship with group performance. She is the co-author of several articles in international journals such as Scientometrics and Minerva.

Gonzalo Sánchez-Gardey, PhD, is an Associate Professor in the Business Management Department of the University of Cádiz, Spain. His investigations are focused on the influence of workforce diversity in strategic HRM. He is the co-author of several papers in international sources such as the Journal of Intellectual Capital, British Journal of Management, the European Management Journal, the International Journal of Human Resource Management, Group Decision and Negotiation, the Journal of Business Ethics and Scientometrics, among others.

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