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International
Labour Review

# The impact of diversity on the performance of work teams: A global study

**Tomáš Michalička**, Faculty of Economics and Management, Slovak University of Agriculture in Nitra, tomas.michalicka@uniag.sk (corresponding author).

**Drahoslav Lančarič**, Faculty of Economics and Management, Slovak University of Agriculture in Nitra, drahoslav.lancaric@uniag.sk.

**Dimuth Nambuge**, Faculty of Economics and Management, Slovak University of Agriculture in Nitra, dimuth.nambuge@uniag.sk.

**Michal Munk**, Faculty of National Sciences and Informatics, Constantine the Philosopher University in Nitra; Science and Research Centre, University of Pardubice, mmunk@ukf.sk.

**Abstract.** This study examines the impact of several diversity dimensions and characteristics in work teams (e.g. nationality, first language, team size and managerial function) on their performance. We use stepwise regression on a research sample of 911 sales teams of a multinational corporation operating in 39 countries or territories. The findings show that diversity in terms of nationality, first language, team size and managerial experience has a significant impact on the performance of teams. The study also highlights the broader implications of integrating variables connected with diversity into performance analyses.

**Keywords:** company performance, diversity management, work team diversity, work team performance.

Responsibility for opinions expressed in signed articles rests solely with their authors, and publication does not constitute an endorsement by the ILO.

This article is also available in French, in *Revue internationale du Travail* 164 (4), and Spanish, in *Revista Internacional del Trabajo* 144 (4).

The *International Labour Review/Revue internationale du Travail/Revista Internacional del Trabajo* is a peer-reviewed open access journal published by the Open Library of Humanities. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited. See https://creativecommons.org/licenses/by/4.0/. Bibliographic references to articles in the *Review* are included in Labordoc, the institutional repository of the International Labour Office. Information on the International Labour Organization (ILO), including ILO publications, is available online at www.ilo.org.

**OPEN ACCESS**

© The author(s) 2025. Journal compilation © International Labour Organization 2025.

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# 1. Introduction

Despite extensive research in this area, results for the effects of team diversity on business performance are ambiguous. Given its strategic importance for organizations, our research thus seeks to improve our understanding of this question by evaluating the impact of various diversity dimensions and characteristics of work teams on their performance using a globally representative sample.

Previous studies have mostly focused on the impact of either only one or a very limited number of diversity characteristics on work team performance. The range of work team diversity factors considered in our research is significantly broader. We examine the effects of two primary and five secondary diversity dimensions and two additional team characteristics on work team performance. The second significant contribution of our research is its comprehensive geographical scope. Existing research has generally considered work teams from either only one or a very limited number of countries or geographical regions. In contrast, our sample contains data for 911 sales work teams within a multinational information technology (IT) company, drawn from 39 selected countries and territories representing all major world regions (Africa, the Americas, Asia, Europe and Oceania).

This article is organized as follows. Section 2 provides a review of the literature on diversity and its effects on organizations and work groups. Section 3 outlines our methodology, describing our data and variables and proposing a number of hypotheses. Section 4 presents our results, which are discussed in section 5. Section 6 summarizes our findings and discusses their implications as well as areas for further research.

# 2. Diversity

The literature offers a variety of perspectives on the concept of diversity. Hubbard (2004) suggests that there is a natural difference between people based on personal characteristics such as age, gender, race and mental and physical capabilities. Hunt et al. (2018) distinguish between primary and secondary diversity. The primary dimensions of diversity include gender, ethnicity, race, sexual orientation, age, and mental and physical abilities and characteristics. They shape our basic image of ourselves, as well as our fundamental view of the world (Mazur 2010). The secondary dimensions of diversity include educational background, geographic location, religion, first language, family status, work style, work experience, military experience, organizational role and level, income and communication style. These dimensions are less visible, exert a more variable influence on personal identity and add a more subtle depth to the primary dimensions of diversity (Mazur 2010).

## 2.1. Diversity in organizations

Many studies have presented workforce diversity and active diversity management as competitive advantages for organizations. Allen et al. (2004) advocate workforce diversity as a competitive advantage for a company, arguing that different perspectives can facilitate unique and creative approaches to problem-solving, thereby increasing creativity and innovation, which can lead to better performance.

According to Bogaert and Vloeberghs (2005), diversity can affect how an organization functions in four ways. First, it can have affective consequences, such as lower organizational engagement or lower satisfaction because people prefer to interact with other similar people. Second, it can have cognitive outcomes related to increased creativity and innovation, as diversity offers the opportunity of interaction with people with different perspectives. Third, a diverse workforce enhances an organization's reputation by projecting an image of progressiveness and social responsibility, making it potentially more attractive to top talent seeking inclusivity and value alignment in the workplace. Lastly, diversity also has clear implications for communication processes within a group or organization, with the potential for both positive and negative effects.
## 2.2. Diversity in work groups

Organizational groups have exhibited a growing diversity over the years and are projected to become even more diverse in the future (Köllen 2021). Work group diversity may have both positive and negative impacts on group performance (van Knippenberg and Schippers 2007). It is therefore important to think about team composition in terms of members' characteristics. Team managers need to pay more attention to less obvious but highly task-relevant traits, such as cognitive styles, which are easily missed. Furthermore, they should carefully consider the configurational aspects of team members' attributes, given the growing emphasis on the effects of team design on team functioning and performance (Aggarwal et al. 2023). Team diversity can enhance learning provided that the organizational environment supports open communication and psychological safety ensures a team's ability to overcome barriers to collaboration (Edmondson and Roloff 2009).

Gender diversity in the workforce is currently one of the most discussed dimensions of diversity, not only in the scientific community but also within companies and governments. A number of extensive studies have been carried out on the subject, particularly in the management literature. Many studies have examined the impact of gender diversity on organizational performance (Yadav and Lenka 2020). The relationships identified are in some cases positive, in some negative (Jehn, Northcraft and Neale 1999) and in others non-significant (Williams and O'Reilly 1998).

Team performance is expected to benefit from diversity, particularly in relation to complex and non-routine tasks (e.g. decision-making and information-processing), where performance is driven by the quality of the outcome, as in the case of research and development. In contrast, routine tasks (e.g. repetitive production tasks) are not associated with extensive information processing; therefore, the role of diversity is expected to be less significant (van Knippenberg, De Dreu and Homan 2004). Given that management challenges are predominantly non-routine, gender-diverse management can offer valuable resources, including market insights, increased creativity, innovation, decision-making capabilities and problem-solving skills. Accordingly, empirical studies have found positive effects for gender diversity in managerial positions (van Knippenberg, De Dreu and Homan 2004). However, other studies indicate that management characterized by an excessive degree of gender diversity may result in employee dissatisfaction and diminished performance (Homberg and Bui 2013). These contradictory findings indicate the importance of examining contextual variables (Johns 2006).

Nationality diversity can be defined as the extent to which the nationalities of team members vary. If someone's nationality is a superordinate determinant of their identity, it is likely to be more important and influential than other demographics, affecting how people communicate, interact and express their characteristics (Hambrick et al. 1998). Diverse and balanced human resources, when properly coordinated and controlled, are protected by knowledge barriers and appear socially complex because they involve a mix of talents and backgrounds that are difficult to identify (Tan and Mahoney 2006).

Different members of a group contribute different knowledge bases and perspectives to the group, which is known as "functional diversity". Differences between the group members can exist in their functionality/area of specialization (e.g. finance, marketing, operations, human resources, strategic planning, research and development, or general management). Having different functional perspectives within a team can help it work more effectively, thanks to knowledge-sharing and information elaboration (Yadav and Lenka 2020). A positive relationship between functional background diversity and team performance is also confirmed by Joshi and Roh (2009). According to Bell et al. (2011), functional background variety diversity has a small positive impact on team performance in general, and on team innovation and creativity.

Emerging research in the field of language diversity within work team members focuses mainly on its disruptive effects, considering it a barrier to productivity and conducting business internationally due to communication issues (Jonsen, Maznevski and Schneider
2011). On the other hand, being able to communicate in one's first language can lead to an open and positive emotional climate in the workplace, where team members do not fear losing face on account of a lack of language proficiency and are under less pressure to express themselves perfectly (Harzing, Köster and Magner 2011).

Diversity can be classified into relations-oriented diversity and job-oriented diversity. Under this classification, diversity of tenure is highly job-oriented. Tenure may refer to either organizational or group tenure. Group tenure is the length of time spent working with a particular group, while the time spent working for an organization as a whole can be defined as organizational tenure. Organizations that comprise employees with long organizational tenure may have greater understanding and knowledge about the organization's culture and systems (Yadav and Lenka 2020). A closer look at the effects of tenure diversity on performance shows that teams that need to be creative and those that work with customers may benefit from having team members with different lengths of tenure (Williams and O'Reilly 1998).

The relationship between team size and productivity provides a relevant focus from an economics, psychology and management science perspective (Mao et al. 2016). Established economics (Holmstrom 1982) and management theories (Malone and Crowston 1994) suggest that increasing team size can hurt productivity for a variety of reasons. In contrast, the economic theory of teams posits that employees within teams have the ability to learn from their peers (Marschak and Radner 1972), which can enhance and expedite the process of specialization by minimizing the need for independent problem-solving. The combination of increasing specialization and observational learning would, therefore, imply that team performance for complex tasks should increase with team size.

## 3. Methodology

We used stepwise regression to analyse the relationship between work team diversity and performance. Stepwise regression combines processes of forward selection and backward elimination. Forward selection starts with the assumption that there are no regressors in the model except the intercept and adds them one by one to find the optimal subset in the model. The largest simple correlation to the response variable (y) is added to the equation; the second regressor considered in the equation also has a high partial correlation towards y after adjusting the effect of the first regressor entered into the model. The F-statistics in equation (1) illustrate that $x_2$ has a high partial correlation when $x_1$ is already in the model (Noryani et al. 2019):

$$F = \frac{SS_R(x_2|x_1)}{MS_{RES}(x_1,x_2)} \qquad (1)$$

In this equation, $SS_R$ is the sum of the square of the regression and $MS_{RES}$ is the mean square of the residual. If the F value exceeds $F_{IN}$, then the regressor is added to the model. In general, the regressor with a high partial correlation with y, which considers the effect of another regressor already in the model, is entered into the model. The process stops when the F-statistics do not exceed $F_{IN}$ or the last regressor is added to the model. Backward elimination is the opposite process, starting with all the potential regressors. $F_{OUT}$ is used to exclude the regressor with the smallest partial correlation in the model (Noryani et al. 2019).

### 3.1. Data and variable description

The performance of many types of jobs – and consequently, work teams – is difficult to measure and quantify. In this study, we chose to analyse the relationship between workforce diversity and team performance using a sample of sales teams, since companies regularly track, measure and evaluate their performance and internal company reporting can provide reliable data on the performance of these work teams.
The business model of the company analysed is organized, according to product offering, into two global business units: the Client Solutions Business Unit (which includes sales of desktops, workstations and notebooks) and the Infrastructure Solutions Business Unit (which offers a portfolio of storage, server and networking solutions). According to this business model, each sales team, located in any geographical region of the world, is, from an organizational perspective, part of one of these two global business units. In practice, this means that all sales teams around the globe within the same business unit can sell the same products and leverage the same tools, technologies and training to support their clients. Considering the differences and various specific aspects of the products, clients and markets of each of these business units, we included only sales teams from the Client Solutions Business Unit in our research sample.

Our study focuses on the impact of various diversity dimensions and characteristics of sales work teams on their performance. As a performance metric, we used revenue generated by work teams and the revenue-based performance ranking of work teams. The model is applied to subsidiaries of a US international IT corporation operating in Africa, the Americas, Asia, Europe and Oceania.

We use two main sets of data in our research. The first set comes from the company's internal employee headcount report. This report contains a list of all company employees and, for each, more than 100 categories of information. Based on our literature review, we identified ten diversity dimensions and characteristics from all the available categories in the report and analysed their impact on team performance (see figure A1 in the appendix).

In our study, we analyse primary diversity dimensions (*gender diversity*, *gender of team manager* and *nationality diversity*), secondary diversity dimensions (*first language*, *seniority score*, *specialization*, *time at management level* and *work experience*) and additional team characteristics (*team size* and *team structure stability*). The *gender diversity* variable (e.g. Solakoglu and Demir 2016; Yadav and Lenka 2020) measures the percentage of female members in the team. *Gender of team manager* (e.g. van Knippenberg, De Dreu and Homan 2004; Mensi-Klarbach 2014) indicates whether the team manager is male or female. *Nationality diversity* (e.g. Stahl et al. 2010; Hunt et al. 2018; Page 2019) measures the extent to which different nationalities are represented in the work team. A value of 0 per cent is assigned to work teams where all team members have the same nationality, while the value of 100 per cent is assigned to teams where each team member has a different nationality. The number of different first languages of team members is represented by the *first language* variable (e.g. Jonsen, Maznevski and Schneider 2011; Tenzer, Pudelko and Harzing 2014; Kundu et al. 2020). In line with studies on the relationship between tenure of employees and team performance (e.g. Williams and O'Reilly 1998; Yadav and Lenka 2020), we identified the *seniority score*, *work experience* and *team structure stability* variables in the internal company report. *Seniority score* measures the average work experience of team members, assigning a value of 1 to entry-level employees with the lowest required qualification for the job and a value of 10 to the highest possible career level that a team member can achieve as an individual contributor within the company hierarchy (outside managerial positions). *Work experience* measures the number of years that team members have been working for the company. *Team structure stability* measures how many years on average team members have been part of their current work team. *Specialization* (e.g. Hunt et al. 2018; Page 2019; Ashikali, Groeneveld and Kuipers 2021) quantifies the different job specializations of team members. *Team size* (e.g. Malone and Crowston 1994; Mao et al. 2016) measures the number of team members, considering that only work teams with at least five team members were selected for the purposes of this study. *Time at management level* (e.g. Osabiya 2015) measures how many years the current team manager has been in a managerial position in the company. The operationalization of the variables is summarized in table 1. Table 2 presents some descriptive statistics.
<table><caption>Table 1. List of variables</caption><thead><tr><th>Variable</th><th>Coding</th><th>Measurement</th></tr></thead><tbody><tr><td>Gender diversity</td><td>GEN_DIV</td><td>1 = 0% (no female team members)<br>2 = 1-20%<br>3 = 21-40%<br>4 = 41-60%<br>5 = 61-100%</td></tr><tr><td>Nationality diversity</td><td>NAT_DIV</td><td>1 = 0% (all team members have the same nationality)<br>2 = 1-20%<br>3 = 21-40%<br>4 = 41-60%<br>5 = 61-100% (each team member has a different nationality)</td></tr><tr><td>First language</td><td>FIRST LANG</td><td>1 = 1 language<br>2 = 2 languages<br>3 = 3 languages<br>4 = ≥4 languages</td></tr><tr><td>Work experience</td><td>WORK_EXP</td><td>1 = ≤4 years<br>2 = >4-6 years<br>3 = >6-9 years<br>4 = >9 years</td></tr><tr><td>Seniority score</td><td>SEN SCORE</td><td>1 = ≤6 years<br>2 = >6-7 years<br>3 = >7</td></tr><tr><td>Specialization</td><td>WORK AREA</td><td>1 = 1 specialization<br>2 = 2 specializations<br>3 = ≥3 specializations</td></tr><tr><td>Gender of team manager</td><td>GEN TEAM MAN</td><td>1 = female<br>2 = male</td></tr><tr><td>Team size</td><td>T_SIZE</td><td>1 = 5-7 team members<br>2 = 8-9 team members<br>3 = 10-11 team members<br>4 = ≥12 team members</td></tr><tr><td>Team structure stability</td><td>T_STR_STAB</td><td>1 = ≤2 years<br>2 = >2-3 years<br>3 = >3-4 years<br>4 = >4 years</td></tr><tr><td>Time at management level</td><td>TIME MAN LEV</td><td>1 = ≤2 years<br>2 = >2-3 years<br>3 = >3-4 years<br>4 = >4 years</td></tr></tbody><tfoot><tr><td colspan="3">Source: Our own compilation based on internal company data.</td></tr></tfoot></table>

<table><caption>Table 2. Descriptive statistics</caption><thead><tr><th></th><th>N</th><th>Mean</th><th>Median</th><th>Standard deviation</th><th>Min.</th><th>Max.</th></tr></thead><tbody><tr><td>(0) Team average country performance ranking</td><td>911</td><td>35.137</td><td>34.000</td><td>18.855</td><td>1.000</td><td>75.000</td></tr><tr><td>(1) Gender diversity</td><td>911</td><td>0.237</td><td>0.2</td><td>0.198</td><td>0.000</td><td>1.000</td></tr><tr><td>(2) Nationality diversity</td><td>911</td><td>0.114</td><td>0.000</td><td>0.212</td><td>0.000</td><td>1.000</td></tr><tr><td>(3) First language</td><td>911</td><td>1.552</td><td>1.000</td><td>1.295</td><td>1.000</td><td>8.000</td></tr><tr><td>(4) Work experience</td><td>911</td><td>7.003</td><td>6.733</td><td>2.843</td><td>1.184</td><td>18.360</td></tr><tr><td>(5) Seniority score</td><td>911</td><td>6.648</td><td>6.700</td><td>0.725</td><td>3.294</td><td>8.889</td></tr><tr><td>(6) Specialization</td><td>911</td><td>1.384</td><td>1.000</td><td>0.685</td><td>1.000</td><td>4.000</td></tr><tr><td>(7) Gender of team manager</td><td>911</td><td>0.789</td><td>1.000</td><td>0.408</td><td>0.000</td><td>1.000</td></tr><tr><td>(8) Team size</td><td>911</td><td>9.765</td><td>9.000</td><td>3.076</td><td>5.000</td><td>23.000</td></tr><tr><td>(9) Team structure stability</td><td>911</td><td>2.536</td><td>2.403</td><td>1.170</td><td>0.072</td><td>7.268</td></tr><tr><td>(10) Time at management level</td><td>911</td><td>2.781</td><td>2.830</td><td>1.071</td><td>0.000</td><td>5.460</td></tr></tbody><tfoot><tr><td colspan="7">Source: Our own compilation based on internal company data.</td></tr></tfoot></table>
Our second set of data comes from the company's internal sales performance report, which provides the data for the metric *team performance* (dependent variable *team average country performance ranking*), detailing the revenue (in US dollars) that the company generated at the sales team level for the calendar year 2019. In this context, revenue refers to the company's sales turnover from products and services. We chose revenue as the performance metric in this study as it is the standard key performance indicator (KPI) used by the company to assess the performance of its sales teams. Additionally, it is employed in scientific studies examining the relationship between workforce diversity and performance, serving as a main financial KPI at both the work team and company levels (e.g. Hunt et al. 2018). We created the country performance ranking of sales work teams based on the revenue of sales work teams within each country or territory. We constructed the global performance ranking by consolidating all the country performance rankings. The sales work team with the highest average performance globally in the calendar year 2019 was at the top of the ranking, while the team with the lowest performance was at the bottom. The construction process for the *team performance* metric is illustrated in figure A2 in the appendix.

Our analysis of the impact of work team diversity dimensions and characteristics on team performance is based on a sample of 911 sales teams. To ensure the representativeness of the research sample, all the sales teams within the Client Solutions Business Unit across 39 selected countries and territories (see table 3) were included in the initial sample, for a total of 1,815 work teams. All work teams with fewer than five team members were then excluded from the research sample, reducing it to the final 911 sales work teams. Accordingly, there is no dominant team size category in the final research sample, with all the categories being relatively balanced (ranging from 23 per cent of teams with 10 to 11 team members, to 29 per cent of teams with 8 to 9 employees). According to the company's internal employee headcount report, there are 192 different job positions in the company - which, based on job content, are consolidated into 63 job categories and 18 specializations. An overview of all 18 specializations is included in figure A3 in the appendix.

According to this organizational structure, table 4 presents the characteristics of the research sample. It indicates that 71 per cent of teams in the sample have members from only 1 specialization and only 9 per cent of teams have team members from more than 3 areas. Most of the teams (70 per cent) in the research sample consist of team members with the same nationality, while only 5 per cent of teams have more than 60 per cent of team members of different nationalities. A total of 20 per cent of teams comprise no women; most of the teams (33 per cent) have up to 20 per cent women, and only 4 per cent of teams have more than 60 per cent women. A significant majority of teams (79 per cent) have male team managers, while only 21 per cent of teams have female team managers. Some 40 per cent of team managers have been in managerial positions for 2 to 3 years, while only 9 per cent have more than 4 years' experience managing people. The members of the majority of teams (77 per cent) speak the same first language, and only 8 per cent of teams are made up of team members speaking 4 or more different first languages. Some 40 per cent of teams consist of members who have been in the company for 6 to 9 years on average, while

<table><caption>Table 3. Geographical regions, countries and territories represented in the sample</caption><thead><tr><td>Geographical region</td><td>Countries and territories</td></tr></thead><tbody><tr><td>Africa</td><td>Egypt, Nigeria, South Africa</td></tr><tr><td>Americas</td><td>Brazil, Canada, Chile, Panama, Peru, United States</td></tr><tr><td>Asia</td><td>China, Hong Kong (China), India, Israel, Japan, Malaysia, Philippines, Republic of Korea, Singapore, Taiwan (China), Türkiye</td></tr><tr><td>Europe</td><td>Croatia, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Poland, Portugal, Romania, Russian Federation, Slovakia, Spain, Sweden, Switzerland, Ukraine, United Kingdom</td></tr><tr><td>Oceania</td><td>Australia</td></tr></tbody></table>
<table><caption>Table 4. Characteristics of research sample</caption><thead><tr><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>Nationality diversity</td><td>%</td><td>Time at management level</td><td>%</td></tr><tr><td>0 (all team members have the same nationality)</td><td>70</td><td>≤2 years</td><td>15</td></tr><tr><td>1-20</td><td>7</td><td>>2-3 years</td><td>40</td></tr><tr><td>21-40</td><td>12</td><td>>3-4 years</td><td>36</td></tr><tr><td>41-60</td><td>6</td><td>>4 years</td><td>9</td></tr><tr><td>61-100</td><td>5</td><td>First language</td><td>%</td></tr><tr><td>Specialization</td><td>%</td><td>1 language</td><td>77</td></tr><tr><td>1 specialization</td><td>71</td><td>2 languages</td><td>10</td></tr><tr><td>2 specializations</td><td>20</td><td>3 languages</td><td>4</td></tr><tr><td>3 and more specializations</td><td>9</td><td>≥4 languages</td><td>8</td></tr><tr><td>Team size</td><td>%</td><td>Work experience</td><td>%</td></tr><tr><td>5-7 team members</td><td>24</td><td>≤4 years</td><td>14</td></tr><tr><td>8-9 team members</td><td>29</td><td>>4-6 years</td><td>24</td></tr><tr><td>10-11 team members</td><td>23</td><td>>6-9 years</td><td>40</td></tr><tr><td>≥12 team members</td><td>24</td><td>>9 years</td><td>21</td></tr><tr><td>Gender diversity</td><td>%</td><td>Seniority score</td><td>%</td></tr><tr><td>0 (no female team members)</td><td>20</td><td>≤6 years</td><td>20</td></tr><tr><td>1-20</td><td>33</td><td>>6-7 years</td><td>51</td></tr><tr><td>21-40</td><td>27</td><td>>7</td><td>29</td></tr><tr><td>41-60</td><td>16</td><td>Team structure stability</td><td>%</td></tr><tr><td>60-100</td><td>4</td><td>≤2 years</td><td>34</td></tr><tr><td>Gender of team manager</td><td>%</td><td>>2-3 years</td><td>37</td></tr><tr><td>Female</td><td>21</td><td>>3-4 years</td><td>18</td></tr><tr><td>Male</td><td>79</td><td>>4 years</td><td>11</td></tr></tbody><tfoot><tr><td colspan="4">Source: Our own calculations based on internal company data.</td></tr></tfoot></table>

only 14 per cent of teams are made up of team members who have been working in the company for less than 4 years on average. Approximately half of the teams in the research sample consist of mid-to-senior employees, while 20 per cent consist of relatively junior employees. Only 11 per cent of teams have members who have been working together in the same team for more than 4 years (on average), and 37 per cent have members who have been working together between 2 and 3 years (on average).

Given that the company's business model organizes sales teams according to their product portfolio rather than geographical location, teams are selling products not in only one, but in multiple countries within their geographical area. In this respect, to better serve the customers from different countries, team members within the same sales team are not necessarily located in the same country but in multiple countries within the geographical region they cover. This is also the reason why the research sample characteristics do not include an overview of sales teams from a country-location perspective.

## 3.2. Hypotheses

Based on the reviewed literature and the characteristics of the research sample, we propose and test the following hypotheses:

*Hypothesis 1:* More diverse work teams (considering primary diversity dimensions) perform better than more homogeneous teams.
*Hypothesis 1a:* Work teams with greater gender diversity perform better than teams that are more homogeneous in this respect.

*Hypothesis 1b:* Work team performance is influenced by the gender of the team manager.

*Hypothesis 1c:* Work teams with greater nationality diversity perform better than teams that are more homogeneous in this respect.

*Hypothesis 2:* More diverse work teams (considering secondary diversity dimensions) perform better than more homogeneous teams.

*Hypothesis 2a:* Work teams with greater diversity of job positions perform better than teams that are more homogeneous in this respect.

*Hypothesis 2b:* Work teams with greater diversity of first languages perform better than teams that are more homogeneous in this respect.

*Hypothesis 2c:* Work teams whose members have more experience of working in the company perform better than teams whose members have less experience in the company.

*Hypothesis 2d:* Work teams consisting of more senior team members perform better than teams consisting of more junior team members.

*Hypothesis 2e:* Work team performance is impacted by the team manager's length of experience in management.

*Hypothesis 3:* Work team performance is impacted by team characteristics (team structure stability and team size).

*Hypothesis 3a:* Work team performance is impacted by team structure stability (the amount of time that team members are part of the same team).

*Hypothesis 3b:* Work team performance is impacted by the size of the team.

# 4. Results

## 4.1. Partial correlation

Partial correlation calculates the correlation between two variables while excluding the effect of a third variable. This allows us to find out whether the correlation $r_{xy}$ between variables x and y is produced by the variable z:

$$r_{xy,z} = \frac{r_{xy} - r_{xz} \cdot r_{yz}}{\sqrt{(1-r_{xz}^2) \cdot (1-r_{yz}^2)}} \quad (2)$$

Partial correlation coefficients are statistically significant at the 5 per cent level. The *specialization* and *nationality diversity* variables have the most significant influence on the *team average country performance ranking* variable, with a small rate of statistically significant negative partial correlation (*p* < 0.001). *Time at management level* and *team size* achieve a small rate of statistically significant positive partial correlation (*p* < 0.001). On the contrary, the smallest effect, although statistically significant (*p* < 0.05), is found in the case of *first language*, with only a trivial rate of positive partial correlation. In this case, the partial and semi-partial correlations are relatively similar (the semi-partial correlation is always lower). If the semi-partial correlation is very small but the partial correlation is relatively large, then the relevant variable can predict a unique part of the variability of the dependent variable (which is not considered in the other variables). The contribution of the remaining variables is statistically insignificant (*p* > 0.05). The overview of statistically significant and insignificant independent variables is outlined in table 5.
## 4.2. Stepwise regression results

The dependent variable in the research models is (0) team average country performance ranking. The independent variables are (1) gender diversity, (2) nationality diversity, (3) first language, (4) work experience, (5) seniority score, (6) specialization, (7) gender of team manager, (8) team size, (9) team structure stability and (10) time at management level.

Extreme values (outliers) were identified when building the models using the Grubbs' test for the independent variables specialization (GT statistic = 7.67, *p* < 0.001), team size (GT statistic = 6.72, *p* < 0.001), time at management level (GT statistic = 4.16, *p* < 0.05) and first language (GT statistic = 5.67, *p* < 0.001). We identified specific value limits from graphs of standardized residues and independent variables (see figure A4). On this basis, we implemented the following inclusion condition:

$$ \textit{First language} < 9 \text{ AND specialization} < 5 \text{ AND team size} < 26 \text{ AND time at management level} < 6 $$

Similarly, when verifying the assumption of the absence of extreme values, other extreme values were identified based on residues, specifically the 2-sigma rule as well as the Mahalanobis distances and Cook's distances.

Based on what was introduced, we excluded 41 values. The result was the absence of extreme values, which is one of the prerequisites for regression analysis. The data file subsequently contains no outliers (all residuals within +/- 2 standard deviations). The aim of the analysis is not to create models to predict or estimate the values of the dependent variable but to evaluate the contributions of each variable examined to explain the *team average country performance ranking* variable. To this end, we created an artificial (dummy) variable: *gender of team manager* (male): 0/1.

The results of the stepwise regression are presented in table 6. They indicate that the *nationality diversity*, *specialization*, *team size*, *time at management level* and *first language* variables are statistically significant when progressively added to models 1-5, and explain the variability of the *team average country performance ranking* variable. The remaining independent variables in models 6-10 are statistically insignificant.

Model 5 is statistically significant but explains only 16 per cent of the variability of the dependent variable, indicating that the latter is also influenced by other variables that are not known since they are not part of this study.

Using standardized regression coefficients, we can estimate the impact of independent variables on the dependent variable (see table 7). The impact of each independent variable is estimated while checking the effect of the influence of the remaining independent variables that are input into the model. Based on standardized regression coefficients, we can determine the relative strength of the influence of the individual independent variables on the dependent variable – and, specifically, which variable has the most significant influence on the variance of the dependent variable. The *national diversity* and *specialization* variables have the most significant relative impact (36 and 22 per cent, respectively) on the variance of the dependent variable *team average country performance ranking*; both are cases of indirectly proportional dependence. In contrast, *first language* (13 per cent), *team size* (12 per cent) and *time at management level* (11 per cent) are cases of direct dependence.

## 4.3. Reasoning

Using stepwise regression, we examined the effect of ten team diversity factors on team performance. Based on the results of stepwise regression analysis, we conclude that five of the ten team diversity factors analysed (*nationality diversity*, *specialization*, *team size*, *time at management level* and *first language*) have a significant impact on team performance. The remaining five team diversity characteristics (*gender diversity*, *work experience*, *seniority score*, *gender of team manager* and *team structure stability*) do not significantly impact the performance of work teams in the company.
<table><caption>Table 5. Correlations matrix</caption><thead><tr><th></th><th>(0)</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th></tr></thead><tbody><tr><td>(0) Team average country performance ranking</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(1) Gender diversity</td><td>0.027</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(2) Nationality diversity</td><td>-0.291***</td><td>-0.055</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(3) First language</td><td>-0.188***</td><td>-0.050</td><td>0.849***</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(4) Work experience</td><td>0.031</td><td>-0.143***</td><td>0.044</td><td>0.082*</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(5) Seniority score</td><td>0.010</td><td>-0.203***</td><td>0.159***</td><td>0.158***</td><td>0.325***</td><td>1</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(6) Specialization</td><td>-0.234***</td><td>0.136***</td><td>0.121***</td><td>0.079*</td><td>0.009</td><td>-0.1377***</td><td>1</td><td></td><td></td><td></td><td></td></tr><tr><td>(7) Gender of team manager</td><td>0.018</td><td>-0.238***</td><td>0.005</td><td>0.008</td><td>0.103**</td><td>0.1083***</td><td>-0.052</td><td>1</td><td></td><td></td><td></td></tr><tr><td>(8) Team size</td><td>0.129***</td><td>0.134***</td><td>-0.094**</td><td>0.064</td><td>-0.006</td><td>-0.0747*</td><td>0.149***</td><td>-0.019</td><td>1</td><td></td><td></td></tr><tr><td>(9) Team structure stability</td><td>0.073*</td><td>0.017</td><td>-0.131***</td><td>-0.093**</td><td>0.449***</td><td>0.219***</td><td>0.011</td><td>-0.007</td><td>0.030</td><td>1</td><td></td></tr><tr><td>(10) Time at management level</td><td>0.126***</td><td>0.044</td><td>-0.032</td><td>-0.015</td><td>0.0284</td><td>-0.018</td><td>-0.017</td><td>-0.001</td><td>0.037</td><td>0.036</td><td>1</td></tr></tbody><tfoot><tr><td colspan="11">*, ** and *** indicate statistical significance at the 5, 1 and 0.1 per cent levels, respectively.</td></tr></tfoot></table>

<table><caption>Table 6. Stepwise regression summary</caption><thead><tr><th></th><th>M1</th><th>M2</th><th>M3</th><th>M4</th><th>M5</th><th>M6</th><th>M7</th><th>M8</th><th>M9</th><th>M10</th></tr></thead><tbody><tr><td>Nationality diversity</td><td>-0.291***</td><td>-0.266***</td><td>-0.251***</td><td>-0.248***</td><td>-0.360***</td><td>-0.356***</td><td>-0.355***</td><td>-0.359***</td><td>-0.356***</td><td>-0.356***</td></tr><tr><td>Specialization</td><td></td><td>-0.202***</td><td>-0.225***</td><td>-0.223***</td><td>-0.216***</td><td>-0.217***</td><td>-0.221***</td><td>-0.217***</td><td>-0.218***</td><td>-0.218***</td></tr><tr><td>Team size</td><td></td><td></td><td>0.139***</td><td>0.135***</td><td>0.115***</td><td>0.116***</td><td>0.113**</td><td>0.114**</td><td>0.114**</td><td>0.113**</td></tr><tr><td>Time at management level</td><td></td><td></td><td></td><td>0.110***</td><td>0.109***</td><td>0.108***</td><td>0.107**</td><td>0.107***</td><td>0.107**</td><td>0.107**</td></tr><tr><td>First language</td><td></td><td></td><td></td><td></td><td>0.129*</td><td>0.123*</td><td>0.124*</td><td>0.123*</td><td>0.123*</td><td>0.124*</td></tr><tr><td>Work experience</td><td></td><td></td><td></td><td></td><td></td><td>0.036</td><td>0.040</td><td>0.033</td><td>0.026</td><td>0.025</td></tr><tr><td>Gender diversity</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.029</td><td>0.033</td><td>0.031</td><td>0.034</td></tr><tr><td>Seniority score</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.024</td><td>0.022</td><td>0.022</td></tr><tr><td>Team structure stability</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.016</td><td>0.016</td></tr><tr><td>Gender of team manager</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.013</td></tr><tr><td>Multiple R<sup>2</sup></td><td>0.085***</td><td>0.125***</td><td>0.144***</td><td>0.156***</td><td>0.160*</td><td>0.161</td><td>0.162</td><td>0.162</td><td>0.163</td><td>0.163</td></tr></tbody><tfoot><tr><td colspan="11">*, ** and *** indicate statistical significance at the 5, 1 and 0.1 per cent levels, respectively.</td></tr></tfoot></table>

Source: Our own calculations based on internal company data.
<table><caption>Table 7. Regression summary (Model 5)</caption><thead><tr><th></th><th>b*</th><th>Standard error of b*</th><th>b</th><th>Standard error of b</th><th>t(905)</th><th>p-value</th></tr></thead><tbody><tr><td>Intercept</td><td></td><td></td><td>31.8714</td><td>2.6178</td><td>12.1747</td><td>0.0000</td></tr><tr><td>Nationality diversity (Diversity %; min = 0%; max = 100%)</td><td>-0.3600</td><td>0.0608</td><td>-31.9944</td><td>5.4038</td><td>-5.9208</td><td>0.0000</td></tr><tr><td>Specialization (No. of areas)</td><td>-0.2163</td><td>0.0313</td><td>-5.9512</td><td>0.8597</td><td>-6.9227</td><td>0.0000</td></tr><tr><td>Team size (No. of team members)</td><td>0.1154</td><td>0.0324</td><td>0.7075</td><td>0.1984</td><td>3.5651</td><td>0.0004</td></tr><tr><td>Time at management level (No. of years)</td><td>0.1089</td><td>0.0305</td><td>1.9171</td><td>0.5372</td><td>3.5690</td><td>0.0004</td></tr><tr><td>First language (No. of first languages)</td><td>0.1292</td><td>0.0602</td><td>1.8814</td><td>0.8770</td><td>2.1452</td><td>0.0322</td></tr></tbody><tfoot><tr><td colspan="7">Note: b* = standardized regression coefficients; b = regression coefficients are statistically significant (p < 0.05). Source: Our own calculations based on internal company data.</td></tr></tfoot></table>

The gender diversity of the work team or the gender of the team manager does not influence team performance owing to the company's approach to diversity and inclusion. It takes these issues very seriously and invests a lot of resources in their active management. The company's organizational structure includes a specific diversity and inclusion department responsible for the implementation and governance of internal diversity and inclusion policy within each subsidiary of the company around the globe. This should ensure that the company culture in this regard is the same in each of the company's offices. In addition, many company branches are operating diversity and inclusion employee resource groups with the aim of promoting an inclusive environment and equal opportunities for employees of all genders and backgrounds by organizing various events, such as workshops, discussions and seminars. All employees, whether individual contributors or people managers, must attend regular training courses on diversity and inclusion, which are also part of the onboarding process for newly hired employees. This mandatory high-quality training, in combination with all other active gender diversity and inclusion management measures, eliminates gender as a differentiating factor among employees since all employees are given the same treatment and opportunities. This is also the reason why gender does not have a significant impact on work team performance in our research. These results reject Hypothesis 1a (work team gender diversity) and Hypothesis 1b (gender of team manager).

Another team diversity characteristic that does not have a significant impact on team performance is the average experience of team members in the company (*work experience*) and the position of team members within the company hierarchy (*seniority score*). These research results reject Hypothesis 2c (work experience) and Hypothesis 2d (seniority of team members). Explanations can be found in the relatively uniform structure of the teams analysed in terms of the age and experience of their members. Most of the team members are in their 30s and average experience in the company is 7 years, which is a relatively short time considering that people may be in the labour market for up to 50 years (depending on the length of studies and retirement age). This uniformity in our sample is partially due to the fact that, after reaching a certain career and experience level as individual contributors, employees usually move (are promoted) to people manager roles, which are not part of our research sample. Accordingly, owing to the relatively small range of these diversity characteristics, they do not significantly impact work team performance.

Lastly, the average time that employees are part of the team (*team structure stability* variable) does not have a significant effect on team performance owing to the company's precise hiring processes and the high-quality onboarding of new employees joining the team, either internally (transferred from another team/department within the company) or externally. Over the multiple phases of the hiring process, candidates are tested on
different skills and personality characteristics to select candidates who fulfil the required criteria for the job to the most satisfactory level. Once selected, candidates must attend and pass different training courses, which should prepare them to be as effective as possible on the job from day one. As a result of these effective hiring and onboarding processes, the impact of *team structure stability* on team performance is eliminated. This result rejects Hypothesis 3a.

Among the team diversity characteristics that have a significant impact on team performance, we find a relationship of indirect dependence between *team size, time at management level* and *first language* and team performance measured by *team average country performance ranking*, such that the higher the team diversity (within a particular category), the lower the team performance.

The negative impact of *team size* on team performance is in line with theories in management studies (Malone and Crowston 1994) and economics (Holmstrom 1982) suggesting that increasing team size can hurt productivity for a variety of reasons: workers find it increasingly tempting to rely on the efforts of others; communication overheads increase with the number of individuals whose efforts must be coordinated; or communication between team members leads to herding and groupthink (Mao et al. 2016). This result supports Hypothesis 3b.

Several factors can drive the indirect dependency between the team manager's experience in a managerial role and the team's performance. The first factor is the quality of training that employees receive when they become managers. The aim of this training is to provide employees with the soft and technical skills required to manage people and to substitute them for a certain level of learning on the job, which will make the transition period shorter and smoother. The second factor is the effect of the promotion to management level as a motivational factor (Osabiya 2015). Newly promoted team managers generally have higher motivation levels than managers who have been in the role for several years, driven by their desire to demonstrate to the leadership that they deserved their promotion. Their increased motivation and efforts often lead to high performance levels, which typically decline over time in the role. The last driver assumes that new managers are not tied to stereotypes and are therefore not afraid to implement innovations and necessary changes in the work of the team, which can result in higher team performance. This result supports Hypothesis 2e.

The last team diversity characteristic to have an indirect impact on team performance is the number of different first languages within the team (*first language*). This result rejects Hypothesis 2b and is in line with studies suggesting that language barriers can have a negative effect on team communication, effectiveness and performance (Jonsen, Maznevski and Schneider 2011; Tenzer, Pudelko and Harzing 2014). This also reflects internal factors in the company analysed, where team members speaking different first languages usually cover customers from different regions and are also based in different countries or territories. Accordingly, although employees may officially be in the same work team, they do not necessarily collaborate on tasks, which removes the opportunity to create synergies and may lower team performance. From this perspective, teams with fewer first languages have certain advantages and may perform better, in line with attraction theory (Byrne 1971).

Our results indicate that the main drivers of work team performance are *nationality diversity* and *specialization*, which have the most significant direct impact on the dependent variable. These results support Hypothesis 1c (nationality diversity) and Hypothesis 2a (specialization). Team members with different job specializations (e.g. in marketing, finance, sales, IT and technical support) bring knowledge, skills and expertise from different areas to the team, which can enhance its performance. Having a diversity of nationalities within a team makes it less standardized but more variable, which creates more flexibility in responding to changes in the external environment and brings a better understanding of local markets and customers. Moreover, different perspectives can facilitate unique and creative approaches to problem-solving, thereby increasing creativity and innovation, which
can lead to better performance. Several authors have previously confirmed the benefits of diversity in these areas (e.g. Hunt et al. 2018; Page 2019). The positive impact of nationality and job specialization diversity on team performance is also supported by the diversity of management practices within the company analysed in this study. As previously mentioned, the company actively manages workforce diversity and seeks to create an inclusive and collaborative working environment for all its employees. Previous studies have confirmed that this active company approach to diversity management is an important factor in effectively leveraging workforce diversity (e.g. Hudson 2017; Otaye-Ebede 2019).

# 5. Discussion

Our findings reveal the complex nature of diversity, highlighting the conditions under which it can enhance or, on the other hand, hinder performance. By identifying key moderators, we advance current understanding of how diversity can be effectively leveraged in organizational settings.

## 5.1. Contributing factors and performance-enhancing effects

Our results confirm a positive relationship between team members' nationality and job specialization diversity and team performance. Nationality diversity contributes to creativity, invention and flexibility, allowing work teams to address problems from a broader perspective. At the same time, this diversity improves teams' ability to adjust to changing external contexts and to effectively identify and address the needs and expectations of consumers (Stahl et al. 2010; Hunt et al. 2018; Page 2019; Behl 2022). In their study, Saha and Patra (2008) concentrate on the requirements of the globalized market and the advantages of workforce diversity. They argue that if the organization does not employ a diversified workforce, sale managers can make their workforce effective and competent by providing appropriate training. As mentioned above, the positive effects of diversity on team performance can be leveraged through an active company approach to diversity management (e.g. Hudson 2017; Otaye-Ebede 2019).

## 5.2. Impeding factors and challenges associated with diversity

Despite its advantages, diversity can cause problems that hamper performance. Our research confirms previous findings that team performance is negatively impacted by first language diversity (Kundu et al. 2020; Dusdal and Powell 2021). Linguistic disparities can cause misunderstandings, decreased cohesion and poorer collaboration, often resulting in lower team performance (Tenzer, Pudelko and Harzing 2014; Ciuk, Śliwa and Harzing 2023). These findings are also consistent with studies that hold that language commonality promotes interpersonal bonds and team cohesion (Byrne 1971; Kassis Henderson 2005; Grossman et al. 2022). In teams whose members largely act independently, as is typical for sales teams, the lack of linguistic consistency exacerbates this lack of cohesion (Forsyth 2021).

Also in line with previous findings (Hajiali et al. 2022), our results identify team size as another of the key elements impacting performance. Larger teams may face coordination challenges and greater communication overheads, both of which reduce team performance (Mao et al. 2016; Forscher et al. 2023). Team managers' experience in the role is adversely associated with performance, indicating possible loss of motivation and inventiveness over time (Obeng et al. 2021). Long-serving managers may become entrenched in established routines, making them less open to new ideas and practices (Osabiya 2015; Arekrans, Ritzén and Laurenti 2023). While diversity can have considerable benefits, it must be properly managed to avoid negative effects, such as higher conflict potential, inefficiency and decreased group cohesion (Roberge and van Dick 2010; Triana et al. 2021).

Surprisingly, our research does not indicate that gender diversity or work experience diversity has any significant impact on team performance. This is a contrast with the quite
significant number of previous studies confirming the (mainly positive) impact of work team
gender diversity on company performance (Richard et al. 2004) or the impact of functional
team diversity on company performance (Pegels, Song and Yang 2000). However, Solakoglu
and Demir (2016) find only weak evidence that gender diversity affects firm performance
and Williams and O'Reilly (1998) show how research on the implications of workforce gender
diversity has produced asymmetrical findings, which call for further research in this regard.

**5.3. Moderating factors**

The impact of diversity on performance is heavily influenced by moderating factors such as leadership, task type and organizational culture (Guillaume et al. 2017; Triana et al. 2021). Effective leadership is necessary to navigate the complexities of diversity. Inclusive leadership styles usually promote collaboration, respect and a sense of belonging among team members, reducing possible disputes and increasing cohesion (Ashikali, Groeneveld and Kuipers 2021; Shore and Chung 2022). The diversity-performance relationship is also influenced by task factors (van Dijk, van Engen and van Knippenberg 2012; Lee, Chung and Hong 2022). Teams working on creative, interdependent tasks are more likely to benefit from the different perspectives of their members. In contrast, routine jobs may suffer from inefficiencies caused by diversity (Mitchell et al. 2015; Liu et al. 2021). Organizational culture is another important moderating factor. The implementation of comprehensive diversity management practices helps standardize diversity initiatives and creates a supportive environment for diverse teams.

**6. Conclusion**

This article has explored the relationship between work team diversity and performance. Significant impact on team performance is confirmed in the case of work team nationality diversity (Hypothesis 1c), specialization diversity (Hypothesis 2a), diversity among the first languages of team members (Hypothesis 2b), the team manager's experience at the managerial level (Hypothesis 2e) and team size (Hypothesis 3b). Of these five work team diversity factors, the main drivers with a positive impact on team performance are nationality diversity and the specialization diversity of team members. Other team diversity characteristics analysed – including gender diversity of team members (Hypothesis 1a), gender of team manager (Hypothesis 1b), team members' work experience in the company (Hypothesis 2c), seniority of team members (Hypothesis 2d) and team structure stability (Hypothesis 3a) – do not significantly impact the performance of work teams in the analysed company. Overall, these results reveal only partial support for Hypothesis 1 (primary diversity dimensions), Hypothesis 2 (secondary diversity dimensions) and Hypothesis 3 (team characteristics). Nationality diversity and specialization diversity improve performance by promoting creativity, adaptability and innovative problem-solving. However, the challenges posed by first language diversity, larger team sizes and the team manager's experience at the managerial level highlight the potential for inefficiencies or conflicts when these dimensions are not effectively managed.

The wide range of factors analysed in our study and our comprehensive geographical approach provide novel contributions to the literature. Owing to our decision not to limit our sample to a certain country or region, it contains 911 work teams from all around the globe (overall, 39 countries and territories were chosen to represent each geographical region). This approach allows us to generalize our results regarding the impact of certain work team diversity dimensions on performance across geographical contexts.

The results suggest that organizations should focus on team composition during work
group formation, prioritizing diversity in terms of nationality and job specialization. This
should be a key strategy for organizations aiming to maximize the benefits of diversity.
Training programmes are crucial in addressing the complexities of linguistic and cultural
diversity within organizations. They should focus on cross-cultural communication skills,
conflict resolution strategies and collaborative behaviours to overcome barriers and ensure that diversity does not become a source of inefficiency or misunderstanding. Effective coordination and collaboration mechanisms are also crucial, especially in larger teams where communication overheads and coordination difficulties are amplified. Organizations can counteract inefficiencies associated with team size and linguistic diversity by implementing structured workflows, defining roles clearly and leveraging advanced communication technologies. Leadership development programmes that prepare managers to navigate diverse teams are essential, emphasizing adaptability, inclusivity and continuous learning. Fostering an inclusive organizational culture is essential for sustaining the advantages of diversity. Diversity policies should be implemented through effective leadership practices, active employee engagement and periodic evaluations of outcomes. Integrating conflict resolution mechanisms into team management practices can mitigate interpersonal friction and ensure productive and collaborative team dynamics. Diversity management is an ongoing process, and organizations must continuously assess and refine their practices to adapt to evolving workforce dynamics.

However, there are several limitations to the generalizability of our findings. First, the focus on a specific industry (IT) and function (sales) may not reflect the broader impact of diversity across different organizational contexts. Second, data from before the COVID-19 pandemic do not capture the evolving dynamics of remote and hybrid work environments, which are likely to influence team interactions and the impact of diversity. Third, our use of data for a single year (2019) does not allow us to consider long-term trends and the enduring effects of diversity. However, while our findings are specific to one multinational organization and its sales teams, they offer a basis for the broader discussion of the role of diversity in organizational performance. Future research could address this study's limitations by exploring the effects of diversity in different industries, organizational contexts and time periods. Additionally, examining how diversity interacts with evolving workplace dynamics, such as hybrid and remote models, will provide critical insights into the future of diverse teams. By combining longitudinal analyses, broader datasets and a focus on emerging trends, future studies can deepen our understanding of how to harness the full potential of diversity in the modern workforce.

## Acknowledgements

This research received funding from the Grant Agency of the Slovak University of Agriculture in Nitra (Project No. 17-GA-SPU-2024) and from Slovakia's recovery and resilience plan (Project No. 09I03-03-V05-00018, "Early Stage Grants at Slovak University of Agriculture in Nitra").

## Competing interests

The authors declare that they have no competing interests.

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# Appendix

<figure><img src="image_3.png" /><figcaption>Figure A1. Identification of analysed team diversity and team characteristics</figcaption></figure>

Source: Our own compilation.

<figure><img src="image_4.png" /><figcaption>Figure A2. Construction of team performance metric</figcaption></figure>

Source: Our own compilation.

<figure><img src="image_5.png" /><figcaption>Figure A3. Structure of specialization categories within final research sample</figcaption></figure>

Source: Our own compilation.
<figure><img src="image_6.png" /><figcaption>Figure A4. Standardized residuals of independent variables</figcaption></figure>

Source: Our own calculations based on internal company data.