Neurodivergence and boundary spanning as predictors of social skills and diversity climate

van Rijswijk, Jan; Curşeu, Petru L.; van Oortmerssen, Lise A. Article Neurodivergence and boundary spanning as predictors of social skills and diversity climate Administrative Sciences Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: van Rijswijk, Jan; Curşeu, Petru L.; van Oortmerssen, Lise A. (2024) : Neurodivergence and boundary spanning as predictors of social skills and diversity climate, Administrative Sciences, ISSN 2076-3387, MDPI, Basel, Vol. 14, Iss. 11, pp. 1-15, https://doi.org/10.3390/admsci14110285 This Version is available at: https://hdl.handle.net/10419/321092 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Administrative Sciences 14: 285. https://doi.org/ 10.3390/admsci14110285 Received: 5 October 2024 Revised: 1 November 2024 Accepted: 2 November 2024 Published: 4 November 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Article Neurodivergence and Boundary Spanning as Predictors of Social Skills and Diversity Climate Jan van Rijswijk 1, Petru L. Cur¸seu 1,2,* and Lise A. van Oortmerssen 1 1Department of Organisation, Open Universiteit, 6419 AT Heerlen, The Netherlands; [email protected] (J.v.R.); [email protected] (L.A.v.O.) 2Department of Psychology, Babes ,—Bolyai University, 400347 Cluj-Napoca, Romania *Correspondence: [email protected] Abstract: We report a multilevel investigation of the interplay between neurodivergence and boundary-spanning activities in groups as predictors of social skills and diversity climate. In a sample of 357 group members nested in 70 organizational groups, we show that neurodivergence negatively affects conversational and work-related social skills. We also show that boundary spanning attenuates the association between neurodivergence and social skills. We discuss differences observed for withinas well as between-group effects and discuss the indirect association between neurodivergence and diversity climate as significantly mediated by conversational skills. Our study makes important contributions to the literature on neurodivergence in the workplace, showing the beneficial role of engaging in boundary-spanning activities. Keywords: neurodivergence; boundary spanning; social skills; communication; neurodiversity; diversity climate 1. Introduction In recent decades, diversity has emerged as a prominent societal issue, with many organizations developing and implementing diversity management and inclusion programs (Austin and Pisano 2017). In this context, neurodiversity and fostering inclusivity among neurodivergent individuals have become key elements of diversity management in modern organizations (LeFevre-Levy et al. 2023;Rao and Polepeddi 2019). Research on neurodiversity in work environments underscores the need to address both the relational as well as task-related consequences related to neurodivergent employee characteristics (Szulc 2022;van Rijswijk et al. 2024). The distinctive thinking styles of neurodivergent employees, particularly when complemented in collaboration with neurotypical peers, can result in significant organizational benefits, including enhanced innovation, elevated entrepreneurial mindset, and lead to an overall increase in output quality and quantity (Austin and Pisano 2017;Houdek 2022;Moore et al. 2021). However, despite the potential for unique skill sets that neurodivergent individuals bring to the table, employers often struggle to move beyond viewing neurodivergence solely through the lens of disability, which complicates the effective inclusion of these individuals into work processes (Krzeminska and Hawse 2020;Rogge and Janssen 2019). One of the essential factors that distinguish neurodiversity-friendly work environments from others is an inclusive diversity climate (Volpone et al. 2022). While the importance of a positive diversity climate at the organizational level is well recognized (Holmes et al. 2021;Volpone et al. 2022), the specific contribution of neurodivergent individuals to a positive diversity climate is currently unclear. Research to date has not extensively explored the way in which neurodiversity is reflected in social interactions at work, as the scarce group-level quantitative studies have focused primarily on group outcomes rather than dissecting social dynamics (Dugan et al. 1995;Kamps et al. 1995;Zolyomi et al. 2018). Moreover, the reliance on student-based samples in nearly Adm. Sci. 2024,14, 285. https://doi.org/10.3390/admsci14110285 https://www.mdpi.com/journal/admsci Adm. Sci. 2024,14, 285 2 of 15 all studies raises questions about the generalizability of findings to actual work groups (van Rijswijk et al. 2024). In particular, understanding the role of neurodiverse work groups in fostering an inclusive group climate remains virtually unexplored. Although research has shown that neurodivergence also relates to individual social skills and the way in which individuals engage in and experience social interactions (Astle et al. 2022; Fletcher-Watson 2022;Shaw et al. 2014;Whittenburg et al. 2020), it is also unclear what impact neurodivergence and social skills have on the emergence of diversity climate in organizations. This lack of clarity challenges researchers seeking to relate neurodiversity with an inclusive and diversity-welcoming organizational climate, and it is one of the potentially contributing factors to the underrepresentation of neurodivergent employees in organizations. We set out to study the association between neurodivergence and perceptions of diversity climate and the extent to which social skills explain this association. Moreover, we explore the moderating role of boundary spanning in the relationship between neurodivergence and social skills. For this purpose, we collected and analyzed research data from 70 working groups in 19 Dutch organizations across different industries. Our paper is among the first to explore neurodivergence within work groups, specifically delving into how neurodivergence influences social dynamics in workplace settings. Given the limited existing research on neurodiversity in work groups and the predominantly student-sample experimental nature of earlier studies on this topic (van Rijswijk et al. 2024), our research fills a gap by providing real-world data-driven insights into the relationship between neurodivergence and perceptions of diversity climate. In addition, because we aim to test the association between boundary spanning and social skills, our study responds to the call for multilevel research on boundary spanning by Marrone (2010) that also includes individual outcomes and looks beyond group performance consequences of engaging in boundary spanning. 2. Theory and Hypotheses Neurodiversity is a particular form of group diversity with the potential to increase the pool of cognitive resources in groups, yet with clear relational challenges, particularly when the level of vertical differentiation is high, namely when group members have unequal access to socially valued resources in the group (van Rijswijk et al. 2024). Although the literature to date advocates the equal treatment of neurodivergent individuals regardless of their neurological diagnosis (Houdek 2022;Krzeminska et al. 2019), neurodivergent employees are often marginalized and navigate in sparse social networks (Chapman 2021; Dwyer 2022), with important consequences for their social skills development. We know relatively little about how differences in social skills unfold in terms of relational dynamics in groups and the perception of inclusiveness. While initially focused on supporting people with Autism, the scope of neurodiversity has expanded over time to encompass neurodivergent characteristics such as Attention Deficit Hyperactivity Disorder (ADHD), Attention Deficit Disorder (ADD), Dyslexia, Tourette’s syndrome, and intellectual giftedness (Astle et al. 2022). Although broadening the score of neurodiversity to include different neurodivergent diagnoses makes comparison across studies difficult, recent transdiagnostic approaches have emphasized various commonalities in cognition, behavior, social interactions, and lived experiences across different neurodiversity types (Astle et al. 2022;Fletcher-Watson 2022). A recent empirical study identified the so-called “N factor” as an underlying shared factor that includes social, cognitive, and emotional commonalities shared across various neurodivergent conditions (Apperly et al. 2024). Studies of neurodivergence usually include at least Autism, ADHD, and ADD, all of which can have a significant impact on social skills. Social skills can be defined as one’s capacity to actively and effectively participate in social interactions (In et al. 2019) and include conversational, emphatic, and work-related social skills. Conversational skills refer to the ability to engage in dialogues without experiencing excessive stress during verbal interactions (Olaz et al. 2009). Neurodivergent individuals may encounter challenges re- Adm. Sci. 2024,14, 285 3 of 15 garding the development of such social skills, depending on their form of neurodivergence, which may include delayed language development, difficulty initiating and sustaining conversations, and overlooking subtle communication signals (Antshel and Russo 2019; Ginapp et al. 2023;Milton 2012b). Empathic skills capture individuals’ capacities to empathize with another’s position, involving conveying appreciation and compliments and standing up for another person’s position (Olaz et al. 2009). Empathy often comes under pressure when neurodivergent individuals participate in group processes, a phenomenon described by Milton (2012a) as the “double empathy problem”: neurodivergent individuals have difficulty putting themselves in the position of neurotypicals and vice versa. Finally, work-related social skills, such as collaboration, public speaking, and expressing opinions, are often necessary for elevated job performance (Olaz et al. 2009). Due to the common challenges neurodivergent individuals face in relation to the development of social skills, seeking assistance from neurotypical colleagues can be desired but often proves challenging in practice (Van den Bosch et al. 2019). That brings us to the following hypothesis: H1: Neurodivergence has a negative association with social skills (conversational, empathic, and work-related). Broad engagement in social interactions in various social settings and contexts fosters the development of social skills (Beauchamp and Anderson 2010). In workgroup contexts, we can distinguish between interactions that unfold within groups and the boundaryspanning activities in which group members interact at work with other employees outside the group. Boundary-spanning activities are essential for enriching the information pool within groups (Cur¸seu and Pluut 2018), and in the context of inter-group collaborations, pressures for effective alignment require effective interpersonal communication. The individual tasked with managing these inter-group relations, also called the boundary spanner, is often required to reconcile different and sometimes divergent group interests, tasks, and objectives. Therefore, tasks substantially improving their social skills are essential to fulfill this role effectively. We expect that the frequency of engaging in boundary-spanning activities will have a positive association with the social skills reported by the group members. These social skills encompass not only personal competencies like reliability, commitment, and respect but also social competencies such as relationship development, leadership, effective communication, empathy, and diplomacy (Williams 2002,2011). Biographical reports on influential neurodivergent individuals, such as Bill Gates and Richard Branson, show that through conscious and systematic engagement in social interactions across a variety of settings, their social skills developed to great lengths (Branson 2011;Lowe 2001). For example, Bill Gates practiced extensively on his public speaking and communication skills to improve his ability to communicate complex ideas clearly and effectively to a broad audience, which was crucial to both Microsoft and his philanthropic activities, and Richard Branson has practiced talks and interacting with unfamiliar people, and he is known for saying that communication is an art form, a skill that anyone can develop. We expect, therefore, that the limitations in social skills associated with neurodivergence are alleviated by systematic engagement in interpersonal interactions at work. We hypothesize that for neurodivergent group members who engage in such boundary-spanning activities, the relational challenges tied to such roles contribute to the development of their social skills. To summarize, we first hypothesize that the frequency of engaging in boundary-spanning activities fosters social skills, and while neurodivergence may often present challenges to social skills, boundary-spanning activities mitigate such limitations and enhance the social skills of neurodivergent employees. That leads us to the following hypotheses: H2: Engagement in boundary-spanning activities has a positive association with social skills (conversational, empathic, and work-related). Adm. Sci. 2024,14, 285 4 of 15 H3: Engagement in boundary-spanning activities attenuates the negative association between neurodivergence and social skills (such that the association between neurodivergence and social skills is less negative if group members engage in boundary-spanning activities). To benefit as a group from the performance advantages that diversity can provide, a positive diversity climate plays an important stimulating role (Volpone et al. 2022). This role applies not only to demographic differentiation like gender, ethnicity, or age but also to cognitive diversity characteristics to which neurodivergence belongs. A diversity climate reflects group members’ shared understanding of discrimination in the work environment (Chin 2009;Goyal and Shrivastava 2013). A positive diversity climate is focused on tolerance and inclusiveness for its group members, actively looking at treating colleagues equally and taking advantage of any benefits that may arise from group diversity. A diversity climate reflects the shared beliefs related to diversity within organizational groups, and it is shaped by formal interventions such as diversity management programs; yet, it is primarily reflected in the quality of social interactions among group members (Herdman and McMillan-Capehart 2010). Therein, the social skills of group members, including factors such as empathy, trust, good communication, and respect, contribute to the development of emotional intelligence at the group level by developing norms and skills that are inclusive and embrace diversity (Cur¸seu et al. 2015) and thereby shape the group’s diversity climate (Gardenswartz et al. 2010). Therefore, we expect that social skills play a mediating role between neurodivergence and boundary-spanning activities, on the one hand, and diversity climate, on the other, which leads us to the following hypotheses: H4: Social skills mediate the association between neurodivergence and a positive diversity climate in groups. H5: Social skills mediate the association between boundary-spanning activities and a positive diversity climate in groups. All hypothesized relations are visualized in Figure 1. Adm. Sci. 2024, 14, x FOR PEER REVIEW 4 of 17 H2: Engagement in boundary-spanning activities has a positive association with social skills (conversational, empathic, and work-related). H3: Engagement in boundary-spanning activities attenuates the negative association between neurodivergence and social skills (such that the association between neurodivergence and social skills is less negative if group members engage in boundary-spanning activities). To benefit as a group from the performance advantages that diversity can provide, a positive diversity climate plays an important stimulating role (Volpone et al. 2022). This role applies not only to demographic differentiation like gender, ethnicity, or age but also to cognitive diversity characteristics to which neurodivergence belongs. A diversity climate reflects group members’ shared understanding of discrimination in the work environment (Chin 2009; Goyal and Shrivastava 2013). A positive diversity climate is focused on tolerance and inclusiveness for its group members, actively looking at treating colleagues equally and taking advantage of any benefits that may arise from group diversity. A diversity climate reflects the shared beliefs related to diversity within organizational groups, and it is shaped by formal interventions such as diversity management programs; yet, it is primarily reflected in the quality of social interactions among group members (Herdman and McMillan-Capehart 2010). Therein, the social skills of group members, including factors such as empathy, trust, good communication, and respect, contribute to the development of emotional intelligence at the group level by developing norms and skills that are inclusive and embrace diversity (Curşeu et al. 2015) and thereby shape the group’s diversity climate (Gardenswartz et al. 2010). Therefore, we expect that social skills play a mediating role between neurodivergence and boundaryspanning activities, on the one hand, and diversity climate, on the other, which leads us to the following hypotheses: H4: Social skills mediate the association between neurodivergence and a positive diversity climate in groups. H5: Social skills mediate the association between boundary-spanning activities and a positive diversity climate in groups. All hypothesized relations are visualized in Figure 1. Figure 1. A conceptual model. 3. Materials and Methods We collected data from employees working in groups in various Dutch organizations in the context of a broader study aimed at exploring cognitive and neurodiversity in groups. This study is the first to use this dataset, and we followed the recommendations for data slicing suggested by Kirkman and Chen (2011), with dependent, moderating, and mediating variables exclusively selected and used for this paper. We used a snowball technique to recruit groups to participate in the study, aiming to obtain as many Neurodivergence Conversational, empathic, and workrelated social skills Positive diversity climate H2 H4 H3 H5 H1 Engagement in boundary-spanning activities Figure 1. A conceptual model. 3. Materials and Methods We collected data from employees working in groups in various Dutch organizations in the context of a broader study aimed at exploring cognitive and neurodiversity in groups. This study is the first to use this dataset, and we followed the recommendations for data slicing suggested by Kirkman and Chen (2011), with dependent, moderating, and mediating variables exclusively selected and used for this paper. We used a snowball technique to recruit groups to participate in the study, aiming to obtain as many individual responses as possible from the group members. The snowball technique began by contacting individuals within our networks and requesting referrals to teams potentially willing to participate. After establishing contact, we explained to a team member or manager the purpose of our Adm. Sci. 2024,14, 285 5 of 15 study and asked if they could also bring us in contact with other possible participating teams. The only requirement was that each group included at least three people working together on tasks. Our final sample included 357 group members (from which 43 reported being officially diagnosed with neurodivergence: 5 with Autism, 16 with ADHD, 8 with ADD, 14 with Dyslexia, 7 with High Giftedness, and none with Dyscalculia or Developmental Coordination Disorder (DCD)) nested in 70 organizational groups (ranging from 3 to 45 members ) from 19 organizations in various branches: 5 in healthcare, 1 in banking, 1 in pensions and insurance, 7 in IT, 2 in construction industry, 1 in employee recruitment, and 2 in education. To preserve anonymity in accordance with the institution’s IRB requirements, we did not collect demographic information on the participants except for their educational level. The study was approved by the Ethical Review Board of the Open Universiteit. Social skills were assessed with sixteen items from the social skills scales presented by Olaz et al. (2009), slightly adapted to a work situation (e.g., changing the word friends to colleagues). Seven items assessed conversational social skills (item examples: “If someone at work or school praises me, I feel embarrassed and don’t know what to do or say”, “I have trouble interrupting a phone conversation even with people I know”) rated from 1 = “never or rarely” to 5 = “always or almost always”. Cronbach’s alpha for this scale was 0.77, indicating acceptable reliability of the scale, while the omega score derived from factorial analysis (Hayes and Coutts 2020) was also 0.76, with all items loading positively into the main factor, with factor loadings ranging from 0.40 to 0.90. Five items assessed work-related social skills (item example: “At work, if I do not understand an explanation about an interesting topic, I ask all the questions I need to in order to clarify it”), and the responses were recorded from 1 = “never or rarely” to 5 = “always or almost always”. Cronbach’s alpha for this scale was 0.76, while the omega score was also 0.76, indicating acceptable reliability of the scale. Four items referred to empathic social skills (item example: “When one of my colleagues achieves something important that took a lot of effort, I praise them for their success”), and answers were recorded from 1 = “never or rarely” to 5 = “always or almost always”. Cronbach’s alpha for this scale was 0.59, and the omega score was 0.61, indicating problematic reliability of the scale. All items had a positive load in the main factor exceeding 0.44, with a single item with a 0.27 load. Given the previous empirical results supporting the validity of the scale in cross-cultural contexts (Olaz et al. 2009), we decided to use it further in the analyses. Diversity supporting work climate was evaluated with fifteen items of the Diversity Climate Questionnaire (Larkey 1996), slightly adapted to work situations and neurodiversity where necessary, assessing four dimensions of a climate that welcomes differences, namely inclusion (item example: “It seems that the real reason employees are denied promotions or raises is that they are seen as not fitting in”), ideation and dissent (item example: “Employees with different neurological and/or ethnic backgrounds have a difficult time getting their ideas across”), understanding (item example: “When employees who are neurological diagnosed, culturally different, or of different genders work together in our group, there is always some amount of miscommunication”), and fair treatment (item example: “Some employees in our group are “talked down to” because they are different”); answers were recorded from 1 = “strongly disagree” to 5 = “strongly agree”, and answers were recoded so the final scores indicate a climate open to diversity. Based on the factor analysis, a single dominant factor emerged, accounting for 46.60% of the variance in scores. The Cronbach’s alpha for this scale was 0.92, and the omega score was 0.92, with all items loading significantly and positively in the dominant factor, showing excellent reliability of this scale. Given the unitary factor structure of this scale, we decided to use Bartlett’s dominant factor score for further analysis as an indicator of the underlying constructs assessed by the items (DiStefano et al. 2019). Neurodivergence was evaluated by asking participants to report whether they were diagnosed with one of the following neurodivergent characteristics: Autism, ADHD, ADD, DCD, Dyslexia, Dyscalculia, and High Giftedness. We combined these neurodivergent Adm. Sci. 2024,14, 285 6 of 15 characteristics into a single variable, an approach supported by a recent study of Apperly et al. (2024) that suggests that traits across these diagnoses can be explained by a general neurodiversity factor, with overlapping characteristics transcending traditional diagnostic categories. Engagement in boundary-spanning activities was evaluated with a single item: “A boundary spanner is someone who (formally or informally) engages in boundary-crossing activities, processes, and practices, such as, for example, organizing alignment with other/different teams, projects, or departments. How often do you perform this role?” (1 = never, 2 = several times/year, 3 = several times/month, 4 = several times/week, 5 = daily). Given the clear description of the boundary-spanning behavior, we consider this an accurate indicator of the frequency of boundary-spanning activities. Educational level was evaluated as a categorical variable by asking participants their highest completed education level: vocational education (1), secondary education (2), university of applied sciences bachelor’s degree (3), university of applied sciences master’s degree or research university bachelor’s degree (4), research university master’s degree (5), and PhD (6). 4. Results The means, standard deviations, and correlations are presented in Table 1. Table 1. Means, standard deviations, and correlations. Mean SD 1 2 3 4 5 6 1. Education 3.3557 1.51196 1 2. Neurdivergence 0.1204 0.32594 0.055 1 3. Boundary spanning 2.82 1.488 0.205 ** −0.037 1 4. Conversation skills 3.6146 0.70967 −0.125 * −0.206 ** 0.208 ** 1 5. Emphatic skills 3.7513 0.65769 −0.024 −0.064 0.146 ** 0.309 ** 1 6. Work-related skills 3.7976 0.67341 0.158 ** −0.103 0.310 ** 0.433 ** 0.289 ** 1 7. Diversity climate 4.0220 0.67668 −0.055 −0.086 0.068 0.282 ** 0.109 * 0.169 ** Note: Neurodivergence was coded as a dummy variable: 1 = group members with a neurodivergence diagnostic, 0 = neurotypical group member; education was coded as a categorical variable ranging from 1 = vocational education to 6 = PhD. * p< 0.05; ** p< 0.01. Because our participants were nested in organizational groups, we used multilevel analyses to test our hypotheses. In order to disentangle within- and between-group effects, we used the MLMed macro Beta 2 version for SPSS developed by Rockwood and Hayes (2017). MLMed is a multilevel mediation procedure that tests moderation and mediation effects. The results of the multilevel moderation analyses are presented in Table 2, and the additional results for the mediation hypotheses are presented in Table 3. Table 2. Results of the within–between-group multilevel analyses predicting social skills. Variable Conversational Social Skills Emphatic Social Skills Work-Related Social Skills Within Between Within Between Within Between Constant −0.09 (0.07) 0.04 (0.07) −0.01 (0.07) Education −0.08 (0.05) − 0.15 ** (0.05) −0.06 (0.05) −0.001 (0.06) 0.05 (0.05) 0.07 (0.06) Neurodivergence (ND) −0.43 * (0.17) −0.64 (0.39) −0.08 (0.18) −0.26 (0.42) −0.35 * (0.17) 0.16 (0.40) Boundary spanning (BS) 0.12 ** (0.04) 0.07 (0.07) 0.13 ** (0.04) 0.004 (0.08) 0.15 *** (0.04) 0.21 *** (0.07) NDxBS 0.23 * (0.11) 0.64 * (0.27) −0.04 (0.12) 0.62 * (0.29) 0.28 * (0.11) 0.31 (0.28) Note: Unstandardized coefficients are presented in the table with SE in parentheses; ND = neurodivergence; BS = boundary spanning. * p< 0.05; ** p< 0.01; *** p< 0.001. Adm. Sci. 2024,14, 285 7 of 15 Table 3. Results of the within–between-group multilevel analyses predicting diversity climate. Variable DC No Control Variables DC No Interaction Effect DC Full Model Within Between Within Between Within Between Constant 0.01 (0.18) 0.001(0.09) 0.01 (0.09) Education −0.07 (0.05) −0.05 (0.07) −0.06 (0.05) −0.04 (0.08) Neurodivergence (ND) −0.14 (0.17) −0.01 (0.44) −0.16 (0.17) 0.03 (0.45) −0.16 (0.17) −0.05 (0.51) Boundary spanning (BS) −0.004 (0.04) 0.01 (0.09) 0.005 (0.04) 0.02 (0.10) −0.02 (0.04) 0.02 (0.09) Conversational social skills 0.30 *** (0.06) 0.19 (0.16) 0.29 *** (0.07) 0.15 (0.17) 0.28 *** (0.06) 0.16 (0.17) Empathic social skills 0.01 (0.06) 0.13 (0.15) 0.004 (06) 0.15 (0.16) 0.01 (0.06) 0.16 (0.16) Work-related social skills 0.02 (0.06) 0.01 (0.15) 0.03 (0.06) 0.04 (0.16) 02 (0.07) 0.04 (0.16) NDxBS 0.19 †(0.11) −0.12 (0.37) Note: Unstandardized coefficients are presented in the table with SE in parentheses; DC—diversity climate; ND—neurodivergence; BS—boundary spanning. †p< 0.10; *** p< 0.001. Education had a negative and significant association between groups with conversational social skills (B = − 0.15, SE = 0.05, p= 0.008), showing that groups in which average education is high report lower conversational social skills than groups in which the average education of the members is low. Neurodivergence had a negative and significant withingroup association with conversational social skills (B = − 0.43, SE = 0.17, p= 0.01) and with work-related social skills (B = − 0.35, SE = 0.17, p= 0.04), while its association with emphatic social skills was not significant. As illustrated in Table 2, none of the between-group associations between neurodivergence and the three social skills were significant. Therefore, we can conclude that Hypothesis 1 was supported within groups only for conversational and work-related social skills. Boundary spanning had positive and significant within-group associations with conversational social skills (B = 0.12, SE = 0.04, p= 0.004), emphatic social skills (B = 0.13, SE = 0.04, p= 0.003), and work-related social skills (B = 0.15, SE = 0.04, p= 0.0006). These results fully support Hypothesis 2 for the within-group effects. Moreover, Hypothesis 2 was partly supported for between groups since boundary spanning had a positive and significant between-group association with work-related social skills (B = 0.21, SE = 0.07, p= 0.005), showing that in groups in which members engage, on average, in more boundary spanning, the average work-related social skills reported by the members are also higher than in groups whose members engage less in boundary spanning. The interaction between neurodivergence and boundary spanning was significant for conversational social skills (within groups: B = 0.23, SE = 0.11, p= 0.04; between groups: B = 0.64, SE = 0.27, p= 0.02). The significant interaction effects are presented in Figures 2and 3, showing that boundary spanning attenuates the strength of the negative association between neurodivergence and conversational social skills, fully supporting what we specified in Hypothesis 3. Moreover, the interaction effect between neurodivergence and boundary spanning was significant between groups for empathic social skills (B = 0.62, SE = 0.29, p= 0.03). The interaction effect is presented in Figure 4, and it is aligned with the hypothesized attenuation effect of boundary spanning as specified in Hypothesis 3. Finally, the interaction effect was significant within groups for workrelated social skills (B = 0.28, SE = 0.11, p= 0.01), and the interaction effect depicted in Figure 5was also aligned with Hypothesis 3. We can, therefore, conclude that Hypothesis 3 received considerable support. As indicated in Table 3, the only types of social skills with a direct and positive association within groups with diversity climate were conversational social skills (B = 0.28, SE = 0.06, p< 0.001). As empathic (B = 0.01, SE = 0.06, p= 0.85) and work-related social skills (B = 0.02, SE = 0.07, p= 0.77) did not have significant within-group associations with diversity climate, the only indirect within-group association between neurodivergence and diversity climate was observed through conversational social skills (B = − 0.12, SE = 0.06, p= 0.03, 95%CI [ − 0.26; − 0.03]), as the confidence interval did not include zero. The other indirect associations between neurodivergence and diversity climate were not significant, as the 95% confidence intervals included zero; thus, Hypothesis 4 was only supported for Adm. Sci. 2024,14, 285 8 of 15 conversational social skills. Similarly, the indirect association between boundary spanning and diversity climate was supported only for conversational social skills (B = 0.04, SE = 0.02, p= 0.006, 95%CI [0.01; 0.08]), lending only weak support for Hypothesis 5. Adm. Sci. 2024, 14, x FOR PEER REVIEW 9 of 17 Figure 2. The interaction effect between neurodivergence and boundary spanning on conversational social skills (within-group effect). The graph represents a significant within-group interaction effect; ND—neurodivergence (Low ND—neurotypical group members; High ND—neurodivergent group members); BS—boundary spanning (Low BS—group members that do not engage in boundary spanning; High BS—group members that engage in boundary spanning). Figure 3. The interaction effect between neurodivergence and boundary spanning on conversational social skills (between-group effect). The graph represents a significant between-group interaction effect; ND—neurodivergence (Low ND—neurotypical group members; High ND—neurodivergent group members); BS—boundary spanning (Low BS—group members that do not engage in boundary spanning; High BS—group members that engage in boundary spanning). -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 Low ND High ND Conversational social skills Low BS High BS -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 Low ND High ND Conversational social skills Low BS High BS Figure 2. The interaction effect between neurodivergence and boundary spanning on conversational social skills (within-group effect). The graph represents a significant within-group interaction effect; ND—neurodivergence (Low ND—neurotypical group members; High ND—neurodivergent group members); BS—boundary spanning (Low BS—group members that do not engage in boundary spanning; High BS—group members that engage in boundary spanning). Adm. Sci. 2024, 14, x FOR PEER REVIEW 9 of 17 Figure 2. The interaction effect between neurodivergence and boundary spanning on conversational social skills (within-group effect). The graph represents a significant within-group interaction effect; ND—neurodivergence (Low ND—neurotypical group members; High ND—neurodivergent group members); BS—boundary spanning (Low BS—group members that do not engage in boundary spanning; High BS—group members that engage in boundary spanning). Figure 3. The interaction effect between neurodivergence and boundary spanning on conversational social skills (between-group effect). The graph represents a significant between-group interaction effect; ND—neurodivergence (Low ND—neurotypical group members; High ND—neurodivergent group members); BS—boundary spanning (Low BS—group members that do not engage in boundary spanning; High BS—group members that engage in boundary spanning). -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 Low ND High ND Conversational social skills Low BS High BS -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 Low ND High ND Conversational social skills Low BS High BS Figure 3. The interaction effect between neurodivergence and boundary spanning on conversational social skills (between-group effect). The graph represents a significant between-group interaction effect; ND—neurodivergence (Low ND—neurotypical group members; High ND—neurodivergent group members); BS—boundary spanning (Low BS—group members that do not engage in boundary spanning; High BS—group members that engage in boundary spanning). Adm. 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