Building effective student project teams: what has problem-solving styles got to do with it?

Abstract

Purpose

The purpose of this study was to examine if team composition based on adaption-innovation (A-I) problem-solving styles is related to the teamwork quality and team effectiveness (TE) of student project teams participating in a [state-gifted program (SGP)].

Design/methodology/approach

A correlational design was conducted with a sample of 72 (SGP) participants, consisting of 15 project teams (n = 15), which formed three groups: (1) the homogeneous adaptive group, which consists of five homogeneous adaptive teams (n = 5); (2) the homogeneous innovative group, which consists of five homogeneous innovative teams (n = 5), and (3) the heterogeneous group (i.e. a mix of innovative and adaptive individuals), which consists of five heterogeneous teams (n = 5).

Findings

A one-way ANOVA and post-hoc test revealed that team composition based on problem-solving styles is related to teamwork quality and TE. Regarding TE, both homogeneous groups (i.e. all adaptive or all innovative individuals) were more effective than the heterogeneous group. However, regarding teamwork quality, only the adaptive group had significantly higher teamwork quality than the heterogeneous group.

Practical implications

We recommend that leadership educators utilize Kirton’s adaption-innovation inventory (KAI) as a tool for building effective student project teams. KAI can be used by leadership educators in two major ways: to assign students to groups (as done in the current study) or for team building, where team members share their KAI scores to better understand their problem-solving preferences.

Originality/value

The findings add to the literature by specifying the type of homogeneous groups (i.e. homogeneous adaptive), which may offer an advantage over heterogeneous groups regarding teamwork quality.

Citation

Alegbeleye, I.D. and Friedel, C.R. (2024), "Building effective student project teams: what has problem-solving styles got to do with it?", Journal of Leadership Education, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JOLE-02-2024-0043

Introduction

It has long been predicted that the organization of the future will have a team-based structure as opposed to a hierarchical organizational structure (Davis & Lawrence, 1978). Realizing that complex organizational problems require interdependence and are seldom solved by individuals, many organizations are now organized as functional work teams (Bolman & Deal, 2021; Hagemann & Kluge, 2017). Functional work teams are continuously tasked with complex problems and their ability (or inability thereof) to effectively solve problems has significant implications for organizational success.

Consequently, teamwork skills have been identified as one of the skills employers desire in college graduates (NACE, 2021). As a result, educators have increasingly prioritized assigning group projects as a way of developing teamwork skills in students (Halonen & Dunn, 2021). However, students often leave these project teams with negative experiences and an aversion to teamwork, to the frustration of educators and employers alike. Many leadership students continue to lack the requisite skills for problem-solving (Visone, 2018), and many employers believe that college graduates lack the requisite skills for both problem-solving and working effectively in teams (NACE, 2021). This suggests that assigning group projects is not enough, and other factors such as how we assign students to project teams deserve our attention.

Traditionally, problem-solving in teams has often been conceptualized as a set of actions or behaviors that team members must adopt to solve problems (e.g. Algozzine et al., 2016; Bransford & Stein, 1993). For example, the IDEAL model by Bransford and Stein (1993) highlights five behaviors necessary for problem-solving: identify the problem, define the problem, explore strategies, act on strategies and look back to evaluate the effectiveness of the solution. Similarly, Algozzine et al. (2016) developed a team problem-solving model for school-based teams, which consists of six behaviors: identify the problem with precision, identify the goal for change, discuss the solution and create an implementation plan, implement the solution with high fidelity, monitor the impact of the solution and compare against the goal and make summative evaluation decisions. While behavioral models like Algozzine et al. (2016) and Bransford and Stein (1993) may be useful for improving the problem-solving capability of teams, they are predicated on team behaviors that result from the collective experience of working together in a team, and they tell us little to nothing about how to select individuals that will be effective in solving problems in teams (Morgeson, Reider, & Campion, 2005; Stevens & Campion, 1994).

An alternative is the trait approach to problem-solving. Personality tests [e.g. Myers–Briggs Type Indicator (MBTI), Gallup’s Clifton StrengthsFinder and True Colors Personality Test] have often been used by leadership educators for team assignments and team-building purposes. One such measure is Kirton’s adaption-innovation inventory (KAI), which measures one’s problem-solving style (a dimension of one’s personality) and has important implications for problem-solving in teams. Kirton’s A-I theory, upon which the KAI is based, is a problem-solving theory that provides insight into how each of us has a measurable problem-solving style along the A-I continuum (Kirton, 2011). According to the A-I theory, everyone solves problems, albeit in different ways based on their problem-solving style (Kirton, 1984). While some prefer to solve problems innovatively, others prefer to solve problems adaptively. Simplifying the difference between more innovative and more adaptive individuals, the more innovative prefer to solve problems by making things differently, while the more adaptive prefer to solve problems by making things better (Stum, 2009).

Although A-I theory has been around for over 45 years, there is a dearth of empirical evidence regarding the relationship between A-I problem-solving styles and teamwork in student project teams (Brodeur, 2007). A-I theory offers an explanation to the science of teams, and more research is needed to better understand its implications for team function (Lamm et al., 2012). A few studies have examined the relationship between A-I problem-solving styles and other team outcomes such as team problem-solving process (Lamm et al., 2012), conflict (Buffinton, Jablokow, & Martin, 2002) and cooperative learning (Bush, Friedel, Hoerbert, & Broyles, 2017) in student project teams. However, a gap remains in the literature regarding the empirical relationship between team composition based on A-I problem-solving style and the teamwork quality of student project teams, which this study attempts to fill.

Theoretical framework

Kirton’s adaption-innovation theory and problem-solving

The A-I theory posits that everyone can solve problems; however, individuals differ in their problem-solving style (Kirton, 1984). According to the theory, problem-solving styles exist on a continuum that ranges from adaption to innovation (Kirton, 2011; Stum, 2009). On one end of the continuum are more adaptive individuals who prefer to solve problems by making incremental changes, while on the other end are more innovative individuals who prefer to solve problems by making revolutionary changes (Kirton, 2011). The more adaptive prefer to offer fewer ideas, which are paradigm consistent, focused on details and efficiency, and have greater regard for group consensus and group conformity. The more innovative prefer to have more ideas, with less awareness of ideas being in the paradigm or outside of the paradigm, prefer a more global perspective and have less regard for group conformity and group consensus (Jablokow & Booth, 2006).

An individual's problem-solving style is innate and stable over time (Kirton, 2011) and is neither dependent on knowledge, skills, intelligence or abilities nor dependent on motivations, cultural orientations, values, situations or ethnicity (Friedel, Clegorne, Kaufman, Seibel, & Anderson, 2016). There is no ideal problem-solving style in general; each style has advantages and disadvantages, depending on the nature of the problem. Although a key tenet of the A-I theory is the stability of an individual’s preferred problem-solving style over time, individuals can operate outside of their preferred style through coping behaviors, which are learned (Buffinton et al., 2002). The skills learned to operate more adaptively or more innovatively than one’s preference are powered by motivation and turned on like a light switch based on the problem needing to be solved (Kirton, 2011). The environment may also determine whether one engages in coping behaviors, as some environments are not conducive to one’s problem-solving preference (Kirton, 2011). For example, in a not-so-conducive environment, an adaptive individual may be inclined to take more risks than they would normally like, while an innovative individual may choose to operate within a more restrictive structure than they would normally prefer. It is evident that the duration of time and intensity of coping along the A-I continuum factors into the longevity of exhibiting coping behavior. Once one is no longer motivated to cope, as it is stressful to behave outside of one’s preference, coping behavior ceases (Kirton, 2011).

The KAI measures one’s problem-solving preference on a scale that ranges from 32 to 160 (Kirton, 2011). The mean score of the general population is 95 [standard deviation (SD) = 18], providing a normal distribution curve, with those scoring between 32 and 95 considered more adaptive and those scoring between 96 and 160 considered more innovative (Kirton, 2011). Individuals who score closer to 32 have a stronger preference for adaption, and individuals who score closer to 160 have a stronger preference for innovation. This presents a relativity factor associated with the A-I continuum, as a person who scores 122 on the KAI is more innovative with respect to the general population, and they are more adaptive than the individual who scores 145. Similarly, a person who scores 75 on the KAI is more adaptive with respect to the general population and is more innovative than an individual who scores 57.

According to the A-I theory, team composition based on problem-solving styles has implications for teamwork processes such as communication, coordination and cohesion (Buffinton et al., 2002; Jablokow & Booth, 2006; Kirton, 2011). The theory suggests that homogeneous teams (i.e. all adaptive or all innovative individuals) are more likely to communicate effectively and have fewer conflicts than heterogeneous (a mix of adaptive and innovative individuals) teams due to their shared cognitive structure (Buffinton et al., 2002; Kirton, 2011). However, heterogeneous teams are predicted to solve a wider range of complex problems, which may require more adaptive and innovative approaches than homogenous teams due to their greater cognitive diversity (Kirton, 2011). According to Kirton (2011), while one may start to notice a slight difference in the problem-solving styles of two individuals with a KAI difference greater than 10 points, a KAI difference of 20 points or more between two people is unmissable and has implications for how people work with others in teams. The KAI has an SD of 18 points, and as a result, many researchers have chosen a difference of 20 KAI points as the benchmark, such that teams with at least 20 KAI point difference among team members can be considered heterogeneous, while those with less can be considered homogeneous (Hammerschmidt, 1996; Jablokow, 2008).

Kirton’s adaption-innovation theory and teamwork quality

The KAI, a measure of an individual’s problem-solving style, has been utilized in team studies to measure a team’s cognitive climate by taking the average problem-solving style of team members (Kirton, 2011). Much can be learned about a team by examining the mean, SD and range of KAI scores among team members. Teams comprising members with similar KAI scores have similar problem-solving styles and are homogenous as a team, while those with differing KAI scores have dissimilar problem-solving styles and are heterogeneous. The difference in the problem-solving styles between individuals in a team is referred to as the cognitive gap, which has implications for teamwork quality (Jablokow & Booth, 2006; Kirton, 2003). According to Jablokow and Booth (2006), the narrower the cognitive gap of a team (i.e. the more homogeneous), the lesser the team’s coping behavior and stress in working with each other. Conversely, the wider the cognitive gap of a team (i.e. the more heterogeneous), the greater the team’s coping behavior and stress. At least three types of problem-solving style teams or groups can be formed based on the KAI cognitive gap: a homogeneous adaptive team, a homogeneous innovative team and a heterogeneous team.

Every team encounters two problems that must be solved while working together: Problem A, which is the problem related to the task at hand, and Problem B, which is the problem related to managing teamwork (Friedel et al., 2016). While Problem A should be the focus of the team in terms of productivity, an inability to solve Problem B well enough may hinder a team from tackling Problem A. There are many reasons for Problem B to interfere with the work of Problem A, and leaders should mitigate each Problem B to the best of their ability so that the focus of the team can be placed on Problem A. One major contributor to Problem B is a large gap in problem-solving styles between two individuals or an individual and a group. Motive plays a part, as the reward of solving Problem A can help unify the team and help resolve Problem B more readily; whereas if individuals are not feeling like they are receiving their fair share of the reward from solving Problem A, the focus begins to be placed on Problem B, and the team may become less effective in solving Problem A (Friedel et al., 2016). Motive also plays a part in fueling coping behavior to allow individuals to operate outside of their preferred problem-solving style in addressing both Problem A and Problem B (Kirton, 2011). However, the A-I theory recommends that teams devote enough energy and attention to solving both Problem A and Problem B in order to be successful.

Team composition has implications for the quality of interaction within a team. For example, evidence indicates that homogeneous teams tend to communicate more effectively than heterogeneous teams (Kirton, 2011). Bush et al. (2017) found that homogeneous student teams rated their communication significantly higher than heterogeneous teams. Moreover, task coordination may be cumbersome for heterogeneous teams, as the “more adaptive” team members may prefer more structure than their “more innovative” teammates (Kirton, 2011). Similarly, heterogeneous teams may encounter team conflict because team members have opposing ideas on the approach to tackling taskwork, which may lead to polarization. Essentially, some members of the team may want to solve the problem with adaption and some members may want to solve the problem with innovation. Buffinton et al. (2002) found that heterogeneous teams with big cognitive gaps experienced more conflicts than homogeneous teams with narrower gaps. While homogeneous teams might be easier to manage, they often suffer from a limited perspective (Kirton, 2011). Jablokow and Booth (2006) suggest that homogeneous teams tend to solve a narrower range of problems along the A-I continuum than heterogeneous teams. However, organizations today are faced with complex problems that require the diverse perspectives of diverse problem-solvers in heterogeneous teams. Unfortunately, to date, research focusing on differences between homogeneous and heterogeneous groups with respect to KAI is limited, with a focus on youth, secondary and college-level students.

The type of task moderates the relationship between problem-solving styles and team outcomes. For example, Hammerschmidt (1996) found that when homogenous teams (either adaptive or innovative) are assigned tasks that match their problem-solving preferences, they tend to outperform homogeneous teams that are assigned tasks that do not align with their problem-solving preferences. Kirton (2011) theorized that homogeneous adaptive teams are frustrated with ambiguous tasks, while homogeneous innovative teams thrive under ambiguity and relish the possibility of changing existing structures. Buffinton et al. (2002) found in their qualitative study that more innovative teams were less bothered with task ambiguity compared to more adaptive teams. Lamm et al. (2012) studied the problem-solving process of homogeneous and heterogeneous teams based on the KAI among student project teams and found that when given an ambiguous project, homogeneous adaptive teams struggled and could not complete their project, while homogeneous innovative and heterogeneous teams succeeded in completing the problem-solving process.

Researchers have explored the relationship between A-I problem-solving styles and personality (Goldsmith, 1984), decision-making (Buttner & Gryskiewicz, 1993), creativity (Kaufmann, 2004), problem-solving process (Lamm et al., 2012) and cooperative learning (Bush et al., 2017). However, empirical evidence regarding the influence of team composition based on A-I problem-solving styles on teamwork quality is scarce in the literature, as studies exploring such relationships are mostly theoretical and preliminary in nature.

Purpose and research question

The purpose of this study was to examine if team composition based on the A-I problem-solving styles is related to self-ratings of teamwork quality and team effectiveness (TE) of student project teams participating in a [state-gifted program (SGP)]. The study was guided by the following research question: (1) What are the problem-solving styles of high school students participating at the SGP? (2) Is there a significant difference in teamwork quality between problem-solving style groups (i.e. homogeneous adaptive vs homogeneous innovative vs heterogeneous)? (3) Is there a significant difference in TE between problem-solving style groups (i.e. homogeneous adaptive vs homogeneous innovative vs heterogeneous)?

Methods

The SGP participants were sampled for this study. The SGP is a four-week pre-college residential program with a mission to develop future leaders and scientists for careers in agriculture. Since 2001, this program has occurred every summer on the college campus of the state university, with participants comprising junior and senior students from various private, public and home schools from across the commonwealth of the state. All student participants were identified as gifted or talented to be able to participate in the SGP program. In order to have a broad knowledge of agriculture, students are required to attend college classes in five core areas, which include agricultural and biological systems engineering, agricultural economics, animal science, food science and plant science. Students are assigned to a project team of four to five members; this project must solve a major societal issue in one of five core areas: climate change, food safety, childhood obesity, sustainable energy and global food security and hunger. In the end, students are required to submit a final team paper, prepare a poster and deliver an oral presentation. An Institutional Review Board (IRB) approval was received for the current study.

The study utilized a quantitative method. A correlational design (Tanner, 2018) was used to measure the relationship between problem-solving styles, teamwork quality and TE. Data were collected from 15 student project teams (N [team] = 15; N [individual] = 72 team members) at the SGP. The instruments used in the study included the following.

KAI instrument by Kirton (2011)

The KAI includes 32 assessment items to measure problem-solving styles along with three sub-scales: originality, efficiency and rule and/or group conformity. The KAI uses a scale ranging from “very easy” to “very hard,” with results totaling a score ranging from 32 to 160. The KAI has a mean of 95 (SD = 18), with a normal distribution curve. How the scale indicates one as more adaptive or more innovative is described previously in the theoretical framework section of this paper. For this study, 72 students (N [individual] = 72), comprising 27 males and 45 females, completed the KAI during the first week of the SGP and were assigned to teams based on their KAI scores. Participants in this study were not aware of their problem-solving styles during the SGP program and did not know their KAI scores were used for team assignments.

Based on KAI scores, students were assigned to 15 project teams, which formed 3 groups: (1) the homogeneous adaptive group, which consists of five homogeneous adaptive teams (n = 23); (2) the homogeneous innovative group, which consists of five homogeneous innovative teams (n = 25), and (3) the heterogeneous group (i.e. a mix of innovative and adaptive individuals), which consists of five heterogeneous teams (n = 24).

In line with the A-I theory, we assigned students to homogeneous teams (adaptive or innovative) only if they had less than 20 KAI points difference between them and to heterogeneous teams only if they had more than 20 KAI points difference between them (Kirton, 2011). Consequently, each homogeneous team in our study had no more than 12 KAI points difference between their members, while each heterogeneous team had no less than 39 KAI points difference between their members. Each heterogeneous team consists of at least two adaptive and two innovative individuals. Sample items of the KAI are not included in this article due to intellectual property and copyright ownership of the KAI Foundation. Many studies have established the reliability of the KAI with reliabilities ranging from 0.74 to 0.86 for adolescents and students (Kirton, 2011).

TWQ by Hoegl and Gemuenden’s (2001)

Teamwork quality instrument (TWQ) was used to measure teamwork quality. This instrument measures teamwork quality along six sub-constructs: communication, coordination, balance of member contributions, mutual support, effort and cohesion (Hoegl & Gemuenden, 2001). This is a 38-item questionnaire that uses a 5-point scale ranging from 1 for “strongly disagree” to 5 for “strongly agree.” Sample items rated by participants were: “The team members communicated mostly directly and personally with each other,” “The team recognized the specific potentials (strengths and weaknesses) of individual team members” and “All members were fully integrated in our team.” The Cronbach’s alpha for the TWQ instrument used in the current study was 0.80.

The TE scale of the TWQ developed by Hoegl and Gemuenden (2001) was used to measure TE. The TE scale was originally intended for project teams in organizations. Since the population in this study comprised student project teams, there was a need to adapt some of the wording of items to reflect a student project team. Eight items were used from the TE scale, which used a five-point scale ranging from 1 for “strongly disagree” to 5 for “strongly agree.” A sample item rated by participants is: “The team was satisfied with the quality of the project result.” The Cronbach’s alpha of the TE scale used in the study was 0.90.

Control variables

Although leadership scholars have often controlled for the effect of individual-level demographic variables (such as age, race and gender) on outcome variables, those variables often lose their meaning at the team level of analysis (Boatwright & Forrest, 2000; Boies, Fiset, & Gill, 2015). As a result, we did not control for individual-level variables in this study. However, we controlled for team size (a team-level variable) during the design phase of the study, and team size (M = 4.80, SD = 0.41) was mostly constant across teams. Of the 15 teams in this study, 12 had a team size of 5, and three teams had a team size of 4.

Data analysis

To answer research question one, descriptive statistics (i.e. mean and standard deviation) of SGP students who participated in the study were reported. To answer research questions two and three, a one-way ANOVA was conducted; the significance level was set at 0.05. After statistically significant differences were found between groups, a “Dunnett T3” post-hoc test was conducted to compare the mean differences between groups (i.e. homogeneous adaptive vs homogeneous innovative vs heterogeneous). The Dunnett T3 test was utilized because Levene’s test of equal variance showed that the equal variance assumption was violated in the dependent variables (p < 0.001 for TWQ and TE).

Shapiro–Wilk’s test showed that TWQ scores for each group were not normally distributed (adaptive p = 0.01, heterogeneous p = 0.001 and innovative p < 0.001), as well as TE scores for all groups (p < 0.001). Although the assumption of normality was violated in the dependent variables, many studies have shown ANOVA to be robust to non-normality (Blanca Mena et al., 2017; Newman, Gatersleben, Wyles, & Ratcliffe, 2022). Nonetheless, Kruskal–Wallis test, a non-parametric test, was conducted to confirm the findings of the ANOVA and to reduce the chance of Type 1 error (Newman et al., 2022). The Kruskal–Wallis test results for TWQ (H (2) = 11.30, p = 0.004) and TE (H (2) = 10.18, p = 0.006) across groups were consistent with the ANOVA results in the study, confirming its appropriateness. As a result, we chose to report the ANOVA results in the findings section.

The data were analyzed with SPSS version 28. The findings are organized based on the three research questions.

Findings

• (1)

  What are the problem-solving styles of high school students participating at the SGP?

At the individual level, descriptive statistics showed that the KAI scores of SGP students enrolled ranged from 61 to 142 points (M = 93, SD = 18). At the group level, the descriptive statistics for the three problem-solving style groups can be found in Table 1.

A few observations can be made from the data in Table 1. The standard deviation (SD = 20.78) of the heterogeneous group was much higher than those of the homogeneous groups, which means that students’ KAI scores were spread further away from the mean in the heterogeneous group than in the homogeneous groups as expected. This is also supported by the boxplot diagram in Figure 1, with the heterogeneous group having longer box lengths and whiskers than the homogeneous groups.

As shown in Table 1 (and Figure 2), the distribution of KAI scores in the heterogeneous group suggests the presence of individuals with strong adaptive and strong innovative styles (with the most adaptive KAI score of 61 and the most innovative score of 142). Nonetheless, the interquartile range (IQR 78–104) showed that the middle 50% of students in the heterogeneous group were neither strongly adaptive nor strongly innovative. Ranking the KAI scores by percentiles revealed that 58% (a little more than half) of students in the heterogeneous group were adaptive (58th percentile KAI score of 94), and the rest (42%) were innovative.

• (2)

  Is there a significant difference in teamwork quality between problem-solving groups?

The result of a one-way ANOVA (see Table 2) revealed that there was a statistically significant difference in teamwork quality between at least two groups (F(2,69) = 6.44, p < 0.01, ω² = 0.07) with a moderate practical significance (Field, 2013). Differences in problem-solving styles explained 7% of the variance in teamwork quality. To further probe which groups were significantly different, the Dunnett T3 post-hoc test was conducted for multiple comparisons (see Table 3). The result showed that the mean teamwork quality of the adaptive group (M = 3.52, SD = 0.14) was significantly higher than that of the heterogeneous group (M = 3.34, SD = 0.16). However, no statistically significant difference was found between the mean teamwork quality of adaptive and innovative groups (p = 0.654), as there was only a marginal difference between their means (mean difference = 0.05). Similarly, no significant difference was found between the innovative and heterogeneous groups (p = 0.075).

In summary, findings suggest a significant difference in teamwork quality between problem-solving style groups, such that the teamwork quality of the adaptive group was significantly higher than that of the heterogeneous group.

• (3)

  Is there a significant difference in TE between problem-solving groups?

A one-way ANOVA (Table 4) showed that there was a statistically significant difference in TE between at least two groups (F(2,69) = 7.62, p < 0.01, ω² = 0.08) with a moderate practical significance (Field, 2013). Differences in problem-solving styles explained 8% of the variance in TE. The Dunnett T3 post-hoc test was conducted to see which groups were significantly different (see Table 5). The findings showed that the mean TE of the adaptive group (M = 4.01, SD = 0.29) was significantly higher than that of the heterogeneous group (M = 3.49, SD = 0.64). Similarly, the mean TE of the innovative group (M = 4.02, SD = 0.60) was significantly higher than that of the heterogeneous group. However, no statistically significant difference was found between the mean TE of adaptive and innovative groups (p = 1.000).

In summary, the findings suggest a significant difference in TE between problem-solving style groups, such that the TE of both the adaptive group and innovative group was significantly higher than that of the heterogeneous group.

Discussions

The problem-solving styles of the SGP students ranged from 61 to 142 points, which falls within the expected problem-solving style range of 45 to 145 points (Kirton, 2011). The mean KAI score for the SGP students was 93 points (SD = 18), which is similar to the mean score of the general population (M = 95 and SD = 18). It should be noted that there is one demographic variable that has consistently had a small difference in mean scores throughout the research in KAI, and that is the variable of one’s sex, male or female (Kirton, 2011). Across cultures, ethnic groups and nationalities, the female mean is 98 and the male mean is 91, as measured by the KAI. Because this group of students had 45 females and 27 males, the overall mean of the group may have been affected. All of this considered, although the participants were self-selected for the SGP and thus the study, their problem-solving style is analogous to the general population.

The findings showed that teamwork quality differs across problem-solving style groups (i.e. adaptive vs heterogeneous vs innovative), as rated by the respective team members. Specifically, the teamwork quality of the homogeneous adaptive group was found to be significantly higher than that of the heterogeneous group in the current study. This finding partially supports prior studies, which predict that homogeneous groups would have higher teamwork quality than heterogeneous groups due to their narrower cognitive gap, which allows them to more easily build consensus and have fewer conflicts (Buffinton et al., 2002; Bush et al., 2017; Kirton, 2011). However, no statistically significant difference was found between the teamwork quality of the homogeneous innovative group and the heterogeneous group. Therefore, the findings of this study add to the literature by specifying the type of homogeneous groups (i.e. homogeneous adaptive) that may offer an advantage over heterogeneous groups regarding teamwork quality. The significant finding may be attributed to the higher regard for group conformity and group consensus by the more adaptive group (Kirton, 2011), while the nonsignificant finding may be ascribed to the higher propensity of the innovative group to challenge structures, with less regard for group consensus and group conformity (Kirton, 2011). This may have negatively influenced their ability to effectively coordinate tasks, thereby leading to task conflicts and harming teamwork quality. Furthermore, conflict in heterogeneous teams may be more related to individual differences in preference for structure in communicating and coordinating efforts, as well as a lack of group cohesion.

The findings revealed that the TE of the homogeneous adaptive group was significantly higher than that of the heterogeneous group. Similarly, the TE of the homogeneous innovative group was significantly higher than that of the heterogeneous group. The findings suggest that homogeneous groups are more effective than heterogeneous groups, as rated by the respective team members. Note that there was no outcome measure or quality of deliverables measured by scoring rubrics in this study to indicate how effective teams were in completing their assignments. Still, this is an interesting finding because previous research seems to advocate for heterogeneous groups due to their adeptness in solving a wider range of complex problems, typically focused on Problem A, such as those encountered at the SGP (Buffinton et al., 2002; Kirton, 2011). One plausible explanation is that the inability of the heterogeneous group to properly manage Problem B (i.e. problems arising from interpersonal relationships associated with how best to work together as a team) may have hindered them from effectively focusing on the task work (i.e. Problem A). While Problem A is extremely important, as it is the reason for which the team is convened, problems relating to interpersonal relationships (i.e. Problem B) are equally important, and it is often a precursor to solving Problem A. In other words, a team’s ability to solve Problem A is mostly dependent on their ability to resolve Problem B.

Moreover, heterogeneous teams may need more time to move through the five stages of team development than homogenous teams due to the divergent problem-solving preferences of their members (Tuckman, 1965). For example, heterogeneous teams are more likely to spend more time storming and norming than homogeneous teams. Therefore, a four-week program like the SGP may not provide enough time for effective team performance in heterogeneous teams. Kirton (2011) argues that if the diversity in heterogeneous teams can be properly managed by leaders and/or supervisors, then they may be more effective than homogeneous teams in the long run.

Practical recommendation

The use of KAI in student project teams is relatively sparse in leadership education, especially when compared to other personality inventories (such as Gallup’s Clifton StrengthsFinder, MBTI and True Colors Personality Test). However, since findings suggest that team composition based on A-I problem-solving styles has implications for teamwork quality and TE, we recommend that leadership educators utilize KAI as a tool for building effective student project teams. KAI can be used by leadership educators in two major ways. First, it can be used to assign students to groups (as done in the current study). Second, it can be utilized for team building, where team members share their KAI scores to better understand their problem-solving preferences. Such practice may go a long way in improving student experiences in teams, while a failure to do so may set them up for negative team experiences, which may lead to reluctance to work with others in teams in the future.

Findings suggest that there are significant differences in teamwork effectiveness based on KAI problem-solving styles, with homogeneous groups exhibiting higher TE than heterogeneous groups. While the findings seem to support team homogeneity, the A-I theory advocates for heterogeneity and diversity in problem-solving styles when solving complex problems over a long period of time, due to the ability of heterogeneous teams to solve a wider range of complex problems (Jablokow & Booth, 2006; Kirton, 2011). To reconcile the conundrum, we believe the question should not be whether homogenous teams are more effective than heterogeneous teams, but rather under what conditions are homogenous teams more effective than heterogeneous teams and vice-versa. Perhaps, it is better to say that the diversity needed to solve a problem should depend on the nature of the problem. We recommend that factors such as the availability to supervise, project duration and project type (in terms of complexity and scope) should be considered when deciding team composition based on problem-solving styles.

Homogeneous teams may have the advantage of communicating and working well with each other, but have the disadvantage of a narrow perspective along the A-I continuum on how best to solve the problem. They may need to engage in coping behaviors or gain outsider opinions when solving complex problems. However, while the cognitive diversity inherent in heterogeneous teams is often encouraged, it can also create a burden in terms of team conflicts and teamwork challenges in general. These conflicts are more likely to occur at the early stages of team development. However, effective leadership provided by leadership educators, especially at the initial stages, can help the team overcome their teamwork challenges. Leadership educators can act in the role of a “bridger” between the more innovative and more adaptive individuals in heterogeneous teams. Or perhaps a bridger could be identified in heterogeneous teams and coached on how to serve as a bridger. According to Kirton (2011), serving as a bridger is a social role that involves managing the wide cognitive gaps in heterogeneous teams. This can lead to heterogeneous teams learning to self-manage themselves if they are aware of the problem-solving preferences of their teammates at the early stages of team development. That way, students can understand how their teammates think (either adaptively or innovatively) early on, which can help them anticipate and mitigate potential conflicts. Therefore, we recommend that leadership educators using the KAI encourage students to share KAI results with each other at the inception of the team and explain what the scores mean and their implications for teamwork quality and effectiveness. This disclosure process should not be required or forced, as one’s problem-solving style reveals an aspect of one’s personality and is therefore only shared at the student’s discretion.

Heterogeneous teams may need more time to move through the five stages of team development (Tuckman, 1965) than homogenous teams and may spend considerable time storming and norming due to their divergent problem-solving styles. As a result, if a project has a short lifecycle (as is the case in the SGP sample used in this study), homogenous teams may have an advantage. A project with a long lifespan would provide heterogeneous teams with ample time to resolve their differences and maximize their diversity if they could maintain focus on Problem A. Depending on the type of problem, homogeneous teams may be more effective with a clearly defined Problem A, aligning with the group members’ preferred problem-solving styles (Kirton, 2011).

Implications for future research and limitations

Although the A-I theory has been around for over 45 years, there is a dearth of empirical evidence regarding the influence of team composition (based on A-I problem-solving styles) on teamwork quality. Studies exploring such relationships are mostly theoretical and preliminary (Brodeur, 2007; Buffinton et al., 2002; Stum, 2009). While the current study brings more understanding to the complexities associated with the science of leading teams, more research is needed to better understand if homogeneous teams exhibit higher teamwork quality than heterogeneous teams, as we found no statistically significant difference between the teamwork quality of one of the two homogeneous groups (i.e. the homogeneous innovative group) and the heterogeneous group. It is therefore recommended that future research replicate the current study.

The study’s sample consists of high school students (juniors and seniors) participating at the SGP. We ask readers to exercise caution in generalizing the findings to professional work teams (and adults). Motivation in the SGP is incredibly high, with a highly structured curriculum to aid student success. In the SGP, there is a greater focus on Problem A and assistance with resolving Problem B that may not be present in the workforce or creative teams in the corporate world. Therefore, results may be different with professional work teams (or adults). Consequently, we recommend that future studies replicate the current study with professional work teams (or adults).

Moreover, the SGP participants were only together for one month. There is a need for longitudinal studies to study the TE of homogeneous and heterogeneous groups over a longer period. Since Kirton (2011) predicts that properly managed heterogeneous teams will be more effective than homogeneous teams when solving complex problems over a longer period of time, it is important for future studies to study both homogeneous and heterogeneous teams over a longer period of time to see if that hypothesis will be supported.

Conclusion

The findings in the current study suggest that team composition based on problem-solving styles is related to teamwork quality and TE among student project teams. Specifically, the findings revealed that the teamwork quality of the homogeneous adaptive group was significantly higher than that of the heterogeneous group. They also revealed that homogeneous groups (i.e. all adaptive or all innovative) are more effective than the heterogeneous group, at least in the short run. The A-I theory advocates that the cognitive diversity of the team should match the nature of the problem to be solved. While the findings of this study seem to support team homogeneity, cognitive diversity in problem-solving styles in heterogeneous teams has an advantage in solving a wider range of complex problems (Jablokow & Booth, 2006; Kirton, 2011). To reconcile the conundrum, we believe the question should not be whether homogenous teams are more effective than heterogeneous teams, but rather under what conditions are homogenous teams more effective than heterogeneous teams and vice-versa. We recommend that factors such as the availability to supervise, project duration and project type (in terms of complexity and scope) should be considered by leadership educators when deciding team composition based on A-I problem-solving styles. Beyond team composition, the awareness of A-I problem-solving style preferences of team members has implications for team building, as students can understand how their teammates think (either more adaptively or more innovatively) early on, which can help them anticipate and mitigate potential conflicts.

Figures

Figure 1

Boxplot of problem-solving style groups

Figure 2

Kai score distribution for the heterogeneous group

Table 1

Group-level descriptive statistics for KAI

Variables	Problem-solving style groups	n	Range	IQR	M	SD
KAI	Adaptive	23	62–94	72–83	78.35	8.39
	Heterogeneous	24	61–142	78–104	94.13	20.78
	Innovative	25	95–124	99–110	106.08	1.47

Source(s): Table by authors

Table 2

ANOVA summary table for teamwork quality

Teamwork quality	SS	df	MS	F	Sig	Omega squared
Between Groups	0.40	2	0.20	6.44	0.003	0.07

Source(s): Table by authors’

Table 3

Post-hoc test for teamwork quality – multiple comparisons using Dunnett T3

Problem-solving group	Problem-solving group	Mean difference	Std. Error	Sig
Adaptive	Heterogeneous	0.18*	0.04	<0.001
	Innovative	0.05	0.05	0.654
Heterogeneous	Adaptive	−0.18*	0.04	<0.001
	Innovative	−0.12	0.05	0.075
Innovative	Adaptive	−0.05	0.05	0.654
	Heterogeneous	0.12	0.05	0.075

Note(s): ^*The mean difference is significant at the 0.05 level

Source(s): Table by authors

Table 4

ANOVA summary table for team effectiveness

Team effectiveness	SS	df	MS	F	Sig	Omega squared
Between Groups	4.39	2	2.19	7.62	0.001	0.08

Source(s): Table by authors

Table 5

Post-hoc test for team effectiveness – multiple comparisons using Dunnett T3

Problem-solving group	Problem-solving group	Mean difference	Std. error	Sig.
Adaptive	Heterogeneous	0.52*	0.14	0.003
	Innovative	−0.01	0.13	1.000
Heterogeneous	Adaptive	−0.52*	0.14	0.003
	Innovative	−0.53*	0.18	0.014
Innovative	Adaptive	0.01	0.13	1.000
	Heterogeneous	0.53*	0.18	0.014

Note(s): ^*The mean difference is significant at the 0.05 level

Source(s): Table by authors