Measurements of Intercultural Teamwork Competence and Its Impact on Design Students’ Competitive Advantages

Abstract

Main issue: This article aims to measure intercultural teamwork competence and assess its impact on career competitive advantages for design students in order to determine how effective and competitive team members are in culturally diverse settings. Sampling: there were 51 participants (48 effective) in pretesting and 405 (338 effective) in formal testing. Participants were students from three colleges of design in Zhejiang Province of China. Statistical tool: this study used the on-line platform of wenjuanxing (wjx.cn) for data collection and SPSS software for data analysis. Methods: data were collected through on-line questionnaires, and then processed through factor analysis, t-test, and stepwise regression. Results: (1) TWC-CQ scale was formed to measure intercultural teamwork competence; (2) there were no statistically significant differences among participating design students (junior and senior) in intercultural teamwork competencies; (3) key competitive advantage = 0.347 × T-behavioral CQ + 0.232 × T-metacognitive CQ + 0.172 × T-motivational CQ + 0.124 × T-cognitive CQ. Conclusions: intercultural teamwork competence and its positive impact on design students’ competitive advantages could be measured. Implications: theoretical and practical implications were summed up for future studies.

1. Introduction

1.1. Research Background

Intercultural understanding and effective approaches to global cooperation and coordination are urgently required to meet the challenges of global crises, such as the COVID-19 pandemic. Design has played an important role in solving cross-cultural problems. The COVID-19 pandemic has given rise to many innovative design responses, including design for epidemic prevention and control [1], co-creative service design for online businesses in a post-COVID-19 context [2], eco-friendly design focused on sustainable solutions in the post-pandemic era [3], and universal design to create equitable access in a pandemic context [4].

To prepare design students to address global critical design problems, design education should provide appropriate training and guidance, transforming responsibility, empathy, and intercultural understanding into design practice capable of addressing global problems. Global competencies, such as intercultural teamwork practice, are effective approaches to achieve this aim. Global competencies are vital for societies to progress in this changing world [5].

To date, in the design industry, worldwide designers have tended to cooperate professionally in groups to accomplish sophisticated design tasks and improve design efficiency using teamwork competencies. Compared with teamwork competence (TWC), intercultural teamwork competence is a more advanced idea which aligns with the development of cross-cultural understanding and worldwide cooperation towards addressing global problems. Specifically in design education, intercultural TWC involves the competencies of sharing and discussion, bringing together different minds and cultures to build new mental models for design purposes, contributing to improved learning outcomes and providing students with a competitive advantage in the design world.

However, the measurement of intercultural TWC and its impact on design students’ competitive advantage is challenging. A sizeable body of research has demonstrated the challenges of integrating cultural diversity into effective measurements of multicultural experiences [6,7,8,9]. Specifically, research on intercultural TWC in the design discipline is sparse and unsystematic, leaving a gap in the understanding of why some designers are more effective and competitive than others in culturally diverse teamwork situations.

This study aims to investigate measurements of intercultural TWC and its impact on the competitive advantage for students in colleges of design. The samples included design students from three design colleges in the Zhejiang province of China. The statistical correlations between intercultural TWC and career competitive advantages were found.

This study was conducted in China, where a progressive shift from products “made in China” to those “designed in China” is evident. Design education plays an important role in this process. Design academia is responding actively in educational innovation through developing students’ intercultural TWC. The findings from this study will support efforts to effectively measure these educational innovations.

1.2. Research Purpose

This study is part of a broader investigation of up-to-date approaches to improve design students’ competitive advantage. It provides further investigation into the effects of TWC on students’ competitive advantage based on Tu et al.’s study [10]. Tu et al. (2020) assessed the impact of cultural intelligence (CQ) on the sustainable career competitive advantage for college design students and found that (1) education level has a significant effect on CQ; and (2) CQ positively contributes to career competitive advantage for students in colleges of design [10]. In comparison, this study considers the cultural impact of TWC as a critical factor in design students’ competitive advantage; specifically, it clarifies the associations between intercultural TWC and sustainable career competitive advantage for students in colleges of design. To achieve this research goal, this study concretized the key research objectives into four feasible goals:

(1)

Form a new scale to measure intercultural TWC;

(2)

Construct the structure of this new scale;

(3)

Assess the impact of education level on intercultural TWC;

(4)

Measure the impact of intercultural TWC on career competitive advantage.

1.3. Research Scope

This study established the research scope from the following three aspects: (1) contents of education level; (2) location of research; and (3) domain of cultural difference.

(1)

Contents of education level: in Tu et al.’s study, education level was found to impact CQ [10]. This study narrowed the range of education level from “bachelor’s, master’s, and PhD” to only “bachelor’s”. Further, this study divided bachelor students into two groups (junior and senior): “junior” includes those students studying in the first and second year, whereas “senior” comprises students studying in their third and fourth years.

(2)

Location of research: this study was carried out in Hangzhou city of Zhejiang Province in China, home to many world-renowned creative enterprises, such as Alibaba, in which entrepreneurship and teamwork are highly valued [11]. Influenced by geographic cultural promotion, universities in Zhejiang are active in higher education reform, particularly with respect to finding approaches to improve graduates’ adaptability to the competitive job market, in which TWC is valued to a significant degree [12,13].

(3)

Domain of cultural difference: cultural differences have tended to be defined based on broad generalized ideas regarding national characteristics. This study narrows the domain of culture in a student-oriented educational setting. This study believed that each student is unique because they think and behave in their own mode and style, shaped by a specific culture. Therefore, cultural difference in this study is based more on individual criteria. There is a mix of cultures among students attending colleges of design in the Zhejiang province in China, which provides the intercultural background and context for this study.

1.4. Structure

This paper is divided into four further sections. Section 2 reviews the theoretical literature on TWC assessment, career competitive advantage scales, and construct of CQ, and also proposes hypotheses. Section 3 presents the research process and methods in detail. Section 4 elaborates and illustrates the results in response to the four research goals. Section 5 discusses the results theoretically and practically. Finally, Section 6 provides conclusions and offers suggestions for future research.

2. Literature Review

The study reviews the literature for each of the following areas: (1) assessment of TWC; (2) the career competitive advantage scale; (3) construct of CQ; and (4) hypotheses based on literature.

2.1. Assessment of Teamwork Competence

Acquisition of TWC is a continuous and progressive process [14]. Studies focused on teamwork competence have developed continuously over the past decade, with the conceptual focus shifting from “teamwork” to “teamwork competence”, and finally to the “assessment of teamwork competence”. A decade ago, discussions about teamwork competence were relatively new, and few authors had tried to define it [15]. Nowadays, in academic settings, teachers’ perceptions are not the only source of assessment, but also consider other agents involved in teamwork, such as the students themselves through peer ratings and self-evaluations [14]. Teamwork competence in higher education settings was highly valued based on the knowledge from team science, and the characteristics of effective teams of students were gradually generated [16], and it is positively related to 21st-century competence (information and communication technologies) [17].

In addition, in contemporary business settings, high levels of competitiveness and ongoing innovation requires a wider variety of skills, high levels of specialist knowledge, rapid responses, and adaptability [15]; therefore, professional work has accordingly been transformed from one focused on individual tasks to one involving interactions with other employees to achieve the required tasks.

Academic enthusiasm to assess teamwork competence has been on the rise recently. However, even as recently as 2015, researchers’ attention to assess teamwork competence was still limited [15]. Frameworks and tools to measure teamwork competence are limited (see

Table 1. Assessment of TWC.

Researchers	Time	Assessment Tools/Methods	Assessment Contents
Lerís et al. [18]	2014	CTMTC methodology	Three aspects: (1) teamwork phases (mission and goals, responsibility maps, planning, implementation, and organization of documentation); (2) collaborative creation of knowledge; and (3) cloud computing technologies (wikis, forums, social networks, and cloud storage systems).
Nadal et al. [15]	2015	Rubric RUTE	Four dimensions: (1) identity (goals, sense of belonging, roles, adaptability, teamwork climate, commitment); (2) communication (information, personal interaction); (3) performance (planning, decision making, carrying out the tasks, monitoring performance); and (4) regulation (collaborative problem solving, negotiation, making improvements).
Viles et al. [14]	2015	Operational processes	Seven processes: (1) participation; (2) conflict management; (3) problem solving; (4) internal communication, mutual respect, and trust; (5) external communication and feedback; (6) collaboration and cooperation; and (7) leadership.
Navarro et al. [16]	2017	Theoretical model	Five components: (1) task interdependence; (2) task uncertainty; (3) team development; (4) team climate for learning; and (5) team effectiveness.

). They are: (1) the CTMTC method [18] which is a proactive method that allows measuring individual and group teamwork acquisition in multidisciplinary contexts [19]; (2) a tool to measure teamworking processes that affect team performance and its effectiveness [14]; (3) the rubric RUTE tool using scales which help to determine the level of teamwork competence that each participant has acquired [15]; and (4) a theoretical model which explains why some teams are more effective than others [16].

Further tools and methods have built on these, such as the evaluation of the CTMTC methodology in computer science [20], the measurement of TWC development in a multidisciplinary project-based learning environment [19], and the cooperative learning (CL) methodology identifying important dimensions of business students’ teamwork competencies [21].

Although these are all promising methods for measuring TWC, none focus on the relationship between TWC and career competitive advantage, nor do they focus on intercultural dimensions, both of which are critical elements of the study presented here.

2.2. Career Competitive Advantage Scale

The scale this study used is the same as the one employed in Tu et al.’s work [10]. Tu et al. (2020) developed a 10-item scale to measure students’ competitive advantage based on Chiu’s (2014) investigation into students’ core competencies in a competitive design market in Taiwan, which includes a 12-item professional ability scale and an 11-item core competence scale [22].

This study used the same 10 items for the following two reasons: (1) “career competitive advantage” is the dependent variable in this study; therefore, this study deliberately controlled its items unchanged while modifying the independent variable (intercultural TWC) to determine the effect of the independent variable; and (2) the 10-item competitive advantage scale in Tu et al.’s (2020) work was found to be of appropriate reliability (Cronbach’s α = 0.941) and validity (passed expert review and tests) [10].

The 10-item career competitive advantage scale is the following: “I can think creatively during design processes (CA1); I can consider consumers’ needs when designing (CA2); I can undertake cross-disciplinary design (CA3); I can use design resources effectively (CA4); I can positively face design challenges (CA5); I can keep learning to improve design skills (CA6); I can master design trends (CA7); I can take part in cross-cultural design projects (CA8); I respect the cultural differences of team members (CA9); I take an appropriate role in design teamwork (CA10)” [10] (p. 9).

2.3. Constructs of CQ

Debates about cultural intelligence (CQ) have been ongoing for relatively longer than those on teamwork competence (TWC), generating a greater quantity of research. One important theoretical study dated back to 2003, when Earley and Ang conceptualized the construct of CQ based on Sternberg’s multiple loci of intelligence [23]. In 2004, Earley and Mosakowski clarified the idea of cultural intelligence as the ability to make sense of unfamiliar contexts and to then be able to blend into these contexts [24]. It verified its three components of CQ: cognitive, physical, and emotional/motivational. Three years later, in 2007, Ang et al. set up a landmark study in the CQ theory by enhancing the theoretical precision of CQ through developing and testing a model that posited different relationships between four distinct CQ dimensions and three intercultural effectiveness outcomes in culturally diverse settings [25]. The four CQ dimensions were metacognitive, cognitive, motivational, and behavioral. Additionally, the three intercultural effectiveness outcomes were cultural judgment and decision making, cultural adaptation, and task performance. Ang et al. (2007) also designed a multidimensional cultural intelligence scale (CQS) to measure cultural intelligence through these four dimensions, and demonstrated the validity and reliability of the CQS across samples, time, and countries (Singapore and the USA) [25]. Later, researchers applied CQS within their own cultural contexts and disciplines, finding more links among CQ dimensions and many other factors.

Regarding the effect of education level on CQ, some previous studies are listed in Table 2. For example, Hong [26] found that elementary school students had a lower level of awareness of global issues (which was a predictor of cultural intelligence) compared to middle and high school students; Tu et al. (2020) found that design students with a master’s degree had a higher motivational CQ than students with a bachelor’s degree [10]; and Chang (2019) found that family members participating in a social integration program with a higher education level had a higher CQ [27]. More recently, researchers have found the promising effects of proper training on improving CQ dimensions, such as on the development of culturally specific knowledge and cultural intervention skills [28], as well as on CQ as a whole [29].

Table 2. Links between CQ and education level.

Links	Researchers
As a predictor of higher cultural intelligence, elementary school students had a lower level of awareness of global issues compared to middle and high school students.	Hong, 2021 [26]
Education level had a significant effect on two dimensions of CQ (cognitive and motivational CQ) for design students.	Tu et al., 2020 [10]
For multicultural family members participating in a social integration program, the higher the educational level, the higher the cultural intelligence.	Chang, 2019 [27]
According to elementary school students’ educational level, all cultural intelligence’s sub-factors had statistically significant differences.	Chang, 2017 [30]
The cultural intelligence of bilingual teachers had differences according to the education level, the experience of multicultural training and education career.	Chang & Park, 2015 [31]

Table 2. Links between CQ and education level.

Links	Researchers
As a predictor of higher cultural intelligence, elementary school students had a lower level of awareness of global issues compared to middle and high school students.	Hong, 2021 [26]
Education level had a significant effect on two dimensions of CQ (cognitive and motivational CQ) for design students.	Tu et al., 2020 [10]
For multicultural family members participating in a social integration program, the higher the educational level, the higher the cultural intelligence.	Chang, 2019 [27]
According to elementary school students’ educational level, all cultural intelligence’s sub-factors had statistically significant differences.	Chang, 2017 [30]
The cultural intelligence of bilingual teachers had differences according to the education level, the experience of multicultural training and education career.	Chang & Park, 2015 [31]

As for the impact of CQ on competitive advantage, there is consistent evidence of a positive link among various studies both in education and in business (see Table 3).

Table 3. Impact of CQ on competitive advantage.

Category	Links	Researchers
Competitive advantage in education	CQ could increase student’s innovative behavior in higher education.	Kistyanto et al., 2021 [32]
	CQ and empowerment were important factors impacting task-related performance for students engaged in an international experiential game-based learning project.	Curran et al., 2021 [33]
	CQ training could help prepare pharmacy learners to be socially responsible health care practitioners.	Minshew et al., 2021 [34]
	Sales students with CQ were able to adjust their selling behaviors and to perform at a higher level in their role-play presentations.	Delpechitre & Baker, 2017 [35]
	CQ had some influence on school leaders’ ability to adapt their leadership style within a diverse working environment.	Aldhaheri, 2017 [36]
	Metacognitive CQ and cognitive CQ predicted cultural judgment and decision making, motivational CQ, and behavioral CQ predicted cultural adaptation, metacognitive CQ and behavioral CQ predicted task performance.	Ang et al., 2007 [25]
Competitive advantage in business	CQ positively contributed to job performance.	Presbitero, 2021 [37]; Wu & Ng, 2021 [38]; Wang & Jin, 2019 [39]; Nam & Park, 2019 [40]; Jyoti & Kour, 2017 [41]
	CQ could significantly affect employee’s innovative work behavior.	Afsar et al., 2021 [42]
	As the level of all four CQ dimensions of top managers increased, the relationship between entrepreneurial orientation and international performance increased in strength.	Sahin & Gurbuz, 2020 [43]
	Teams with high CQ tended to exhibit a greater degree of team knowledge-sharing and receive higher evaluations of their innovative performance.	Ratasuk & Charoensukmongkol, 2020 [44]
	Motivational and behavioral facets of CQ had the largest effect on job performance in expatriation.	Burakova & Filbien, 2020 [45]
	Behavioral CQ enhanced the effects of proactive resource acquisition tactics on task performance and contextual performance.	Zhao et al., 2020 [46]
	A new training program in developing CQ could improve innovative work behavior and resilience.	Azevedo & Shane, 2019 [47]
	Meta-cognitive CQ improved social performance, while social performance was significantly associated with innovation performance improvements.	Awan et al., 2018 [48]
	Organizational CQ was a competitive capability for strategic alliances in the international construction industry.	Yitmen, 2013 [49]

Table 3. Impact of CQ on competitive advantage.

Category	Links	Researchers
Competitive advantage in education	CQ could increase student’s innovative behavior in higher education.	Kistyanto et al., 2021 [32]
	CQ and empowerment were important factors impacting task-related performance for students engaged in an international experiential game-based learning project.	Curran et al., 2021 [33]
	CQ training could help prepare pharmacy learners to be socially responsible health care practitioners.	Minshew et al., 2021 [34]
	Sales students with CQ were able to adjust their selling behaviors and to perform at a higher level in their role-play presentations.	Delpechitre & Baker, 2017 [35]
	CQ had some influence on school leaders’ ability to adapt their leadership style within a diverse working environment.	Aldhaheri, 2017 [36]
	Metacognitive CQ and cognitive CQ predicted cultural judgment and decision making, motivational CQ, and behavioral CQ predicted cultural adaptation, metacognitive CQ and behavioral CQ predicted task performance.	Ang et al., 2007 [25]
Competitive advantage in business	CQ positively contributed to job performance.	Presbitero, 2021 [37]; Wu & Ng, 2021 [38]; Wang & Jin, 2019 [39]; Nam & Park, 2019 [40]; Jyoti & Kour, 2017 [41]
	CQ could significantly affect employee’s innovative work behavior.	Afsar et al., 2021 [42]
	As the level of all four CQ dimensions of top managers increased, the relationship between entrepreneurial orientation and international performance increased in strength.	Sahin & Gurbuz, 2020 [43]
	Teams with high CQ tended to exhibit a greater degree of team knowledge-sharing and receive higher evaluations of their innovative performance.	Ratasuk & Charoensukmongkol, 2020 [44]
	Motivational and behavioral facets of CQ had the largest effect on job performance in expatriation.	Burakova & Filbien, 2020 [45]
	Behavioral CQ enhanced the effects of proactive resource acquisition tactics on task performance and contextual performance.	Zhao et al., 2020 [46]
	A new training program in developing CQ could improve innovative work behavior and resilience.	Azevedo & Shane, 2019 [47]
	Meta-cognitive CQ improved social performance, while social performance was significantly associated with innovation performance improvements.	Awan et al., 2018 [48]
	Organizational CQ was a competitive capability for strategic alliances in the international construction industry.	Yitmen, 2013 [49]

2.4. Hypotheses Based on Literature

The contents of intercultural TWC in this study originated from CQ. Based on the literature review on CQ above, which found that (1) most of the research to date has found that education level is a predictor of CQ and (2) the positive impact of CQ on competitive advantage, this study had two hypotheses.

Hypothesis 1.

Education levels (junior and senior) have a significant impact on intercultural TWC.

Hypothesis 2.

Intercultural TWC has a positive impact on career competitive advantage for students in college of design.

3. Research Method and Process

To fulfill the research purposes, the research methods and processes were conducted in the following four steps: (1) build a questionnaire and test reliability; (2) apply factor analysis to identify structures of factors; (3) apply t-test to assess differences among variables; and (4) apply regression to predict trends.

3.1. Build Questionnaire and Test Reliability

3.1.1. Questionnaire

The questionnaire was composed of three parts: (1) basic data; (2) intercultural TWC; and (3) career competitive advantage.

(1)

Basic data—comprising two items: education level (four levels were identified) and teamwork experience. Students were required to answer whether they had any teamwork experience. Students without teamwork experience were excluded from the study because this study wanted data based on the students’ own experience.

(2)

Intercultural TWC—comprising 20 items modified from the original CQS. Modifications were designed specifically to assess design students’ CQ. This study altered the wording of the original CQS to make it appropriate for the study purpose. This was followed by a test to check the reliability of the modifications. To distinguish the modified scale, this study named the new scale the TWC-CQ scale (TWC and CQ scale). These 20 items were measured on a 7-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree).

(3)

Career competitive advantages—comprising 10 items from Tu et al.’s (2020) study [10] without any modifications. The only difference lies in the research location. Tu et al. (2020) conducted the research in Taiwan, whereas this study performed the research in Zhejiang. These 10 items were measured on a 7-point Likert scale, ranging from 1 (extremely disagree) to 7 (extremely agree).

3.1.2. Sample

(1)

Sampling sources: the sample was made up of college students majoring in design at three colleges in Zhejiang Province in China (Zhejiang Sci-Tech University, Zhejiang Gongshang University, and Zhejiang University of Science & Technology). Participants in this study must have teamwork experience through school study or internships.

(2)

Population: in the pretest, 51 online questionnaires were collected, and only 48 were effective. In the formal test, 405 online questionnaires were collected, and only 338 were effective. Education levels were divided into junior (1st and 2nd years) and senior (3rd and 4th years).

3.1.3. Test

This study used SPSS Statistics Version 22 to process data. Cronbach’s α was used to determine the reliability of the TWC-CQ scale, the competitive advantage scale, and the entire questionnaire. The factor being analyzed is regarded to be of high reliability if the value is higher than 0.7 [50]. The pretest conducted before the formal test contributes to the validity of the questionnaire.

3.2. Apply Factor Analysis to Identify Structures of Factors

3.2.1. TWC-CQ Scale

This study developed a new scale (the TWC-CQ scale) to measure intercultural TWC based on the structure and scale of CQS, as established by Ang et al. in 2007 [25]. CQS has four independent dimensions: metacognitive CQ, cognitive CQ, motivational CQ, and behavioral CQ. These four dimensions of CQS have specific functions and values in CQ’s structure and together form a 20-item CQ scale (CQS) to measure people’s capability to function and manage effectively in culturally diverse settings [10].

The Kaiser–Meyer–Olkin (KMO) sampling adequacy test and Bartlett’s test were first conducted to test the applicability for a factor analysis of the new TWC-CQ scale. The KMO value should be between 0 and 1 [51], and this study expected the largest KMO value within this range for an applicable result. Principle component analysis (PCA) and “rotation by Varimax” were applied. The sum of an eigenvalue greater than 1 was the screening condition.

3.2.2. Competitive Advantage Scale

Although this scale has been examined in Tu et al.’s (2020) study [10], this study still performed the KMO sampling adequacy test and Bartlett’s test to check the applicability for a factor analysis. The extraction of common factors, the rotation method, and the screening condition were the same as the standard factor analysis of the TWC-CQ scale.

3.3. Apply t-Test to Assess Differences among Variables

Because there are two groups of variables, an independent sample t-test (referred to as the t-test in this study) was applied to investigate the scoring differences between junior and senior students on intercultural TWC. The statistical significance was set to 0.05 in this study.

3.4. Apply Regression to Predict Trends

This study used a stepwise regression analysis to test the impact of intercultural TWC on career competitive advantage. Multiple indicators were progressively tested to improve the reliability and validity of the results. (1) Sample size: the sample number should exceed 40 times the number of predictor variables, according to Tabachnick and Fidell [52]. (2) Multicollinearity: this issue should be avoided. The values of both “tolerance” and “variance inflation factor (VIF)” were used to test whether there was a multicollinearity problem. To avoid this issue, the value of “tolerance” should be between 0 and 1 [53], and the VIF should below 30 [54]. (3) Singularity: this should also be avoided; hence, separate scales should be used rather than the entire scale [55]. (4) Normal distribution, linearity, and homoscedasticity: a scatterplot made of regression standardized residuals and predicted values were checked to test these three indicators. All values should scatter evenly around the 0-value residual line to fit the requirements of normal distribution, linearity, and homoscedasticity [55]. (5) Outliers: this study used Mahalanobis distance and Chi-square tests to avoid outliers. Because outliers may have negative effects on the final regression equation, samples with outliers will be detected (judging by a p-value less than 0.001) and then deleted from the data [55].

Finally, based on the regression results, the most convincing model will be chosen, and equations extracted from this chosen model will statistically illustrate the impact of intercultural TWC on career competitive advantage.

4. Research Results

The research findings are summarized by conducting the methods outlined above from these six aspects: (1) statistics of samples; (2) contents of the TWC-CQ scale; (3) results of reliability test; (4) factor analysis of the TWC-CQ scale and career competitive advantage scale; (5) t-test of the education level on intercultural TWC; and (6) regression of intercultural TWC on career competitive advantage.

4.1. Statistics of Samples

This study summarizes the frequency distribution by students’ year of study (see

Table 4. Basic data of respondents.

Grade	Number (N)	Percent (%)	Category	Number (N)	Percent (%)
1st	132	39.1	Junior	209	61.8
2nd	77	22.8
3rd	80	23.7	Senior	129	38.2
4th	49	14.5

), and the results of the numerical analysis were as follows:

Among the 338 participants, most were freshmen (132 in number), accounting for nearly 40% of the sample. Students in their second and third years were almost even in number, accounting for 22.8% and 23.7% of the sample, respectively. Students in the fourth year showed the least interest in participating in this study, accounting for only 14% of the final sample (49 in number).

Further, according to the pre-set sample division of junior and senior categories, there were 209 samples from junior and 129 from senior levels. The junior sample was almost double the size of the senior sample.

4.2. TWC-CQ Scale

This study adapted the wording of the original items of the CQS for a teamwork-targeted setting. To make the necessary distinctions, this study named the new factors in the TWC-CQ scale: “T-metacognitive CQ”, “T-cognitive CQ”, “T-motivational CQ”, and “T-behavioral CQ”. Specifically, T-metacognitive CQ covers the four items modified from metacognitive CQ, T-cognitive CQ covers the six items modified from cognitive CQ, T-motivational CQ covers the five items modified from motivational CQ, and T-behavioral CQ covers the five items modified from behavioral CQ. The items in the TWC-CQ scale are listed in

Table 5. Items in CQS and the TWC-CQ scale.

CQS *			TWC-CQ Scale
Factor		Item	Factor		Item
Metacognitive CQ	MC1	I am conscious of the cultural knowledge I use when interacting with people with different cultural backgrounds.	T-metacognitive CQ	T-MC1	I am conscious of the cultural knowledge I use when interacting with team members with different cultural backgrounds.
	MC2	I adjust my cultural knowledge as I interact with people from a culture that is unfamiliar to me.		T-MC2	I adjust my cultural knowledge as I interact with team members from a culture that is unfamiliar to me.
	MC3	I am conscious of the cultural knowledge I apply to cross-cultural interactions.		T-MC3	I am conscious of the cultural knowledge I apply to cross-cultural teamwork.
	MC4	I check the accuracy of my cultural knowledge as I interact with people from different cultures.		T-MC4	I check the accuracy of my cultural knowledge as I interact with team members from different cultures.
Cognitive CQ	COG1	I know the legal and economic systems of other cultures.	T-cognitive CQ	T-COG1	I know the legal and economic systems of team members’ cultures.
	COG2	I know the rules (e.g., vocabulary, grammar) of other languages.		T-COG2	I know the rules (e.g., vocabulary, grammar) of team members’ languages.
	COG3	I know the cultural values and religious beliefs of other cultures.		T-COG3	I know the cultural values and religious beliefs of team members’ cultures.
	COG4	I know the marriage systems of other cultures.		T-COG4	I know the marriage systems of team members’ cultures.
	COG5	I know the arts and crafts of other cultures.		T-COG5	I know the arts and crafts of team members’ cultures.
	COG6	I know the rules for expressing nonverbal behaviors in other cultures.		T-COG6	I know the rules for expressing nonverbal behaviors in team members’ cultures.
Motivational CQ	MOT1	I enjoy interacting with people from different cultures.	T-motivational CQ	T-MOT1	I enjoy interacting with team members from different cultures.
	MOT2	I am confident that I can socialize with locals in a culture that is unfamiliar to me.		T-MOT2	I am confident that I can socialize with team members in a culture that is unfamiliar to me.
	MOT3	I am sure I can deal with the stresses of adjusting to a culture that is new to me.		T-MOT3	I am sure I can deal with the stresses of adjusting to a team’s culture that is new to me.
	MOT4	I enjoy living in cultures that are unfamiliar to me.		T-MOT4	I enjoy the teamwork in cultures that are unfamiliar to me.
	MOT5	I am confident that I can get accustomed to the shopping conditions in a different culture.		T-MOT5	I am confident that I can get accustomed to the teamwork conditions in a different culture.
Behavioral CQ	BEH1	I change my verbal behavior (e.g., accent, tone) when a cross-cultural interaction requires it.	T-behavioral CQ	T-BEH1	In teamwork, I change my verbal behavior (e.g., accent, tone) when a cross-cultural interaction requires it.
	BEH2	I use pause and silence differently to suit different cross-cultural situations.		T-BEH2	In teamwork, I use pause and silence differently to suit different cross-cultural situations.
	BEH3	I vary the rate of my speaking when a cross-cultural situation requires it.		T-BEH3	In teamwork, I vary the rate of my speaking when a cross-cultural situation requires it.
	BEH4	I change my nonverbal behavior when a cross-cultural situation requires it.		T-BEH4	In teamwork, I change my nonverbal behavior when a cross-cultural situation requires it.
	BEH5	I alter my facial expressions when a cross-cultural interaction requires it.		T-BEH5	In teamwork, I alter my facial expressions when a cross-cultural interaction requires it.

* Derived from CQS by Ang et al. [25] (p. 366); this study listed the two scales here for comparison and distinguishment.

, with bold marks highlighting the modification for clear presentation, as well as the original items for comparison.

4.3. Reliability Test

4.3.1. Pretest

Based on Guilford’s standard [50], the value of Cronbach’s α in the pretest showed high reliability for the TWC-CQ scale (Cronbach’s α = 0.948), the career competitive advantage scale (Cronbach’s α = 0.940), as well as the entire questionnaire (Cronbach’s α = 0.964), all of which provide a satisfactory basis for the formal test.

4.3.2. Formal Test

The value of Cronbach’s α in the formal test also showed high reliability for the TWC-CQ scale (Cronbach’s α = 0.950), the career competitive advantage scale (Cronbach’s α = 0.938), as well as the entire questionnaire (Cronbach’s α = 0.964), all of which provide a satisfactory basis for this study.

4.4. Factor Analysis

4.4.1. Factor Analysis of TWC-CQ Scale

The KMO value of the TWC-CQ scale was 0.941, and the significance by Bartlett’s test was 0.000, indicating high applicability for conducting a factor analysis. Four factor components were extracted from the TWC-CQ scale, and the total variance was 74.0% (see

Table 6. Total variance explained.

Component	Extraction Sums of Squared Loadings
	Total	% of Variance	Cumulative %
1	10.464	52.318	52.318
2	1.761	8.804	61.122
3	1.453	7.263	68.385
4	1.113	5.567	73.951

Extraction method: principal component analysis.

).

This study further rotated the components in the matrix and visualized the results by the coloring feature of a heat map (see

Table 7. Rotated component matrix.

Items of TWC-CQ Scale	Component
	1	2	3	4
T-COG3	0.846	0.198	0.174	0.140
T-COG4	0.816	0.257	0.085	0.180
T-COG6	0.797	0.226	0.229	0.248
T-COG5	0.763	0.276	0.228	0.185
T-COG2	0.713	0.203	0.307	0.195
T-COG1	0.706	0.113	0.372	0.247
T-BEH3	0.278	0.810	0.269	0.225
T-BEH4	0.328	0.792	0.218	0.200
T-BEH5	0.267	0.756	0.252	0.218
T-BEH2	0.149	0.721	0.204	0.230
T-BEH1	0.164	0.66	0.163	0.407
T-MC1	0.167	0.200	0.796	0.134
T-MC2	0.249	0.220	0.791	0.171
T-MC3	0.267	0.205	0.771	0.233
T-MC4	0.290	0.305	0.742	0.107
T-MOT4	0.066	0.279	0.003	0.809
T-MOT2	0.369	0.166	0.265	0.719
T-MOT3	0.364	0.201	0.278	0.717
T-MOT5	0.222	0.378	0.211	0.687
T-MOT1	0.360	0.372	0.282	0.511

Extraction method: principal component analysis; rotation method: Varimax with Kaiser normalization.

). The four extracted components were the same as the original four dimensions of the CQS.

4.4.2. Factor Analysis of Career Competitive Advantage Scale

The KMO value of the career competitive advantage scale was 0.936, and the significance by Bartlett’s test was 0.000, indicating high applicability for conducting a factor analysis. Only one-factor components were extracted, and the total variance was 64.6% (see

Table 8. Total variance explained.

Component	Extraction Sums of Squared Loadings
	Total	% of Variance	Cumulative %
1	6.456	64.560	64.560

Extraction method: principal component analysis.

).

The component matrix of the career competitive advantage model is listed in

Table 9. Component matrix.

	Component
	1
CA5	0.855
CA3	0.849
CA2	0.844
CA6	0.830
CA7	0.829
CA4	0.818
CA8	0.817
CA1	0.780
CA10	0.711
CA9	0.682

Extraction method: principal component analysis.

. This study named the only factor as the “key competitive advantage”.

4.4.3. Descriptive Statistics of Key Factors

There were five key factors in this study: (1) T-metacognitive CQ; (2) T-cognitive CQ; (3) T-motivational CQ; (4) T-behavioral CQ; and (5) key competitive advantage. Descriptive statistics of these five key factors are listed in

Table 10. Descriptive statistics of key factors.

	N	Minimum	Maximum	Mean	Std. Deviation	Skewness		Kurtosis
	Statistic	Statistic	Statistic	Statistic	Statistic	Statistic	Std. Error	Statistic	Std. Error
T-metacognitive CQ	338	1	7	5.453	0.938	−0.206	0.133	0.381	0.265
T-cognitive CQ	338	1	7	4.800	1.091	−0.142	0.133	0.395	0.265
T-motivational CQ	338	1	7	4.697	1.090	−0.250	0.133	0.701	0.265
T-behavioral CQ	338	1	7	4.944	1.046	−0.348	0.133	1.023	0.265
key competitive advantage	338	1	7	5.220	0.877	−0.247	0.133	1.110	0.265
Valid N (listwise)	338

.

4.5. T-Test of Education Level on the TWC-CQ Scale

This study applied a t-test to determine the impact of education level on intercultural TWC. However, the scores sorted by education level (junior and senior) did not reach significance levels, with a p-value of more than 0.05 (see

Table 11. Independent samples test.

	Levene’s Test for Equality of Variances			t-Test for Equality of Means
		F	Sig.	t	df	Sig. (2-Tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
									Lower	Upper
T-Metacognitive CQ	Equal variances assumed	0.19	0.664	1.193	336	0.234	0.125	0.105	−0.081	0.332
	Equal variances not assumed			1.184	264.337	0.238	0.125	0.106	−0.083	0.334
T-Cognitive CQ	Equal variances assumed	0.802	0.371	0.001	336	0.999	0.000	0.122	−0.241	0.241
	Equal variances not assumed			0.001	250.248	0.999	0.000	0.125	−0.247	0.247
T-Motivational CQ	Equal variances assumed	0.928	0.336	0.505	336	0.614	0.062	0.122	−0.179	0.302
	Equal variances not assumed			0.491	247.385	0.624	0.062	0.126	−0.186	0.309
T-Behavioral CQ	Equal variances assumed	1.105	0.294	1.331	336	0.184	0.156	0.117	−0.074	0.386
	Equal variances not assumed			1.29	244.379	0.198	0.156	0.121	−0.082	0.393

). Therefore, neither junior or senior students had statistically significant differences on intercultural TWC (T-metacognitive, T-cognitive, T-motivational, or T-behavioral CQs). Hence, Hypothesis 1 was not supported.

4.6. Regression of Intercultural Teamwork Competence on Key Competitive Advantage

4.6.1. First Round

This study conducted a stepwise regression to examine the predictors of intercultural TWC on key competitive advantage. Several indicators of stepwise regression were checked successively:

(1)

Sample size: there were 338 samples in this study, and this number meets the standard set by Tabachnick and Fidell [52].

(2)

Multicollinearity: this study used both tolerance and variance inflation factor (VIF) values to check multicollinearity. All predicted variables (T-metacognitive, T-cognitive, T-motivational, and T-behavioral CQs) had tolerance values above the cutoff line (range = 0.437–0.541). In addition, the data met the criterion for VIF with values less than 10 (range = 1.847–2.290). Hence, there was no multicollinearity.

(3)

Singularity: to avoid singularity, this study used separate scales (T-metacognitive, T-cognitive, T-motivational, and T-behavioral CQs) as predicted variables instead of the entire TWC-CQ scale.

(4)

Normal distribution, linearity, and homoscedasticity: a scatterplot of regression standardized residuals showed that all values were scattered evenly around the 0-value residual line, therefore supporting the requirements of normal distribution, linearity, and homoscedasticity.

(5)

Outliers: This study found nine outlier samples (p < 0.001) after a Mahalanobis distance test. Therefore, this study deleted the invalid samples and ran the stepwise regression test for the second round.

4.6.2. Second Round

The sample size was 329 for the second round of stepwise regression. This study tested the aforementioned indicators again for a more reliable result. Specifically, the sample size still met the requirement to run a stepwise regression (see

Table 12. Descriptive statistics.

Factors	M	SD	N
T-metacognitive CQ	5.459	0.908	329
T-cognitive CQ	4.822	1.056	329
T-motivational CQ	4.732	1.035	329
T-behavioral CQ	4.959	1.000	329
Key competitive advantage	5.229	0.836	329

).

There were still no multicollinearity problems in the equation (tolerance values ranged from 0.393 to 0.529, whereas VIF values were 1.890–2.545) (see

Table 13. Coefficients *.

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.	Correlations			Collinearity Statistics
		B	Std. Error	Beta			Zero-Order	Partial	Part	Tolerance	VIF
1	(Constant)	2.360	0.169		13.990	0.000
	T-behavioral CQ	0.579	0.033	0.692	17.347	0.000	0.692	0.692	0.692	1.000	1.000
2	(Constant)	1.576	0.198		7.960	0.000
	T-behavioral CQ	0.419	0.040	0.502	10.614	0.000	0.692	0.507	0.398	0.630	1.588
	T-metacognitive CQ	0.288	0.044	0.313	6.614	0.000	0.618	0.344	0.248	0.630	1.588
3	(Constant)	1.516	0.195		7.791	0.000
	T-behavioral CQ	0.309	0.048	0.369	6.380	0.000	0.692	0.334	0.235	0.403	2.479
	T-metacognitive CQ	0.250	0.044	0.272	5.711	0.000	0.618	0.302	0.21	0.598	1.674
	T-motivational CQ	0.172	0.045	0.214	3.818	0.000	0.641	0.207	0.14	0.432	2.314
4	(Constant)	1.492	0.194		7.707	0.000
	T-behavioral CQ	0.290	0.049	0.347	5.964	0.000	0.692	0.314	0.218	0.393	2.545
	T-metacognitive CQ	0.213	0.046	0.232	4.615	0.000	0.618	0.248	0.169	0.529	1.890
	T-motivational CQ	0.139	0.047	0.172	2.956	0.003	0.641	0.162	0.108	0.393	2.545
	T-cognitive CQ	0.098	0.042	0.124	2.353	0.019	0.594	0.130	0.086	0.477	2.095

* Dependent variable: key competitive advantage.

).

Separate scales (T-metacognitive, T-cognitive, T-motivational, and T-behavioral CQs) were used to avoid singularity; the deleted sample still met the requirement of normal distribution, linearity, and homoscedasticity (see Figure 1).

The final regression result consisted of four models (see

Table 14. Summary of four models.

Model		R	R2	Adj. R2	F	F Change	B	β	Tolerance	VIF	Condition Index
1	T-behavioral CQ	0.692	0.479	0.478	300.904	300.904	0.579	0.692 ***	1.000	1.000	10.029
2	T-behavioral CQ	0.735	0.541	0.538	191.991	43.744	0.419	0.502 ***	0.630	1.588	12.279
	T-metacognitive CQ						0.288	0.313 ***	0.630	1.588	16.231
3	T-behavioral CQ	0.749	0.561	0.556	138.182	14.576	0.309	0.369 ***	0.403	2.479	12.347
	T-metacognitive CQ						0.250	0.272 ***	0.598	1.674	18.093
	T-motivational CQ						0.172	0.214 ***	0.432	2.314	19.401
4	T-behavioral CQ	0.754	0.568	0.563	106.468	5.537	0.290	0.347 ***	0.393	2.545	13.511
	T-metacognitive CQ						0.213	0.232 ***	0.529	1.890	16.503
	T-motivational CQ						0.139	0.172 **	0.393	2.545	20.538
	T-cognitive CQ						0.098	0.124 *	0.477	2.095	22.193

* p < 0.05; ** p < 0.01; *** p < 0.001.

). Model 1 consisted of only one factor, Model 2 consisted of two factors, Model 3 consisted of three factors, and Model 4 consisted of four factors. The four models were gradually accumulated and tightly connected. Specifically, Model 1 included the T-behavioral CQ factor as a predictor and explained 47.9% of the variance in the key competitive advantage; when T-metacognitive CQ was added, Model 2 explained 54.1% of the variance; with the addition of T-motivational CQ factor, Model 3 explained 56.1% of the variance; T-cognitive CQ was the last factor to be involved, and Model 4 completely explained 56.8% of the variance.

Compared with the other three models, Model 4 explained most of the variance in the key competitive advantage (56.8%); at the same time, the coefficients of the four predicted variables (T-behavioral, T-metacognitive, T-motivational, and T-cognitive CQs) in Model 4 were statistically significant (p < 0.05). Therefore, Model 4 was statistically the most appropriate model to represent the impact of the intercultural TWC on key competitive advantage. Specifically, Model 4 consisted of four factors: T-behavioral CQ (β = 0.347, p < 0.001), T-metacognitive CQ (β = 0.232, p < 0.001), T-motivational CQ (β = 0.172, p < 0.01), and T-cognitive CQ (β = 0.124, p < 0.05).

This study further clarified the associations between intercultural TWC and key competitive advantage through equations based on the data of Model 4: key competitive advantage = 0.347 × T-behavioral CQ + 0.232 × T-metacognitive CQ + 0.172 × T-motivational CQ + 0.124 × T-cognitive CQ. Hence, Hypothesis 2 was supported.

From this equation, it can be illustrated that T-behavioral CQ contributes the most to key competitive advantage, followed by T-metacognitive CQ and T-motivational CQ, whereas T-cognitive CQ was the least influential. This study extracted the key data of the regression results and presents them in Table 14.

5. Discussion

This section looks at the following dimensions: (1) limitations of this study; (2) future research direction; (3) theoretical contributions; and (4) practical contributions.

5.1. Limitations of the Study

(1)

Scale Limitation

The TWC-CQ scale is created largely based on CQS. This choice was a relatively better option to measure intercultural TWC to fit the purpose of this study, and it was statistically proven that the TWC-CQ scale was of high reliability (Cronbach’s α = 0.950). However, limitations exist in terms of the range and content of intercultural TWC. Just as with CQS, the TWC-CQ scale also consisted of 20 items, which were modified items to fit an intercultural teamwork context (such as altering the original word “people” in CQS to “team members” and changing the phrase “other cultures” to “team members’ cultures”). To present the correlations, this study named the extracted four factors as T-cognitive CQ, T-behavioral CQ, T-metacognitive CQ, and T-motivational CQ. However, the range and the contents were set within the boundaries of the original metacognitive, cognitive, motivation, and behavioral domain, with the four factors of TWC-CQ explaining only 74% of the total variance.

(2)

Sample Limitation

This study did not find any impact of the education level on intercultural TWC, which was different from most studies in the literature review. Limitations with the sample may be one possible reason for this: first, the sample distribution is uneven (209:129) owing to the questionnaire’s random distribution, which is a factor beyond the study’s control; second, the sample quality was checked through reversed questions, but the study still cannot guarantee that each participant was responsible for providing their own answers. As for the study’s generalizability, design students from three colleges in the Zhejiang province participated in the investigation; therefore, the collected data may represent the feedback of targeted college students only.

(3)

Variable Limitation

For a comparison with Tu et al.’s (2020) study [10], this study chose education level as the only variable to distinguish among design students. Limitations followed accordingly in two ways. (1) Participants’ lack of intercultural teamwork experience. Although participants in this study must have teamwork experience, some juniors in their first year only had one or two experiences, thus they did not have a stable and mature perception toward teamwork experience. (2) The lack of education duration. Differences in education duration within the sample were within 1 and 3 years. This may be insufficient in duration to have a significant impact on cultural intelligence because the accumulation of cultural intelligence requires time and experience.

5.2. Future Research Direction

(1)

Modify Scales

Future studies could modify the scales by extending the bounded range and contents. Since TWC-CQ explained 74% of the total variance, the remaining 26% provide promising spaces to explore. One possible direction is to amplify the 74%. According to the research results, four factors of TWC-CQ contribute differently to competitive advantage. If T-behavioral CQ had the greatest impact, then the items in T-behavioral CQ could remain. However, if T-cognitive CQ had the smallest impact, then the items in T-cognitive CQ could be improved. Another possible way is to fill the gap of 26%. Open-ended interviews among experienced designers could be carried out to provide up-to date insights from real intercultural teamwork encounters, and this could largely supplement the range and content of the TWC-CQ scale.

(2)

Selected Participants

The distribution of junior and senior participants was uneven in the study. Thus, in future research, the study could be undertaken among a more even distribution of participants. As for the generalizability of the results, more design colleges in Zhejiang could get involved into the research. Further, researchers could consider inserting the survey into school courses, making a formal introduction to the questionnaire before testing, and even bonding the survey with benefits to students, such as credits.

(3)

Add Variables

Educational guidance could be considered as a related new factor to include in the future. After the questionnaire, follow-up surveys were conducted, finding that educational guidance of intercultural TWC was not given enough attention in all the three colleges. However, for competencies’ development, it is necessary to provide supervised learning experiences to students throughout their degrees [14]. This partly explains why junior and senior students did not display significant differences in intercultural TWC. Students may neglect cultural differences and corresponding reactions in their teamwork practice if appropriate guidance is missing. Therefore, educational guidance could be a valuable factor to moderate the impact between education level and intercultural TWC. In addition, based on previous literature, because training could improve CQ and task performance [28], it is worth testing the impact of educational guidance on the improvement of intercultural TWC as well as competitive advantages.

5.3. Theoretical Contributions

5.3.1. Contribution to the TWC Assessment Theory

In terms of extending the theory, this study has pushed forward the assessment theory of TWC from “assessment of teamwork competence” to “assessment of intercultural teamwork competence”, which means that cultural considerations are involved. Specifically, this study contributed to the TWC theory by creating a TWC-CQ scale to measure intercultural TWC.

First, the idea of a TWC-CQ scale was consistent with previous studies. For example, the TWC-CQ scale provided a platform for peer ratings and self-evaluations, which is consistent with Viles et al.’s (2015) [14] opinion to get more agents involved. The TWC-CQ scale also provided “characteristics” for “effective teams” in culturally diverse settings according to Navarro et al.’s (2017) [16] opinion. Finally, the information and communication contents in the TWC-CQ scale echoed with Almerich et al.’s (2020) [17] call to accumulate 21st-century competencies. In addition, the TWC-CQ scale covered abilities, such as high levels of specialist knowledge, rapid responses, and adaptability, which Nadal et al. (2015) [15] emphasized.

Second, the dimensions of the TWC-CQ scale were supplements based on previous studies in terms of the contents. The biggest differences between the TWC-CQ scale and other methods lie in the object of the TWC-CQ scale, i.e., the “people” in a teamwork, while the objects in many other assessment methods seemed to focus on the teamwork itself. Specifically, the four dimensions of the TWC-CQ scale focus on what people should do in an intercultural teamwork setting to improve communication and task efficiency. However, in many other methods, the dimensions were not people-focused, such as three aspects of CTMTC methodology [18], seven processes of operational processes [14], and task-oriented components of the theoretical model [16].

5.3.2. Contribution to the Competitive Advantage Theory

This study contributed to the competitive advantage theory by making connections with TWC in a statistical manner. TWC has long been theoretically regarded as a key factor and a source of competitive advantage by many researchers in multiple disciplines [56,57,58,59]. However, although the value of intercultural TWC to competitive advantage has almost been regarded in academia as common sense, few theories have been formed to precisely calculate the connections. This study identified the key factors in intercultural TWC and their priority ranking in terms of impacting career competitive advantage. Further, this study formed an equation by using SPSS software to statistically present these connections, providing an approach to predict students’ career competitive advantage in numbers and data. In this sense, this study contributes to the competitive advantage theory.

5.3.3. Contribution to the CQ Theory

This study contributed to the CQ theory by extending the application of CQS. Although the aim of the TWC-CQ scale was to measure intercultural TWC, the spirit of this scale was highly connected with CQS. The reliability test and factor analysis of this new scale (TWC-CQ scale) showed noteworthy results: the value of reliability was very high (Cronbach’s α = 0.950), and the extracted new factors were the same as the CQS in number (four factors extracted) and structure (each new factor contained the same items adapted from CQS). On the one hand, this result agreed well with that of Ang and colleagues, noting that “cross-validity analyses provide strong support for the validity and reliability of the CQS across samples, time and countries” [25] (pp. 359, 362). On the other hand, this study provided an example of applying CQS into a domain which highly valued cultural intelligence.

5.4. Practical Contributions

The results of this research are expected to be inspiring to young designers. Different from the friendly and encouraging atmosphere in school education, career development in the job market is highly competitive and relatively cruel. At schools, students are being educated, whereas in the market, designers are being selected. When young designers become involved in a design project, they typically work in groups with teammates with substantially more complicated cultural backgrounds than teammates in colleges; hence, applying intercultural TWC effectively is a test for them. To begin with, based on the research results, T-behavioral CQ is what designers need to improve in the first place because it counts the most in the final equation. Items in T-behavioral CQ include communication skills, such as varying the rate of speaking and changing nonverbal behavior through intercultural interactions, which represent respect and understanding when communicating with teammates. However, designers need to bear in mind that efficiency is crucial in work; every minute counts, which means they do not have a lot of time to learn and practice, as is the case in colleges. Rather, they are required to implement intercultural TWC into design projects rapidly and effectively. These requirements are challenging for young designers, but this competitive advantage would contribute to a sustainable career in design. On the other hand, this competence could be one of the criteria for companies to select designers for intercultural projects because people with such competence could improve design efficiency and bring corresponding profits for companies.

6. Conclusions and Suggestions

6.1. Conclusions

This study is part of a series of studies that intend to help students in colleges of design to increase their career competitive advantages under culturally diverse settings. Tu et al.’s (2020) study initially applied and assessed the CQ model in design education and investigated the impact of CQ factors on career competitive advantage [10]. This study further discovered the key cultural factors, contributing to career competitive advantage by focusing on the intercultural teamworking context.

To achieve this research purpose, this study concretized the key objectives into four feasible goals and carried out specific methods accordingly. The four goals were all accomplished: (1) a new scale (TWC-CQ) to measure intercultural teamwork competence was formed; (2) a four-factor structure of the TWC-CQ scale was constructed; (3) the impact of education level on intercultural teamwork competence was assessed; and (4) the impact of intercultural teamwork competence on career competitive advantage was measured.

Based on the results, intercultural teamwork competence has a positive impact on career competitive advantage for students in college of design; specifically, key competitive advantage = 0.347 × T-behavioral CQ + 0.232 × T-metacognitive CQ + 0.172 × T-motivational CQ + 0.124 × T-cognitive CQ. However, there were no statistically significant differences among participating design students (junior and senior) in intercultural teamwork competencies.

Although there is a research foundation for this study, limitations still exist, and some of the results did not meet research expectations. Therefore, this study elaborated upon the research processes while presenting research details. Discussions and reflections were made to thoroughly analyze the possible reasons. It is sincerely hoped that this study could be inspiring to other researchers who are devoted to design education or even other disciplines, and together find approaches to help students in college to improve their competitive advantage.

6.2. Suggestions

Regarding the results, this study suggested that future researchers be careful when applying the findings or further pushing the investigation. Boundaries have been made clear in the paper in that this study was conducted in a specific cultural setting (teamwork context), targeting one specific group (college students majoring in design), and taking a limited sample from three design colleges in the Zhejiang province. Thus, the findings may not be generalizable to different situations, such as the educational impact on competitive advantage in other colleges and locations. Therefore, corresponding studies in other contexts could be conducted to verify the findings and make comparisons. In terms of applying the final equations to test career competitive advantage in the job market, this study suggests duplicating the test among experienced designers who may have a mature perception and understanding of intercultural competitive advantage that is different from young school students.