How to Assess Generic Competencies: From Sustainable Development Needs among Engineering Graduates in Industry

Abstract

Achieving many of the UN’s 17 Sustainable Development Goals requires the active contribution of skilled engineers. Globally, however, there appears to be a mismatch between the sustainable competencies that engineering graduates possess and those required by industry. Closing this gap requires a reliable and valid means of establishing which competencies are of greatest importance to engineering practitioners. In this research, we developed a model of generic engineering competency and designed a scale comprising 55 skills in total. This instrument was then used to survey two samples of engineering graduates working in the Chinese industry, with 746 in the first round of surveys, and 1183 in the second. Using exploratory factor analysis (EFA), seven subscales were extracted from the data: (1) leadership, (2) engineering design, (3) professionalism, (4) problem solving, (5) lifelong learning, (6) technical theory, and (7) communication. Confirmatory factor analysis (CFA) demonstrated that the total number of generic engineering competencies was represented by a second-order, single-factor model that adequately fitted the data. Further, the Cronbach’s alpha values and composite reliability of the scale indicate its reliability. Overall, the evidence shows that the instrument offers a valid and reliable means of researching and assessing engineering education practices.

1. Introduction

Many of the UN’s 17 Sustainable Development Goals can only be achieved through the work of skilled engineers and technology experts. However, the advent of new technologies, automation, and employment mobility will require the engineering profession to demonstrate sustainability competencies [1]. Currently, the increasing pace of technological change, the growth in automation, and the frequency with which engineers change jobs within or between organizations are driving the need for generic engineering competencies [2]. These can be defined as the knowledge, skills, talents, attitudes, and other characteristics that transcend the specialized area of a specific engineering discipline and allow for rational decision-making and effective action in complex and uncertain situations [3]. Similar concepts include key competencies [4], holistic competencies [5], employability skills [6], etc. Generic competency is not only related to the overall future growth of individuals but also contributes to socio-economic development and national competitiveness. Therefore, building generic competencies has become an important goal of higher education internationally [7].

However, evidence from across the globe suggests that many graduates lack such skills. For instance, Australian universities are under increasing pressure to ensure their courses meet the demands of employers for skilled graduates [8]. Although China produces more engineering graduates than any other country, there is a clear mismatch between these graduates’ generic competencies and what is required in their professional roles [9]. To this end, the country’s “Emerging Engineering” construction plan was launched in 2017 [10]. This plan requires engineering educators to analyze the knowledge, abilities, skills, and professional qualities that engineers require in an era of rapid scientific, technological, industrial, and social change. Several researchers have attempted to categorize generic skills and assess their relative value to industry via meta-analytic and empirical studies [11,12,13]. However, there are currently few valid and reliable instruments for assessing generic competencies in the engineering workplace.

2. Literature Review

2.1. Classification Framework of Generic Engineering Competencies

China has established a registered engineer system in the fields of structure, civil engineering, electrical, chemical industry, and others and implemented qualification management for technical personnel engaged in engineering. However, the existing regulations on the qualification of engineers emphasize professional and technical capabilities and rarely involve cross-disciplinary general capabilities. As the importance of generic competencies increased, the generic competencies required by practical engineers gained attention, and several related classification studies emerged. The cognitive and practical skills, knowledge, value orientations, attitudes, emotions, and other social and behavioral components that comprise generic competencies are mobilized for effective action in interrelated patterns. Consequently, an oblique (direct oblimin) rather than an orthogonal rotation should be used for factor analysis. Some researchers have used factor analysis to divide general engineering abilities into 11 categories, including applying technical theory, professionalization, problem solving, and communication [13]; others have identified eight categories, including academic and problem-solving skills, interpersonal skills, and professional effectiveness [14]. Despite their differences, these researchers’ classifications overlap. This section provides a detailed review of the generic engineering competencies literature (including the above studies) to identify eight generic competencies of relevance to sustainability in the profession (see

Table 1. Dimensions, definitions, examples, and sources of eight generic engineering competencies.

Dimensions	Definitions	Examples	Sources
Leadership	The ability to fulfill multiple leadership roles in coordination and planning, motivating and supervising team members, and building team cohesion	Presenting vision, decision-making, team building, motivating and influencing others	[10,13,15,16,17]
Design	The ability to express a system, a component, or a process in advance under realistic constraints	Designing a system, component, or process, taking into account external constraints (e.g., environment and sustainability, ethics), using tools	[3,13,18,19]
Professionalism	The ability to understand professionalism in terms of professional conduct and responsibility in engineering practices	Dedication to the organization, using initiative, loyalty	[10,13,20]
Lifelong Learning	The ability to learn independently and continuously adapt to the development	Acquiring new skills (information), self-directed learning, adaptability, time management	[10,12,13,14,21]
Technical Theory	The ability to apply mathematics, science, and engineering knowledge and skills	Understanding and applying knowledge of mathematics, information science, and natural sciences	[10,22,23]
Communication	The ability to communicate effectively in speech and writing	Written communication, oral communication, listening, negotiation, speech	[10,13,24,25]
Problem Solving	The ability to understand the nature of problems and solve these to achieve the goal	Creativity, innovation, lateral thinking, critical thinking, logical thinking, synthesizing information	[21,23,26]
Teamwork	The ability to work with people of different cultures, races, genders, and disciplines	Interdisciplinary skills, diversity skills, teamwork	[10,13,27]

).

Distributed leadership approaches identify four key capabilities: sensemaking, relating, visioning, and inventing [15]. In an engineering context, sensemaking refers to the project leader’s attempt to understand and/or anticipate the direction of the project and the business environment in which it operates; relating denotes the leader’s understanding of his or her team members and their viewpoints, as well as his or her ability to build functional relationships; visioning describes the development and articulation of compelling and achievable future visions; inventing refers to devising methods and pathways to realize these visions [15,16]. Engineering design is divided into content and context. Content is embedded in the environment, which includes the objects (such as systems, components, or processes), methods, and techniques involved in performing the actual design, as well as addressing the “how” question. Context denotes the environment where engineering design occurs and is realized. It involves consumer and social needs, as well as the multiple constraints of global, economic, historical, cultural, and natural environments [18]. Professionalism is largely related to the elements of professional attitude and personal psychology, such as organizational commitment, initiative, and self-confidence [13,20]. Lifelong learning requires engineers to possess information literacy while learning independently and to continuously adapt to ongoing social and technological changes. Some research has found that information literacy and communication skills belong to the same construct [12,14], with one study identifying the former as the most important component of lifelong learning, which also includes autonomous learning and adaptability [21]. Thus, lifelong learning and communication may belong to two distinct constructs. At the generic level, knowledge of technical engineering theory covers mathematics, physics, chemistry, and engineering science, as established by the ASEE’s Report of the Committee on Evaluation of Engineering Education 70 years ago [22]. The quality of engineers stems from their ability to combine and apply this knowledge. The considerable import of cognitive skills such as critical thinking, analysis, synthesis, innovation, and creativity in problem solving is now widely recognized [26]. Some studies have identified various general skills (such as self-management skills and interpersonal skills) as teamwork skills [27]. However, we differentiated between these to reduce the likelihood of our target group of engineering graduates making different assumptions about teamwork.

2.2. The Assessment of Generic Engineering Competencies

The relative importance of generic competencies in engineering varies according to the practical context and the sub-discipline. That is, the importance accorded to specific competencies varies by disciplinary area and work environment: graduates do not evaluate competencies based on time-related or demographic variables but according to their specialization (major) or workplace. For example, industrial and manufacturing engineers rate particular competencies differently, as do aerospace engineering specialists and managers [28]. Nonetheless, all engineering graduates share a common view of the importance of overarching competencies such as teamwork, communication, data analysis, and problem solving [3].

Self-report is a well-established method of gathering data about competencies and learning outcomes. However, the current self-report instruments for investigating the perceptions of generic competencies in engineering are less than optimal [14]. First, many have been developed ad hoc and not tested for psychometric performance [29]. While grounded in the existing literature, earlier instruments designed to measure the relative importance of engineering competencies to graduate professionals in the field have not been validated on a larger scale [13] and, therefore, lack an adequate evidence base.

This study was motivated by the need to better understand the structure of generic engineering competencies and assist in the principled development of engineering course curricula and assessments. To develop the necessary framework, we devised a competencies questionnaire, distributed it to a sample of engineering graduates working in Chinese science and technology enterprises, and then intensively tested its validity and reliability. Our research addressed the following four questions: (1) How important is each generic engineering competency relative to the others? (2) How are the generic engineering competencies structured? (3) How well do the data fit the hypothesized factor structure? (4) What is the evidence for the reliability and validity of the total scale and of each subscale used to measure the required competencies?

3. Methodology

3.1. Questionnaire Development

We devised the Generic Engineering Competencies Questionnaire (GECQ) based on the eight general skill areas detailed in the previous section (see Table 1). After thoroughly reviewing previous surveys of engineering skills [11], we extracted the key competencies and established a pool of 58 survey items. As suggested by Morgeson et al. [30], verbs were used when formulating the items to reduce the probability of respondents overestimating the importance of each ability. The questionnaire was scored using a five-point Likert scale on which engineering professionals were asked to rate the importance of various generic skills to the engineer’s job, from 1 (not at all necessary) to 5 (completely necessary).

To optimize the content validity, we discussed the skills with four Ph.D. engineering lecturers from majors in materials, environment, computer science and technology, and naval architecture, and three researchers in higher engineering education. We asked them to evaluate the logical relationship between each generic skill and its relevance and applicability to the work of an engineer. We also conducted cognitive interviews with four senior engineering students majoring in mechanics, electrical, chemistry, and computer science and technology. At the same time, experts in psychometrics were invited to assess the face validity of the questionnaire items. Written in Chinese, the GECQ contains 58 items and consists of two parts. The first part gathers demographic data on the respondents, including their gender, industry, position, years of work, and the highest level of education. The second part is the main body of the questionnaire and aims to investigate how engineering practitioners rate the importance of 58 generic competencies.

3.2. Participants

The survey was conducted in two stages with a sample of engineers from 20 Chinese high-tech companies, such as Huawei, Baidu, Alibaba, and Tencent. The first stage aimed to explore the structure of generic engineering competencies. The electronic questionnaire sent to 1000 respondents received 746 valid responses, an effective recovery rate of 74.6%. To verify the model obtained from the first stage, a further 2000 electronic questionnaires were sent out, comprising the formal survey. As with the first stage, information about the formal survey’s purposes and uses was supplied to the respondents, whose participation was entirely voluntary. A total of 1183 valid questionnaires were recovered, a rate of 59.15%. Overall, 948 (80.14%) respondents at this stage were male engineers and 235 (19.86%) were female; the distribution of sample industries, position, education, and professional experience is shown in

Table 2. Descriptive statistics for the formal survey.

Variable	Level	Number	Percentage
Gender	Male	948	80.14
	Female	235	19.86
Industry	Manufacturing	749	63.31
	Construction	255	21.56
	Information technology and other industries	179	15.13
Position	Engineering Technology	927	78.36
	Technical management	145	12.26
	Operations and Administration	111	9.38
Degree	Bachelor’s degree	690	58.33
	Master’s degree	466	39.39
	Ph.D.	27	2.28
Working experience	<1 year	590	49.87
	2–5 years	237	20.03
	6–15 years	309	26.12
	16 years+	47	3.97

.

3.3. Data Analysis

To test the validity of the survey, descriptive statistics, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA) were performed. First, EFA was performed using principal component analysis to explore how each general competency item reflected the underlying structure to be measured through SPSS. An oblique or PROMAX rotation was used due to correlations found among factors within constructs. In higher education research, the threshold value is generally held to be no less than 0.4 [31]. Second, we assessed the goodness-of-fit of the data and the hypothesized factor structure using CFA via AMOS 21.0. In CFA, the most widely used model estimation method, maximum likelihood (ML), was used. For the model’s basic fit index, the factor loading between the latent variable and its measurement index should be between 0.50 and 0.95 [32]. According to Awang [33], any factor loading below 0.6 and an R² (square of normalized loading) less than 0.4 should be removed from the measurement model. The asymptotic residual mean square and root mean square error of approximation (RMSEA) offers a relatively stable and more accurate measurement of absolute fit than other index values [34]. RMSEA values below 0.06 are acceptable for model fit [35]. In terms of value-added fit statistics, the values of the comparative fit index (CFI), the Tucker–Lewis Index (TLI), and the incremental fit index (IFI) should all be above 0.90 [34]. Finally, values above 0.5 for the average variance extracted (AVE) of the latent variables demonstrated the model’s intrinsic structural fit [32].

Cronbach’s alpha was used to test the reliability of the entire scale, and Cronbach’s alpha and composite reliability (CR) were used to test the reliability and relative stability of each subscale. A Cronbach’s alpha value of >0.70 is the threshold for a scale’s reliability, with values of >0.8 preferred, while the CR score should be greater than 0.6 [32].

4. Results

4.1. Descriptive Statistics

The mean, standard deviation, and standard error of the engineering graduates’ scores for each item are shown in

Table 3. Descriptive statistics for each survey dimensions and items.

Dimensions and Items	Mean	Standard Error	Standard Deviation
Technical theory	4.30	0.03	0.87
Apply physical and life sciences fundamentals	4.13	0.03	0.94
Apply mathematical and information science fundamentals	4.27	0.03	0.89
Apply engineering science	4.30	0.03	0.87
Understand the cutting edge of one’s profession	4.32	0.03	0.86
Apply technical knowledge	4.49	0.02	0.79
Leadership	4.40	0.02	0.78
Possess humanities and social science literacy	4.28	0.02	0.85
Empathize and work with people from a wide range of backgrounds	4.67	0.02	0.63
Work with culturally, racially, and gender diverse people	4.41	0.02	0.80
Build and maintain working relationships	4.49	0.02	0.76
Understand the business context in which engineering is practiced	4.21	0.03	0.88
Make decisions based on relevant information	4.48	0.02	0.73
Ensure resources are available	4.43	0.02	0.76
Create vision and direction	4.36	0.02	0.79
Motivate others to achieve great things	4.46	0.02	0.76
Coach and mentor colleagues	4.45	0.02	0.71
Coordinate the work of team members	4.51	0.02	0.69
Supervise work and people	4.19	0.03	0.87
Build team cohesion	4.39	0.02	0.80
Chair and participate constructively in meetings	4.28	0.02	0.85
Lifelong learning	4.61	0.02	0.65
Manage time and meet deadlines	4.54	0.02	0.71
Always gain new skills	4.66	0.02	0.63
Obtain relevant information	4.58	0.02	0.67
Learn autonomously	4.65	0.02	0.64
Detect and adapt to changing conditions	4.63	0.02	0.63
Communication	4.49	0.02	0.72
Write concisely	4.55	0.02	0.70
Communicate verbally, accurately	4.57	0.02	0.67
Negotiate to reach a decision	4.47	0.02	0.74
Make effective presentations to clients	4.29	0.03	0.86
Listen to different points of view	4.58	0.02	0.65
Problem solving	4.50	0.02	0.72
Develop and implement solutions for a broad array of issues involving many disciplines and conflicting objectives	4.55	0.02	0.69
Use creativity and ingenuity	4.45	0.02	0.76
Develop innovative approaches	4.47	0.02	0.75
Frame the problem in a logical way	4.59	0.02	0.67
Think critically	4.44	0.02	0.77
Think systematically	4.54	0.02	0.68
Think laterally	4.42	0.02	0.78
Synthesize principles, technology, environment, and other factors	4.52	0.02	0.70
Professionalism	4.55	0.02	0.69
Work on practical engineering projects	4.59	0.02	0.66
Be familiar with workplace politics	4.40	0.02	0.78
Commit to achieving objectives, which requires high expectations and a positive attitude	4.45	0.02	0.77
Take initiative	4.54	0.02	0.70
Demonstrate personal integrity	4.63	0.02	0.65
Possess self-confidence	4.63	0.02	0.64
Cope with work pressure and stress	4.62	0.02	0.65
Be prepared to take calculated risks	4.54	0.02	0.69
Remain calm under pressure	4.56	0.02	0.68
Engineering design	4.34	0.02	0.85
Design a system, component, or process	4.41	0.02	0.80
Be aware of political, social, and economic issues	4.39	0.02	0.76
Operate in an international and multicultural context	4.21	0.03	0.93
Use a foreign language for listening, speaking, reading, and writing	4.26	0.03	0.97
Design and conduct experiments; analyze and interpret the resulting data	4.33	0.03	0.87
Understand professional ethics and responsibilities	4.34	0.02	0.85
Exert high levels of effort; strive to achieve goals	4.43	0.02	0.77
Use engineering equipment	4.50	0.02	0.74
Research literature on a topic and draw conclusions	4.27	0.03	0.90
Be aware of environmental and sustainable development issues	4.30	0.03	0.89

. The average values for all competencies lie between 4.13 and 4.67, with the lowest for “understanding and application of natural science knowledge”, and the highest for “team cooperation”. The high overall averages for each item reflect their importance to the engineers, while the standard deviations are all between 0.63 and 0.97, within one standard deviation, which indicates the ratings’ consistency. In ascending order, the importance of each dimension is as follows: technical theory, engineering design, leadership, communication, problem solving, professionalism, and lifelong learning. The normality of the distributions of all the items was acceptable (skewness ranging from −1.91 to −0.72; kurtosis ranging from −0.05 to 2.83).

4.2. Construct Validity

4.2.1. Exploratory Factor Analysis

A stepwise exploratory factor analysis was carried out, and only one item was excluded at each step. After several rounds of factor analysis, 56 items were retained based on eigenvalues greater than 1, with a seven-factor solution extracted. These factors explained 67.18% of the total variance. As shown in

Table 4. Factor loading and communality in the exploratory factor analysis.

Dimensions and Items	1	2	3	4	5	6	7	Commonality
Leadership
Ensure resources are available	0.81							0.73
Understand the business context in which engineering is practiced	0.80							0.63
Motivate others to achieve great things	0.75							0.71
Create vision and direction	0.75							0.70
Build and maintain working relationships	0.70							0.60
Make decisions based on relevant information	0.69							0.66
Possess humanities and social science literacy	0.69							0.52
Build team cohesion	0.66							0.63
Coach and mentor colleagues	0.63							0.69
Work with culturally, racially, and gender-diverse people	0.61							0.48
Supervise work and people	0.60							0.51
Coordinate the work of team members	0.57							0.71
Empathize and work with people from a wide range of backgrounds	0.55							0.62
Chair and participate constructively in meetings	0.48							0.60
Engineering design
Operate in an international and multicultural context		0.91						0.70
Use a foreign language for listening, speaking, reading, and writing		0.82						0.54
Research literature on a topic and draw conclusions		0.81						0.69
Be aware of environmental and sustainable development issues		0.75						0.71
Design and conduct experiments; analyze and interpret the resulting data		0.73						0.67
Understand professional ethics and responsibilities		0.71						0.69
Design a system, component, or process		0.66						0.65
Exert high levels of effort; strive to achieve goals		0.64						0.71
Be aware of political, social, and economic issues		0.54						0.66
Use engineering equipment		0.54						0.66
Professionalism
Cope with work pressure and stress			0.89					0.71
Commit to achieving objectives, which requires high expectations and a positive attitude			0.81					0.67
Possess self-confidence			0.80					0.73
Take the initiative			0.80					0.72
Demonstrate personal integrity			0.79					0.61
Be familiar with workplace politics			0.72					0.67
Be prepared to take calculated risks			0.68					0.66
Remain calm under pressure			0.49					0.64
Work on practical engineering projects			0.48					0.56
Problem solving
Develop innovative approaches				0.85				0.74
Think systematically				0.77				0.73
Think laterally				0.76				0.68
Use creativity and ingenuity				0.74				0.72
Think critically				0.72				0.71
Frame the problem in a logical way				0.65				0.72
Synthesize principles, technology, environment, and other factors				0.57				0.68
Develop and implement solutions for a broad array of issues involving many disciplines and conflicting objectives				0.53				0.67
Lifelong learning
Always gain new skills					0.83			0.73
Obtain relevant information					0.77			0.76
Learn autonomously					0.74			0.76
Detect and adapt to changing conditions					0.71			0.75
Manage time and meet deadlines					0.56			0.69
Technical theory
Apply mathematical and information science fundamentals						0.81		0.67
Apply physical and life sciences fundamentals						0.80		0.64
Apply engineering science						0.78		0.69
Apply technical knowledge						0.65		0.69
Understand the cutting edge of one’s profession						0.56		0.59
Communication
Negotiate to reach a decision							0.75	0.76
Make effective presentations to clients							0.69	0.70
Communicate verbally, accurately							0.68	0.77
Write concisely							0.62	0.73
Listen to different points of view							0.42	0.63

, the loading of each item on each home factor was between 0.42 and 0.91, reaching the established standard. The commonality of each item ranged from 0.48 to 0.77, indicating that each item was highly correlated with other factors [31].

The seven factors incorporating the items were as follows:

Factor 1:

Leadership (14 items), consisting of organization, decision-making, motivation, vision, project management, networking, teamwork (including dealing with people from different backgrounds), breadth of knowledge, team-building, supervision, coaching, influence, coordination, and meeting skills.

Factor 2:

Engineering Design (10 items), consisting of design, internationalization, foreign languages, literature research, engineering ethics, experimentation, environmental and sustainable development, achievement orientation, consideration of external constraints, and the use of modern tools.

Factor 3:

Professionalism (9 items), consisting of honesty, coping with pressure, organizational commitment, self-confidence, risk-taking, initiative, calmness, familiarity with the workplace, and practical experience.

Factor 4:

Problem Solving (8 items), consisting of solving complex problems, innovation, creativity, lateral thinking, critical thinking, systems analysis, logical thinking, and synthesis.

Factor 5:

Lifelong Learning (5 items), consisting of autonomous learning, information acquisition, lifelong learning, adaptability, and time management.

Factor 6:

Technical Theory (5 items), consisting of the application of knowledge in mathematics and information science, natural science, engineering, and professional technology, as well as an understanding of professional frontiers.

Factor 7:

Communication (5 items), consisting of written communication, oral communication, negotiation, speech, and listening.

In addition to examining the construct validity of all items as a whole, we also used principal component analysis to examine the construct validity of the subscales that make up the whole. The Kaiser–Meyer–Olkin (KMO) value for each scale ranged from 0.83 to 0.96, indicating that factor analysis was an appropriate method for investigating the data. With eigenvalues greater than 1, a single-factor solution could be extracted from each subscale. As shown in

Table 5. Evidence for the validity of the general ability subscales (N = 746).

Subscale	Number of Items	KMO Sampling Suitability Quantity	Bartlett Sphericity Test		Eigenvalue	Percentage of Variance	Factor Loadings
			Approximate Chi-Square	Degree of Freedom
Leadership	14	0.96	7007.77	91	8.14	58.14	0.63–0.85 ***
Engineering Design	10	0.93	5203.21	45	6.37	63.69	0.69–0.84 ***
Professionalism	9	0.95	4411.16	36	5.82	64.68	0.74–0.85 ***
Problem solving	8	0.93	4542.79	28	5.57	69.68	0.81–0.85 ***
Lifelong Learning	5	0.9	2653.29	10	3.8	76	0.84–0.90 ***
Technical Theory	5	0.83	1640.14	10	3.19	63.89	0.76–0.84 ***
Communication	5	0.87	2059.15	10	3.48	69.61	0.78–0.87 ***

*** p < 0.001.

, the factor loading of each item was between 0.63 and 0.90; for each subscale, the total variance explained by the main factor was between 58.14% and 76.00%. These results suggest that each scale contained only a single structure.

4.2.2. Confirmatory Factor Analysis

According to the parameter estimation of standardized loadings and modified indicators, the “breadth of knowledge” data were deleted because the factor loadings for these were lower than 0.6 and the R² was lower than 0.4. The generic engineering competencies model was accordingly modified. As shown in

Table 6. Factor loadings and reliability estimates for second-order confirmatory factor analysis.

Dimensions and Items	Standardized Factor Loadings	Item Reliability (R2)	Relevance between Items	Composite Reliability (CR)	Average Variance Extracted (AVE)
Technical theory			0.47–0.68 ***	0.86	0.56
Apply physical and life sciences fundamentals	0.68	0.46
Apply mathematical and information science fundamentals	0.73	0.54
Apply engineering science	0.77	0.59
Understand the cutting edge of one’s profession	0.75	0.57
Apply technical knowledge	0.81	0.65
Leadership			0.39–0.78 ***	0.94	0.57
Empathize and work with people from a wide range of backgrounds	0.69	0.48
Work with culturally, racially, and gender-diverse people	0.65	0.43
Build and maintain working relationships	0.74	0.54
Understand the business context in which engineering is practiced	0.70	0.49
Make decisions based on relevant information	0.78	0.61
Ensure resources are available	0.83	0.68
Create vision and direction	0.82	0.66
Motivate others to achieve great things	0.81	0.66
Coach and mentor colleagues	0.81	0.65
Coordinate the work of team members	0.82	0.67
Supervise work and people	0.67	0.44
Build team cohesion	0.77	0.59
Chair and participate constructively in meetings	0.70	0.49
Lifelong learning			0.65–0.76 ***	0.93	0.71
Manage time and meet deadlines	0.81	0.66
Always gain new skills	0.84	0.70
Obtain relevant information	0.86	0.74
Learn autonomously	0.88	0.77
Detect and adapt to changing conditions	0.84	0.71
Communication			0.56–0.74 ***	0.90	0.64
Write concisely	0.83	0.69
Communicate verbally, accurately	0.84	0.71
Negotiate to reach a decision	0.82	0.67
Make effective presentations to clients	0.73	0.54
Listen to different points of view	0.77	0.60
Problem solving			0.58–0.83 ***	0.94	0.66
Develop and implement solutions for a broad array of issues involving many disciplines and conflicting objectives	0.78	0.61
Use creativity and ingenuity	0.80	0.63
Develop innovative approaches	0.80	0.64
Frame the problem in a logical way	0.83	0.69
Think critically	0.82	0.67
Think systematically	0.85	0.72
Think laterally	0.82	0.68
Synthesize principles, technology, environment, and other factors	0.83	0.68
Professionalism			0.52–0.75 ***	0.94	0.63
Work on practical engineering projects	0.75	0.56
Be familiar with workplace politics	0.76	0.57
Commit to achieving objectives, which requires high expectations and a positive attitude	0.80	0.64
Takes the initiative	0.83	0.68
Demonstrate personal integrity	0.76	0.57
Possesses self-confidence	0.85	0.73
Cope with work pressure and stress	0.82	0.68
Be prepared to take calculated risks	0.80	0.64
Remain calm under pressure	0.78	0.61
Engineering design			0.46–0.76 ***	0.94	0.62
Design a system, component, or process	0.79	0.63
Be aware of political, social, and economic issues	0.80	0.65
Operate in an international and multicultural context	0.74	0.54
Use a foreign language for listening, speaking, reading, and writing	0.66	0.43
Design and conduct experiments; analyze and interpret the resulting data	0.81	0.66
Understand professional ethics and responsibilities	0.82	0.67
Exert high levels of effort; strive to achieve goals	0.84	0.71
Use engineering equipment	0.81	0.66
Research literature on a topic and draw conclusions	0.79	0.62
Be aware of environmental and sustainable development issues	0.83	0.69
Generic competencies			0.52–0.86 ***	0.95	0.74
Lifelong learning	0.87	0.75
Technical theory	0.65	0.42
Communication	0.90	0.81
Engineering design	0.85	0.73
Leadership	0.87	0.75
Problem solving	0.92	0.85
Professionalism	0.92	0.85

*** p < 0.001.

, the results of the CFA show that the loading of each factor in overall engineering competence was between 0.65 and 0.88, indicating that the basic fit index of the estimated results was good and did not violate the rules for model identification. The model fit indices were as follows: RMSEA = 0.05, CFI = 0.91, IFI = 0.91, and TLI = 0.91, all of which meet the corresponding critical value requirements. The AVE for each factor was between 0.56 and 0.71—all higher than the threshold value, indicating that the items adequately reflect the main factors, each of which has good convergent validity. In short, the values for each fit index meet the acceptable standards, indicating that the theoretical model was a good fit to the actual data.

Self-reported data in surveys could contain a common method bias. We evaluated the potential presence of this bias by calculating the common latent factor. Using this method, two different models were tested. The first model was the proposed model, and in the second model, a method factor variable was introduced. The resulting path coefficients were not substantially different between these two models: ∆GFI = 0, ∆IFI = 0.002, ∆NFI = 0.001, ∆TLI = 0.006, ∆CFI = 0.002, ∆RMSEA = 0.002, RMR = 0.001. GFI, IFI, NFI, TLI, and CFI did not exceed 0.1, and RMSEA and RMR did not exceed 0.05, which indicates that there was no significant bias related to common methods.

Further investigation found that the correlation coefficients between the factors ranged from 0.52 to 0.86 (p < 0.001; see Table 6). These medium-high correlations indicate that the factor construct was measuring a higher order. Table 6 shows that in this higher-order factor model, the factor loading of the second-order factor was between 0.65 and 0.92, the AVE was 0.74, and its fit index values were RMSEA= 0.06, CFI = 0.91, IFI = 0.91, and TLI = 0.90. These fit index values all lie within the thresholds of acceptability, indicating that the generic engineering competencies framework can be described as a single-factor second-order model.

4.3. Evidence of Reliability

Cronbach’s alpha was used to test the reliability of the GECQ, with a result of 0.98 (N = 1183) indicating excellent reliability. Table 6 shows that the inter-item correlations between each subscale were between 0.39 and 0.83 and all associations were significantly positive. The correlation coefficients between the remaining items were all at moderate-to-high levels. The combined reliability of each subscale ranged from 0.86 to 0.94. The high degree of internal correlation between the items demonstrates the very high internal consistency of each factor.

5. Discussion

This investigation into the perceptions of engineering graduates in China generated a seven-dimensional structure of generic engineering competencies. The results show that the Cronbach’s alpha coefficient of the GECQ and its seven dimensions was approximately 0.9, indicating the scale’s high overall reliability as well as that of its subscales. The CFA results validate the seven-dimensional structure obtained by EFA, indicating that lifelong learning, technical theory, communication, engineering design, leadership, problem solving, and professionalism cover industry requirements for generic engineering competencies. This result overlaps with, yet differs from, earlier classifications of generic engineering competencies into eleven dimensions [13]. This earlier study also specified the competencies of leadership, communication, professionalism, technical theory, and problem solving, but overlooked lifelong learning and engineering design [13], which are both essential competencies [36]. The seven dimensions form an integral whole, and each is essential to the effective work of engineers.

The leadership dimension consists of a skill set that helps to organize the effort, creates a vision, and enables others to work effectively to achieve organizational and project goals. These leadership skills are similar to those defined by Male, Bush, and Chapman [13]. Engineers can demonstrate the first capability (sensemaking) by understanding the environment and basic rules of the project and business operations. The second of the four capabilities (relating) resembles the competence to build relationships within and across organizations and interpersonal skills in dealing with others that were identified as important by engineering respondents in the current study. Third, to motivate members and unify their actions to achieve project goals, engineers need to create a compelling picture of the future for the organization or project (visioning) [15]. To realize their vision, engineers require a range of skills, including correct decision-making, motivating, supervising, mentoring and influencing, and coordinating subordinate members. They must be able to effectively organize and manage people, finances, and materials to maximize their effectiveness. Of course, the performance of these types of leadership skills is not linear but staggered [16].

The dimension of engineering design consists of a set of methods, tools, means, and environment-related items, which can be divided into design content and design context [18]. The focus of the former is design processes, methods, and technologies that are applied in a problem-solving approach. Engineers need to develop, select, and use appropriate technologies, resources, modern engineering tools, and information technology tools to conduct professional analysis, calculation, and analysis of complex engineering problems. Design, simulation, and prediction entail the ability to develop experimental protocols, conduct experiments, and analyze and interpret data. In many cases, a design project begins by reviewing the relevant literature [18], using additional languages if necessary, and drawing on the relevant experience of design in various cultural settings—all of which make the quality of one’s design stand out. The context of engineering design highlights the temporal and spatial aspects of its contents. Only by fully integrating the environment into engineering design can its achievements promote a convivial society and enhance users’ quality of life. In an increasingly globalized world, engineering designs must reflect their ecological, political, ethical, economic, social, historical, and psychological contexts [18] and acknowledge the global community in which they are embedded. In short, engineering design is not only a “solo” of design content but also an “ensemble” of design content and environmental/contextual considerations.

The dimension of professionalism includes a group of relatively stable personality traits related to the occupational field. These include honesty, self-confidence, initiative, calmness, risk-taking, coping with pressure, and loyalty to the organization. The traits are similar to items included in the “Personality Characteristics” category in corresponding taxonomies of professional educational goals [11,12] and reflect the content of the “professionalism” factor previously obtained via EFA [13]. It is crucial to identify such traits when selecting candidates for engineering roles and to support their development in existing employees [12]. Since these qualities are often found in the professional environment, they are closely linked to workplace politics and practical experience.

The results of this study make an important contribution to resolving the dispute over the role played by information literacy in the dimension of lifelong learning and communication. Although some researchers regard information literacy (an important aspect of lifelong learning) and communication as aspects of the same construct [12,14], this study indicates that they are separate. Information acquisition, active learning, adaptation, time management, continuous learning, etc. are competencies needed for meeting the challenge of ongoing, rapid technological and social change. However, written and oral communication, negotiation, presentation, and listening are information-mediated interpersonal skills. This result is consistent with the findings of Whittle et al. [37] and Burke et al. [38], who viewed communication and information literacy as two distinct constructs.

Similarly, although some studies separate mental skills and problem solving [11,12], this study shows that the problem-solving dimension encompasses a variety of mental skills. Various thinking skills, such as systems analysis, synthesis of information, and creative, logical, critical, and lateral thinking, differ in their focus and operation. Nevertheless, all involve information processing and reasoning that involve the brain [39] and are critical to problem solving [40,41]. Therefore, it seems reasonable to conclude that thinking and problem solving form the same construct.

Although Brumm et al. [42] regarded technical theory as the most important competency, this view was not shared by the engineering graduates in this study, who rated its importance as low, a finding consistent with those of Shaoxue et al. [43] and Male et al. [20]. This low rating reflects the gap between the demand for competencies in industry and the supply of competencies in higher education: the ability to apply basic scientific knowledge at university is conventionally valued much more than other specialized skills. The deep-seated reason for this difference is the disjunction between the academic culture of universities and the professional culture of industry [43]. Therefore, greater communication and collaboration between higher education and enterprises are required to nurture the talents that the industry requires.

6. Conclusions and Limitations

Generic engineering competencies must be developed by graduates who wish to become good citizens and skilled practitioners. These competencies should, therefore, inform the development of the higher education engineering curriculum. However, the relative emphasis given to each competency in the curriculum should be determined by its relative importance in professional practice. This means the generic competencies required of professional engineers must be assessed using a valid and reliable survey instrument. The generic engineering competencies framework validated in this study provides an industry perspective, a pool of important educational objectives or learning outcomes, and an analytical framework for curriculum design in engineering education. The GECQ developed in this study can be used to assess the generic competency required by engineering graduates in the industry.

Several limitations that impacted the development of the GECQ must be addressed in future research. First, the sample was drawn from 20 companies in China. Although this was representative of industries and employment roles in these sectors, the results cannot be generalized to all enterprises across China owing to the large numbers of companies from other sectors that were not sampled. While the sample exceeded the minimum recommended size, it was small compared to the total number of Chinese engineers. Because this may have affected the model fit, we will expand the sample size to address this issue in future studies. Second, in order to increase the wide applicability of the content, the future sampling of engineers for content validity verification will also be extended to mechanical, aerospace, nuclear, and other fields. In addition, this scale was developed based on the current Chinese context and may not define the generic competencies required by engineers in other cultural contexts. Future research in multicultural contexts should be carried out to test the validity and credibility of the scale. Evidence for the validity of an assessment instrument should be diverse. While we have provided evidence of content, face, and construct validity in this study, future research may investigate the extent to which the GECQ correlates with other measures to test its criterion-related validity.