The Effect of Collaborative Learning Service Quality on the Innovative Work Behavior of High-Tech Engineers

Abstract

Corporations are increasingly looking beyond inter-company collaborations to multidimensional collaborative activities between departments and organizational members within the company to strengthen innovative market competitiveness. High-tech corporations, specifically, are adopting collaborative learning approaches to promote work capability among engineers from the perspective of developing new technologies and increasing productivity. This study aimed to empirically verify the effect of the service quality of collaborative learning programs on the innovative work behavior of engineers in high-tech companies. Job autonomy, self-efficacy, and learning transfer were set as mediating variables and their effects on innovative work behavior were analyzed. The sample comprised 298 high-tech engineers in South Korea. Collaborative learning service quality was found to have a direct positive effect on job autonomy, self-efficacy, and learning transfer. However, job autonomy and self-efficacy did not influence innovative work behavior through learning transfer. On the other hand, collaborative learning quality had a positive effect on innovative work behavior through learning transfer. Thus, improving the service quality of collaborative learning programs in high-tech corporations can enhance learning transfer within the organization, leading to innovative business outcomes. Moreover, to maximize the effectiveness of collaborative learning, the service quality of learning programs can be improved by prioritizing learning transfer rather than job autonomy or self-efficacy.

1. Introduction

Technological collaboration between corporations has been on the rise in recent years, and it is recognized as an important element of corporate and technological advancement. This trend of technological collaboration is being actively promoted by governments in various countries and has become a central element of the “technology-globalism” analysis of future international economy and technological development. The size of the global enterprise collaboration market is estimated at USD 60.33 billion in 2024. It is expected to reach USD 100.29 billion by 2029, growing at a compound annual growth rate of 10.70% (Markets and Markets 2021). The increasing use of accessible smartphones in emerging economies, such as China and India, is also fueling the growth of an enterprise collaboration market. In North America, market corporations, such as Meta Platforms Inc., Microsoft Corporation, Google LLC, and Slack Technologies Inc., hold a large share of the enterprise collaboration market (Fortune Business Insights 2024).

In addition, Gartner (2023) reported that the number of workers using collaboration tools increased from 55% in 2019 to 79% in 2021. Beyond business-to-business collaboration, collaborative activities are being reinforced across departments and organizations within an enterprise. Advances in digital technology in particular have led to the rise in advanced tools such as virtual reality, robotic process automation, and artificial intelligence robots. This has led to a proliferation of collaborative activities that extend in-house productivity and foster communication with employees. In response, corporations are striving to improve productivity by providing advanced solutions and products to facilitate internal and external communication. The expansion of global organizations, the high rate of productivity, and the increased application of bring-your-own-device policies and the IoT (Internet of Things) have led to the development of enterprise collaboration solutions, enabling businesses to collaborate efficiently and quickly in order to drive market growth (Heckscher and Heckscher 2007; Porter and Heppelmann 2015).

In high-tech fields, such as semiconductors, products require multiple processes and complex procedures to be completed. While the technologies applied to each process are important, it is their coordination that matters. Corporations that lack inter-process collaborations are prone to errors and low efficiency. In fields such as software development and data analytics, a wide range of specialized knowledge and skills are required, so seamless communication and collaboration between team members play a critical role in project success (Keeble and Wilkinson 1999). Globalization and the proliferation of remote work have also made collaboration skills between physically separated team members even more important. Therefore, it is essential to train employees to use collaboration tools and communicate effectively (Jassawalla and Sashittal 1998; Quan-Haase et al. 2005; Assbeihat 2016).

High-tech corporations are developing and operating a variety of intra-company collaborative learning programs to adopt and effectively utilize innovative technologies. Collaboration skill-learning programs run by corporations take many forms. For example, some corporations organize regular workshops and seminars to train employees on using collaboration tools and team-building activities to strengthen bonds between team members. Others utilize online platforms to provide learning materials and courses that are easily accessible in a remote work environment. These programs are aimed not only at the acquisition of collaboration skills but also at their application and effective use in the performance of actual work (Ciborra 1991; Ford and Meyer 2013; Sun and Huang 2023).

Global high-tech companies must operate state-of-the-art specialized technologies, and the importance of collaboration in complex production processes also requires sophisticated collaboration tools and structured learning (Neilson 1997). Collaborative learning between technology company engineers goes beyond simple skill acquisition to strengthen trust and communication among team members in integrated tasks or task execution, strengthen work capabilities through learning transfer (Stump et al. 2011; Mora et al. 2020), and can affect the business innovation of an organization. For high-tech companies, innovation is a concept that puts more emphasis on new attempts in technology than on change. Moreover, technological innovation includes attempts at the technological level, processes, and results. Specifically, it refers to new materials, new processes, and new products, as well as the respective processes in which they are created and produced (Pandya 2024; Zhang et al. 2024; Wang and Tan 2021). Therefore, technologically advanced companies should provide training to lead innovation through the development of engineers’ capabilities and collaboration between teams to improve work efficiency, productivity, and innovation.

Previous studies conducted on collaborative learning have suggested the use of collaborative tools in companies (Bhat et al. 2020) and examined the impact of collaborative learning programs on teams’ problem-solving abilities (Anitha and Kavitha 2022). These studies have shown that collaborative skill-learning has a positive effect on team performance and corporate innovation (Zhou et al. 2013; Lee and Bonk 2014; Cheng et al. 2021). However, previous studies have focused only on the effects of collaborative learning in companies, and there is a lack of research that considers the quality of learning programs and participant satisfaction (Hendarwati et al. 2021). As argued by Horvat et al. (2024), collaborative learning can help develop new ideas, share technological know-how, promote new technology development, and promote innovative technology production. Therefore, above all, discussions on the quality, learning satisfaction, and effectiveness of collaborative learning for engineers, who are the main agents of innovation, are needed.

The purpose of this study was to empirically verify the effect of cooperative-learning service quality on the innovative work behavior of engineers in high-tech companies. In particular, we looked at how cooperative-learning service quality affects engineers’ innovative work behavior through self-efficacy, job autonomy, and learning transfer. These findings have academic value that not only emphasizes the importance of cooperative learning but also presents empirical results on the quality and impact of cooperative learning. In addition, it is significant in the study that it presents research results from the perspective of organizational behavior on the learning competency factors and the innovative behaviors of engineers in high-tech companies. As a result, this study emphasizes the importance of cooperative learning for engineers in high-tech companies and presents specific implications for improving the quality of cooperative-learning services to improve the job competency of engineers. In addition, we will present developmental directions for promoting cooperative learning in high-tech companies and discuss ways to strengthen the capabilities of organizational members to strengthen technological innovation.

Accordingly, this study consists of a total of six chapters. In Section 2, this study examines prior studies on collaborative learning and establishes hypotheses based on these prior studies by factors. The methodologies, such as research models and variable settings, are presented in Section 3. In Section 4, the analysis results are presented, and in Section 5, the interpretation and discussion of the analysis results are presented. Finally, in Section 6, the implications and limitations of the study and future tasks are presented.

2. Literature Review and Hypothesis Development

2.1. Collaborative Learning in Technologically Advanced Corporations

Collaborative learning provides team members with the skills and knowledge required to work together effectively to achieve a common goal (Yang 2023). It goes beyond teaching people how to use technical tools and seeks to cultivate an understanding of roles within a team, build mutual trust, and improve collaborative problem-solving skills (Laal and Laal 2012; O’Donnell and Hmelo-Silver 2013). Collaborative skill-learning promotes teamwork and helps diverse members of an organization to interact and create synergy (Nevgi et al. 2006; Mena-Guacas et al. 2023). Further, collaborative learning workshops and seminars give team members opportunities for participation and practical training (Unger et al. 2022). Online learning platforms can be utilized to provide collaboration skill-learning opportunities (Ajayi and Ajayi 2020), and mentoring and coaching programs can help individual team members improve their collaboration skills (Gilbert 2021).

Collaborative learning for technology development engineers consists of several components: teamwork, communication, problem solving, conflict management, and decision making. As shown in

Table 1. Purpose of and approach to collaborative learning.

Category	Content	Previous Research
Teamwork and leadership	Teamwork is a key element of collaboration; it refers to the ability of team members to understand each other’s roles and responsibilities and work together toward a common goal.	Riivari et al. (2021)
Communication	This component emphasizes clear and efficient communication skills, which helps team members understand each other clearly and give and receive feedback.	Li (2002)
Problem solving	This enables team members to learn to propose different perspectives and integrate them to arrive at optimal solutions.	Wismath and Orr (2015)
Conflict management	This component includes techniques to help team members manage their emotions and find constructive solutions in conflict situations.	Walker and Daniels (2019)
Decision making	This component focuses on developing the skills needed for teams to make decisions collaboratively.	Baumberger-Henry (2005)

, previous research has shown that these factors have a significant impact on organizational performance and innovation.

Previous studies on collaborative learning in enterprises have emphasized the importance of learning effectiveness. First, Johnson and Johnson (2021) argued that, in terms of contribution to improving organizational performance, effective collaboration increases productivity and creativity and enables complex problems to be solved quickly. Felder and Brent (2001) explained that, in terms of improving communication within an organization, collaborative skill-learning helps team members communicate clearly and effectively. Dowell et al. (2014) claimed that it facilitates the flow of information, reduces misunderstandings and conflict, and increases team cohesion.

Collaborative learning, therefore, contributes to a positive organizational culture based on trust and respect among team members. Blasco-Arcas et al. (2013) explained that collaborative learning increases employee satisfaction and engagement, which, in turn, promotes sustainable organizational growth in the long run. As such, research findings have shown that collaborative learning improves the problem-solving, communication, and leadership competencies of individual team members, which has a positive impact on their career advancement and sense of accomplishment.

Furthermore, regarding prior studies on collaborative learning for high-tech corporations and engineers, O’Sullivan et al. (2017) claimed that collaborative learning maximizes learning transfer and enhances collaboration competencies in practice-oriented projects. Apte and Bhave-Gudipudi (2020) explained that, in the field of engineering, collaborative learning environments play a crucial role in helping engineers solve complex problems and overcome technical challenges. Their study reported that learning through collaboration not only contributes to individual skill development but also strengthens an organization’s problem-solving skills.

In addition, Chang (2001) stressed that collaborative learning assumes a critical role in bridging theory and practice and is effective in promoting technology learning and engineering. Fischer (2013) explained that collaborative learning and peer assessment foster interactions among team members, which allows engineers to better utilize what they learn in real work situations. Finally, Zhuang and Zhou (2023) asserted that coordination and collaboration between industry and academia in engineering education can maximize learning transfer and academic performance. As a result, collaborative learning for employees or engineers in technology-based corporations can increase learning transfer and problem-solving effects within the organization, playing a positive role in enhancing work effectiveness, including skill development and productivity improvement.

2.2. The Roles of Service Quality, Job Autonomy, and Self-Efficacy in Corporate Learning

Corporate learning is the process of acquiring the knowledge and skills required for a specific job and includes all the aspects of development, education, and training (Crocetti 2001). Calisir et al. (2013) explained that the purpose of corporate learning is to improve the effectiveness and efficiency of corporate structures and corporate activities and enhance the development of organizational members. Corporate learning training refers to educational programs and related systematic activities conducted for employees or stakeholders to achieve organizational objectives by improving their capabilities (Martin and Reyes 2023).

Learning service refers to all educational activities and administrative services provided to employees by corporations, organizations, and instructors, aiming at carrying out training to achieve educational objectives (Hebles et al. 2023). It is a set of activities in which a supplier provides tangible and intangible services to employees to fulfill educational objectives, thereby increasing employees’ material and mental satisfaction. Sadiq Sohail and Shaikh (2004) defined learning service quality as the overall assessment or attitude of learners toward the quality of the learning service provided by their institution. Felten and Clayton (2011) defined learning service quality as learners’ overall judgment or attitude toward the excellence of a training program. Uppal et al. (2018) argued that e-learning service providers should provide efficient learning services to increase members’ satisfaction and facilitate the achievement of educational goals.

The essential purpose of corporate learning is to enable the employees of an organization to develop the ability to effectively acquire knowledge related to their work, develop their work competencies, and apply them to work situations. In corporate job-training programs, learning service quality is an indicator of the effectiveness and satisfaction of the programs offered within an organization. The content of learning should be closely related to the job in question as it has a direct impact on job satisfaction and performance (Egan et al. 2004). High-quality learning helps employees gain the knowledge and skills they need and apply them during the performance of actual work. Therefore, learning methods should be tailored to the learning styles of participants and reflect the needs and job characteristics of organizational members to maximize learning effectiveness (Nielsen and Kreiner 2017).

Consequently, improving the quality of a corporate learning service affects the improvement of individual or team competencies for job performance, such as job autonomy, self-efficacy, and learning transfer (Tho 2017; Islam and Ahmed 2018). First, in terms of job autonomy, learning that provides up-to-date job-related knowledge builds employees’ confidence in regard to performing their jobs more autonomously (Tabiu et al. 2020). It also strengthens self-efficacy, the belief that individuals have in their abilities to achieve their goals. In addition, employees share the knowledge or information they have gained through education, creating a learning transfer effect that positively impacts their coworkers and teams.

Slåtten (2014) noted that high-quality learning programs play a crucial role in increasing employees’ job autonomy and self-efficacy. In addition, Qureshi (2019) claimed that, when employees’ self-efficacy is enhanced by corporate learning, it has a positive impact on learning transfer as well. Malureanu et al. (2021) argued that high-quality learning enhances job autonomy and learning transfer, leading to a comprehensive impact on employees’ job satisfaction and performance. Furthermore, Dermol and Čater (2013) and Argote (2014) asserted that operating an ongoing corporate learning program enhances problem solving and creativity through enhanced team learning transfer.

Ultimately, these prior studies showed that improving the service quality of a company’s collaborative learning program can have a positive impact on employees’ job autonomy, self-efficacy, and learning transfer. Thus, enhancing the quality of learning services for collaborative learning among engineers in high-tech corporations will improve engineers’ job autonomy, self-efficacy, and learning transfer. Therefore, the following hypotheses were proposed for this study:

H1. 

The service quality of collaborative learning programs of high-tech corporations will have a positive (+) effect on engineers’ job autonomy.

H2. 

The service quality of collaborative learning programs of high-tech corporations will have a positive (+) effect on engineers’ self-efficacy.

H3. 

The service quality of collaborative learning programs of high-tech corporations will have a positive (+) effect on engineers’ learning transfer.

2.3. Job Autonomy, Self-Efficacy, and Learning Transfer

Learning transfer refers to the process by which knowledge, skills, and attitudes acquired through learning or training are successfully applied in a real work environment. This serves as an important metric that measures the actual effectiveness of learning, and the ability to apply the learned content to one’s job is directly related to improved organizational performance (Holton and Baldwin 2003). Further, learning transfer is correlated with factors that bolster employees’ work behaviors and competencies, such as job autonomy and self-efficacy. In this regard, Tho (2017) explained that, in an environment with high job autonomy, employees have the opportunity to freely apply what they have learned, which enhances the effectiveness of learning transfer. The author argued that employees with high self-efficacy have greater confidence in applying what they have learned on the job, which increases the success rate of learning transfer.

In terms of the relationship between job autonomy and learning transfer, job autonomy is a concept that describes how much autonomy and control an employee has over the process of performing their job (Saragih 2011). It means freedom for employees to decide how they work, control their work schedules, and choose how they solve problems. Job autonomy is considered especially important for knowledge workers or those who perform creative tasks. In organizations with high autonomy, employees have the opportunity to perform to the best of their abilities (Ozlati 2015) and can freely choose how they work and how they solve problems, and so they are better able to apply what they learn to their work (Maggi-da-Silva et al. 2022).

Job autonomy is a critical factor that affects learning transfer. Llopis and Foss (2016) demonstrated that increasing job autonomy is an effective strategy for promoting learning transfer among employees. Buch et al. (2015) found that learning programs are more effective in organizations with higher levels of job autonomy. They explained that, in these organizations, learning transfer is seamless because employees are given many opportunities to apply their new skills and knowledge to real work situations. Lee and Jin (2023) concluded that, the higher the level of employees’ job autonomy, the more effectively they engage in transfer learning. Based on the literature cited above, this study proposed the hypothesis that job autonomy can also facilitate the transfer of learning acquired through education among engineers in high-tech corporations:

H4. 

The job autonomy of engineers in high-tech corporations that have received collaborative learning will have a positive (+) effect on learning transfer.

Furthermore, considering the relationship between self-efficacy and learning transfer, self-efficacy refers to an individual’s belief in their ability to successfully perform a specific task (Holladay and Quiñones 2003). People with high self-efficacy have greater confidence when faced with challenges and are more proactive in overcoming failures and achieving success (Chiaburu and Lindsay 2008). This confidence is directly linked to an individual’s job performance, and employees with high self-efficacy are more likely to be satisfied with their jobs and perform better. Self-efficacy can be strengthened through learning and training, feedback, and successful experiences (Wen and Lin 2014).

Jackson (2002) explained that employees with high self-efficacy are better able to apply what they learn to their work. Godinez and Leslie (2015) emphasized the positive impact of self-efficacy on learning transfer. They explained that learners with higher self-efficacy are better able to remember what they learn and use it effectively in real work situations. Hasan et al. (2020) argued that educational strategies that increase self-efficacy are necessary to promote learning transfer. They suggested that self-efficacy can be strengthened by ensuring that the educational content is relevant to actual tasks and that learners are given the opportunity to check their performance during the educational process. Sookhai and Budworth (2010) found that employees with high self-efficacy are more willing to apply what they learn, thus maximizing the effectiveness of learning transfer. They also underscored the need for support and motivation at the organizational level to facilitate learning transfer.

In turn, learners with high self-efficacy have greater confidence in applying what they have learned to actual tasks, which raises the success rate of learning transfer. Hence, it is important to include strategies to strengthen learners’ self-efficacy when designing learning programs. Based on these previous studies, the following hypothesis was proposed for this study:

H5. 

The self-efficacy of engineers in high-tech corporations that have received collaborative learning will have a positive (+) effect on learning transfer.

2.4. Learning Transfer and Innovative Work Behavior

Scott and Bruce (1998) defined innovative work behavior as the intentional creation, adoption, and application of new ideas that help improve the performance of one’s job role, the group to which one belongs, or the organization; it is a concept that best encompasses innovation at the individual level. While creativity focuses on the development of new and useful ideas, innovative work behavior should be understood as a broader concept than creativity because it encompasses not only the development of ideas but also their promotion, implementation, and diffusion (Volery and Tarabashkina 2021). Innovative work behavior is closely related to the tasks assigned to members, and, in particular, the degree to which a task is routine will play a pivotal role in determining the need to create or introduce new ideas. If a job is highly routine, it is unlikely that workers will be able to experiment and utilize new ways or procedures in performing their tasks. Besides the non-routineness of tasks, task intensity also affects innovative work behavior.

Various prior studies have emphasized the importance of employee creativity and innovative behavior in the context of innovative work behavior and have proposed various variables that influence them. Gegenfurtner et al. (2013) claimed that extrinsic reward systems, job design, and organizational culture, as well as the attributes of the innovation itself, either inhibit or facilitate the relationship between environmental variables and innovation. Ramos et al. (2018) identified goal clarity, organizational culture, a decentralized decision-making system, and political support as important influencing variables that affect organizational innovation and organizational performance. Hui and Lee (2000) classified internal locus of control, organization-based self-esteem, task intensity, task non-routineness, leader–member exchange relationships, team member exchange relationships, innovation supportive organizational culture, and decentralization as important influencing variables. The relationship between the degree of decentralization of authority and innovation behavior, as well as leaders’ support, trust, and autonomy, enhances creative idea generation and innovative behavior (Arad et al. 1997; Dedahanov et al. 2017). Other studies have particularly emphasized the importance of organizational culture among the variables that explain innovation behavior (Khan and Hussain 2020; Ekmekcioglu and Öner 2024).

As noted by Lai et al. (2016), learning transfer, which is the concept of applying knowledge, skills, and attitudes gained through learning on the job, has a positive effect on employees’ innovative work attitude. Cangialosi et al. (2020) defined learning transfer as the act of effectively applying the knowledge, skills, and competencies acquired by learners through education and training programs to actual work innovations. In this context, Battistelli et al. (2019) referred to learning transfer as the effective application of what participants in an education and training program have learned to their jobs and explained that, through this process, learners form their own creative ideas about what the learning means and how it can be applied.

Wang et al. (2023) also posited that learning transfer can extend beyond knowledge, skills, and attitudes to include cognitive strategies; thus, learning transfer formed through education can stimulate new attempts and innovative work behavior. Eventually, as Akhavan et al. (2015) and Kmieciak (2021) have argued, knowledge sharing as learning transfer is utilized on the job and manifested as innovative work behavior. Based on these previous studies, the following hypothesis was proposed for this study:

H6. 

Learning transfer among engineers in high-tech corporations that have received collaborative learning will have a positive (+) effect on innovative work behavior.

3. Research Method

3.1. Research Model

Based on the above hypotheses, a research model was postulated, as shown in Figure 1. This study aimed to empirically analyze the influence of collaborative learning service quality on innovative work behavior. To this end, collaborative learning service quality was set as the independent variable. The following parameters were analyzed: job autonomy, self-efficacy, and learning transfer. Innovative work behavior was considered the dependent variable. Through this, the causal relationship among learning service quality, job autonomy, self-efficacy, and learning transfer was established, as well as the influential relationship of how job autonomy and self-efficacy affects learning transfer. Finally, this study constructed a structural equation model for path analysis to analyze the influence of learning transfer on innovative work behavior.

3.2. Measurement of Variables and Analysis Method

A survey was conducted to collect data and analyze the proposed model. To construct the survey, the survey questions shown in Table 1 below were prepared based on previous studies. This study then defined the components of the variables that comprised the survey. Most of the questions on “collaborative learning” refer to mutual learning for collaboration in the course of performing work to improve understandings of not only one’s own work within the organization but also the work of the entire company. Collaborative learning is a factor that affects the learning activities of members within an organization and their effectiveness; it includes factors that corporations should consider in order to enhance collaboration effectiveness.

The components of collaborative learning service quality can be categorized into basic service quality factors, such as tangibility, reliability, responsiveness, assurance, and empathy in the SERVQUAL model proposed by Parasuraman et al. (1988) and the SERVPERF model proposed by Cronin and Taylor (1994). In terms of learning service quality, Abdullah (2006) presented an instructor factor, a university reputation factor, a learning program factor, a student care factor, a teaching assistant staff factor, and an accessibility factor in their HEdPERF model. Latif et al. (2019) categorized instructor expertise, learner responsiveness, educational content reliability, and program tangibility. Khodayari and Khodayari (2011) proposed instructor expertise, curriculum organization, learning contents, and learning process.

Based on these prior studies, this study defined “instructor expertise”, “responsiveness”, and “program tangibility”, as proposed by Hasan et al. (2008) and Uppal et al. (2018), as the factors influencing “collaborative learning service quality”. First, instructor expertise refers to the impact of an instructor’s knowledge, skills, and experience on learner engagement and learning outcomes (Yeo 2008). Second, responsiveness refers to the learning support factors that instructors and staff need to provide to increase members’ willingness to learn and complete activities and strengthen learning effectiveness (Watkins et al. 2020). Third, program tangibility is defined as the factor that influences the type and characteristics of learning programs for members. This includes the design and structure of the programs, providing a variety of learning contents to meet learners’ needs, and the flexibility of the programs (Rozak et al. 2022).

“Job autonomy” is defined as a variable that has a significant impact on learning and work performance (Saragih 2011; Ozlati 2015). It is the ability of employees to perform their jobs autonomously, while “self-efficacy” is the belief that an individual has about their ability to successfully perform a task (Wen and Lin 2014; Jackson 2002). “Learning transfer” refers to the behavior of applying or utilizing what is learned in an educational program provided by the organization to the performance of tasks (Maggi-da-Silva et al. 2022). The dependent variable, “innovative work behavior”, refers to employees’ behavior to improve job performance by incorporating creative ideas or new problem-solving methods when performing their work (Cangialosi et al. 2020). The variables defined in

Table 2. Variable definitions and measurement items.

Variables		Measurement Items	Previous Research
Collaborative Learning Service Quality	Instructor expertise	- The instructor has enough expertise. - The instructor clearly communicates educational concepts or principles. - The instructor understands what learners need. - The instructor explains the educational content in a way that is easy to understand.	Hasan et al. (2008) and Uppal et al. (2018) Yeo (2008) Watkins et al. (2020) Rozak et al. (2022)
	Responsiveness	- The learning program actively monitors students’ responses during class and adjusts the pace of the lesson accordingly. - The instructor provides appropriate feedback on class-related questions. - The learning program seeks to actively engage with students in a variety of ways. - The learning program is caring and kind to students. - Questions are answered quickly.
	Program tangibility	- The educational content is consistent and relevant to the company’s work. - The curriculum is organized in a way that is practical and aligned with the learning objectives. - The educational content covers the basics of what needs to be learned. - The learning program is meeting my learning objectives. - The learning program has identified learner interests and needs.
Job autonomy		- I can make many of my own decisions regarding the duties of my job. - My organization provides autonomy and opportunities for deciding how I can do my job. - I develop and propose issues with regard to work to my supervisor. - I actively express my opinions in the course of my work.	Saragih (2011) Ozlati (2015)
Self-efficacy		- I have the ability to overcome difficult situations. - Most of the time, I think I can perform my work better than others. - I constantly strive hard even when there are challenges. - I do not give up before I finish my goal, and I persevere. - I do not avoid but enjoy facing challenges.	Wen and Lin (2014) Jackson (2002)
Learning transfer		- I try to apply the knowledge or know-how gained from the course to actual tasks. - I try to use what I learn to find ways to do my job more efficiently. - I tend to use a lot of the things I learned during the course on the job. - The things I learned in the course helped me solve problems in my actual tasks.	Maggi-da-Silva et al. (2022)
Innovative work behavior		- I convince others of the importance of a new idea or solution. - I promote new ideas to be implemented in the organization. - I drive change that benefits my organization or customers. - I find new ways to solve problems in my work’s progress. - I incorporate new ideas to meet work goals and outcomes.	Cangialosi et al. (2020)

consist of a total of 32 questions that make up the questionnaire. This study employed a systematic approach to analyze quantitative data collected from survey responses. Each response was quantified using a 5-point Likert scale, accurately reflecting the perceptions of the respondents. SPSS 25.0 was used to analyze demographic characteristics for descriptive statistics and to conduct exploratory factor analysis. AMOS 22.0 was used to conduct a confirmatory factor analysis based on structural equation modeling, model validation, and a path analysis of the hypotheses.

3.3. Demographic Information of Survey Participants

For this study, we conducted an online survey with a random sample of engineers from high-tech corporations. Prior to the actual survey, we conducted a preliminary survey and pilot sampling with a group of five people, including three semiconductor experts, a collaborative skill training instructor, and an in-house university student. The questionnaire was available for seven days and verified to increase its reliability and validity. A total of 308 questionnaires were collected, and, after excluding 10 questionnaires that were found to provide insincere responses, 298 valid questionnaires were used for analysis. The survey was conducted for three weeks from 12 May to 2 June 2024. The demographic characteristics of the survey participants are presented in

Table 3. Demographic information of survey participants.

Category		Frequency	Percentage (%)
Total		298	100.0
Gender	Male	181	60.7
	Female	117	39.3
Age	20–29 years old	80	26.8
	30–39 years old	170	57.0
	40–49 years old	39	13.2
	50 years old or older	9	3.0
Education level	High school graduate or below	0	0.0
	High school graduate	7	2.4
	College student or graduate	270	90.6
	Graduate school student or graduate	21	7.0
Occupation	Executive or above	7	2.3
	General manager	109	36.6
	Assistant manager, manager	88	29.6
	Staff, senior staff	94	31.5
Corporate size	Large enterprise	125	41.9
	Small and medium-sized enterprise	173	58.1
Work experience	5 years or less	155	52.0
	5–10 years	29	9.7
	10–20 years	107	35.9
	20 years or more	7	2.4

.

4. Results

4.1. Analysis Results of Reliability and Validity

As shown in

Table 4. Descriptive statistical analysis results.

Variable	N	Minimum	Maximum	Mean	Standard Deviation	Skewness	Kurtosis
Instructor expertise	298	1	5	5.90	0.505	0.014	−0.078
Responsiveness	298	1	5	4.06	0.783	−0.302	0.052
Program tangibility	298	1	5	5.22	0.599	−0.356	−0.183
Job autonomy	298	2	5	4.82	0.712	−0.075	−0.281
Self-efficacy	298	2	5	5.52	0.798	−0.123	−0.391
Learning transfer	298	2	5	6.07	0.678	0.014	−0.300
Innovative work behavior	298	2	5	4.87	0.695	0.019	−0.349

, a descriptive statistical analysis was performed to determine the descriptive statistics of variables. Normality tests were performed for both univariate and multivariate forms. Skewness and kurtosis were used to confirm the normality of the data. If skewness never exceeded 3 and kurtosis did not exceed 8, the variables were considered normal and confirmed to be appropriate. The normality check of the responses confirmed that the univariate normality assumption was not invalidated by skewness and kurtosis.

As shown in

Table 5. Results of reliability and convergent validity test.

Variables		Measurement Items	Standard Loading Value	Standard Error	t-Value (p)	CR	AVE	Cronbach α
Collaborative learning service quality	Instructor expertise	IE1	0.722			0.858	0.604	0.845
		IE2	0.843	0.089	13.472 ***
		IE3	0.702	0.105	10.486 ***
		IE4	0.831	0.093	13.33 ***
	Responsiveness	RE1	0.78			0.915	0.684	0.913
		RE2	0.84	0.064	16.143 ***
		RE3	0.904	0.056	17.713 ***
		RE4	0.811	0.063	15.438 ***
		RE5	0.793	0.064	15.002 ***
	Program tangibility	PT1	0.602			0.819	0.579	0.823
		PT2	0.582	0.099	9.814 ***
		PT3	0.714	0.122	9.561 ***
		PT4	0.762	0.118	9.957 ***
		PT5	0.777	0.123	10.079 ***
Job autonomy		JA1	0.751			0.867	0.620	0.900
		JA2	0.745	0.069	13.308 ***
		JA3	0.801	0.069	14.458 ***
		JA4	0.849	0.073	15.448 ***
Self-efficacy		SE1	0.818			0.852	0.544	0.856
		SE2	0.874	0.064	16.968 ***
		SE3	0.815	0.063	15.73 ***
		SE4	0.602	0.058	10.785 ***
		SE5	0.512	0.064	8.935 ***
Learning transfer		LT1	0.814			0.881	0.649	0.875
		LT2	0.865	0.063	17.891 ***
		LT3	0.770	0.081	13.241 ***
		LT4	0.770	0.065	15.273 ***
Innovative work behavior		IW1	0.619			0.900	0.646	0.899
		IW2	0.820	0.103	12.750 ***
		IW3	0.856	0.114	11.788 ***
		IW4	0.842	0.115	11.657 ***
		IW5	0.856	0.118	11.789 ***

Note: *** p < 0.001.

, the reliability and convergent validity analysis results of the measurement model were found to be fair. The factor loadings ranged from 0.512 to 0.904, with all of them being greater than 0.5, and the internal reliability (CR) values ranged from 0.819 to 0.915, with a t-value of at least 8.935, which was statistically significant. The average variance extracted (AVE) values ranged from 0.544 to 0.684 and the Cronbach’s α values ranged from 0.823 to 0.913, demonstrating convergent validity.

The reliability and validity of the derived coefficients, including their adequacy, were verified through exploratory and confirmatory factor analyses, as well as correlation analyses, prior to testing the hypothesis through path analysis and examining mediating effects (see Figure 2).

As shown

Table 6. Analysis of the goodness-of-fit of the measurement models.

Model	χ2(df)	p	DF	χ2/(df)	RMR	GFI	AGFI	NFI	TLI	CFI	RMSEA
Original Model	213.339	0	78	1.847	0.079	0.855	0.769	0.777	0.854	0.879	0.087
Final Model	108.613	0	75	2.491	0.075	0.815	0.877	0.858	0.897	0.909	0.069

Notes: DF, degrees of freedom; RMR, root mean square residual; GFI, goodness-of-fit index; AGFI, adjusted goodness-of-fit index; NFI, normal fit index; TLI, Tucker–Lewis index; CFI, comparative fit index; RMSEA, root mean square error of approximation.

, the original model included all the questions, whereas the final model was based on the variables used in the actual structural equation model fitting. In this study, the fit of the model was evaluated using the comparative fit index (CFI), Tucker–Lewis Index (TLI), and a root mean square error of approximation (RMSEA), which are established standards for evaluating model fit.

The goodness-of-fit analysis of the structural equation measurement model showed an χ² (df) of 108.613 and χ²/degree of freedom of 2.491. The goodness-of-fit index (GFI) value was 0.815, the adjusted GFI (AGFI) was 0.877, the normed fit index (NFI) was 0.858, and the root mean square error of approximation (RMSEA) was 0.069, indicating that the goodness-of-fit component values of the measurement model were statistically significant. The TLI and GFI almost reached the cutoff value of 0.9, and the CFI was found to exceed this cutoff. The RMSEA value was less than the cutoff value of 0.10, showing good overall fit, and the appropriateness of the CFA model was confirmed.

The AVE values and correlation coefficients among the latent variables were analyzed in this study. As shown in

Table 7. Correlation matrix and AVE.

Variables	IE	RE	PT	JA	SE	LT	IW
Instructor expertise (IE)	0.777
Responsiveness (RE)	0.580 **	0.827
Program tangibility (PT)	0.529 **	0.579 **	0.761
Job autonomy (JA)	0.452 **	0.636 **	0.586 **	0.787
Self-efficacy (SE)	0.470 **	0.489 **	0.456 **	0.570 **	0.737
Learning transfer (LT)	0.525 **	0.660 **	0.583 **	0.897 **	0.583 **	0.805
Innovative work behavior (IW)	0.579 **	0.706 **	0.558 **	0.687 **	0.558 **	0.725 **	0.804

Note: ** p < 0.01. The square root of AVE is shown in bold letters.

, the root mean square value of the AVE of each latent variable was greater than the correlation coefficients between the latent variables, confirming discriminant validity.

4.2. Analysis Results of Structural Equation Model

As presented in

Table 8. Analysis of the goodness-of-fit of structural model.

Model	χ2(df)	p	DF	χ2/(df)	RMR	GFI	AGFI	NFI	TLI	CFI	RMSEA
Structural Model	1101.425	0	75	2.470	0.076	0.814	0.880	0.856	0.898	0.908	0.070

Notes: DF, degrees of freedom; RMR, root mean square residual; GFI, goodness-of-fit index; AGFI, adjusted goodness-of-fit index; NFI, normal fit index; TLI, Tucker–Lewis index; CFI, comparative fit index; RMSEA, root mean square error of approximation.

, the goodness-of-fit analysis of the structural equation model showed that the χ² (df) was 1101.425 (446) and the χ²/degree of freedom was 2.470. The results were fair, with a GFI of 0.814 and an NFI of 0.856. The root mean square residual (RMR) was 0.076, the AGFI was 0.880, and the RMSEA was 0.070, indicating that the model’s goodness-of-fit was significant with excellent goodness-of-fit component values. The comparative fit index (CFI), which indicates the explanatory power of the model without being affected by the sample size, was 0.908, and the Tucker–Lewis index (TLI), which determines the explanatory power of the structural model, was 0.898, indicating that the basic model fit very well. The results were derived by conducting a path analysis, as shown in Figure 3.

The hypotheses were tested via path analysis of the structural equation model, and all six hypotheses were supported, as shown in

Table 9. Results of hypothesis test.

	Hypothesis (Path)	S.E.	t-Value (p)	Support
H1	Collaborative learning service quality → Job autonomy	0.118	2.210 *	Supported
H2	Collaborative learning service quality → Self-efficacy	0.101	2.171 *	Supported
H3	Collaborative learning service quality → Learning transfer	0.098	6.752 ***	Supported
H4	Job autonomy → Learning transfer	1.008	13.813 ***	Supported
H5	Self-efficacy → Learning transfer	0.060	2.141 *	Supported
H6	Learning transfer → Innovative work behavior	0.848	9.833 ***	Supported

Note: * p < 0.05, *** p < 0.001.

and Figure 4. Collaborative learning service quality had a positive (+) effect on job autonomy (2.210, p < 0.05), self-efficacy (2.171, p < 0.05), and learning transfer (6.752, p < 0.001). Job autonomy (13.813, p < 0.001) and self-efficacy (2.141, p < 0.05) had a positive (+) effect on learning transfer. On the other hand, learning transfer (9.833, p < 0.001) also had a positive (+) effect on innovative work behavior. As such, all hypotheses were supported.

4.3. Direct and Indirect Effects

As shown in

Table 10. Results of direct and indirect effects.

Hypothesis (Path)	LLCI	ULCI	Direct Effects	Indirect Effects	Total Effect
Collaborative learning quality → Job autonomy → Learning transfer	0.199	0.474	0.232	0.344	0.576
Collaborative learning quality → Self-efficacy → Learning transfer	0.379	0.771	0.056	0.025	0.081
Collaborative learning quality → Learning transfer → Innovative work behavior	−0.116	0.217	0.674	0.040 ***	0.714
Job autonomy → Learning transfer → Innovative work behavior	0.211	0.610	0.922	0.054 ***	0.976
Self-efficacy → Learning transfer → Innovative work behavior	−0.209	−0.048	0.074	0.004 *	0.078

LLCI (lower limit confidence interval), ULCI (upper limit confidence interval). Note: * p < 0.05, *** p < 0.001.

, to test the significance of the indirect effects, we used the bootstrapping method to derive the direct, indirect, and total effects. The results of the path analysis showed that the independent variable—collaborative learning service quality—did not affect learning transfer through the mediation of job autonomy or self-efficacy. However, it was found that collaborative learning service quality influenced innovative work behavior through learning transfer. Further, job autonomy and self-efficacy influenced innovative work behavior through learning transfer. The results confirmed that, when learning service quality is mediated by learning transfer, innovative work behavior can be improved.

5. Discussion

This study analyzed the effect of collaborative learning service quality on the innovative work behavior of engineers in high-tech companies through job autonomy, self-efficacy, and learning transfer. Accordingly, the following research results could be found. First, the quality of the collaborative learning service of high-tech engineers had a positive effect on job autonomy and self-efficacy. This suggests that collaborative skill-learning effectively gives learners the knowledge and skills they need for their jobs, giving them autonomy and confidence in their work. Learners with high job autonomy apply what they have learned more freely to real tasks, while those with high self-efficacy have more confidence and will in their ability to cope with new challenges. As Shih et al. (2011) mentioned, collaborative learning provides learners with confidence in performing tasks and the ability to respond autonomously to designated tasks in preparation for a variety of situations they may face in real work. Technical engineers have to carry out constant research and technology development, so they were able to confirm that confidence in their abilities and willingness to develop innovations is important in promoting innovative technologies. In addition, as Lee and Jin (2023) pointed out, innovation results can be more successful when collaborative learning in technology development creates an atmosphere of freedom while creating competency development and job cooperation. Eventually, engineers of high-tech companies were able to confirm that they need an education and work environment that takes into account individual self-efficacy and job autonomy in strengthening innovative job behaviors through collaborative training.

Engineers view the job as challenging and important and approach it with the recognition that they should actively utilize their capabilities in the job. Also, job innovation is shaped by job autonomy awareness. In addition, the autonomy to choose one’s own work goals and methods is closely related to challenges. In particular, autonomy strengthens an individual’s sense of responsibility by making them recognize that their skills, abilities, and knowledge determine the success or failure of a task. Through the action of these two components, the cooperative education of engineers can reinforce innovation orientation.

Second, the quality of cooperative learning services had a positive effect on innovative work behavior. This is consistent with previous studies that show that learners can positively influence job performance by applying the skills and knowledge acquired in the curriculum to their actual work (Wang et al. 2023). As Cangialosi et al. (2020) pointed out, innovative job behavior refers to the process of creating new ideas that benefit the organization and converting them into differentiated products and services, systems, and operational methods through them. The subject of innovative behavior is individual, and innovative behavior is a purpose-oriented activity that is implemented to improve performance (Egan et al. 2004). In the end, innovative behavior consists of a process of developing, promoting, and realizing creative ideas, and the idea of innovative behavior is characterized by how it must be new in a universal sense. However, it does not just mean changing job-related matters; it is a process of discovering new ideas, commercializing them, and putting them into action in order to secure a competitive advantage in the market as well as the performance and development of the organization. Therefore, the educational effect can take place only when goal-oriented innovation-oriented behavior through collaborative education appears. In this respect, the results of this study show that collaborative training for engineers can play a positive role in the innovative job behavior of engineers pursued by technology companies.

Third, the quality of cooperative learning service had a direct effect on job autonomy and self-efficacy, but the mediating effect of job autonomy and self-efficacy on innovative work behavior through learning transfer has not been proven. These results suggest that mediating factors at the team and organizational levels, such as learning transfer, play a more important role in the impact of cooperative learning service quality on innovative work behavior than individual competency factors such as job autonomy or self-efficacy. Learning transfer is an important indicator of how effectively knowledge and skills acquired through learning are used in real work. All of these factors facilitate learning transfer and consequently contribute to strengthening innovative work behavior. Learning transfer, which changes behavior through acquired knowledge, skills, and attitudes, pursues innovation through effective use of knowledge, skills, and attitudes acquired in educational programs for work and strengthens new work orientation; consequently, it is involved in job performance and contributes positively.

Moreover, Hebles et al. (2023) argued that cooperative learning mainly consists of team- or organizational learning and focuses on enhancing the ability of team members to cooperate and communicate with each other. Therefore, team performance and cooperative ability may be more important. In addition, since cooperative learning creates an environment in which learners work together to achieve common goals, the team’s collective efficacy can play a more important role than individual autonomy and self-efficacy. Eventually, the results of this study also show that, if the quality of collaborative education is strengthened and has a positive effect on learning transfer, the innovative job behavior of engineers can be strengthened in the end. This shows that strengthening the quality of collaborative education plays an important role in the orientation of technological innovation and development.

6. Conclusions

6.1. Research Implications

Based on the findings of this study, the following specific implications can be suggested for high-tech corporations to enhance their collaborative skill-learning. Above all, from a theoretical point of view, this study is meaningful in that it suggested the importance of improving the effectiveness and quality of cooperative education. Until now, research related to cooperative education has focused on discussing educational tools and methods. However, as research on educational effects and reinforcement measures such as education application and satisfaction with educational subjects is necessary, this study is meaningful in that it empirically proved and presented the importance of strengthening cooperative education services for companies.

In particular, it defined the quality of collaborative education of high-tech companies and suggested that collaborative education affects the innovative work behavior of engineers. This is valuable as a research result that suggests the importance of strengthening collaborative education in technology companies and further improving the quality of education services. It shows the academic value of this study in that it presents research results that empirically prove that collaborative education in technology companies pursuing technological innovation can lead to innovative work performances beyond technical competency education.

As practical implications, three aspects can be considered. First, to maximize the effectiveness of collaborative learning among engineers in high-tech corporations, learning transfer should be considered a critical factor. To ensure that engineers can effectively apply the knowledge and skills they learn in educational programs to their actual work, the curriculum should be designed to reflect the actual work environment as closely as possible. It is essential that programs link theory and practice so that what is learned is immediately applicable to the current work. In particular, they should include working-level projects, simulations, and problem-solving tasks to create an environment where engineers can put the learned educational content to practical use.

Ultimately, maximizing learning transfer plays a key role in ensuring a company’s technological competitiveness. In the process of setting the direction of future technological development and solving challenges with diverse ideas, it is vital to create an environment where the knowledge and skills acquired through education can be effectively applied in the current job. To this end, corporations should build open research platforms to expand external collaboration and continue to foster a one-team culture through collaborative skill-building programs. This series of processes will contribute significantly to strengthening organizations’ competitiveness and improving their performance.

Second, a team-based learning environment should be cultivated. As collaboration skill-learning focuses more on team collaboration and improved performance than in-dividual job autonomy or self-efficacy, it is imperative to foster an environment where learners can engage in problem solving in teams. This can be accomplished through team projects, group discussions, role playing, and other teaching methods that help learners understand their role in the team and develop the ability to collaborate with teammates to solve problems. It is also necessary to provide physical and virtual collaboration spaces where learners can freely share ideas and collaborate.

Collaboration and communication with key device makers are becoming increasingly important, especially with the increase in technical complexity. For high-tech corporations, collaborative learning among engineers is a core component that ensures technological competitiveness and is essential for maximizing inter-departmental collaborations and practical applicability. It is important that the curriculum is designed to reflect actual work environments so that the knowledge and skills learned are effectively transferable to employees’ current jobs. This can be accomplished through high-level collaborative technical group discussion meetings and working-level deep technology councils. Through these councils, high-tech corporations can share their technology development roadmaps with device makers, align technology development timing, and strengthen collaborative relationships based on mutual respect and trust.

Third, high-tech corporations should consider designing learning programs not only to improve technical collaborations but also learning service quality. The service, including the instructors, contents, processes, curriculum, and other parts of the collaborative learning training, should be delivered in a way to maximize the learning style and its effectiveness in terms of instructor expertise, responsiveness, and program tangibility. The program design should be supported by an understanding of cutting-edge technology, especially with programs that target engineers. It is also necessary to design learning programs that are relevant to practical tasks so that collaborative experiences can be shared in actual work and collaboration skills can be practically improved.

Therefore, collaboration tools or methodologies that incorporate cutting-edge technology and represent engineers’ job responsibilities well must be applied. It is also possible to operate a program where experienced employees provide mentoring and coaching to newer employees or those who need to improve their collaboration skills; in this way, they can share their actual collaboration experiences and practically improve the learners’ collaboration skills. For example, it is necessary to lay the foundation for the growth of collaborative engineers by combining technical learning related to processes and equipment with systematic learning on new product development models from the beginning of employment. Moreover, considering the changes in manufacturing methods and organizational systems for next-generation product development and mass production in the digital technology era, corporations can redefine the roles and responsibilities of teams and consider running customized learning programs to enhance mutual competencies.

6.2. Research Limitations and Future Plans

This study has several limitations that should be addressed in future research. First, this study was conducted among Korean corporations only. Therefore, the findings are specific to the Korean corporate culture and environment and may not be directly applicable to corporations in other countries or with other cultural backgrounds. Korean corporations have a specific organizational culture and management style, and these factors may have influenced the study’s results. Thus, future research should expand the study to include corporations from different countries and cultural backgrounds to increase the generalizability of the findings.

Second, the collaborative learning service quality variable was defined and analyzed based on general service quality components in this study. However, for collaborative learning aimed at skill development, unlike general learning, there may be differentiated learning service quality factors that need to be considered. Therefore, in future research, it may be necessary to define the learning quality factor to reflect the work characteristics of technologically advanced corporations and the collaborative learning characteristics of engineers involved in technology development for in-depth discussion of and research into the learning qualities specific to these corporations.

Third, this study primarily used quantitative research methods for data collection and analysis. While this is useful for statistically analyzing the relationship between variables, it may not fully reflect the qualitative aspects of collaborative learning for engineers or the subjective experiences of learners. To better understand the actual effectiveness of collaborative learning and learners’ experiences in the process, qualitative research may be needed to explore the specific experiences and perceptions of learners based on phenomenology or grounded theory.