2.1. QFD and Kano Model
QFD (Quality Function Deployment) is a holistic concept that translates customer needs into appropriate technical requirements and provides each step method for production and development of each product, thus the whole department is to cooperate closely, such as marketing strategy, planning, product design, engineering, product development, and sales department, etc. [
30]. There is also a definition [
32] that “QFD is a method for an organization to predict customer needs, prioritize them, and to effectively embody the products or services which would be provided to the end consumer. Bicknell and Bicknell [
33] defined QFD as “a systematic approach that measures customer needs using objective and quantified methods and translates them into product and process factors that can be unambiguously identified.”
“Quality” in “Quality Function Deployment” means quality, features, or attributes of a product, “function” means a function or mechanization, and “deployment” means diffusion, development, deployment, or evolution [
34].
According to Runte [
35], “quality” is used to mean “attributes”, and the attributes that are the most important are investigated first and are the basis for other attributes are the attributes requested by customers among the attributes that appear in QFD. “Function” means “various functions within an organization”; for example, it is a function of a subordinate organization such as marketing, engineering, manufacturing, and purchasing part that forms and implements quality of products. “Development” means “to put or distribute widely for future use”, and technically it can be used in a narrower sense. It is said that a selected portion of the vertical attributes of the first matrix of the QFD called the House of Quality is used as the input element for the second matrix, the part of the vertical attribute, which is partly attributed to the second matrix and is used as the input element of the next matrix. In this way, the fact that QFD data are applied to both functions of the organization through the QFD process is said to be “deployed”.
Thus, QFD is created to connect design, development, engineering, marketing, manufacturing, services, and other organizational functions with the desired quality of the customer’s expectations through a systematic arrangement that seeks to mitigate the growing demand differences between producers and users in the process of manufacturing and business development [
36].
Recently, QFD has been very closely associated with concurrent engineering and Lean Production, which emphasizes product quality and innovation [
37,
38].
According to Tan and Shen [
39], QFD was invented and presented as a quality technique in 1966 by Yoji Akao in Japan. QFD was applied for the first time in 1972 at Mitsubishi Heavy Industries’ Kobe shipyard when making deep-sea fishing boats [
40], and Toyota Motor Corporation in 1975 has begun to apply it to the automobile industry, beginning to be applied to all industries.
QFD has been introduced by global companies such as Ford, GM, Hewlett Packard, AT&T, and ITT in the United States since the mid-1980s, with great success, and it was mainly used in the field of manufacturing, but gradually the scope of application has been expanded and applied not only to industry but also to the fields of administration and services [
41].
Regarding the effect of introducing QFD, Toyota Motor Corporation has reflected the needs of various customers in its products, and since 1984, Toyota’s pre-mass injection cost has been 61% compared to 1977, which was the time before the introduction of QFD. It was found that the input cost before mass production was reduced by 61% compared to 1977, which was the time before the introduction of QFD, the time until market shipment was shortened to about one-third, and the product quality was improved [
30].
The QFD and Kano Model are complex and require professionals to run them, so they have been done by large companies with resources and qualified professionals [
16]. Cristiano et al. [
31] reports that 30% of the 400 companies selected by the supporting members of the Japanese Union of Scientists and Engineers responded to the e-mail survey and it takes a meaningful period of six years for them to settle QFD in their organization, taking two years to systematize this. They also explain that the most important impediment to performing QFD is the ability to capture and understand customer needs and complete a large QFD matrix.
Looking at the current situation of QFD utilization through open literature, it has been used in various fields and industries such as construction, communication, aircraft, horticulture, clothing, medical service, food, library, university education, milk, postal service, petrochemistry, VR/AR, Mobile-Government Service, Logistics Service, Tire, and IOT. Also, it can be confirmed that QFD is applied from various countries such as the United States, South Korea, Italy, China, Pakistan, United Kingdom, Spain, Indonesia, India, Iran, The Netherlands, UAE, and Turkey through the literature. An average of approximately 350 documents has been published annually from 2015 to 2020 when searching the number of documents such as cases utilizing QFD through Web of Science, Elsevier, Scopus, etc.
Sharma et al. [
42] investigated 400 documents with a QFD theme applied from 1988 to 2006 and divided them into the categorical functional fields of QFD and the pure QFD (pure QFD) research field. The categorical functional fields are categorized into primary, secondary, and tertiary. The primary functional fields consisted of product development, customer requirement analysis, and quality management system. The secondary functional fields are concurrent engineering, management science, planning, operational research, education, software, and specialized systems fields such as artificial intelligence (AI), artificial neural networks (ANN), and fuzzy logic. The tertiary fields are further expanded by the application of QFD, for example, in the areas of construction and housing, cost, environment, decision making, and services sector application.
Looking at the literature on the shortcomings of QFD, four issues can be summarized [
43]. The first is that traditional QFD cannot be used when multi-criteria considerations are needed [
44]. The second is that using customer’s terminology leads to ambiguity and inaccurate characteristics, raising questions about the validity of QFD results [
45,
46]. Thirdly, QFD is mainly useful only for new products and is not well utilized for improving existing products [
47,
48]. Fourth, QFD is complex and very difficult to perform while analysis of the data is performed from a subjective point of view; thus, results are lacking in consistency, leading to the concerns on the vague relationship displayed between customer needs (Whats) and product specifications (Hows) [
46,
49,
50]. The advanced models developed to solve the limitations of traditional QFD have been proposed such as Fussy QFD, Kano-based QFD, AHP QFD, ANP QFD, Project QFD, and TQFD [
51].
The quality research section of the Japan Quality Control Association conducted the most extensive survey on the actual state of QFD use among Japanese companies Also, in 1986 [
52,
53], the survey was conducted by e-mail to more than 400 companies in Japan selected by the supporting members of the Japanese Union of Scientists and Engineers (JUSE). According to the survey results, 30% of the respondents applied QFD widely to multiple products, and the House of Quality was the most used matrix. However, the results of the study only described the use situation of QFD and did not provide any information about the effectiveness of QFD [
41].
In addition, there is a limit to how best practice research remains in the country, mainly to case studies and research studies [
41] while QFD has been used extensively in a variety of industries worldwide [
29,
30]. Franceschini and Terzago [
54] also points out that the effectiveness of training courses is not handled, only staying in case studies despite the new approach to applying QFD to industrial training courses. An analysis of prior research shows that QFD is applied to various fields, such as the manufacturing industry, service industry, and even public services, but most of the studies are limited to cases in an individual industry. In addition, few studies have tried to analyze the feasibility of application results and only presented application cases of QFD. This research verifies the effectiveness of QFD after applying it comprehensively to the technology-based SMEs in various industries in order to overcome the limitation [
41,
54] of the verification absence of the effectiveness of QFD, which differentiated this paper from the existing studies focusing on only a case of the single industry.
Traditional QFD analysis has been a difficult problem to understand accurately for customer needs [
55] as explained on the shortcomings of QFD [
43]. Researchers have combined QFD with Kano’s Model for customer satisfaction over the years in order to accurately understand customer requirements [
36,
56,
57,
58,
59]. Kano Model is a Customer Satisfaction measurement model studied by Noriaki Kano of Tokyo Rica University in Japan in 1984 [
40].
The Kano Model has been applied to classify the characteristics of a product or service and used as a way to satisfy the needs of its customers since being developed by Kano and his colleagues [
60]. The Kano Model has also been widely applied in several industries with an effective tool for examining customer preferences [
61]. Lee et al. [
62] applied the Kano Model to QFD to confirm the needs of consumers. Shen et al. [
63] insist on a customer delight element to provide an innovative product that brings higher consumer satisfaction than the competitor while presenting an innovative product development process and on applying the Kano Model utilizing questionnaire in the QFD process.
In this study, the QFD training program is enabled for technology-based SMEs to confirm their customers’ needs and lead to sales expansion because the Kano Model is used when investigating customer requirements. They can find the customer delight factor, that is, the attractive quality factor, so that the technology-based SMEs can have a competitive advantage with their competitor in the market through the Kano Model, and gain the market share more than the competitor. The reason that the customer delight factor is important for the technology-based SMEs is that they lack resources; not only human resources but also brand power compared to their competitors. Those customer delight factors generate an effect of impressing a customer and attracting another customer by the customer when the firm penetrates into the market. Therefore, it becomes possible to outperform competitors and survive in the market when factors of customer delight are dissolved in products or services.
2.2. Application QFD for SMEs
As stated by the European Commission [
64], SMEs are companies that employ less than 250 employees and of which the annual turnover is less than 50 million euros or of which the total assets are less than 43 million euros. The definition of SMEs in South Korea is that SMEs require maximally 109 million euros or less average sales or annual sales depending on the type of industry for enterprises that conduct business for the purpose of profit, and total assets without industry classification must be less than 364.9 euros as stated by Framework Act on Small and Medium Enterprises Article 2 Paragraph 1 Item 1 [
65]. It is about double the turnover and about eight times the total assets, compared to European standards.
SMEs are the backbone of the economy in most countries. In addition, systematically integrating the needs of consumers in the design of products is an important issue for all industries, especially SMEs with insufficient technical means and resources [
1]. According to Zhou et al. [
16], most SMEs, which account for 80% of the global economy [
2], have insufficient financial resources and experts who carry out QFD and Kano Model in them, so there is a limit to applying it to them. Therefore, they propose to convert and use Customer Requirement (CRs) in a way called Customer Requirement Information (CRIs) for easy and proper application to SMEs. However, if an expert who is basically familiar with the basic structure of QFD does not work for SMEs, QFD cannot be utilized even if the customer’s requirements are simply converted into customer requirement information.
There are relatively few studies applying QFD to SMEs. A total of almost 50 journal papers from 2001 to 2019 have been published when the QFD and SMEs are commonly searched and displayed documents that utilize QFD for SMEs through the Web of Science, Elsevier, Scopus, etc. This is a very small number when compared with the publication of more than 350 QFD-related studies each year. In addition, cases of SMEs in India, China, Malaysia, Singapore, and Hong Kong, which are mainly in Asia, have been announced.
Rozar et al. [
66] applied QFD to promote Green Supply Chain Management and applied it to Malaysian SMEs for Sustainability performance approach, and O’Gorman et al. [
67] applied QFD for the design and manufacture of innovative hybrid doors for SMEs. Hsu et al. [
68] applied QFD to SMEs to overcome the limitations that SMEs have shortages of resources in most cases (unlike large enterprises, as successful sustainable enterprises) and to search for Balanced scorecard factors that meet sustainability development requirements through using QFD and fuzzy MADM (Multiple Attribute Decision-Making). QFD was applied to the e-business planning system for SMEs in the research of Tan et al. [
69], and Barad and Gien [
70] utilized QFD as a tool to help priority selection of SMEs to improve their performance.
Looking at the above literature comprehensively, the papers that apply QFD to SMEs are mainly the case studies where they are limited in each industry, as are the cases with the existing QFD researches. Especially, there is a tendency to concentrate on sustainable enterprises and supply chain management. However, there is no research that presents a QFD utilization method that is structured to suit SMEs and early technology-based SMEs or that should be used in common with various types of SMEs and early technology-based SMEs.
In this research, one QFD professional coach is arranged per early technology-based SMEs in order to solve the problem that QFD is complicated and difficult, which has been pointed out as a limitation point in applying QFD to SMEs in the previous research. Also, we utilize Kano-based QFD to improve the ambiguity of customer requirements priority accuracy. Moreover, we verify the effectiveness of the Kano QFD training program applicable to early technology-based SMEs that can be used not only in one industrial field but also in various industrial fields.
2.3. Effectiveness of Training Program
Tai [
17] argued that the effectiveness of training was the extent to which the purpose of training was achieved. The concept of training program evaluation is to present the information necessary to improve the training program, to decide whether the program is sustainable, to show how the relevant departments contribute to the organization’s goals and objectives, and to justify the feasibility of the training program [
18,
19]. Based on these theoretical backgrounds, we apply a model that measures the effect of training to verify the effectiveness of the Kano QFD program applied to early technology-based SMEs in this study.
Training effectiveness is usually determined by some combination of categories presented in Kirkpatrick’s [
71] hierarchical model of training outcomes [
23]. The Kirkpatrick model consists of four stages. Reaction assessment, which is the first step, is a measure of the degree of reaction of the program content and the training course of the people participating in the curriculum. Also, it aims to measure the satisfaction of participants through the step, which is “reaction evaluation” to investigate for lecture evaluation and course summary at educational sites. This step consists of evaluation items for correcting and supplementing educational process and operational problems rather than evaluating the effects and outcomes of education. The second evaluation of learning measures the change of the learner’s attitude, the degree of knowledge acquired, and the degree of acquisition of technology as a result of the curriculum. The third step evaluation is a behavior evaluation, which is an evaluation of whether or not what the participants learned through the training process caused a change in the actual execution of duties, and usually the in-service applicability is evaluated. The final fourth step is to evaluate the results and whether there are any specific improvements to the individual or organization. For example, it evaluates substantial changes such as sales increase, cost reduction, productivity improvement, and profit increase. Reaction evaluation, learning evaluation, and behavior evaluation is an assessment of an individual who has undergone a training course, and result evaluation is to evaluate the effect of the results of the curriculum at the individual/organizational level on the business performance [
10,
17,
23].
Many prior studies support Kirkpatrick’s hierarchical model as an evaluation method for education and training [
17,
20,
21,
22,
23]. Although Alliger and Janek [
72] criticized the suitability of relationships in measuring the four-step Kirkpatrick model [
17], the Kirkpatrick model is still a useful and valuable method in evaluating the results of education and training [
17,
73,
74,
75].
Positive reactions and learning outcomes of participants, behavioral changes due to the results of the training, and progress improvement outcomes related to work can be expected when the training program is well designed and well-administered in Kirkpatrick’s hierarchical evaluation model. However, the attitudes, interests, values, and expectations of participants can undermine or enhance the effectiveness of training. Determining the characteristics of a particular individual that affects the effectiveness of training is of great importance when we want to know how to increase the potential for behavioral changes and performance improvements that result from participating in a training program [
23].
Therefore, this study focuses on the personal characteristics of training participants that influence the effectiveness of training, and in particular the learning transfer effect, which examines whether or not there is an intention to apply what has been obtained through the training into the field.
2.4. Learning Transfer Intention Model
The term “Learning transfer” was started in the concept of “transfer” in the 1910s and has been used as various terms such as education transfer and training transfer [
76]. Many researchers have argued that “Motivation to Transfer” is one of the important factors that explain the learning transfer, and that it is an essential element in the process of training transfer [
27,
77,
78]. Studies on the motives for transfer have been conducted continuously rather than studies on transfer intentions.
Motivation to Transfer is defined as the desire of learners or trainees to apply what they have learned through education and training to the field of work [
23]. Yamnill and McLean [
79] define transfer motivation as a participant’s hope for using the knowledge and skills learned in the company training program on the job site. It may also define a transfer motivation as the intended effort to utilize the skills and knowledge learned through training in the field of actual work [
80].
These definitions are expressed in a desire, hope, intended effort to emphasize the willingness of the learner. The concept of motivation to transfer has begun to be noticed in attempts to evaluate the effectiveness of training, and Kirkpatrick’s four-level evaluation model has emerged and has been gradually materialized. The performance of training consists of reactions, learning, behaviors, and outcomes according to a Kirkpatrick’s study [
81], and the concept closely related to learning transfer among these is the behavior evaluation corresponding to the third step [
76].
Rowold [
82] argued that a participation in training will improve job outcomes in the future if learners are motivated to apply the content of the training to their work when they enter their work environment. Intention in the transfer intention means “the idea or plan to do what you want to do, or what you want to attempt,” and is treated as an important factor in the academic domain of consumer behavior. Academically, it is defined as “reflecting the cognition of an individual’s will to perform a given action” [
76]. The behavioral intention when the actual behavior is expressed is treated as an important factor that predicts the actual behavior in the closest distance according to the representative theoretical planned behavior theory [
26] in the field of psychological behavior [
76].
The three types proposed by Baldwin and Ford [
27] have been accepted as the most influential conceptual models to date as factors affecting the transfer intention. [
28]. Three factors are classified into learner characteristic factors, training design factors, and work environment factors. This model includes various variables compared to the Holton model [
83], and is used by many researchers [
84].
In this study, we target CEOs or employees of 3–7-year-old technology-based SMEs who have participated in the Product Improvement Academy under the control of Ministry of SMEs and Startups of the South Korean government ministry in charge of SMEs and Korea Institute of Startup and Entrepreneurship Development of its affiliated organization. The learner characteristics factor and the design factor of training are selected as the two factors of the transfer intention impact factors among the three types proposed by Baldwin and Ford [
27] in consideration of the peculiarity of this program.
Regarding the business environment factor, it is very difficult to select the measurement question, considering that the type of industry is different for each learner and the number of years since its foundation is different. So, we decided to mention it as the limits and the subsequent research in this study. We investigate the attitudes of learners to tackle learning as their readiness, that is, their motives and expectations for learner’s characteristic factors. Based on these theoretical foundations, the first hypothesis is set as follows.
Hypothesis 1 (H1). A learner’s readiness has effects on transfer intention.
Self-efficacy, which is performance self-efficacy that gained from one’s experience and surrounding feedback in relation to learning transfer ability, and the perceived content validity has the mediating effect on motivation to transfer in the learning transfer model of Kirwan and Birchall [
85]. The term “Self Efficacy” means trust in one’s ability, and is a concept developed by Bandura’s theory of social learning [
86]. Self-efficacy has been treated to be one of major factors in various research areas in terms of that a person with a high perception of self-efficacy can perform better than someone with the same level of knowledge and skill [
84]. Therefore, the second hypothesis is set as follows.
Hypothesis 2 (H2). A learner’s readiness has effects on self-efficacy.
The learner readiness is expressed as a direct effect on the transfer motivation, that is, the transfer intention, apart from self-efficacy in the model of Kirwan and Birchall [
85], but the learner readiness and self-efficacy are set as the factors that influence the transfer motivation in the same way from the model of Holton III et al. [
83]. Gegenfurtner et al. [
78], in various studies investigating the relationship between self-efficacy and transfer motivation, put the focus of self-efficacy on “learning efficacy”, “computer efficacy”, and “general or execution self-efficacy”, but he emphasizes post-training self-efficacy as the factor that most strongly explains the transfer motivation among them. Machin and Fogarty [
87] ascertained the direct relationship between self-efficacy and transfer after training. It was Axtell et al. [
88] and Al-Eisa et al. [
89] who found that self-efficacy affects transfer intention in a general sense [
84]. Therefore, the third hypothesis is set as follows.
Hypothesis 3 (H3). A learner’s self-efficacy has effects on transfer intention.
There is insufficient empirical research to clarify what role the design factors of training play in the transfer process [
84,
90]. However, we decide to select the perceived content validity as a training design factor among the complicated factors of the learning transfer model of Kirwan and Birchall [
85] in this study. Therefore, the fourth and fifth hypotheses are set as follows.
Hypothesis 4 (H4). A perceived content validity has effects on transfer intention.
Hypothesis 5 (H5). A perceived content validity has effects on self-efficacy.
As described above, self-efficacy has a mediating effect on the relationship between the perceived content validity and the motivation to transfer in the learning transfer model of Kirwan and Birchall [
84,
85]. We also select a self-efficacy as a mediating factor of learning transfer intention as in the study by Kirwan and Birchall [
85]. Therefore, the sixth and seventh hypotheses are set as follows.
Hypothesis 6 (H6). A self-efficacy has mediating effects on the relationship between learner readiness and transfer intention.
Hypothesis 7 (H7). A self-efficacy has mediating effects on the relationship between perceived content validity and transfer intention.
Figure 1 shows the above hypothesis as a theoretical model.