- Email: [email protected]
Using Kano model to differentiate between future vehicle-driving services
International Journal of Industrial Ergonomics 69 (2019) 142–152
Contents lists available at ScienceDirect
International Journal of Industrial Ergonomics journal homepage: www.elsevier.com/locate/ergon
Using Kano model to differentiate between future vehicle-driving services a
b,∗
Min-Yuan Ma , Chun-Wei Chen a b c
T
c
, Yu-Ming Chang
Department of Industrial Design, National Cheng Kung University, No. 1, Dasyue Rd., East District, Tainan City, 701, Taiwan, ROC Department of Crafts and Creative Design, National University of Kaohsiung, 700, Kaohsiung University Rd., Nanzih District, Kaohsiung, 811, Taiwan, ROC Department of Creative Product Design, Southern Taiwan University, No. 1, Nantai St., Yongkang City, Tainan County, 710, Taiwan, ROC
A R T I C LE I N FO
A B S T R A C T
Keywords: Kano model Feature Customer satisfaction Car steering wheel Autonomous driving
Services are constantly changing with the introduction of new technologies, which affect the service systems of both conventional and autonomous driving. New theories and technologies are also key factors affecting the design and development trends of service models and solutions. Major automobile manufacturers aspire to provide customers with unique services and experiences, resulting in a growing demand for systematic approaches to characterize customer behaviors and scientific methods to accurately interpret data stored in databases. This study proposes a scientific engineering and operation framework for driving services that enables conventional automobile manufacturers to re-evaluate their service models and solutions as they expand into the domain of autonomous driving, integrating customized consumer interactions and mass production efficiency to develop new technologies, and subsequently applying these technologies to innovate their driving services, form service innovation guidelines, and accelerate the development of smart applications for the automobile industry. A Kano two-dimensional model of quality was employed. A Kano questionnaire was administered to analyze consumers' perceived satisfaction concerning different service quality elements; the elements were then ranked in the order of requiring improvement to determine the elements that are essential in conventional vehicles. Finally, suggestions were proposed for improving the service quality of driving products and evaluating driver satisfaction. A total of 56 valid questionnaires were collected from potential buyers of four-door sedans. The questionnaire evaluated respondents’ perceived value and satisfaction of 30 product elements categorized into two groups (specific functions and intangible value-added services) across eight major quality dimensions (basic safety functions, multimedia entertainment systems, information and communication systems, value-added systems, active matching, automatic service systems, hardware–software integration, and customer service and support). In addition, Kano quality categories were statistically analyzed to elucidate whether significant differences existed between groups. Using the Kano quality categories, 30 design elements were classified: 10 as “attractive,” 7 as “one-dimensional,” 3 as “must-be,” 4 as “indifferent,” and 6 as “reverse.” Enterprises can effectively reduce customer dissatisfaction and enhance customer satisfaction based on the quality category of the product and the product improvement order proposed in this study. Relevance to industry: This study determined that using the Kano quality categories, enterprises can effectively reduce customer dissatisfaction and enhance customer satisfaction based on the quality category of the product and the product improvement order proposed in this study.
1. Introduction
electric vehicles will continue to grow; more importantly, the automobile market will experience an even greater reform, which is autonomous driving. Automotive companies need to consider what is attractive to drivers and what consumers consider to be “driver-friendly” (Dominici et al., 2016). Due to the warming at the automotive market in the last years and consequently the growth of vehicle production has been moved and placed emphasis on the segment (Cagnin et al., 2016). Whether future
1.1. Background and study motivation An automobile is an exceptional product that, aside from numerous practical functions, also interacts intimately with its owner's family life and reflects the personal economic status. However, the automobile industry is facing drastic change. In the next ten years, the presence of
∗ Corresponding author. Department of Creative Design and Architecture, National University of Kaohsiung, 700, Kaohsiung University Rd., Nanzih District, Kaohsiung, 811, Taiwan, ROC. Tel.: +886 915334637. E-mail addresses: [email protected] (M.-Y. Ma), [email protected] (C.-W. Chen), [email protected] (Y.-M. Chang).
https://doi.org/10.1016/j.ergon.2018.11.003 Received 6 July 2017; Received in revised form 26 October 2018; Accepted 27 November 2018 0169-8141/ © 2018 Elsevier B.V. All rights reserved.
International Journal of Industrial Ergonomics 69 (2019) 142–152
M.-Y. Ma et al.
once reliable autonomous driving is realized. However, current autonomous driving systems can only be classified as an assisted-driving function. Therefore, the functions of the steering wheel cannot be fully delegated to the vehicle yet. The car part that a driver interacts with most directly and frequently is neither the appearance nor power system of the car, but rather its interior design, of which the steering wheel is the most fundamental component. In addition to the shape of the steering wheel, the gripping experience and the convenience during operation are also crucial (Chang and Chen, 2016). This study predicts that steering wheels will expand beyond their existing function for navigation, such as becoming the point of contact for the vehicle to monitor drivers. For example, Toyota unveiled a steering wheel equipped with a built-in sensor to track drivers’ alertness and adjust the environment of the vehicle accordingly. With increasing concerns on customer needs in today's competitive market, the issue of incorporating customer requirements into product design arises the interest of both researchers and practitioners (Ji et al., 2014). The concept of two-dimensional quality was introduced by Noriaki Kano and his colleagues, who developed a highly effective model to classify customer preference into five categories, namely onedimensional quality, attractive quality, indifferent quality, must-have quality, and reverse quality. Thus, the two-dimensional concept in the Kano model enables describing the relationships of customer satisfaction with product quality or service quality. Kano's model, which studies the nature of customer needs, provides a way for a better classification of customer needs (Ji et al., 2014). Given this consumercentered mentality, consumers may not always seek a product with the best attributes. Rather, they seek products that can satisfy their emotional needs. Consumers evaluate products using multi-quality/multiprinciple strategies (Chen and Chuang, 2008). Using the Kano model to define quality categories helps designers determine the true needs of consumers and enable them to more accurately control quality and satisfaction during product design and development (Chen and Li, 2008). The Kano model of quality addresses consumers' needs rather than their preferences (Kano et al., 1984). Existing questions and answering alternatives included in the Kano methodology must be adapted to the nature of experiences (Högström, 2011). For this reason, the present study adopted the Kano model for further classification of the service quality in driving. Past studies on this topic have mostly relied on statistical methods to identify the service qualities that notably affect the customers, but they have usually been unable to determine the degree to which customer satisfaction (or the lack of it) can be improved once measures regarding the service qualities in question have been implemented, and nor could they identify the exact service qualities that actually matter for the customers. To overcome this problem, a two-dimensional quality model was employed to effectively allocate the resources of automobile manufacturers, thereby improving the standard of driving service. Does adding technical services to the specific function of driving service products enhance product value and consumer satisfaction? Experiment focuses on the functional requirements and service satisfaction aspects of qualityservices are constantly changing with the introduction of new technologies. In this study, we applied research results concerning consumers' “basic demands” for conventional driving services to autonomous driving to identify the functions or services that promote consumer satisfaction. The experimental results highlight the indispensable feelings and services in conventional driving. These data were then used to determine the associations between product functions, services, and customer satisfaction, as well as how these associations can be incorporated into the provision of desirable customer service in autonomous driving and the identification of vital Kansei quality factors (attractive elements) for enhancing customer satisfaction. In addition, changes in the use of conventional vehicles, as well as various intangible services of autonomous vehicles, were examined to determine their functions in the provision of services and their effects on customer demand and expectations. We further reviewed numerous
autonomous vehicles should be equipped with a steering wheel or a user-controllable braking system is a question that has triggered substantial debate among conventional automobile manufacturers and major Internet companies. The existing line of autonomous vehicles developed by Google is equipped with a control system that enables the driver to fully brake the vehicle in emergency situations. However, these vehicles are not equipped with a steering wheel. Conventional automobile manufacturer Audi stated that drivers should still follow driving protocols amidst the prevalence of autonomous driving technologies and remain vigilant on the road, even with the absence of a steering wheel and brake pedal. Audi works to improve the safety elements of existing autonomous driving systems, such as monitoring drivers’ conditions during autonomous driving. Tesla recommends that drivers keep their hands on the steering wheel so that they can control their vehicles where necessary. The University of Michigan conducted a survey on autonomous and semi-autonomous driving systems in April 2016. Among the 618 respondents, 66% expressed reserved attitudes towards completely autonomous driving. However, 94.5% of the respondents opted for autonomous vehicles with steering wheels, 37% expressed safety concerns towards fully autonomous vehicles, and 17% expressed safety concerns towards semi-autonomous vehicles. In recent years, as studies on advanced driver assistance systems have garnered more attention, the behavior patterns and service experiences of the driver have also started to be emphasized by relevant scholars. An understanding of the pressure patterns that are appropriate for the human body (that is, the patterns that reduce discomfort or improve what is called comfort) can make product design and usage very satisfying and fulfilling (Goonetilleke, 2000). To quote the Dansk Design Center, the term “service design“ refers to the design of systems and process around the idea of rendering a service to the user (Bedford and Lee, 2008). Therefore, the primary task of service design is making the service you deliver useful, usable, efficient, effective and desirable (Design Council, 2010). In the field of service design, the touch point has always been an essential concept; where automobiles are concerned, the touch points consist of the items and scenarios in the service system that interact with the driver. Increasing the number of touch points generates more user experience, which facilitates enhancing the value of the service system and customer satisfaction. However, from the perspective of cognitive psychology, further analysis suggests that ambiguity, uniqueness and dominance are three important aspects to consider when designing and developing icons (Goonetilleke et al., 2001). In the case of automobiles, service designers must address the core service, operational service, and servicescape all together, to meet drivers' preferences and expectations for journey-related service encounters (Grace and O'Cass, 2004). Therefore, the topic of how should corporations develop diversified strategies (Nunes and Cespedes, 2003) to understand the user behaviors (Kumar and Venkatesan, 2005) and service experiences (Patrício et al., 2011) of their customers has merited further investigation. Although service is a crucial factor affecting quality, most service providers tend to perceive it as one-dimensional. The so-called onedimensional quality refers to the one-dimensional thinking that when a quality element is known to satisfy the customer, the greater the degree to which the product features this quality element, the more satisfied the customers will be, and vice versa. However, the truth is that not all quality elements are one-dimensional, some of them can be two-dimensional, which means that the full presence of certain quality elements would not necessarily satisfy the customers; sometimes, the customers would even become displeased, or remain totally indifferent. Thus, how to improve driving service quality, enhance customer satisfaction, create competitive edge, and reduce customer attrition are key aspects of driving service that must be addressed. 1.2. Research question and motivation People commonly believe that steering wheels will become obsolete 143
International Journal of Industrial Ergonomics 69 (2019) 142–152
M.-Y. Ma et al.
considered for operating a steering wheel were collected, including built-in functionality. The data were compiled into a spreadsheet. 2.2. Establishing quality evaluation dimensions and evaluation characteristics The two-way Kano quality questionnaire was based on a number of quality evaluation dimensions and characteristics that affect the perceived satisfaction of drivers when they operate vehicles. In line with Kano's model for examining consumer “demand” rather than “preference” (Kano et al., 1984), a focus group was organized to discuss the steering wheel samples in Fig. 2 to identify the demand attributes and characteristics. A total of 15 elements across four dimensions were identified. The dimensions were basic safety functions, multimedia entertainment, information and communication, and value-added systems. These elements were collectively named the “specific functions.” To apply the aforementioned elements in future autonomous driving environments, a number of experts were invited to participate in a brainstorming session. All experts were between the ages of 25 and 50, and had a driver's license and at least two years of driving experience. A diffusion mode of creative thinking was applied to discuss future autonomous driving scenarios, which included the concepts of “criticism is ruled out,” “freewheeling is welcomed,” “go for quantity,” and “hitchhike (improve) on ideas.” A total of 15 quality evaluation elements across four dimensions were identified. The dimensions were active matching, automated system services, software–hardware integration, and customer service and support. These elements were collectively named “Intangible Value-Added Services.” A total of 30 elements were selected to evaluate, define, and explain the positive and negative quality elements associated with demand. Subsequently, 56 respondents were invited to score each element according to their preferences. The elements were measured using a fivepoint Likert scale (Table 1).
Fig. 1. Functional requirements and service satisfaction research framework.
studies on customer satisfaction to prioritize the improvement of product functions and uncover the underlying flaws in drivers’ service experiences. These observations helped us envision the future ecosystem of autonomous driving services. 2. Methods of study and implementation The research framework for Experiment is illustrated in Fig. 1. All quality elements associated with driving service quality were identified, and then, a questionnaire was formulated based on the Kano model of quality and administered to the study respondents. The service quality perceptions of the respondents were categorized based on a two-dimensional quality classification scheme. This scheme was devised to overcome the flaws of the original one-dimensional scheme by including a psychological dimension for quality evaluation. Interpreting the correlation between product quality and consumer satisfaction in two dimensions reveals that favorable product quality may not always satisfy consumers, but sometimes leads to indifference or dissatisfaction, instead.
2.3. Two-way Kano quality satisfaction survey and quality determination 2.1. Stimulation test sample selection The questionnaire was based on a Kano two-dimensional model of quality. The survey results were categorized into five dimensions, namely, “attractive,” “one-dimensional,” “must-be,” “indifferent,” and “reverse,” based on the respondents' opinions. Survey participants were general office workers with two or more years of driving experience. A literature review was performed to categorize the service quality of steering wheels into two major groups, “specific functions” and “intangible value-added services.” The questionnaire was designed based on the Kano two-dimensional model of quality. Participants’ identities were kept anonymous, with only the demographics of gender, years of driving, and daily average driving time solicited. The questionnaire items were the quality evaluation elements. The respondents answered the items based on a two-dimension system, specifically, with or without a specific quality element. The survey comprised two parts. The first part was the two-way Kano quality questionnaire to elucidate consumers' demands. The second part evaluated the performance of the samples (steering wheel
In the development of products, methods enabling the designer to create an appropriate image for a product so that it may communicate with the user are always critical issues (Chuang and Ma, 2001). To avoid discrepancy regarding form vs content, the focus team, through direct visual perception, classified the test samples according to the visual characteristics of the images. To classify the 114 test samples by form contrast(Chang and Chen, 2016), the KJ method was adopted. This method is used for qualitative analysis based on intuition and for innovating through teamwork. Because this method is used for analyzing how participants perceive the images, it enables the characteristics of images to be more clearly understood (Yang, 2003). The main objective of Experiment was from to examine service quality. The focus group was invited to categorize the 45 stimuli samples into 12 clusters based on design similarities. The most representative sample from each cluster was selected for a total of 12 representative samples in Experiment (Fig. 2). Then, data concerning the potential factors that may be
Fig. 2. Final samples for experiment (n = 12). 144
International Journal of Industrial Ergonomics 69 (2019) 142–152
M.-Y. Ma et al.
Table 1 Quality elements associated with customer demand. Group
Dimension
Element
Specific Functions
Basic Safety Functions
A1. Horn button A2. Paddle shift controls A3. Lane departure warning indicator A4. Hazard warning indicator A5. Airbag deployment indicator A6. Fast U-turn B1. Audio device play button B2. Audio device scroll button B3. Audio device volume buttons C1. Pick up/end call buttons C2. Navigator activation button C3. Cruise control activation button D1. Air conditioner control buttons D2. Rear seat light control buttons D3. Four-directional control pad
Multimedia Entertainment System
Information and Communication System Value-Added Systems
Iintangible Value-Added Services
Active Matching Automatic Service System
Software–Hardware Integration
Customer Service and Support
E1. Automatic recording of drivers' habits and frequently used commands E2. Steering wheel height and headrest adjustment based on facial recognition F1. Arbitrary manual driving override F2. Driver attention detection while the steering wheel is held F3. Autopilot when hands leave steering wheel F4. Easy return of steering wheel after turning F5. Auto-complete lane change after initiating auto-turning F6. Active cruise control support system F7. Automatic lane departure warning support system (steering wheel vibrates to warn the driver when leaving a marked lane without turning on indicators) G1. App-enabled (drivers can input their preferred parameters into a dedicated app; the parameters are automatically synced with the steering wheel activation control system) G2. Fatigue detection based on facial recognition G3. Fatigue relief (lights are activated to stimulate drivers' sympathetic nervous system when fatigue is detected) G4. Stress relief (light music, warm lights, and a pleasant fragrance are provided to stimulate drivers' parasympathetic nerve system when stress is detected) H1. Voice commands H2. Audio reminders
(1) to apply the respondents' feedback and the Kano two-dimensional model of quality to classify the quality attributes of product design; (2) to determine the respondents' perceived importance of the various items and elucidate the customer satisfaction coefficients for the various products; (3) to determine the differences between the customer satisfaction coefficients of the five product quality attributes; and (4) to determine whether the Kano evaluation rules support or reject the product classifications developed in this study.
Table 2 Kano two-way questionnaire format (using hardware–software integration services as an example). How do you find steering wheels with “autopilot when hands leave steering wheel?” □ Delightful □ Necessary □ No Comment □ Tolerable □ Unacceptable How do you find steering wheels without “autopilot when hands leave steering wheel?” □ Delighted □ Necessary □ No Comment □ Tolerable □ Unacceptable
designs) for each quality element (demand attribute) and overall satisfaction (Table 2). A total of 56 participants aged 25–50 (35 men and 21 women) were invited to participate in the two-way Kano quality questionnaire, which comprised a series of “paired items” (with and without a specific quality element) to survey the respondents’ opinions. The Kano quality attribute determination matrix was used to determine the quality classification of each demand element (Table 3). Based on the aforementioned procedures, the objectives of Experiment were as follows:
2.4. Data analysis 2.4.1. Questionnaire content validation Reliability refers to the scoring consistency among groups of participants. In this study, Cronbach's alpha was used to measure item consistency. 2.4.2. Data analysis and hypothesis testing 2.4.2.1. Basic descriptive statistical analysis. The descriptive statistics
Table 3 Decision matrix for determining quality attributes (three by three) (five-by-five). Product demand
Sufficient quality
Insufficient quality
Satisfied It must be that way It is indifferent I can live with it Dissatisfied
Satisfied
It must be that way
It is indifferent
I can live with it
Dissatisfied
contradictory reverse reverse reverse reverse
charming indifference indifference indifference reverse
charming indifference indifference indifference reverse
charming indifference indifference indifference reverse
one-dimensional necessary necessary necessary contradictory
145
International Journal of Industrial Ergonomics 69 (2019) 142–152
M.-Y. Ma et al.
comprised general sample size, driver demographics, questionnaire response rate, mean values of quality elements, and standard deviations. The objective of the descriptive statistical analysis was to elucidate the characteristics of the returned samples, the importance of the quality elements, participants’ perceived satisfaction, and the classification of the Kano quality elements.
Table 5 Respondent demographics (N = 56).
Gender Male Female Age 25–30 31–35 36–40 41–45 46–50
2.4.2.2. One-way analysis of variance. A one-way analysis of variance (ANOVA) was conducted to determine the differences between the average values of specific variables among various independent populations. A small variance denotes that the effect of the variable is consistent among the various independent populations. This method is a common tool for measuring causal relationships.
No. of respondents
%
Total sample size
Cumulative percentage
35 21
62.5 37.5
56
100
8 12 13 12 11
14.3 21.4 23.2 21.4 19.6
56
100
play button,” “cruise control activation button,” “automatic recording of drivers’ driving habits and frequently used commands,” “arbitrary manual driving override,” “driver attention detection while the steering wheel is held,” “easy return of steering wheel after turning,” and “voice commands” were classified as attractive quality elements. A considerable proportion of the participants expressed that they would not be dissatisfied even when the quality elements were inadequate; however, they were satisfied when the elements were adequate.
3. Results and discussion 3.1. Questionnaire reliability The Cronbach's α coefficient was used in this study to measure the internal consistency of the questionnaire items. Hair et al. (1998) asserted that although the alpha coefficient lacks a standard absolute value, an alpha coefficient of 0.7 is universally accepted as the reliability standard, and items with alpha coefficient lower than 0.35 should be rejected. Wortzel (1979) maintained that a reliability coefficient of 0.6 is acceptable, but one between 0.7 and 0.98 is preferred, and that coefficients lower than 0.35 should be rejected. In this study, Cronbach's α coefficients concerning the importance and perceived satisfaction of specific functions (i.e., basic safety functions, multimedia entertainment, information and communication, and value-added systems) and intangible value-added services (active matching, automatic service system, software-hardware integration, and customer service and support) were measured. Outcomes are tabulated in Table 4. Apart from the product design construct, all the remaining coefficients were 0.733 (exceeding the recommended level of 0.6), suggesting that the questionnaire achieved excellent reliability.
3.3.2. One-dimensional “Audio device scroll button,” “audio device volume button,” “pick up/end call buttons,” “autopilot when hands leave steering wheel,” “active cruise control support system,” “automatic lane deviation warning support system,” and “fatigue detection based on facial recognition” were classified as one-dimensional quality elements. The participants expressed satisfaction when the quality elements were adequate and dissatisfaction when the elements were inadequate. 3.3.3. Must-be “Steering wheel height and headrest adjustment based on facial recognition,” “auto-complete lane change after initiating auto-turning,” and “app-enabled” were classified as must-be quality elements. A considerable proportion of the participants expressed no satisfaction when the quality elements were adequate but dissatisfaction when the elements were inadequate, suggesting that they valued these quality elements.
3.2. Returned sample characteristics analysis A total of 56 valid questionnaires were returned. Participants demographics are given in Table 5; among them, 62.5% were men and 37.5% were women, and most of them were aged between 31 and 45 years (66%).
3.3.4. Indifferent
3.3. Kano quality elements classification and analysis
“Lane departure warning indicator,” “air conditioner control buttons,” “four-directional control pad,” and “audio reminders” were classified as indifferent quality elements. The respondents expressed neither satisfaction nor dissatisfaction for these elements.
For the Kano two-dimensional quality analysis, the respondents were instructed to express their perceived satisfaction for 30 quality elements presented in two hypothetical scenarios (adequate or inadequate). Perceived satisfaction was measured using a five-point Likert scale, where a higher score represented higher perceived satisfaction. The results were used to categorize the Kano two-dimensional quality elements, as tabulated in Tables 6 and 7. Among the 30 design elements, 10 were classified as “Attractive,” seven were classified as “One-Dimensional,” three were classified as “Must-Be,” four were classified as “Indifferent,” and six were classified as “Reverse” (Table 8).
3.3.5. Reverse “Hazard warning indicator,” “airbag deployment indicator,” “navigator activation button,” “rear seat light control buttons,” “fatigue relief,” and “stress relief” were classified as reverse quality elements. A considerable proportion of the respondents expressed dissatisfaction when these quality elements were adequate and satisfaction when the elements were inadequate. The classification results of the Kano two-dimensional quality elements indicated significant differences among the five groups of quality elements. Therefore, the hypothesis that product quality elements have different classifications in the Kano model was supported.
3.3.1. Attractive “Horn button,” “paddle shift controls,” “fast U-turn,” “audio device Table 4 Variable reliability: Cronbach's α.
3.4. Respondents’ perceived importance of vehicle functions
Cronbach's Alpha
N of Items
.733
30
A five-point Likert scale was used to measure the participants' perceived importance concerning the 30 quality elements, where a higher score represented a higher perceived importance or satisfaction 146
International Journal of Industrial Ergonomics 69 (2019) 142–152
M.-Y. Ma et al.
Table 6 Kano quality element percentage and classification for specific functions. Group
Dimension
Element
(A)
(O)
(M)
(I)
(R)
Kano Quality Classification
Specific Functions
Basic Safety Functions
A1. Horn button A2. Paddle shift controls A3. Lane departure warning indicator A4. Hazard warning indicator A5. Airbag deployment indicator A6. Fast U-turn B1. Audio device play button B2. Audio device scroll button B3. Audio device volume buttons C1. Pick up/end call buttons C2. Navigator activation button C3. Cruise control activation button D1. Air conditioner control buttons D2. Rear seat light control buttons D3. Four-directional control pad
44% 42% 0% 0% 0% 43% 54% 20% 7% 32% 0% 48% 0% 0% 0%
38% 39% 11% 5% 6% 14% 7% 42% 50% 52% 3% 45% 2% 3% 10%
9% 9% 13% 2% 2% 41% 30% 34% 38% 9% 4% 5% 24% 4% 26%
10% 10% 46% 41% 34% 2% 9% 4% 5% 7% 43% 3% 64% 43% 57%
0% 0% 30% 52% 51% 0% 0% 0% 0% 0% 51% 0% 10% 50% 7%
A A I R R A A O O O R A I R I
Multimedia Entertainment System
Information and Communication System
Value-Added Systems
average extent of dissatisfaction (Fig. 3). The extent of satisfaction and extent of dissatisfaction coefficients ranged between absolute values of 0 and 1. A customer satisfaction coefficient closer to 1 represents that customers' perceived satisfaction increases significantly when the quality element is improved, whereas an extent of dissatisfaction coefficient plotted closer to −1 represents that customers’ perceived dissatisfaction increases significantly if that quality element is not satisfied. According to the preceding table, the following findings were derived. Using the example of “E1. Automatic collection of drivers' driving habits and frequently used commands,” we can ascertain that the quality ratio of this element for the “attractive,” “one-dimensional,” “must-be,” “indifferent,” and “reverse” constructs was 52%, 40%, 5%, 3%, and 0.0%, respectively (Table 11). This indicates that over half the participants identified this quality element as attractive. From Eqs. (3) and (4), the extents of satisfaction and dissatisfaction were found to be 0.92 and −0.45, respectively, implying that customers’ perceived satisfaction for adequate quality outweighed their perceived dissatisfaction with inadequate quality and validating the reliability of classifying this element under the “attractive” construct. These results are consistent with the evaluation standard of M > O > A > I proposed by Matzler and Hinterhuber (1998). In other words, during product design or development, quality elements in the “must-be” construct must first be satisfied to prevent a sharp increase in dissatisfaction. Then, attractive design elements should be introduced to enhance customer satisfaction. In the customer satisfaction matrix (Fig. 3), the quality elements plotted in the first quadrant (high extent of satisfaction, low extent of dissatisfaction) are the key design elements for future vehicle development. These elements include “cruise control activation button,” “automatic recording of drivers’ driving habits and frequently used commands,” “voice commands,” “driver attention detection while holding the steering wheel,” and “arbitrary manual driving override.”
(Table 9). The quality groups, in descending order of importance as expressed by the participants, were automated system services, multimedia entertainment, active matching, information and communication, customer service and support, basic safety functions, softwarehardware integration, and value-added systems. In this order, the average scores of the first five groups were higher than the overall average. The standard deviation values of the constructs were analyzed to determine consensus. The level of dispersion of the eight groups of standard deviation values was relatively low, suggesting that the respondents’ opinions were consistent. Table 10 indicates the respondents' perceived importance concerning the various functional design elements. The five top-ranking quality elements were “fatigue detection based on facial recognition” in software-hardware integration, “cruise control activation button” in information and communication, “automatic recording of drivers’ driving habits and frequently used commands” in active matching, “arbitrary manual driving override” in automated system services, and “horn button” in basic safety functions. The five bottom-ranking quality elements were “rear seat light control buttons” in value-added systems, “airbag deployment indicator” and “hazard warning indicator” in basic safety functions, and “fatigue relief” and “stress relief” in software–hardware integration. Although these factors seemed less important, an additional analysis was performed to determine whether they were key elements affecting satisfaction. 3.5. Kano customer satisfaction coefficient calculation and matrix creation Although the data collected from the Kano questionnaire enabled the researchers of this study to classify the two-dimensional quality elements of the product designs, they are inadequate to facilitate managers in their efforts to enhance customer satisfaction. Therefore, a customer satisfaction matrix was created to highlight the quality elements that can be incorporated into product designs to maximize customer satisfaction. First, the two-dimensional quality elements classification results and the literature review results concerning the Kano two-dimensional model of quality were applied to calculate the extent of customer satisfaction and the extent of dissatisfaction coefficients given by Eqs. (1) and (2) (Table 11): (A + O)/(A + O + M + I) and
(1)
−(M + O)/(A + O + M + I),
(2)
3.6. Demand for future automatic vehicle-driving services The 30 quality characteristics were plotted on an XY diagram (Fig. 4). In the Kano two-dimensional quality model, the horizontal axis represents the sufficiency of the quality characteristics, whereas the vertical axis represents customer satisfaction. Of all the quality characteristics, three were classified under the “must-be” category, indicating characteristics that companies had to improve first; improvements in this category could immediately lower customer dissatisfaction. Seven quality characteristics were classified under the “one-dimensional” category; improvements in this category would elevate customer satisfaction and lower customer dissatisfaction. A total of 10 quality characteristics were classified under the “attractive”
where A: Attractive; O: One-dimensional; M: Must-be; and I: Indifferent. The extents of customer dissatisfaction were then plotted on the Xaxis (Eq. (1)), and the extents of satisfaction coefficients on the Y-axis (Eq. (2)), to create the customer satisfaction matrix. The center line of the X-axis was the average extent of satisfaction of the 30 quality elements, and the center line of the Y-axis was the 147
Active Matching
Iintangible Value-Added Services
148
Customer Service and Support
Software–Hardware Integration
Voice commands
Automatic recording of drivers' habits and frequently used commands ‧ Arbitrary manual driving override ‧ Driver attention detection while the steering wheel is held ‧ Easy return of steering wheel after turning
Active Matching
Automatic Service System
Cruise control activation button
Information and Communication System Value-Added Systems
‧ Paddle shift controls ‧ Fast U-turn
Audio device play button
A
Multimedia Entertainment System
Basic Safety Functions
Group
‧ Autopilot when hands leave steering wheel ‧ Active cruise control support system ‧ Automatic lane departure warning support system Fatigue detection based on facial recognition
‧ Audio device scroll button ‧ Audio device volume buttons Pick up/end call buttons
O
App-enabled
Steering wheel height and headrest adjustment based on facial recognition Auto-complete lane change after initiating auto-turning
M
E1. Automatic recording of drivers' habits and frequently used commands E2. Steering wheel height and headrest adjustment based on facial recognition F1. Arbitrary manual driving override F2. Driver attention detection while the steering wheel is held F3. Autopilot when hands leave steering wheel F4. Easy return of steering wheel after turning F5. Auto-complete lane change after initiating auto-turning F6. Active cruise control support system F7. Automatic lane departure warning support system (steering wheel vibrates to warn the driver when leaving a marked lane without turning on indicators) G1. App-enabled (drivers can input their preferred parameters into a dedicated app; the parameters are automatically synced with the steering wheel activation control system) G2. Fatigue detection based on facial recognition G3. Fatigue relief (lights are activated to stimulate drivers' sympathetic nervous system when fatigue is detected) G4. Stress relief (light music, warm lights, and a pleasant fragrance are provided to stimulate drivers' parasympathetic nerve system when stress is detected) H1. Voice commands H2. Audio reminders
Element
Table 8 Kano quality element classification for the various quality elements.
Customer Service and Support
Software–Hardware Integration
Automatic Service System
Dimension
Group
Table 7 Kano quality element percentage and classification for intangible value-added services.
4%
0%
11% 34%
9%
22% 3%
45%
5% 54% 10% 11% 9% 22% 58% 20% 34%
(M)
2% 57%
43%
5% 38%
2%
3% 36% 5% 3% 2% 3% 30% 3% 2%
(I)
Audio reminders
‧ Air conditioner control buttons ‧ Cruise control activation button
Lane departure warning indicator
I
34% 5%
55% 2%
18% 0%
57% 0%
43%
40% 8% 33% 35% 61% 18% 7% 42% 50%
(O)
10%
52% 0% 52% 52% 28% 57% 5% 35% 14%
(A)
0% 4%
44%
0% 57%
0%
0% 2% 0% 0% 0% 0% 0% 0% 0%
(R)
‧ Hazard warning indicator ‧ Airbag deployment indicator
‧ Fatigue relief ‧ Stress relief
Rear seat light control buttons
Navigator activation button
R
A I
R
O R
M
A M A A O A M O O
Kano Quality Classification
M.-Y. Ma et al.
International Journal of Industrial Ergonomics 69 (2019) 142–152
International Journal of Industrial Ergonomics 69 (2019) 142–152
M.-Y. Ma et al.
improving service procedures.
Table 9 Ranking of the quality constructs based on perceived importance. Constructs
Items
Perceived Importance Mean
Basic Safety Functions Multimedia Entertainment System Information and Communication System Value-Added Systems Active Matching Automatic Service System Software–Hardware Integration Customer Service and Support
4. Conclusions
Standard Deviation
Rank
6 3 3
2.8988 3.8214 3.4405
0.6698 0.6646 0.5376
6 2 4
3 2 7 4 2
1.9464 3.5715 4.0153 2.7232 3.3839
0.4833 0.5114 0.6219 0.5189 0.6307
8 3 1 7 5
The Kano model is an instrument to evaluate users' perception of quality, enabling users' current expectations and demand to be determined. This illustrates the value of user satisfaction and allows designers or companies to make related assessments, ultimately improving and elevating users’ overall satisfaction. This study used the vehicle-driving quality assessment model developed from a previous study, which contains 2 dimensions and 30 quality characteristics. The Kano model was used to identify the quality attribute of each characteristic. The study results showed that the quality attribute most commonly used to classify the characteristics was “attractive,” followed by “one-dimensional,” “reverse,” “indifferent,” and “must-be.” This confirms users’ varying demand for quality attributes. This study identified various vehicle-driving quality characteristics and evaluated which ones exhibit greater effects on user satisfaction. Such findings allow companies to implement customer segmentation and achieve higher customer satisfaction levels. The Kano model shows the following customer satisfaction results. From the perspective of human–machine interaction, consumers may not notice all the media they come into contact with. They also have affective experiences from intangible feedback. Many innovative services will emerge amidst fierce market competition and as the
category; improvements in this category could significantly enhance customer satisfaction (Table 12). Finally, four quality characteristics were classified under the “indifferent” category; the diagram for this category is not shown, because customers are not concerned with the performance of these characteristics and companies may therefore choose to not improve them. By referring to where quality characteristics are located on a Kano model diagram, companies can prioritize the importance of quality characteristics, develop improvement strategies to elevate customer satisfaction, and identify the quality characteristics that they should pay attention to when developing or Table 10 Importance of each construct characteristic. Group
Specific Functions
Dimension
Basic Safety Functions
Multimedia Entertainment System Information and Communication System Value-Added Systems
Iintangible Value-Added Services
Active Matching
Automatic Service System
Software–Hardware Integration
Customer Service and Support
Element
Importance Mean
Standard Deviation
Rank
A1. Horn button A2. Paddle shift controls A3. Lane departure warning indicator A4. Hazard warning indicator A5. Airbag deployment indicator A6. Fast U-turn B1. Audio device play button B2. Audio device scroll button B3. Audio device volume buttons C1. Pick up/end call buttons C2. Navigator activation button C3. Cruise control activation button D1. Air conditioner control buttons D2. Rear seat light control buttons D3. Four-directional control pad
4.4464 4.4286 1.7143 1.4464 1.3750 3.9821 4.1429 3.7857 3.5357 4.3036 1.5000 4.5179 2.1607 1.4286 2.2500
.65836 .65663 .67995 .53664 .52440 .96278 .96160 .49412 .53815 .50162 .57208 .53906 .37059 .49935 .57997
5 6 21 24 25 11 10 12 14 8 22 2 20 26 19
E1. Automatic recording of drivers' habits and frequently used commands E2. Steering wheel height and headrest adjustment based on facial recognition F1. Arbitrary manual driving override F2. Driver attention detection while the steering wheel is held F3. Autopilot when hands leave steering wheel F4. Easy return of steering wheel after turning F5. Auto-complete lane change after initiating auto-turning F6. Active cruise control support system F7. Automatic lane departure warning support system (steering wheel vibrates to warn the driver when leaving a marked lane without turning on indicators) G1. App-enabled (drivers can input their preferred parameters into a dedicated app; the parameters are automatically synced with the steering wheel activation control system) G2. Fatigue detection based on facial recognition G3. Fatigue relief (lights are activated to stimulate drivers' sympathetic nervous system when fatigue is detected) G4. Stress relief (light music, warm lights, and a pleasant fragrance are provided to stimulate drivers' parasympathetic nerve system when stress is detected) H1. Voice commands H2. Audio reminders
4.5000 2.6429
.53936 .48349
3 17
4.4643 4.4107 4.2143 4.1429 2.7143 4.5000 3.6607
.60194 .68162 .59435 .99870 .49412 .50452 .47775
4 7 9 10 16 3 13
3.4286
.49935
15
4.5536 1.4464
.50162 .53664
1 24
1.4643
.53815
23
4.4107 2.3571
.70780 .55362
7 18
149
International Journal of Industrial Ergonomics 69 (2019) 142–152
M.-Y. Ma et al.
Table 11 Customer satisfaction coefficients. Group
Dimension
Element
Kano Quality Element
(CS)
(DS)
Specific Functions
Basic Safety Functions
A1. Horn button A2. Paddle shift controls A3. Lane departure warning indicator A4. Hazard warning indicator A5. Airbag deployment indicator A6. Fast U-turn B1. Audio device play button B2. Audio device scroll button B3. Audio device volume buttons C1. Pick up/end call buttons C2. Navigator activation button C3. Cruise control activation button D1. Air conditioner control buttons D2. Rear seat light control buttons D3. Four-directional control pad
A A I R R A A O O O R A I R I
0.82 0.81 0.16 0.10 0.14 0.57 0.61 0.62 0.57 0.84 0.06 0.93 0.02 0.06 0.11
−0.47 −0.48 −0.34 −0.15 −0.16 −0.55 −0.37 −0.76 −0.88 −0.64 −0.14 −0.50 −0.29 −0.14 −0.39
E1. Automatic recording of drivers' habits and frequently used commands E2. Steering wheel height and headrest adjustment based on facial recognition F1. Arbitrary manual driving override F2. Driver attention detection while the steering wheel is held F3. Autopilot when hands leave steering wheel F4. Easy return of steering wheel after turning F5. Auto-complete lane change after initiating auto-turning F6. Active cruise control support system F7. Automatic lane departure warning support system (steering wheel vibrates to warn the driver when leaving a marked lane without turning on indicators) G1. App-enabled (drivers can input their preferred parameters into a dedicated app; the parameters are automatically synced with the steering wheel activation control system) G2. Fatigue detection based on facial recognition G3. Fatigue relief (lights are activated to stimulate drivers' sympathetic nervous system when fatigue is detected) G4. Stress relief (light music, warm lights, and a pleasant fragrance are provided to stimulate drivers' parasympathetic nerve system when stress is detected) H1. Voice commands H2. Audio reminders
A M A A O A M O O
0.92 0.08 0.85 0.87 0.89 0.72 0.12 0.77 0.64
−0.45 −0.63 −0.43 −0.46 −0.70 −0.40 −0.65 −0.62 −0.84
M
0.53
−0.88
O R
0.73 0.05
−0.77 −0.15
R
0.07
−0.23
A I
0.91 0.05
−0.45 −0.40
Multimedia Entertainment System Information and Communication System Value-Added Systems
Iintangible Value-Added Services
Active Matching Automatic Service System
Software–Hardware Integration
Customer Service and Support
Fig. 3. Customer satisfaction matrix. Fig. 4. Customer satisfaction diagram obtained using the Kano model.
driving service market continues to change. For example, with the advent of autonomous driving, designers are challenged with selecting attractive Kansei qualities for their designs to meet consumer expectations and enhance “service” and “satisfaction.” This is one of the main topics this study focuses on. In this context, we examined driving devices that were technologically mature and emphasized technology services using the Kano model of quality to elucidate consumers’ demands for different Kansei elements (Fig. 5). Based on the “attractive”
quality element of consumer demands, the classification results were subsequently used to create the “Golden Circle of Product Services” selfdefined by this study.
Conflicts of interest The authors declare that there are no conflicts of interest. 150
International Journal of Industrial Ergonomics 69 (2019) 142–152
M.-Y. Ma et al.
Table 12 Recommendations for vehicle-driving service demand. C3 E1 H1 F2 F1 A1 A2 F4 B1 A6
Cruise control activation button Automatic recording of drivers' habits and frequently used commands Voice commands Driver attention detection while the steering wheel is held Arbitrary manual driving override Horn button Paddle shift controls Easy return of steering wheel after turning Audio device play button Fast U-turn
F3 C1 F6 G2 F7
Autopilot when hands leave steering wheel Pick up/end call buttons Active cruise control support system Fatigue detection based on facial recognition Automatic lane departure warning support system (steering wheel vibrates to warn the driver when leaving a marked lane without turning on indicators) Audio device scroll button Audio device volume buttons
B2 B3
G1 F5 E2
App-enabled (drivers can input their preferred parameters into a dedicated app; the parameters are automatically synced with the steering wheel activation control system) Auto-complete lane change after initiating auto-turning Steering wheel height and headrest adjustment based on facial recognition
References Bedford, C., Lee, A., 2008. Would you like service with that? Des. Manage. Rev. 19 (1), 39–43. Cagnin, F., Oliveira, M.C., Simon, A.T., Helleno, A.L., Vendramini, M.P., 2016. Proposal of a method for selecting suppliers considering risk management: an application at the automotive industry. Int. J. Qual. Reliab. Manag. 33 (4), 488–498. Chang, Y.M., Chen, C.W., 2016. Kansei assessment of the constituent elements and the overall interrelations in car steering wheel design. Int. J. Ind. Ergon. 56, 97–105. Chen, C.C., Chuang, M.C., 2008. Integrating the Kano model into a robust design approach to enhance customer satisfaction with product design. Int. J. Prod. Econ. 114 (2), 667–681. Chen, C.C., Li, Y.J., 2008. The application of Kano model on exploring the attractive attributes of cultural product design. J. Des. 13 (4), 25–41. Chuang, M.C., Ma, Y.C., 2001. Expressing the expected product images in product design of micro-electronic products. Int. J. Ind. Ergon. 27, 233–245. Design Council, 2010. What Is Service Design? http://www.designcouncil.org.uk/aboutdesign/Types-of-design/Service-design/What-is-service-design/, Accessed date: 10 November 2010. Dominici, G., Roblek, V., Abbate, T., Tani, M., 2016. Click and drive: consumer attitude to product development: towards future transformations of the driving experience. Bus. Process Manag. J. 22 (2), 420–434. Goonetilleke, R.S., 2000. The comfort-discomfort phase change. In: Karwowski, W. (Ed.), International Encyclopedia of Ergonomics and Human Factors. Taylor and Francis, London, pp. 399–402. Goonetilleke, R.S., Shih, H.M., On, H.K., Fristch, J., 2001. Effects of training and representational characteristics in icon design. Int. J. Hum. Comp. Stud. 55 (5), 741–760. Grace, D., O'Cass, A., 2004. Examining service experiences and post-consumption evaluations. J. Serv. Market. 18 (6), 450–461. Hair Jr., J.F., Anderson, R.E., Thatham, R.L., Black, W.C., 1998. Multivariate Date Analysis, third ed. Prentice-Hall Inc., Upper Saddle River, NJ, pp. 326–383. Högström, C., 2011. The theory of attractive quality and experience offerings. TQM J. 23 (2), 111–127. Ji, P., Jin, J., Wang, T., Chen, Y., 2014. Quantification and integration of Kano's model into QFD for optimising product design. Int. J. Prod. Res. 52 (21), 6335–6348. Kano, N., Seraku, N., Takahashi, F., Ichi, S., 1984. Attractive quality and must-be quality. Qual. Contr. J. 21 (5), 33–41. Kumar, V., Venkatesan, R., 2005. Who are the multichannel shoppers and how do they
Fig. 5. Golden circle for vehicle-driving product and services.
Acknowledgements This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
151
International Journal of Industrial Ergonomics 69 (2019) 142–152
M.-Y. Ma et al.
Patrício, L., Fisk, R.P., e Cunha, J.F., Constantine, L., 2011. Multilevel service design: from customer value constellation to service experience blueprinting. J. Serv. Res. 14 (2), 180–200. Wortzel, L., 1979. Multivariate Analysis. Prentice-Hall Inc., Upper Saddle River, NJ. Yang, J., 2003. The icon recognition affected selection intention and behavior pattern of college students in playing lucky-hole poking game. J. Des. 8 (1), 51–63.
perform? Interact. Market. 9 (2), 44–62. Matzler, K., Hinterhuber, H.H., 1998. How to make product development projects more successful by integrating Kano’s model of customer satisfaction into quality function deployment. Technovation 18 (1), 25–38. Nunes, P.F., Cespedes, F.V., 2003. The customer has escaped. Harv. Bus. Rev. 81 (11), 96–105.
152
Contents lists available at ScienceDirect
International Journal of Industrial Ergonomics journal homepage: www.elsevier.com/locate/ergon
Using Kano model to differentiate between future vehicle-driving services a
b,∗
Min-Yuan Ma , Chun-Wei Chen a b c
T
c
, Yu-Ming Chang
Department of Industrial Design, National Cheng Kung University, No. 1, Dasyue Rd., East District, Tainan City, 701, Taiwan, ROC Department of Crafts and Creative Design, National University of Kaohsiung, 700, Kaohsiung University Rd., Nanzih District, Kaohsiung, 811, Taiwan, ROC Department of Creative Product Design, Southern Taiwan University, No. 1, Nantai St., Yongkang City, Tainan County, 710, Taiwan, ROC
A R T I C LE I N FO
A B S T R A C T
Keywords: Kano model Feature Customer satisfaction Car steering wheel Autonomous driving
Services are constantly changing with the introduction of new technologies, which affect the service systems of both conventional and autonomous driving. New theories and technologies are also key factors affecting the design and development trends of service models and solutions. Major automobile manufacturers aspire to provide customers with unique services and experiences, resulting in a growing demand for systematic approaches to characterize customer behaviors and scientific methods to accurately interpret data stored in databases. This study proposes a scientific engineering and operation framework for driving services that enables conventional automobile manufacturers to re-evaluate their service models and solutions as they expand into the domain of autonomous driving, integrating customized consumer interactions and mass production efficiency to develop new technologies, and subsequently applying these technologies to innovate their driving services, form service innovation guidelines, and accelerate the development of smart applications for the automobile industry. A Kano two-dimensional model of quality was employed. A Kano questionnaire was administered to analyze consumers' perceived satisfaction concerning different service quality elements; the elements were then ranked in the order of requiring improvement to determine the elements that are essential in conventional vehicles. Finally, suggestions were proposed for improving the service quality of driving products and evaluating driver satisfaction. A total of 56 valid questionnaires were collected from potential buyers of four-door sedans. The questionnaire evaluated respondents’ perceived value and satisfaction of 30 product elements categorized into two groups (specific functions and intangible value-added services) across eight major quality dimensions (basic safety functions, multimedia entertainment systems, information and communication systems, value-added systems, active matching, automatic service systems, hardware–software integration, and customer service and support). In addition, Kano quality categories were statistically analyzed to elucidate whether significant differences existed between groups. Using the Kano quality categories, 30 design elements were classified: 10 as “attractive,” 7 as “one-dimensional,” 3 as “must-be,” 4 as “indifferent,” and 6 as “reverse.” Enterprises can effectively reduce customer dissatisfaction and enhance customer satisfaction based on the quality category of the product and the product improvement order proposed in this study. Relevance to industry: This study determined that using the Kano quality categories, enterprises can effectively reduce customer dissatisfaction and enhance customer satisfaction based on the quality category of the product and the product improvement order proposed in this study.
1. Introduction
electric vehicles will continue to grow; more importantly, the automobile market will experience an even greater reform, which is autonomous driving. Automotive companies need to consider what is attractive to drivers and what consumers consider to be “driver-friendly” (Dominici et al., 2016). Due to the warming at the automotive market in the last years and consequently the growth of vehicle production has been moved and placed emphasis on the segment (Cagnin et al., 2016). Whether future
1.1. Background and study motivation An automobile is an exceptional product that, aside from numerous practical functions, also interacts intimately with its owner's family life and reflects the personal economic status. However, the automobile industry is facing drastic change. In the next ten years, the presence of
∗ Corresponding author. Department of Creative Design and Architecture, National University of Kaohsiung, 700, Kaohsiung University Rd., Nanzih District, Kaohsiung, 811, Taiwan, ROC. Tel.: +886 915334637. E-mail addresses: [email protected] (M.-Y. Ma), [email protected] (C.-W. Chen), [email protected] (Y.-M. Chang).
https://doi.org/10.1016/j.ergon.2018.11.003 Received 6 July 2017; Received in revised form 26 October 2018; Accepted 27 November 2018 0169-8141/ © 2018 Elsevier B.V. All rights reserved.
International Journal of Industrial Ergonomics 69 (2019) 142–152
M.-Y. Ma et al.
once reliable autonomous driving is realized. However, current autonomous driving systems can only be classified as an assisted-driving function. Therefore, the functions of the steering wheel cannot be fully delegated to the vehicle yet. The car part that a driver interacts with most directly and frequently is neither the appearance nor power system of the car, but rather its interior design, of which the steering wheel is the most fundamental component. In addition to the shape of the steering wheel, the gripping experience and the convenience during operation are also crucial (Chang and Chen, 2016). This study predicts that steering wheels will expand beyond their existing function for navigation, such as becoming the point of contact for the vehicle to monitor drivers. For example, Toyota unveiled a steering wheel equipped with a built-in sensor to track drivers’ alertness and adjust the environment of the vehicle accordingly. With increasing concerns on customer needs in today's competitive market, the issue of incorporating customer requirements into product design arises the interest of both researchers and practitioners (Ji et al., 2014). The concept of two-dimensional quality was introduced by Noriaki Kano and his colleagues, who developed a highly effective model to classify customer preference into five categories, namely onedimensional quality, attractive quality, indifferent quality, must-have quality, and reverse quality. Thus, the two-dimensional concept in the Kano model enables describing the relationships of customer satisfaction with product quality or service quality. Kano's model, which studies the nature of customer needs, provides a way for a better classification of customer needs (Ji et al., 2014). Given this consumercentered mentality, consumers may not always seek a product with the best attributes. Rather, they seek products that can satisfy their emotional needs. Consumers evaluate products using multi-quality/multiprinciple strategies (Chen and Chuang, 2008). Using the Kano model to define quality categories helps designers determine the true needs of consumers and enable them to more accurately control quality and satisfaction during product design and development (Chen and Li, 2008). The Kano model of quality addresses consumers' needs rather than their preferences (Kano et al., 1984). Existing questions and answering alternatives included in the Kano methodology must be adapted to the nature of experiences (Högström, 2011). For this reason, the present study adopted the Kano model for further classification of the service quality in driving. Past studies on this topic have mostly relied on statistical methods to identify the service qualities that notably affect the customers, but they have usually been unable to determine the degree to which customer satisfaction (or the lack of it) can be improved once measures regarding the service qualities in question have been implemented, and nor could they identify the exact service qualities that actually matter for the customers. To overcome this problem, a two-dimensional quality model was employed to effectively allocate the resources of automobile manufacturers, thereby improving the standard of driving service. Does adding technical services to the specific function of driving service products enhance product value and consumer satisfaction? Experiment focuses on the functional requirements and service satisfaction aspects of qualityservices are constantly changing with the introduction of new technologies. In this study, we applied research results concerning consumers' “basic demands” for conventional driving services to autonomous driving to identify the functions or services that promote consumer satisfaction. The experimental results highlight the indispensable feelings and services in conventional driving. These data were then used to determine the associations between product functions, services, and customer satisfaction, as well as how these associations can be incorporated into the provision of desirable customer service in autonomous driving and the identification of vital Kansei quality factors (attractive elements) for enhancing customer satisfaction. In addition, changes in the use of conventional vehicles, as well as various intangible services of autonomous vehicles, were examined to determine their functions in the provision of services and their effects on customer demand and expectations. We further reviewed numerous
autonomous vehicles should be equipped with a steering wheel or a user-controllable braking system is a question that has triggered substantial debate among conventional automobile manufacturers and major Internet companies. The existing line of autonomous vehicles developed by Google is equipped with a control system that enables the driver to fully brake the vehicle in emergency situations. However, these vehicles are not equipped with a steering wheel. Conventional automobile manufacturer Audi stated that drivers should still follow driving protocols amidst the prevalence of autonomous driving technologies and remain vigilant on the road, even with the absence of a steering wheel and brake pedal. Audi works to improve the safety elements of existing autonomous driving systems, such as monitoring drivers’ conditions during autonomous driving. Tesla recommends that drivers keep their hands on the steering wheel so that they can control their vehicles where necessary. The University of Michigan conducted a survey on autonomous and semi-autonomous driving systems in April 2016. Among the 618 respondents, 66% expressed reserved attitudes towards completely autonomous driving. However, 94.5% of the respondents opted for autonomous vehicles with steering wheels, 37% expressed safety concerns towards fully autonomous vehicles, and 17% expressed safety concerns towards semi-autonomous vehicles. In recent years, as studies on advanced driver assistance systems have garnered more attention, the behavior patterns and service experiences of the driver have also started to be emphasized by relevant scholars. An understanding of the pressure patterns that are appropriate for the human body (that is, the patterns that reduce discomfort or improve what is called comfort) can make product design and usage very satisfying and fulfilling (Goonetilleke, 2000). To quote the Dansk Design Center, the term “service design“ refers to the design of systems and process around the idea of rendering a service to the user (Bedford and Lee, 2008). Therefore, the primary task of service design is making the service you deliver useful, usable, efficient, effective and desirable (Design Council, 2010). In the field of service design, the touch point has always been an essential concept; where automobiles are concerned, the touch points consist of the items and scenarios in the service system that interact with the driver. Increasing the number of touch points generates more user experience, which facilitates enhancing the value of the service system and customer satisfaction. However, from the perspective of cognitive psychology, further analysis suggests that ambiguity, uniqueness and dominance are three important aspects to consider when designing and developing icons (Goonetilleke et al., 2001). In the case of automobiles, service designers must address the core service, operational service, and servicescape all together, to meet drivers' preferences and expectations for journey-related service encounters (Grace and O'Cass, 2004). Therefore, the topic of how should corporations develop diversified strategies (Nunes and Cespedes, 2003) to understand the user behaviors (Kumar and Venkatesan, 2005) and service experiences (Patrício et al., 2011) of their customers has merited further investigation. Although service is a crucial factor affecting quality, most service providers tend to perceive it as one-dimensional. The so-called onedimensional quality refers to the one-dimensional thinking that when a quality element is known to satisfy the customer, the greater the degree to which the product features this quality element, the more satisfied the customers will be, and vice versa. However, the truth is that not all quality elements are one-dimensional, some of them can be two-dimensional, which means that the full presence of certain quality elements would not necessarily satisfy the customers; sometimes, the customers would even become displeased, or remain totally indifferent. Thus, how to improve driving service quality, enhance customer satisfaction, create competitive edge, and reduce customer attrition are key aspects of driving service that must be addressed. 1.2. Research question and motivation People commonly believe that steering wheels will become obsolete 143
International Journal of Industrial Ergonomics 69 (2019) 142–152
M.-Y. Ma et al.
considered for operating a steering wheel were collected, including built-in functionality. The data were compiled into a spreadsheet. 2.2. Establishing quality evaluation dimensions and evaluation characteristics The two-way Kano quality questionnaire was based on a number of quality evaluation dimensions and characteristics that affect the perceived satisfaction of drivers when they operate vehicles. In line with Kano's model for examining consumer “demand” rather than “preference” (Kano et al., 1984), a focus group was organized to discuss the steering wheel samples in Fig. 2 to identify the demand attributes and characteristics. A total of 15 elements across four dimensions were identified. The dimensions were basic safety functions, multimedia entertainment, information and communication, and value-added systems. These elements were collectively named the “specific functions.” To apply the aforementioned elements in future autonomous driving environments, a number of experts were invited to participate in a brainstorming session. All experts were between the ages of 25 and 50, and had a driver's license and at least two years of driving experience. A diffusion mode of creative thinking was applied to discuss future autonomous driving scenarios, which included the concepts of “criticism is ruled out,” “freewheeling is welcomed,” “go for quantity,” and “hitchhike (improve) on ideas.” A total of 15 quality evaluation elements across four dimensions were identified. The dimensions were active matching, automated system services, software–hardware integration, and customer service and support. These elements were collectively named “Intangible Value-Added Services.” A total of 30 elements were selected to evaluate, define, and explain the positive and negative quality elements associated with demand. Subsequently, 56 respondents were invited to score each element according to their preferences. The elements were measured using a fivepoint Likert scale (Table 1).
Fig. 1. Functional requirements and service satisfaction research framework.
studies on customer satisfaction to prioritize the improvement of product functions and uncover the underlying flaws in drivers’ service experiences. These observations helped us envision the future ecosystem of autonomous driving services. 2. Methods of study and implementation The research framework for Experiment is illustrated in Fig. 1. All quality elements associated with driving service quality were identified, and then, a questionnaire was formulated based on the Kano model of quality and administered to the study respondents. The service quality perceptions of the respondents were categorized based on a two-dimensional quality classification scheme. This scheme was devised to overcome the flaws of the original one-dimensional scheme by including a psychological dimension for quality evaluation. Interpreting the correlation between product quality and consumer satisfaction in two dimensions reveals that favorable product quality may not always satisfy consumers, but sometimes leads to indifference or dissatisfaction, instead.
2.3. Two-way Kano quality satisfaction survey and quality determination 2.1. Stimulation test sample selection The questionnaire was based on a Kano two-dimensional model of quality. The survey results were categorized into five dimensions, namely, “attractive,” “one-dimensional,” “must-be,” “indifferent,” and “reverse,” based on the respondents' opinions. Survey participants were general office workers with two or more years of driving experience. A literature review was performed to categorize the service quality of steering wheels into two major groups, “specific functions” and “intangible value-added services.” The questionnaire was designed based on the Kano two-dimensional model of quality. Participants’ identities were kept anonymous, with only the demographics of gender, years of driving, and daily average driving time solicited. The questionnaire items were the quality evaluation elements. The respondents answered the items based on a two-dimension system, specifically, with or without a specific quality element. The survey comprised two parts. The first part was the two-way Kano quality questionnaire to elucidate consumers' demands. The second part evaluated the performance of the samples (steering wheel
In the development of products, methods enabling the designer to create an appropriate image for a product so that it may communicate with the user are always critical issues (Chuang and Ma, 2001). To avoid discrepancy regarding form vs content, the focus team, through direct visual perception, classified the test samples according to the visual characteristics of the images. To classify the 114 test samples by form contrast(Chang and Chen, 2016), the KJ method was adopted. This method is used for qualitative analysis based on intuition and for innovating through teamwork. Because this method is used for analyzing how participants perceive the images, it enables the characteristics of images to be more clearly understood (Yang, 2003). The main objective of Experiment was from to examine service quality. The focus group was invited to categorize the 45 stimuli samples into 12 clusters based on design similarities. The most representative sample from each cluster was selected for a total of 12 representative samples in Experiment (Fig. 2). Then, data concerning the potential factors that may be
Fig. 2. Final samples for experiment (n = 12). 144
International Journal of Industrial Ergonomics 69 (2019) 142–152
M.-Y. Ma et al.
Table 1 Quality elements associated with customer demand. Group
Dimension
Element
Specific Functions
Basic Safety Functions
A1. Horn button A2. Paddle shift controls A3. Lane departure warning indicator A4. Hazard warning indicator A5. Airbag deployment indicator A6. Fast U-turn B1. Audio device play button B2. Audio device scroll button B3. Audio device volume buttons C1. Pick up/end call buttons C2. Navigator activation button C3. Cruise control activation button D1. Air conditioner control buttons D2. Rear seat light control buttons D3. Four-directional control pad
Multimedia Entertainment System
Information and Communication System Value-Added Systems
Iintangible Value-Added Services
Active Matching Automatic Service System
Software–Hardware Integration
Customer Service and Support
E1. Automatic recording of drivers' habits and frequently used commands E2. Steering wheel height and headrest adjustment based on facial recognition F1. Arbitrary manual driving override F2. Driver attention detection while the steering wheel is held F3. Autopilot when hands leave steering wheel F4. Easy return of steering wheel after turning F5. Auto-complete lane change after initiating auto-turning F6. Active cruise control support system F7. Automatic lane departure warning support system (steering wheel vibrates to warn the driver when leaving a marked lane without turning on indicators) G1. App-enabled (drivers can input their preferred parameters into a dedicated app; the parameters are automatically synced with the steering wheel activation control system) G2. Fatigue detection based on facial recognition G3. Fatigue relief (lights are activated to stimulate drivers' sympathetic nervous system when fatigue is detected) G4. Stress relief (light music, warm lights, and a pleasant fragrance are provided to stimulate drivers' parasympathetic nerve system when stress is detected) H1. Voice commands H2. Audio reminders
(1) to apply the respondents' feedback and the Kano two-dimensional model of quality to classify the quality attributes of product design; (2) to determine the respondents' perceived importance of the various items and elucidate the customer satisfaction coefficients for the various products; (3) to determine the differences between the customer satisfaction coefficients of the five product quality attributes; and (4) to determine whether the Kano evaluation rules support or reject the product classifications developed in this study.
Table 2 Kano two-way questionnaire format (using hardware–software integration services as an example). How do you find steering wheels with “autopilot when hands leave steering wheel?” □ Delightful □ Necessary □ No Comment □ Tolerable □ Unacceptable How do you find steering wheels without “autopilot when hands leave steering wheel?” □ Delighted □ Necessary □ No Comment □ Tolerable □ Unacceptable
designs) for each quality element (demand attribute) and overall satisfaction (Table 2). A total of 56 participants aged 25–50 (35 men and 21 women) were invited to participate in the two-way Kano quality questionnaire, which comprised a series of “paired items” (with and without a specific quality element) to survey the respondents’ opinions. The Kano quality attribute determination matrix was used to determine the quality classification of each demand element (Table 3). Based on the aforementioned procedures, the objectives of Experiment were as follows:
2.4. Data analysis 2.4.1. Questionnaire content validation Reliability refers to the scoring consistency among groups of participants. In this study, Cronbach's alpha was used to measure item consistency. 2.4.2. Data analysis and hypothesis testing 2.4.2.1. Basic descriptive statistical analysis. The descriptive statistics
Table 3 Decision matrix for determining quality attributes (three by three) (five-by-five). Product demand
Sufficient quality
Insufficient quality
Satisfied It must be that way It is indifferent I can live with it Dissatisfied
Satisfied
It must be that way
It is indifferent
I can live with it
Dissatisfied
contradictory reverse reverse reverse reverse
charming indifference indifference indifference reverse
charming indifference indifference indifference reverse
charming indifference indifference indifference reverse
one-dimensional necessary necessary necessary contradictory
145
International Journal of Industrial Ergonomics 69 (2019) 142–152
M.-Y. Ma et al.
comprised general sample size, driver demographics, questionnaire response rate, mean values of quality elements, and standard deviations. The objective of the descriptive statistical analysis was to elucidate the characteristics of the returned samples, the importance of the quality elements, participants’ perceived satisfaction, and the classification of the Kano quality elements.
Table 5 Respondent demographics (N = 56).
Gender Male Female Age 25–30 31–35 36–40 41–45 46–50
2.4.2.2. One-way analysis of variance. A one-way analysis of variance (ANOVA) was conducted to determine the differences between the average values of specific variables among various independent populations. A small variance denotes that the effect of the variable is consistent among the various independent populations. This method is a common tool for measuring causal relationships.
No. of respondents
%
Total sample size
Cumulative percentage
35 21
62.5 37.5
56
100
8 12 13 12 11
14.3 21.4 23.2 21.4 19.6
56
100
play button,” “cruise control activation button,” “automatic recording of drivers’ driving habits and frequently used commands,” “arbitrary manual driving override,” “driver attention detection while the steering wheel is held,” “easy return of steering wheel after turning,” and “voice commands” were classified as attractive quality elements. A considerable proportion of the participants expressed that they would not be dissatisfied even when the quality elements were inadequate; however, they were satisfied when the elements were adequate.
3. Results and discussion 3.1. Questionnaire reliability The Cronbach's α coefficient was used in this study to measure the internal consistency of the questionnaire items. Hair et al. (1998) asserted that although the alpha coefficient lacks a standard absolute value, an alpha coefficient of 0.7 is universally accepted as the reliability standard, and items with alpha coefficient lower than 0.35 should be rejected. Wortzel (1979) maintained that a reliability coefficient of 0.6 is acceptable, but one between 0.7 and 0.98 is preferred, and that coefficients lower than 0.35 should be rejected. In this study, Cronbach's α coefficients concerning the importance and perceived satisfaction of specific functions (i.e., basic safety functions, multimedia entertainment, information and communication, and value-added systems) and intangible value-added services (active matching, automatic service system, software-hardware integration, and customer service and support) were measured. Outcomes are tabulated in Table 4. Apart from the product design construct, all the remaining coefficients were 0.733 (exceeding the recommended level of 0.6), suggesting that the questionnaire achieved excellent reliability.
3.3.2. One-dimensional “Audio device scroll button,” “audio device volume button,” “pick up/end call buttons,” “autopilot when hands leave steering wheel,” “active cruise control support system,” “automatic lane deviation warning support system,” and “fatigue detection based on facial recognition” were classified as one-dimensional quality elements. The participants expressed satisfaction when the quality elements were adequate and dissatisfaction when the elements were inadequate. 3.3.3. Must-be “Steering wheel height and headrest adjustment based on facial recognition,” “auto-complete lane change after initiating auto-turning,” and “app-enabled” were classified as must-be quality elements. A considerable proportion of the participants expressed no satisfaction when the quality elements were adequate but dissatisfaction when the elements were inadequate, suggesting that they valued these quality elements.
3.2. Returned sample characteristics analysis A total of 56 valid questionnaires were returned. Participants demographics are given in Table 5; among them, 62.5% were men and 37.5% were women, and most of them were aged between 31 and 45 years (66%).
3.3.4. Indifferent
3.3. Kano quality elements classification and analysis
“Lane departure warning indicator,” “air conditioner control buttons,” “four-directional control pad,” and “audio reminders” were classified as indifferent quality elements. The respondents expressed neither satisfaction nor dissatisfaction for these elements.
For the Kano two-dimensional quality analysis, the respondents were instructed to express their perceived satisfaction for 30 quality elements presented in two hypothetical scenarios (adequate or inadequate). Perceived satisfaction was measured using a five-point Likert scale, where a higher score represented higher perceived satisfaction. The results were used to categorize the Kano two-dimensional quality elements, as tabulated in Tables 6 and 7. Among the 30 design elements, 10 were classified as “Attractive,” seven were classified as “One-Dimensional,” three were classified as “Must-Be,” four were classified as “Indifferent,” and six were classified as “Reverse” (Table 8).
3.3.5. Reverse “Hazard warning indicator,” “airbag deployment indicator,” “navigator activation button,” “rear seat light control buttons,” “fatigue relief,” and “stress relief” were classified as reverse quality elements. A considerable proportion of the respondents expressed dissatisfaction when these quality elements were adequate and satisfaction when the elements were inadequate. The classification results of the Kano two-dimensional quality elements indicated significant differences among the five groups of quality elements. Therefore, the hypothesis that product quality elements have different classifications in the Kano model was supported.
3.3.1. Attractive “Horn button,” “paddle shift controls,” “fast U-turn,” “audio device Table 4 Variable reliability: Cronbach's α.
3.4. Respondents’ perceived importance of vehicle functions
Cronbach's Alpha
N of Items
.733
30
A five-point Likert scale was used to measure the participants' perceived importance concerning the 30 quality elements, where a higher score represented a higher perceived importance or satisfaction 146
International Journal of Industrial Ergonomics 69 (2019) 142–152
M.-Y. Ma et al.
Table 6 Kano quality element percentage and classification for specific functions. Group
Dimension
Element
(A)
(O)
(M)
(I)
(R)
Kano Quality Classification
Specific Functions
Basic Safety Functions
A1. Horn button A2. Paddle shift controls A3. Lane departure warning indicator A4. Hazard warning indicator A5. Airbag deployment indicator A6. Fast U-turn B1. Audio device play button B2. Audio device scroll button B3. Audio device volume buttons C1. Pick up/end call buttons C2. Navigator activation button C3. Cruise control activation button D1. Air conditioner control buttons D2. Rear seat light control buttons D3. Four-directional control pad
44% 42% 0% 0% 0% 43% 54% 20% 7% 32% 0% 48% 0% 0% 0%
38% 39% 11% 5% 6% 14% 7% 42% 50% 52% 3% 45% 2% 3% 10%
9% 9% 13% 2% 2% 41% 30% 34% 38% 9% 4% 5% 24% 4% 26%
10% 10% 46% 41% 34% 2% 9% 4% 5% 7% 43% 3% 64% 43% 57%
0% 0% 30% 52% 51% 0% 0% 0% 0% 0% 51% 0% 10% 50% 7%
A A I R R A A O O O R A I R I
Multimedia Entertainment System
Information and Communication System
Value-Added Systems
average extent of dissatisfaction (Fig. 3). The extent of satisfaction and extent of dissatisfaction coefficients ranged between absolute values of 0 and 1. A customer satisfaction coefficient closer to 1 represents that customers' perceived satisfaction increases significantly when the quality element is improved, whereas an extent of dissatisfaction coefficient plotted closer to −1 represents that customers’ perceived dissatisfaction increases significantly if that quality element is not satisfied. According to the preceding table, the following findings were derived. Using the example of “E1. Automatic collection of drivers' driving habits and frequently used commands,” we can ascertain that the quality ratio of this element for the “attractive,” “one-dimensional,” “must-be,” “indifferent,” and “reverse” constructs was 52%, 40%, 5%, 3%, and 0.0%, respectively (Table 11). This indicates that over half the participants identified this quality element as attractive. From Eqs. (3) and (4), the extents of satisfaction and dissatisfaction were found to be 0.92 and −0.45, respectively, implying that customers’ perceived satisfaction for adequate quality outweighed their perceived dissatisfaction with inadequate quality and validating the reliability of classifying this element under the “attractive” construct. These results are consistent with the evaluation standard of M > O > A > I proposed by Matzler and Hinterhuber (1998). In other words, during product design or development, quality elements in the “must-be” construct must first be satisfied to prevent a sharp increase in dissatisfaction. Then, attractive design elements should be introduced to enhance customer satisfaction. In the customer satisfaction matrix (Fig. 3), the quality elements plotted in the first quadrant (high extent of satisfaction, low extent of dissatisfaction) are the key design elements for future vehicle development. These elements include “cruise control activation button,” “automatic recording of drivers’ driving habits and frequently used commands,” “voice commands,” “driver attention detection while holding the steering wheel,” and “arbitrary manual driving override.”
(Table 9). The quality groups, in descending order of importance as expressed by the participants, were automated system services, multimedia entertainment, active matching, information and communication, customer service and support, basic safety functions, softwarehardware integration, and value-added systems. In this order, the average scores of the first five groups were higher than the overall average. The standard deviation values of the constructs were analyzed to determine consensus. The level of dispersion of the eight groups of standard deviation values was relatively low, suggesting that the respondents’ opinions were consistent. Table 10 indicates the respondents' perceived importance concerning the various functional design elements. The five top-ranking quality elements were “fatigue detection based on facial recognition” in software-hardware integration, “cruise control activation button” in information and communication, “automatic recording of drivers’ driving habits and frequently used commands” in active matching, “arbitrary manual driving override” in automated system services, and “horn button” in basic safety functions. The five bottom-ranking quality elements were “rear seat light control buttons” in value-added systems, “airbag deployment indicator” and “hazard warning indicator” in basic safety functions, and “fatigue relief” and “stress relief” in software–hardware integration. Although these factors seemed less important, an additional analysis was performed to determine whether they were key elements affecting satisfaction. 3.5. Kano customer satisfaction coefficient calculation and matrix creation Although the data collected from the Kano questionnaire enabled the researchers of this study to classify the two-dimensional quality elements of the product designs, they are inadequate to facilitate managers in their efforts to enhance customer satisfaction. Therefore, a customer satisfaction matrix was created to highlight the quality elements that can be incorporated into product designs to maximize customer satisfaction. First, the two-dimensional quality elements classification results and the literature review results concerning the Kano two-dimensional model of quality were applied to calculate the extent of customer satisfaction and the extent of dissatisfaction coefficients given by Eqs. (1) and (2) (Table 11): (A + O)/(A + O + M + I) and
(1)
−(M + O)/(A + O + M + I),
(2)
3.6. Demand for future automatic vehicle-driving services The 30 quality characteristics were plotted on an XY diagram (Fig. 4). In the Kano two-dimensional quality model, the horizontal axis represents the sufficiency of the quality characteristics, whereas the vertical axis represents customer satisfaction. Of all the quality characteristics, three were classified under the “must-be” category, indicating characteristics that companies had to improve first; improvements in this category could immediately lower customer dissatisfaction. Seven quality characteristics were classified under the “one-dimensional” category; improvements in this category would elevate customer satisfaction and lower customer dissatisfaction. A total of 10 quality characteristics were classified under the “attractive”
where A: Attractive; O: One-dimensional; M: Must-be; and I: Indifferent. The extents of customer dissatisfaction were then plotted on the Xaxis (Eq. (1)), and the extents of satisfaction coefficients on the Y-axis (Eq. (2)), to create the customer satisfaction matrix. The center line of the X-axis was the average extent of satisfaction of the 30 quality elements, and the center line of the Y-axis was the 147
Active Matching
Iintangible Value-Added Services
148
Customer Service and Support
Software–Hardware Integration
Voice commands
Automatic recording of drivers' habits and frequently used commands ‧ Arbitrary manual driving override ‧ Driver attention detection while the steering wheel is held ‧ Easy return of steering wheel after turning
Active Matching
Automatic Service System
Cruise control activation button
Information and Communication System Value-Added Systems
‧ Paddle shift controls ‧ Fast U-turn
Audio device play button
A
Multimedia Entertainment System
Basic Safety Functions
Group
‧ Autopilot when hands leave steering wheel ‧ Active cruise control support system ‧ Automatic lane departure warning support system Fatigue detection based on facial recognition
‧ Audio device scroll button ‧ Audio device volume buttons Pick up/end call buttons
O
App-enabled
Steering wheel height and headrest adjustment based on facial recognition Auto-complete lane change after initiating auto-turning
M
E1. Automatic recording of drivers' habits and frequently used commands E2. Steering wheel height and headrest adjustment based on facial recognition F1. Arbitrary manual driving override F2. Driver attention detection while the steering wheel is held F3. Autopilot when hands leave steering wheel F4. Easy return of steering wheel after turning F5. Auto-complete lane change after initiating auto-turning F6. Active cruise control support system F7. Automatic lane departure warning support system (steering wheel vibrates to warn the driver when leaving a marked lane without turning on indicators) G1. App-enabled (drivers can input their preferred parameters into a dedicated app; the parameters are automatically synced with the steering wheel activation control system) G2. Fatigue detection based on facial recognition G3. Fatigue relief (lights are activated to stimulate drivers' sympathetic nervous system when fatigue is detected) G4. Stress relief (light music, warm lights, and a pleasant fragrance are provided to stimulate drivers' parasympathetic nerve system when stress is detected) H1. Voice commands H2. Audio reminders
Element
Table 8 Kano quality element classification for the various quality elements.
Customer Service and Support
Software–Hardware Integration
Automatic Service System
Dimension
Group
Table 7 Kano quality element percentage and classification for intangible value-added services.
4%
0%
11% 34%
9%
22% 3%
45%
5% 54% 10% 11% 9% 22% 58% 20% 34%
(M)
2% 57%
43%
5% 38%
2%
3% 36% 5% 3% 2% 3% 30% 3% 2%
(I)
Audio reminders
‧ Air conditioner control buttons ‧ Cruise control activation button
Lane departure warning indicator
I
34% 5%
55% 2%
18% 0%
57% 0%
43%
40% 8% 33% 35% 61% 18% 7% 42% 50%
(O)
10%
52% 0% 52% 52% 28% 57% 5% 35% 14%
(A)
0% 4%
44%
0% 57%
0%
0% 2% 0% 0% 0% 0% 0% 0% 0%
(R)
‧ Hazard warning indicator ‧ Airbag deployment indicator
‧ Fatigue relief ‧ Stress relief
Rear seat light control buttons
Navigator activation button
R
A I
R
O R
M
A M A A O A M O O
Kano Quality Classification
M.-Y. Ma et al.
International Journal of Industrial Ergonomics 69 (2019) 142–152
International Journal of Industrial Ergonomics 69 (2019) 142–152
M.-Y. Ma et al.
improving service procedures.
Table 9 Ranking of the quality constructs based on perceived importance. Constructs
Items
Perceived Importance Mean
Basic Safety Functions Multimedia Entertainment System Information and Communication System Value-Added Systems Active Matching Automatic Service System Software–Hardware Integration Customer Service and Support
4. Conclusions
Standard Deviation
Rank
6 3 3
2.8988 3.8214 3.4405
0.6698 0.6646 0.5376
6 2 4
3 2 7 4 2
1.9464 3.5715 4.0153 2.7232 3.3839
0.4833 0.5114 0.6219 0.5189 0.6307
8 3 1 7 5
The Kano model is an instrument to evaluate users' perception of quality, enabling users' current expectations and demand to be determined. This illustrates the value of user satisfaction and allows designers or companies to make related assessments, ultimately improving and elevating users’ overall satisfaction. This study used the vehicle-driving quality assessment model developed from a previous study, which contains 2 dimensions and 30 quality characteristics. The Kano model was used to identify the quality attribute of each characteristic. The study results showed that the quality attribute most commonly used to classify the characteristics was “attractive,” followed by “one-dimensional,” “reverse,” “indifferent,” and “must-be.” This confirms users’ varying demand for quality attributes. This study identified various vehicle-driving quality characteristics and evaluated which ones exhibit greater effects on user satisfaction. Such findings allow companies to implement customer segmentation and achieve higher customer satisfaction levels. The Kano model shows the following customer satisfaction results. From the perspective of human–machine interaction, consumers may not notice all the media they come into contact with. They also have affective experiences from intangible feedback. Many innovative services will emerge amidst fierce market competition and as the
category; improvements in this category could significantly enhance customer satisfaction (Table 12). Finally, four quality characteristics were classified under the “indifferent” category; the diagram for this category is not shown, because customers are not concerned with the performance of these characteristics and companies may therefore choose to not improve them. By referring to where quality characteristics are located on a Kano model diagram, companies can prioritize the importance of quality characteristics, develop improvement strategies to elevate customer satisfaction, and identify the quality characteristics that they should pay attention to when developing or Table 10 Importance of each construct characteristic. Group
Specific Functions
Dimension
Basic Safety Functions
Multimedia Entertainment System Information and Communication System Value-Added Systems
Iintangible Value-Added Services
Active Matching
Automatic Service System
Software–Hardware Integration
Customer Service and Support
Element
Importance Mean
Standard Deviation
Rank
A1. Horn button A2. Paddle shift controls A3. Lane departure warning indicator A4. Hazard warning indicator A5. Airbag deployment indicator A6. Fast U-turn B1. Audio device play button B2. Audio device scroll button B3. Audio device volume buttons C1. Pick up/end call buttons C2. Navigator activation button C3. Cruise control activation button D1. Air conditioner control buttons D2. Rear seat light control buttons D3. Four-directional control pad
4.4464 4.4286 1.7143 1.4464 1.3750 3.9821 4.1429 3.7857 3.5357 4.3036 1.5000 4.5179 2.1607 1.4286 2.2500
.65836 .65663 .67995 .53664 .52440 .96278 .96160 .49412 .53815 .50162 .57208 .53906 .37059 .49935 .57997
5 6 21 24 25 11 10 12 14 8 22 2 20 26 19
E1. Automatic recording of drivers' habits and frequently used commands E2. Steering wheel height and headrest adjustment based on facial recognition F1. Arbitrary manual driving override F2. Driver attention detection while the steering wheel is held F3. Autopilot when hands leave steering wheel F4. Easy return of steering wheel after turning F5. Auto-complete lane change after initiating auto-turning F6. Active cruise control support system F7. Automatic lane departure warning support system (steering wheel vibrates to warn the driver when leaving a marked lane without turning on indicators) G1. App-enabled (drivers can input their preferred parameters into a dedicated app; the parameters are automatically synced with the steering wheel activation control system) G2. Fatigue detection based on facial recognition G3. Fatigue relief (lights are activated to stimulate drivers' sympathetic nervous system when fatigue is detected) G4. Stress relief (light music, warm lights, and a pleasant fragrance are provided to stimulate drivers' parasympathetic nerve system when stress is detected) H1. Voice commands H2. Audio reminders
4.5000 2.6429
.53936 .48349
3 17
4.4643 4.4107 4.2143 4.1429 2.7143 4.5000 3.6607
.60194 .68162 .59435 .99870 .49412 .50452 .47775
4 7 9 10 16 3 13
3.4286
.49935
15
4.5536 1.4464
.50162 .53664
1 24
1.4643
.53815
23
4.4107 2.3571
.70780 .55362
7 18
149
International Journal of Industrial Ergonomics 69 (2019) 142–152
M.-Y. Ma et al.
Table 11 Customer satisfaction coefficients. Group
Dimension
Element
Kano Quality Element
(CS)
(DS)
Specific Functions
Basic Safety Functions
A1. Horn button A2. Paddle shift controls A3. Lane departure warning indicator A4. Hazard warning indicator A5. Airbag deployment indicator A6. Fast U-turn B1. Audio device play button B2. Audio device scroll button B3. Audio device volume buttons C1. Pick up/end call buttons C2. Navigator activation button C3. Cruise control activation button D1. Air conditioner control buttons D2. Rear seat light control buttons D3. Four-directional control pad
A A I R R A A O O O R A I R I
0.82 0.81 0.16 0.10 0.14 0.57 0.61 0.62 0.57 0.84 0.06 0.93 0.02 0.06 0.11
−0.47 −0.48 −0.34 −0.15 −0.16 −0.55 −0.37 −0.76 −0.88 −0.64 −0.14 −0.50 −0.29 −0.14 −0.39
E1. Automatic recording of drivers' habits and frequently used commands E2. Steering wheel height and headrest adjustment based on facial recognition F1. Arbitrary manual driving override F2. Driver attention detection while the steering wheel is held F3. Autopilot when hands leave steering wheel F4. Easy return of steering wheel after turning F5. Auto-complete lane change after initiating auto-turning F6. Active cruise control support system F7. Automatic lane departure warning support system (steering wheel vibrates to warn the driver when leaving a marked lane without turning on indicators) G1. App-enabled (drivers can input their preferred parameters into a dedicated app; the parameters are automatically synced with the steering wheel activation control system) G2. Fatigue detection based on facial recognition G3. Fatigue relief (lights are activated to stimulate drivers' sympathetic nervous system when fatigue is detected) G4. Stress relief (light music, warm lights, and a pleasant fragrance are provided to stimulate drivers' parasympathetic nerve system when stress is detected) H1. Voice commands H2. Audio reminders
A M A A O A M O O
0.92 0.08 0.85 0.87 0.89 0.72 0.12 0.77 0.64
−0.45 −0.63 −0.43 −0.46 −0.70 −0.40 −0.65 −0.62 −0.84
M
0.53
−0.88
O R
0.73 0.05
−0.77 −0.15
R
0.07
−0.23
A I
0.91 0.05
−0.45 −0.40
Multimedia Entertainment System Information and Communication System Value-Added Systems
Iintangible Value-Added Services
Active Matching Automatic Service System
Software–Hardware Integration
Customer Service and Support
Fig. 3. Customer satisfaction matrix. Fig. 4. Customer satisfaction diagram obtained using the Kano model.
driving service market continues to change. For example, with the advent of autonomous driving, designers are challenged with selecting attractive Kansei qualities for their designs to meet consumer expectations and enhance “service” and “satisfaction.” This is one of the main topics this study focuses on. In this context, we examined driving devices that were technologically mature and emphasized technology services using the Kano model of quality to elucidate consumers’ demands for different Kansei elements (Fig. 5). Based on the “attractive”
quality element of consumer demands, the classification results were subsequently used to create the “Golden Circle of Product Services” selfdefined by this study.
Conflicts of interest The authors declare that there are no conflicts of interest. 150
International Journal of Industrial Ergonomics 69 (2019) 142–152
M.-Y. Ma et al.
Table 12 Recommendations for vehicle-driving service demand. C3 E1 H1 F2 F1 A1 A2 F4 B1 A6
Cruise control activation button Automatic recording of drivers' habits and frequently used commands Voice commands Driver attention detection while the steering wheel is held Arbitrary manual driving override Horn button Paddle shift controls Easy return of steering wheel after turning Audio device play button Fast U-turn
F3 C1 F6 G2 F7
Autopilot when hands leave steering wheel Pick up/end call buttons Active cruise control support system Fatigue detection based on facial recognition Automatic lane departure warning support system (steering wheel vibrates to warn the driver when leaving a marked lane without turning on indicators) Audio device scroll button Audio device volume buttons
B2 B3
G1 F5 E2
App-enabled (drivers can input their preferred parameters into a dedicated app; the parameters are automatically synced with the steering wheel activation control system) Auto-complete lane change after initiating auto-turning Steering wheel height and headrest adjustment based on facial recognition
References Bedford, C., Lee, A., 2008. Would you like service with that? Des. Manage. Rev. 19 (1), 39–43. Cagnin, F., Oliveira, M.C., Simon, A.T., Helleno, A.L., Vendramini, M.P., 2016. Proposal of a method for selecting suppliers considering risk management: an application at the automotive industry. Int. J. Qual. Reliab. Manag. 33 (4), 488–498. Chang, Y.M., Chen, C.W., 2016. Kansei assessment of the constituent elements and the overall interrelations in car steering wheel design. Int. J. Ind. Ergon. 56, 97–105. Chen, C.C., Chuang, M.C., 2008. Integrating the Kano model into a robust design approach to enhance customer satisfaction with product design. Int. J. Prod. Econ. 114 (2), 667–681. Chen, C.C., Li, Y.J., 2008. The application of Kano model on exploring the attractive attributes of cultural product design. J. Des. 13 (4), 25–41. Chuang, M.C., Ma, Y.C., 2001. Expressing the expected product images in product design of micro-electronic products. Int. J. Ind. Ergon. 27, 233–245. Design Council, 2010. What Is Service Design? http://www.designcouncil.org.uk/aboutdesign/Types-of-design/Service-design/What-is-service-design/, Accessed date: 10 November 2010. Dominici, G., Roblek, V., Abbate, T., Tani, M., 2016. Click and drive: consumer attitude to product development: towards future transformations of the driving experience. Bus. Process Manag. J. 22 (2), 420–434. Goonetilleke, R.S., 2000. The comfort-discomfort phase change. In: Karwowski, W. (Ed.), International Encyclopedia of Ergonomics and Human Factors. Taylor and Francis, London, pp. 399–402. Goonetilleke, R.S., Shih, H.M., On, H.K., Fristch, J., 2001. Effects of training and representational characteristics in icon design. Int. J. Hum. Comp. Stud. 55 (5), 741–760. Grace, D., O'Cass, A., 2004. Examining service experiences and post-consumption evaluations. J. Serv. Market. 18 (6), 450–461. Hair Jr., J.F., Anderson, R.E., Thatham, R.L., Black, W.C., 1998. Multivariate Date Analysis, third ed. Prentice-Hall Inc., Upper Saddle River, NJ, pp. 326–383. Högström, C., 2011. The theory of attractive quality and experience offerings. TQM J. 23 (2), 111–127. Ji, P., Jin, J., Wang, T., Chen, Y., 2014. Quantification and integration of Kano's model into QFD for optimising product design. Int. J. Prod. Res. 52 (21), 6335–6348. Kano, N., Seraku, N., Takahashi, F., Ichi, S., 1984. Attractive quality and must-be quality. Qual. Contr. J. 21 (5), 33–41. Kumar, V., Venkatesan, R., 2005. Who are the multichannel shoppers and how do they
Fig. 5. Golden circle for vehicle-driving product and services.
Acknowledgements This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
151
International Journal of Industrial Ergonomics 69 (2019) 142–152
M.-Y. Ma et al.
Patrício, L., Fisk, R.P., e Cunha, J.F., Constantine, L., 2011. Multilevel service design: from customer value constellation to service experience blueprinting. J. Serv. Res. 14 (2), 180–200. Wortzel, L., 1979. Multivariate Analysis. Prentice-Hall Inc., Upper Saddle River, NJ. Yang, J., 2003. The icon recognition affected selection intention and behavior pattern of college students in playing lucky-hole poking game. J. Des. 8 (1), 51–63.
perform? Interact. Market. 9 (2), 44–62. Matzler, K., Hinterhuber, H.H., 1998. How to make product development projects more successful by integrating Kano’s model of customer satisfaction into quality function deployment. Technovation 18 (1), 25–38. Nunes, P.F., Cespedes, F.V., 2003. The customer has escaped. Harv. Bus. Rev. 81 (11), 96–105.
152