1. Introduction
Indoor navigation systems realize the output of navigation paths in buildings through road network construction [1], indoor positioning [2], and path planning [3]. It is necessary and important to develop an indoor navigation system that reflects the general demands of users in shopping malls. Recently, many scholars have carried out independent research on the three important content factors of an indoor navigation system. In road network construction, IFC is a commonly used road network extraction method, combined with the BIM model [4,5]. In indoor positioning, Liu et al. [6] fused magnetic and visual sensors to study indoor localization without infrastructure, while Farahsari et al. [7] studied Internet of Things (IoT)-based indoor localization. In path planning, Deng et al. [8] studied path planning under fire evacuation scenarios, while Lee and Medioni [9] used an improved D* algorithm to plan paths. However, at present, there is no mature indoor navigation system [10,11,12] that combines the three independent parts, and in particular, the existing indoor navigation systems do not really understand the users’ demand, and there is no indoor navigation system that combines the users’ general demand and dynamic environmental changes. At present, the visual navigation service only uses the fixed point where the LED display screen is located as the navigation starting point, and the user cannot navigate in real time on their mobile phone. The navigation service only provides the resulting output of the shortest path, and only a few shopping malls provide a personalized choice of priority vertical elevators or priority horizontal escalators, without considering environmental changes (such as congestion and emergencies) and their impact on user navigation experience and satisfaction. This leads to user dissatisfaction with indoor navigation deficiency. Users often find that the positioning of the navigation is inaccurate, the planned shortest route is congested, the traffic time is wasted, and navigation in the vertical direction is not clear. The reason for these existing affairs and problems is that the indoor navigation system has not undergone an adequate demand functional analysis. As a functional service system for space management and operation and maintenance management, the indoor navigation system is used by “people” with independent will. Therefore, it is not enough for designers to only satisfy the planning of the shortest path between the start point and the end point. Only by conducting sufficient user research and demand analysis can the indoor navigation system achieve the greatest practical role and meet the individual needs of users.
In general, existing indoor navigation systems of shopping malls have problems such as ignoring the general demand of users and ignoring the dynamic changes of the scene. The lack of a complete survey for demand analysis of indoor navigation in shopping malls is, thus, the key issue to be resolved. General demand analysis has been proven to be the key starting point of system design, with successful examples such as industrial energy demand models [13], cooling demand models to design cooling systems for large office buildings [14], and hotel room demand analysis, which changed the direction of the hospitality industry [15]. However, there is a gap in the effective analysis of the general demand for indoor navigation systems.
There are several mainstream models for demand analysis, including Maslow’s hierarchy of needs [16], SWOT analysis [17], the Boston matrix [18], and PEST analysis [19]. Among them, Maslow’s Hierarchy of Needs method divides people’s needs into five levels, physiology, safety, emotional belonging, respect, and self-realization, which is suitable for determining the macro function of products, such as designing internet products with the function of making friends out of emotional belonging. SWOT analysis divides events into four dimensions, strengths, weaknesses, opportunities, and threats, and constructs a matrix to facilitate the design of product positioning and competitive strategies. The Boston Matrix divides products into four categories, star category, thin dog category, problem category, and golden bull category, which is helpful for sales strategy adjustment. The PEST analysis method obtains the macro-environmental analysis of the product through the analysis of the political environment, economic environment, social environment, and technical environment. Compared with these methods, the Kano model is another important model that better suits the purpose of the study. The Kano model is a theory invented and proposed by Professor N. Kano in 1984. With its essence of reflecting the nonlinear relationship between satisfaction and performance [20], the model can classify and rank the demand of users. The Kano model helps to increase the value of a system or product, and will focus on the service design, development and verification phases, and functional design by real customers in the development design phase [21]. Integrating the Kano model into existing design methods can improve users’ satisfaction with product design [22]. At present, the Kano model has been fully applied and studied in various aspects such as product development and the healthcare industry. Asian et al. [20] used this model to study the effective variables of third-party logistics providers in the automobile manufacturing industry. Hashim and Dawal [23] improved ergonomic design with the help of the Kano model. Li et al. [3] studied the user needs of an eco-city based on the Kano model. Materla et al. [24] summarized the application of the Kano model in the healthcare industry. However, no report on the application of the Kano model in the building operation and maintenance management stage has been found, especially in the design of indoor navigation products in shopping malls.
In order to fill the gap in which there is a lack of effective general demand analysis for indoor navigation, which causes deviation between the functions of the indoor navigation system and the user’s general demand, this paper uses the Kano model with 498 questionnaires to determine the priority of different general demands in the functional design for indoor navigation in shopping malls. The design of a shopping mall indoor navigation system based on users’ general demand and dynamic environmental changes is also proposed to inspire future designers for related products. The main contribution of this paper is to apply the Kano model, for the first time, to determine the general demand and functions of indoor navigation in shopping malls. Existing research (Table 1) on user demand analysis of indoor navigation systems has mostly focused on the navigation needs of special populations [25,26], and mobility needs in the navigation process [27,28]. Compared to the existing literature in terms of user demand research for indoor navigation, the general demand analysis method based on the Kano model in this paper is able to reveal the user accreditation degree of the different functions of indoor navigation systems in shopping malls and meet the general demand of most people. Furthermore, the findings of this paper provide insight into users’ behaviors and preferences from questionnaire research, which will benefit further studies on indoor navigation systems for shopping malls.
The following is a summary of the framework of this paper. Section 2 describes the Kano model and the related evaluation indicators. Section 3 presents the results of the questionnaire survey, including the analysis of the basic information of the questionnaire, the correlation analysis, and the related indicators of the Kano model. Section 4 is a practical implementation of the general demand for an indoor navigation system in shopping malls, which is based on the results of Section 3.
2. Materials and Methods
The method used in this paper is shown in Figure 1, which mainly includes literature research, offline interviews, and questionnaire research. Through literature research and offline interviews, several major functions of general demand can be initially considered in the indoor navigation of shopping malls, as well as related qualitative indicators. Except for a few customers who are particularly familiar with shopping malls, most customers have high indoor navigation demands. Zhou et al. [29] considered path complexity, congestion, and blocking events when planning indoor paths. Basu et al. [30] believe that the Pedestrian Route Choice (PRC) needs to consider the relationship between perceptual factors and objective factors. The qualitative indicators mentioned here are mainly crowd-density indicators and traffic-speed indicators. Based on the above discussion, this paper identifies five general demands (2.1 Identify 5 general demands), namely “Avoid crowded/emergency roads”, “Passing by specific types of shops”, “Bypass specific types of shops”, “Vertical elevator first”, and “Escalator first”. Next, we outline the questionnaire design based on these five general demands. The questionnaire is divided into three parts, including the basic personal information of users, objective data of users related to the indoor navigation of shopping malls, and subjective data of users. Due to the COVID-19 pandemic, this questionnaire was collected online. After obtaining the results of the questionnaire research, the analysis work was carried out. The reliability, validity, and correlation were tested, and the Kano model and related evaluation indicators were introduced. The types of general demands and the priority of consideration were determined through Kano model classification, mixed class, and coefficient analysis. A design of the indoor navigation system of a shopping mall incorporating general demands identified from the results is presented to show how the conclusions drawn by the Kano model can be applied to the system design.
2.1. The Kano Model
The Kano model divides demand attributes into 5 types, as shown in Table 2 and Figure 2 [31,32]. The X-axis represents the level of quality performance (from insufficient to sufficient) and the Y-axis represents the level of user satisfaction (from dissatisfaction to satisfaction), which can be divided into five categories. The must-be quality (M) means that when this factor is applied or improved, the user’s satisfaction with the product will not be improved. If this factor is not considered or is weakened, the user’s satisfaction with the product will drop significantly. This factor must be considered in product design. The indifferent quality (I) means that regardless of whether this factor is applied or not, the user’s satisfaction with the product does not fluctuate, and this factor does not need to be considered in product design. The one-dimensional quality (O) means that when the factor is applied or improved, the user’s satisfaction with the product is greatly improved. If the factor is not considered or is weakened, the user’s satisfaction with the product will decrease accordingly. This factor is a competitive attribute and is an important factor in product design. The considered part is different from other conventional products and reflects unique, special, and high-quality characteristics. The attractive quality (A) means that when the factor is applied or improved, the user’s satisfaction with the product is greatly improved. If the factor is not considered or is weakened, the user’s satisfaction with the product does not change, and the factor can be developed within the scope of the cost. The reversal quality (R) means that when the factor is applied or improved, the user’s satisfaction with the product does not rise but falls, and the user has no demand for the factor, which should be eliminated in the design.
Figure 2Relationship between quality performance and user satisfaction of Kano types [31,32].
[Figure omitted. See PDF]
2.2. Questionnaire Design Based on Kano Model
The premise of the Kano model is questionnaire research. Functional questions and dysfunctional questions were set in the questionnaire. Functional questions aim to ask whether the user is satisfied if this demand is considered in the product design. The purpose of the dysfunctional question is to ask if the user would be satisfied if the product design left this demand out. Therefore, in the questionnaire design, a maximum of five demand/function questions should be set in the same questionnaire, and the keywords of the functional question and dysfunctional question should be bolded in different colors to prevent the questionnaire results from being affected by unclear questions. In addition, multiple-choice questions were utilized when setting the question type, avoiding using an array of questions and preventing the respondents from answering questions in confusion due to the small degree of distinction.
Therefore, the questionnaire developed in this study adopted the form of “Single choice + Multiple choice”, combined with the interviews of pedestrians in shopping malls, to obtain three elements, including user’s basic information, objective data, and subjective data about users related to indoor navigation in shopping malls. The questionnaire included the following three main parts, and the specific item settings are shown in Figure 3.
Part1: User’s personal basic information, including gender, age, and occupation.
Part2: Objective data of users related to the indoor navigation of shopping malls, including the frequency of visiting shopping malls, the purpose of visiting shopping malls, the types of shopping malls visited, whether users are familiar with shopping mall maps, the types of shops they often visit, whether they can accurately find the shortest path, etc.
Part3: Subjective data of users related to the indoor navigation of shopping malls, including opinions and demands on indoor navigation in shopping malls, and satisfaction with existing indoor navigation signs/systems. Among them, the functional and dysfunctional questions for indoor navigation in shopping malls were combined with 5 general demands, including “Avoid crowded/emergency roads”, “Passing by specific types of shops”, “Bypass specific types of shops”, “Vertical elevator first”, and “Escalator first”. The sources references of the 5 general demands are shown in Table 3, which prove that the investigated functions cover most shopping mall users. The setting of functional and dysfunctional questions is shown in Table 4. The questionnaire respondents are allowed to fill in personalized needs in addition to the 5 general demands. Among them, because “Avoid congestion/emergency roads” involves quantitative analysis, the choice of indicators for the congestion environment was added to the questionnaire. We adopted results from Zhou et al. [29], which divided the congestion index into the traffic density index and the traffic speed index. The traffic density index (person/m2) was divided into four grades ([0, 0.75], (0.75, 2.00), (2.00, 3.50), and (3.5, +∞]). The traffic speed index (m/s) was divided into four levels ((1.40, +∞], (1.08, 1.40], (0.30, 1.08], and [0, 0.30]). Based on this, the questionnaire for this study was divided into multiple levels, and the traffic density index was divided into six grades (0.25 people/m2, 0.5 people/m2, 0.75 people/m2, 1.25 people/m2, 2 people/m2, and ≥2 people/m2). The traffic speed index was divided into four grades (1.5 m/s, 0.75 m/s, 0.3 m/s, and ≤ 0.3 m/s). In order to improve people’s engagement with the questionnaire, pictures of the scene under different traffic densities were simulated (shown in Figure 4), which is analogous to the normal walking speed.
Table 3Source of 5 general demands.
| Items | Corresponding Source | References |
|---|---|---|
| Avoiding crowded/emergency roads | Literature research | Zhou et al. [29] |
| Passing by specific types of shops | Offline interviews | Randomly interviewed 5 male customers and 5 female customers, the age of the customers involved “old”, “middle” and “youth” generations |
| Bypassing specific types of shops | ||
| Vertical elevators first | Shopping mall field research | Hankyu Ningbo |
| Escalator first |
Pictures with the scene under different crowd densities.
[Figure omitted. See PDF]
2.3. Determination of Kano Model by Berger
There were five options for functional and dysfunctional questions, including “Like”, “Must-be”, “Neutral”, “Live with”, and “Dislike”. According to the choices of the people investigated, the Kano type of this general demand was obtained, as shown in Table 5. Among them, the Kano types obtained according to the dysfunctional and functional questions correspond to the Kano type in Table 2. Then, the principle of relative majority was adopted to summarize all Kano types of each demand item and determine the final Kano type of each demand item. Berger [33] proposed an improved Kano category, which is defined as follows:
If f (O+A+M) > f (I+R+Q), the Kano type is the highest frequency type among O, A, and M;
If f (O+A+M) < f (I+R+Q), the Kano type is the highest frequency type among I, R, and Q;
If f(O+A+M) = f (I+R+Q), the Kano type is the highest frequency type among O, A, M, and I;
where f (X) is the frequency of the demand type.
2.4. Determination of TS and CS of the Kano Model
The second part is mixed class analysis, proposed by Newcomb [35], which includes two indicators (as shown in Equations (1) and (2)), TS (Total Strength) and CS (Category Strength). If the TS value ≥ 0.6 and the CS value ≤ 0.06, it indicates that the demand belongs to the mixed class H, and the two mixed types, namely the two types with the highest frequency, should be explained.
TS = f(O + A + M)/f(O + A + M + I + R + Q)(1)
(2)
2.5. Determination of Better-Worse Coefficient of Kano Model
The third part is the analysis of the Better–Worse coefficient, which is calculated by the percentage of each demand to the classification. The Better–Worse coefficient is used to indicate the influence degree of whether the demand is satisfied or not for the respondents. The better coefficient is an “increased satisfaction coefficient”, indicating that when the demand/function is satisfied, the user’s satisfaction will be improved. The closer the coefficient is to 1, the more obvious the improvement in satisfaction will be. The Worse coefficient is “dissatisfaction coefficient after elimination”, indicating that when the demand/function is eliminated, the user’s satisfaction will decrease. The closer the coefficient is to −1, the more obvious the decrease in satisfaction will be. The Better coefficient and Worse coefficient are calculated as follows (Equations (3) and (4)):
(3)
(4)
The evaluation of the Average Satisfaction Coefficient (ASC) based on the Better–Worse coefficient was proposed by Park [36], which is strongly correlated with the Better and Worse coefficients and can reflect the priority degree in functional design. The higher the ASC value (as shown in Equation (5)), the higher priority the change demand/function should be.
(5)
If we plot a four-quadrant graph based on the Better–Worse coefficients of the demand factors, the “Better” coefficient is along the X-axis and the absolute value of the “Worse” coefficient is along the Y-axis. The origin is the average value of the absolute value of the “Better” coefficient and the “Worse” coefficient at each point.
3. Findings from the General Demand Analysis
As shown in Figure 5, 78.51% of respondents indicated that they needed software/systems to guide them to specific shops. As shown in Figure 6, 47.19% of respondents indicated that they were not satisfied with the current indoor navigation signage/system.
3.1. Reliability and Validity Analysis
In this survey, 502 questionnaires were issued in total, of which 498 were effective, accounting for 99.20%. SPSS 22.0 software (IBM SPSS Statistics, Norman H. Nie & C. Hadlai (Tex) Hull & Dale H. Bent Chicago, IL, USA) was used for reliability and validity statistics. The gender, age, and occupation of the respondents are shown in Table 6 and Figure 7, Figure 8 and Figure 9. The gender, age, and occupation of the respondents in the questionnaire conform to the actual access situation of the shopping mall, and also conform to the conclusion of Wu [37] on customers of different ages in user surveys of a shopping mall. Due to economic ability and desire, users are mainly concentrated between 18 and 49 years old. Professional office workers with stable economic incomes accounted for the largest proportion. Therefore, the questionnaire is in line with the actual situation and the general results of previous studies.
The user profile of the indoor navigation system of the shopping mall is 18–49-year-old non-retired users with a certain economic ability and frequent access to different shopping malls.
As a basic index of a questionnaire survey, reliability and validity are divided into two levels. Reliability is used to test the internal stability and consistency of the evaluation results, while validity is used to judge whether the results obtained from the questionnaire can accurately represent the evaluated functional requirements. The reliability measurement indexes include Cronbach’s Alpha value, the halved coefficient, retest reliability, etc. The validity measurement is factor analysis, and the KMO value and Bartlett sphericity test value are used in this paper. As shown in Table 7, Cronbach’s Alpha of the functional questions is between 0.7 and 0.8 with good reliability, the KMO value is between 0.7 and 0.8, and Bartlett’s sphericity test corresponds to a p value < 0.05, indicating that the results of functional questions in this questionnaire are suitable for factor analysis. Cronbach’s Alpha of dysfunctional questions was between 0.7 and 0.8, with good reliability, the KMO value was between 0.7 and 0.8, and Bartlett’s sphericity test corresponded to a p value < 0.05, indicating that the results of dysfunctional questions in this questionnaire were suitable for factor analysis.
3.2. Correlation Analysis
Correlation analysis can determine the degree of correlation between variables and judge the degree of correlation between basic personal information and objective data and subjective data in the questionnaire, using the Pearson correlation coefficient and significance SIG value. The correlation analysis framework of this questionnaire is shown in Figure 10.
3.2.1. Correlation Analysis from Objective Data
As shown in Table 8, there is a certain correlation between respondents’ basic information and objective data. There is a significant difference between “shopping mall frequency” and “age” at 0.05 (double tail), and an extremely significant difference between “occupation” at 0.01 (double tail). The correlation between “familiar with indoor map of shopping mall”, “age”, and “occupation” is significantly different at 0.05 (double tail). The correlation between “whether can find the shortest path”, “gender”, and “age” is significant at 0.01 (double tail), and the correlation between “occupations” is significant at 0.05 (double tail). In addition to the correlation between characteristics (personal basic information) and state variables (objective data) in Table 7, there is also a correlation between status variables (objective data). The r value of “frequency of shopping mall” and “familiarity with indoor map of shopping mall” is 0.377, with a SIG value of 0.000, and the correlation is very significant at 0.01 (double tail). The r value and SIG value of “are you familiar with the indoor map of the shopping mall” and “can you find the shortest path to the destination” are 0.297 and 0.000, showing a very significant difference in the correlation at 0.01 (double tail).
Figure 11 shows the basic information of “gender” and the frequency of the personal data contrast, according to the Pearson correlation coefficient and the significant SIG value of “gender” and “if you can find the shortest path”. Males’ perceptions of finding the shortest path were evenly balanced, while more than half of females felt they were unsure whether they would find the shortest path.
Males feel that they have a clearer grasp of pathfinding, while females are not sure.
Figure 12 shows the comparison between the frequency of “age” and personal data. According to the Pearson correlation coefficient and significance SIG value, it can be seen that “age” is correlated with personal data related to shopping behavior. Although the proportion of respondents aged 30–49 is 1.23 times higher than that of respondents aged 18–29, their frequency of going to the mall more than once a week is 1.97 times higher than that of respondents aged 18–29, indicating that respondents aged 30–49 visit shopping malls more frequently. The frequency of respondents aged 18 to 29 years old visiting shopping malls mainly ranges from once a week (8.83%) to once every two weeks (9.03%) to less than once a month (10.04%), which is related to different work and rest schedules for different age groups. Young users need to consider academic and economic pressures, while middle-aged users have more disposable time and consumption demands. Secondly, in terms of familiarity with the indoor map of the shopping mall, all age groups are concentrated in “not very familiar”, and the proportion of “very familiar” with the indoor map is less than 2%. This again highlights the need for a properly designed indoor navigation system. Similar to “whether familiar with store indoor map”, the 18-to-29-year-old group of respondents’ answers to “if you can find the shortest path” focus on “no” (15.66%) and “uncertainty” (14.26%). Respondents in the 30–49 age group, however, concentrated on “yes” (15.26%) and “not sure” (19.48%), which were related to lifestyle fixation and familiarity with frequent mall visits. Respondents generally reflected that they could not find the shortest route in a new shopping mall. In addition, 55.18% of the respondents aged over 50 answered “yes”, indicating that the elderly tend to go to a fixed shopping mall. They often go to the nearest shopping mall to buy daily necessities and are quite familiar with the route.
Users of different age groups go to shopping malls with different frequencies, which is closely related to the lifestyle, economic strength, and work schedule of different age groups.
Users that frequently go to particular malls think they are familiar with the indoor map of the shopping mall, but still need a navigation system when they go to a new mall.
Users over the age of 50 tend to go to particular shopping malls due to habits and physical limitations, etc., and they do not use new navigation systems. Therefore, they are not the target users of indoor navigation systems in shopping malls.
Figure 13 shows the frequency comparison between “occupation” and personal data related to shopping behavior. According to the Pearson correlation coefficient and significance SIG value, it can be seen that “occupation” is correlated with personal data related to shopping behavior. The frequency of students visiting shopping malls is concentrated at least once every two weeks, which is similar to the related results of “age”. The frequency of visiting shopping malls of the self-employed and retired respondents was relatively balanced. Office workers accounted for the largest proportion of respondents, with a frequency of mainly once a week. Secondly, in terms of whether they are familiar with the indoor map of shopping malls, students and office workers answered “not very familiar”; in contrast, 26% of office workers chose “relatively familiar”, which also matched the middle-aged survey respondents’ “regular life” and “regular shopping malls” mentioned above. In the part concerning “whether the shortest path can be found”, 64.23% of the students believe that they cannot find it or are not sure, while 75.28% of the office workers focus on “yes” and “not sure”, which also matches the previous conclusion.
Similar results of “occupation” and the personal data related to shopping behavior can be corroborated with “age”.
3.2.2. Correlation Analysis from Subjective Data
As shown in Table 9, there is a certain correlation between people’s basic information and data related to shopping behavior. The correlation between “need navigation software/system” and “occupation” is significant at 0.05 (two tails). In addition to the correlation between characteristics (personal basic information) and demand variables (subjective data) in Table 8, there is also a correlation between demand variables (subjective data). The r value of “whether you need navigation software/system” and “Yes or no you are satisfied with the current navigation system” is −0.156, the SIG value is 0.000, and the correlation is significant at 0.01 (double tail).
Figure 14 shows the frequency comparison of “gender”, a person’s basic information, and the personal data related to shopping behavior, while Figure 15 shows the frequency comparison of “age”, a person’s basic information, and personal data related to shopping behavior. According to the Pearson correlation coefficient and significance sig value, it can be seen that “gender” and “age” are not correlated with them.
“Gender” and “Age” are not correlated with personal data related to shopping behavior.
Figure 16 shows the basic information of “professional” respondents and the frequency of personal data related to shopping behavior contrast, according to the Pearson correlation coefficient and the significant sig value of “professional” and “whether need navigation software/system”. Except for the retired group, the respondents of other occupational types are not satisfied with the current indoor navigation signage or system, especially students (10.84%) and office workers (22.69%).
Satisfaction with indoor navigation systems is low among students and office workers, while retirement groups do not care. This also verifies that the retired group is not the target user of the indoor navigation system.
Students and office workers are selected as the main users. Young people, limited by their academic and economic abilities, tend to choose uncertain shopping malls, and they are more willing to try different shopping malls, having a greater demand for indoor navigation. Middle-aged people have a more fixed life trajectory and are very familiar with the indoor map of a specific shopping mall, but they also have a greater demand for indoor navigation when going to a shopping mall they have never been to.
There is a certain correlation between the characteristics of users and their behavior in shopping malls.
3.3. Kano Model Classification
As shown in Table 10, the Kano type of the five demands is consistent with the modified Kano types of Berger.
3.4. Mixed Classification
As shown in Table 11, combined with the Kano model classification and mixed classification, the five demands of this questionnaire are all non-mixed types, which are consistent with the Kano model classification. “Vertical elevator first” belongs to the Attractive quality (A), while “Escalator first” belongs to the Indifferent quality (I). Both vertical elevators and horizontal escalators are important means of vertical transportation in shopping malls. Users prefer to take the vertical elevator because they think the vertical elevator is faster and more convenient while consuming less energy. Some users stated that they are afraid of the height of the horizontal escalator.
Although vertical elevators are more difficult to find than horizontal escalators, users prefer to use vertical elevators, which may be related to the user’s belief that the vertical elevator is faster.
“Passing by a particular type of store” belongs to the Attractive quality (A), while “Bypass specific types of shops” belongs to the Indifferent quality (I). This shows that users do not accept unnecessary detours, but only detours due to congestion.
The users prefer that the navigation system passes the desired space when reaching a certain destination, but users who are not interested will tend to avoid certain spaces.
3.5. Better–Worse Coefficient Analysis
Table 12 shows the Better–Worse coefficient and ASC values of indoor navigation demand factors in shopping malls. According to AEC values, the priority of the five demands can be obtained: “Avoid crowded/emergency sections” > “Vertical elevator first” > “passing by specific types of shops” > “Escalator first” > “Bypass specific types of shops”.
The four-quadrant diagram is drawn according to the Better–Worse coefficient of the demand factors for indoor navigation in shopping malls, as shown in Figure 17. “Avoid crowded/emergency roads” is located in the first quadrant, which is an expected requirement and should be given first priority in functional design. “Passing by specific types of shops” and “Vertical elevator first” are in the second quadrant, which should also be given priority. “Bypass specific types of shops” and “Escalator first” are located in the third quadrant, which are undifferentiated requirements and can be ignored in functional design.
In terms of functional design, it can be concluded that users prefer vertical elevators for vertical transportation. In addition, it is generally recognized as necessary to avoid congested road sections or emergencies. In terms of going to a certain destination and passing the rest of the space, the users tend to think that it is necessary to pass a certain type of store that they want to pass through, but it is not necessary to make a detour to avoid passing through a certain type of store.
The three indoor navigation system functions identified in this paper are “Avoid crowded/emergency roads”, “Vertical elevator first”, and “Passing by a particular type of store”.
4. Practical Implementations of Findings from the Survey
Based on literature research and the questionnaire analysis of the Kano model, it can be concluded that “Avoid crowded/emergency roads” is an important demand, while “Vertical elevator first” and “Passing by specific types of shops” are expected general demands when designing the indoor navigation system of shopping malls. “Bypass specific types of shops” and “Escalator first” are undifferentiated requirements and may not be considered. Table 13 shows answers to “When planning the path of indoor navigation system, you want it to consider: ______” in the questionnaire. The survey results are similar to those of the Kano model. “Avoiding crowded/emergency sections”, “Vertical elevator first”, and “Passing by specific types of shops” account for the largest proportion, while “Bypassing specific types of shops” and “Escalator first” account for less than 40%.
In addition to the analysis of the five general demands, the questionnaire also set the following question: “What are your requirements for indoor navigation in shopping malls/what do you think the most convenient and effective indoor navigation system should have: _____”. A total of 141 valid answers were collected. The users’ expectation of the system is being clear and easy to use, having detailed precision, and real-time accuracy. In terms of specific needs, some users mentioned the demand to identify shopping mall activities, indicate the location of toilets, and provide a variety of options and non-graphic designs by floor.
This paper determines demand functions of three levels, as shown in Table 14, including the function level, use level, and effect level. The function level is to realize three exciting needs and expectation needs and can choose whether to implement specific functions through a personalized interface. The interface should be simple and clear, with navigation made easy enough even for people with a weak sense of direction. At the effect level, new demands are put forward for the specific environment of shopping malls. For example, the path congestion index needs to change in real time, and even predict future congestion based on past long-term data. It is necessary to solve the three-dimensional navigation results of the shopping mall from the two aspects of positioning and navigation, rather than a two-dimensional plane. More consideration should be given to special spaces such as toilets, vertical elevators, and horizontal escalators. Multiple paths can be provided if the user desires.
Based on the determined system functions, the shopping mall indoor navigation system as shown in Figure 18 was designed. The dotted box in Figure 16 is the preparatory work for the indoor navigation system of the shopping mall. First, based on the BIM model, the 3D road network is obtained through IFC and stored in the form of a matrix. Meanwhile, a database of indoor images of shopping malls is established, and 200 photos were selected for each of the four different types of scenes of elevators, atriums, gates, and ordinary indoor space. Subsequently, computer vision was used to determine whether a location in the mall is a congested route. Then, the multi-source heterogeneous information such as the congestion situation, shops on the intended route, three-dimensional coordinates, and vertical elevators are integrated into the shortest path routing algorithm. The parameters are continuously adjusted to obtain the most suitable route evaluation algorithm. Among them, the login page of the indoor navigation system of the shopping mall is shown in Figure 19, which provides the basic information of the user. In addition, the functional page design of the shopping mall indoor navigation system is shown in Figure 20. The user scans the QR code of the nearest store through the mobile phone, and their three-dimensional coordinates can be obtained in the background. On the mobile phone terminal, the user enters the destination, the shops they intend to pass through, whether to avoid crowded road sections, and whether vertical elevators are preferred, and then the optimal path calculated by the path planning algorithm can be obtained. The mobile terminal of the mobile phone presents the visual effect of the path, and displays the important store nodes of the path to improve readability and understandability.
5. Conclusions
This paper provides a detailed analysis of users’ general demand for indoor navigation systems for shopping malls. Three important functions that need to be considered in the design of shopping mall indoor navigation system are obtained. According to the questionnaire, this paper also drew some interesting conclusions, which will benefit further studies on indoor navigation systems for shopping malls: (1) Both vertical elevators and horizontal escalators are important means of vertical transportation in shopping malls. Although vertical elevators are more difficult to find than horizontal escalators, users prefer to use vertical elevators, which may be related to users’ beliefs that the vertical elevator is faster. (2) Users prefer that the navigation system can indicate the passage to the desired space when reaching a certain destination, such as buying milk tea and other beverages along the way when going to the cinema, but users who are not as interested will avoid certain spaces. (3) This paper finds the correlation between “gender”, “age”, “occupation”, and users’ behaviors in shopping malls. For example, due to the singularity of moving lines, retired groups do not care about the indoor navigation signs of shopping malls. (4) Retirees over the age of 50 are not the target users of indoor navigation systems in shopping malls.
There are still some deficiencies in this paper. The functions formulated in the questionnaire survey are still insufficient, and the multi-source heterogeneous data fusion and application in the design of shopping mall indoor navigation systems are not fully explored. In future work, more in-depth research will be carried out on these two aspects.
Y.X.: Methodology (lead), formal analysis, investigation, writing—original draft. H.D.: Resources, validation, writing—review and editing, funding acquisition. Y.D.: Conceptualization (lead), methodology, writing—review and editing, funding acquisition. All authors have read and agreed to the published version of the manuscript.
Not applicable.
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Enlarge this image.Figure 11. Frequency comparison between “Gender” and three objective data. (a) Shopping mall frequency; (b) Be familiar with the indoor map of shopping mall; (c) Whether the shortest path can be found.
Enlarge this image.Figure 12. Frequency comparison between “Age” and personal data related to shopping behavior. (a) Shopping mall frequency; (b) Be familiar with the indoor map of shopping mall; (c) Whether the shortest path can be found.
Enlarge this image.Figure 12. Frequency comparison between “Age” and personal data related to shopping behavior. (a) Shopping mall frequency; (b) Be familiar with the indoor map of shopping mall; (c) Whether the shortest path can be found.
Enlarge this image.Figure 13. Frequency comparison between “Occupation” and personal data related to shopping behavior. (a) Shopping mall frequency; (b) Be familiar with the indoor map of shopping mall; (c) Whether the shortest path can be found.
Enlarge this image.Figure 14. Frequency comparison between “Gender” and personal data related to shopping behavior. (a) Do you need a software system to guide you to the destination store? (b) Are you satisfied with the current signage/system for indoor navigation?
Enlarge this image.Figure 15. Frequency comparison between “Age” and personal data related to shopping behavior. (a) Do you need a software system to guide you to the des-tination store; (b) Are you satisfied with the current signage/system for indoor navigation.
Enlarge this image.Figure 16. Frequency comparison of “Occupation” and personal data related to shopping behavior. (a) Do you need a software system to guide you to the destination store? (b) Are you satisfied with the current signage/system for indoor navigation?
Comparison between the method in this paper and the existing research.
| Research Content | Advantages | Limitations | Author | References |
|---|---|---|---|---|
| An indoor navigation system for the blind and visually impaired (PERCEPT) | Focused on the subjective needs of special populations | The study only focused on the blind population | Ganz et al. | [ |
| A speech-based, infrastructure-free indoor navigation system (MagNav) | The study only focused on validating the importance of voice navigation | Giudice et al. | [ |
|
| Navigation aid based on mobile projector | Demonstrates the advantages of mobile screen navigation | Developed only for navigation pages/apps | Li et al. | [ |
| Using smartphones and WiFi-based indoor navigation system (SWiN) | Consider the mobility and dynamic properties of the navigation system | Only focused on the use process | Zhang et al. | [ |
Definitions of Kano types.
| Kano Type | Definition |
|---|---|
| Must-be quality (M) | When this factor is applied or improved, the user’s satisfaction of the product will not be improved. |
| Indifferent quality (I) | No matter whether this factor is applied or not, the user’s satisfaction of the product does not fluctuate. |
| One-dimensional quality (O) | when the factor is applied or improved, the user’s satisfaction of the product is greatly improved. |
| Attractive quality (A) | When the factor is applied or improved, the user’s satisfaction of the product is greatly improved. |
| Reversal quality (R) | When the factor is applied or improved, the user’s satisfaction of the product does not rise but falls. |
Sample functional and dysfunctional questions of Kano questionnaire.
| Like | Must-Be | Neutral | Live with | Dislike | |
|---|---|---|---|---|---|
| Do you accept avoiding crowded/emergency roads on the way to your destination (Functional question) | √ | ||||
| Do you accept not avoiding crowded/emergency sections on the way to your destination (Dysfunctional question) | √ |
Dysfunctional and functional questions [
| Scale | Dysfunctional Question Evaluation | |||||
|---|---|---|---|---|---|---|
| Like | Must-Be | Neutral | Live with | Dislike | ||
| Functional question evaluation | Like | Q 1 | A | A | A | O |
| Must-be | R | I | I | I | M | |
| Neutral | R | I | I | I | M | |
| Live with | R | I | I | I | M | |
| Dislike | R | R | R | R | Q 1 | |
1 Note: Q is a suspicious result due to incorrectly set question or misunderstanding of respondent.
Sex and age cross-comparison table.
| Distribution of Options | Male | Female | ||
|---|---|---|---|---|
| Number | Proportion | Number | Proportion | |
| <18 | 0 | 0.00% | 5 | 2.01% |
| 18~29 | 95 | 38.15% | 87 | 34.94% |
| 30~39 | 61 | 24.50% | 70 | 28.11% |
| 40~49 | 43 | 17.27% | 50 | 20.08% |
| 50~59 | 40 | 16.06% | 31 | 12.45% |
| ≥60 | 10 | 4.02% | 6 | 2.41% |
| Total | 249 | 100.00% | 249 | 100.00% |
Reliability and validity analysis of forward and reverse problems of Kano model.
| Type of Test | Cronbach’s Alpha | KMO Value | Bartlett’s Sphericity Test Sig Value |
|---|---|---|---|
| Functional question | 0.710 | 0.767 | 0.000 |
| Dysfunctional question | 0.707 | 0.739 | 0.000 |
Correlation analysis between characteristics (personal basic information) and status variables (objective data) 1.
| Gender | Age | Occupation | ||||
|---|---|---|---|---|---|---|
| r | p | r | p | r | p | |
| Shopping mall frequency | −0.012 | 0.787 |
|
|
|
|
| Be familiar with the indoor map of shopping mall | −0.075 | 0.093 |
|
|
|
|
| Whether the shortest path can be found |
|
|
|
|
|
|
1 r is Pearson correlation coefficient. p is significance sig value. Blue indicates significant difference. Red indicates very significant difference.
Correlation analysis between characteristics (personal basic information) and demand variables (subjective data) 1.
| Gender | Age | Occupation | ||||
|---|---|---|---|---|---|---|
| r | p | r | p | r | p | |
| Do you need a software system to guide you to the destination store? | 0.028 | 0.538 | 0.057 | 0.205 |
|
|
| Are you satisfied with the current signage/system for indoor navigation? | 0.010 | 0.832 | −0.075 | 0.097 | 0.039 | 0.383 |
1 r is Pearson correlation coefficient. p is significance sig value. Blue is significant difference.
Frequency statistics and classification of indoor navigation demand factors in shopping malls.
| Demand Item | Demand Type Frequency f(X) | Kano Category | Berger Improved the Kano Category | |||||
|---|---|---|---|---|---|---|---|---|
| O | A | M | I | R | Q | |||
| Avoid crowded/emergency roads | 226 | 58 | 27 | 151 | 18 | 18 | O | O |
| Passing by specific types of shops | 40 | 263 | 3 | 142 | 23 | 27 | A | A |
| Bypass specific types of shops | 11 | 111 | 3 | 306 | 31 | 36 | I | I |
| Vertical elevator first | 50 | 260 | 8 | 148 | 10 | 22 | A | A |
| Escalator first | 48 | 89 | 14 | 311 | 11 | 25 | I | I |
Mixed analysis of indoor navigation demand factors in shopping malls.
| Demand Item | TS Value | CS Value | Mixed Type |
|---|---|---|---|
| Avoid crowded/emergency roads | 0.624498 | 0.150602 | O |
| Passing by specific types of shops | 0.614458 | 0.242972 | A |
| Bypass specific types of shops | 0.251004 | 0.391566 | I |
| Vertical elevator first | 0.638554 | 0.224900 | A |
| Escalator first | 0.303213 | 0.445783 | I |
Analysis of Better–Worse coefficient and ASC value of indoor navigation demand factors in shopping malls.
| Demand Item | Better Coefficient | Worse Coefficient | ASC Value | Importance Ranking |
|---|---|---|---|---|
| Avoid crowded/emergency roads | 0.614719 | −0.547619 | 0.581169 | 1 |
| Passing by specific types of shops | 0.676339 | −0.095982 | 0.386161 | 3 |
| Bypass specific types of shops | 0.283063 | −0.032483 | 0.157773 | 5 |
| Vertical elevator first | 0.665236 | −0.124464 | 0.394850 | 2 |
| Escalator first | 0.296537 | −0.134199 | 0.215368 | 4 |
Percentage of indoor navigation considerations (multiple choice) in the questionnaire.
| Demand Item | Number | Percentage | Percentage of the Observed Value |
|---|---|---|---|
| Avoid crowded/emergency roads | 274 | 24.3% | 55.0% |
| Passing by specific types of shops | 338 | 29.9% | 67.9% |
| Bypass specific types of shops | 114 | 10.1% | 22.9% |
| Vertical elevator first | 231 | 20.5% | 46.4% |
| Escalator first | 159 | 14.1% | 31.9% |
| Else | 13 | 1.2% | 2.6% |
Classification and design of shopping mall interior navigation requirements.
| Level | Specific Requirements |
|---|---|
| Function | Avoid crowded/emergency roads |
| Vertical elevator first | |
| Passing by specific types of shops | |
| Use | Convenient, portable use |
| Simple and clear | |
| Effect | Updates environment data in real time and prompts for specific activities |
| Positioning and navigation are three-dimensional and accurate | |
| Multiple paths are available | |
| Special calibration for toilet, vertical elevator, horizontal escalator and other shopping mall special space |
References
1. Geoinformation; Reports from International Institute of Information Technology Describe Recent Advances in Geoinformation. Politics & Government Week 2020. Available online: https://geoinformation.com/ (accessed on 11 August 2022).
2. Luo, H.; Hu, X.; Zou, Y.; Jing, X.; Song, C.; Ni, Q. Research on a reference signal optimisation algorithm for indoor Bluetooth positioning. Appl. Math. Nonlinear Sci.; 2021; 6, pp. 525-534. [DOI: https://dx.doi.org/10.2478/amns.2021.2.00111]
3. Li, J.; Wang, Q.; Xuan, Y.; Zhou, H. User demands analysis of Eco-city based on the Kano model—An application to China case study. PLoS ONE; 2021; 16, e0248187. [DOI: https://dx.doi.org/10.1371/journal.pone.0248187] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33690633]
4. Mirahadi, F.; McCabe, B.; Shahi, A. IFC-centric performance-based evaluation of building evacuations using fire dynamics simulation and agent-based modeling. Autom. Constr.; 2019; 101, pp. 1-16. [DOI: https://dx.doi.org/10.1016/j.autcon.2019.01.007]
5. Liu, L.; Li, B.; Zlatanova, S.; van Oosterom, P. Indoor navigation supported by the Industry Foundation Classes (IFC): A survey. Autom. Constr.; 2021; 121, 103436. [DOI: https://dx.doi.org/10.1016/j.autcon.2020.103436]
6. Liu, Z.; Zhang, L.; Liu, Q.; Yin, Y.; Cheng, L.; Zimmermann, R. Fusion of Magnetic and Visual Sensors for Indoor Localization: Infrastructure-Free and More Effective. IEEE Trans. Multimed.; 2016; 19, pp. 874-888. [DOI: https://dx.doi.org/10.1109/TMM.2016.2636750]
7. Farahsari, P.S.; Farahzadi, A.; Rezazadeh, J.; Bagheri, A. A Survey on Indoor Positioning Systems for IoT-Based Applications. IEEE Internet Things J.; 2022; 9, pp. 7680-7699. [DOI: https://dx.doi.org/10.1109/JIOT.2022.3149048]
8. Deng, H.; Ou, Z.; Zhang, G.; Deng, Y.; Tian, M. BIM and Computer Vision-Based Framework for Fire Emergency Evacuation Considering Local Safety Performance. Sensors; 2021; 21, 3851. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34199640][DOI: https://dx.doi.org/10.3390/s21113851]
9. Lee, Y.H.; Medioni, G. RGB-D camera based wearable navigation system for the visually impaired. Comput. Vis. Image Underst.; 2016; 149, pp. 3-20. [DOI: https://dx.doi.org/10.1016/j.cviu.2016.03.019]
10. Schuldt, C.; Shoushtari, H.; Hellweg, N.; Sternberg, H. L5IN: Overview of an Indoor Navigation Pilot Project. Remote Sens.; 2021; 13, 624. [DOI: https://dx.doi.org/10.3390/rs13040624]
11. Yao, D.; Park, D.-W.; An, S.-O.; Kim, S.K. Development of Augmented Reality Indoor Navigation System based on Enhanced A* Algorithm. KSII Trans. Internet Inf. Syst.; 2019; 13, pp. 4606-4623. [DOI: https://dx.doi.org/10.3837/tiis.2019.09.016]
12. Bian, W.; Guo, Y.; Qiu, Q. Research on Personalized Indoor Routing Algorithm. Proceedings of the 2014 13th International Symposium on Distributed Computing and Applications to Business, Engineering and Science; Xi’an, China, 24–27 November 2014; pp. 275-277. [DOI: https://dx.doi.org/10.1109/dcabes.2014.60]
13. Maaouane, M.; Zouggar, S.; Krajačić, G.; Zahboune, H. Modelling industry energy demand using multiple linear regression analysis based on consumed quantity of goods. Energy; 2021; 225, 120270. [DOI: https://dx.doi.org/10.1016/j.energy.2021.120270]
14. Zeferina, V.; Wood, F.R.; Edwards, R.; Tian, W. Sensitivity analysis of cooling demand applied to a large office building. Energy Build.; 2021; 235, 110703. [DOI: https://dx.doi.org/10.1016/j.enbuild.2020.110703]
15. Ozdemir, O.; Kizildag, M.; Dogru, T.; Madanoglu, M. Measuring the effect of infectious disease-induced uncertainty on hotel room demand: A longitudinal analysis of U.S. hotel industry. Int. J. Hosp. Manag.; 2022; 103, 103189. [DOI: https://dx.doi.org/10.1016/j.ijhm.2022.103189]
16. Hopper, E. Maslow’s Hierarchy of Needs Explained. ThoughtCo ThoughtCo; 2020; 24, Available online: https://www.thoughtco.com/maslows-hierarchy-of-needs-4582571 (accessed on 11 August 2022).
17. Leigh, D. SWOT Analysis. Handbook of Improving Performance in the Workplace: Volumes 1–3; Wiley Online Library: Hoboken, NJ, USA, 2009; pp. 115-140. [DOI: https://dx.doi.org/10.1002/9780470592663.ch24]
18. Tao, Z.-Q.; Shi, A.-M. Application of Boston matrix combined with SWOT analysis on operational development and evaluations of hospital development. Eur. Rev. Med. Pharmacol. Sci.; 2016; 20, pp. 2131-2139. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27249614]
19. Sammut-Bonnici, T.; Galea, D. PEST Analysis. 2014; Available online: https://www.um.edu.mt/library/oar/bitstream/123456789/21816/1/sammut-bonnici%20pest.pdf (accessed on 11 August 2022).
20. Asian, S.; Pool, J.K.; Nazarpour, A.; Tabaeeian, R.A. On the importance of service performance and customer satisfaction in third-party logistics selection: An application of Kano model. Benchmarking Int. J.; 2019; 26, pp. 1550-1564. [DOI: https://dx.doi.org/10.1108/BIJ-05-2018-0121]
21. Yao, M.-L.; Chuang, M.-C.; Hsu, C.-C. The Kano model analysis of features for mobile security applications. Comput. Secur.; 2018; 78, pp. 336-346. [DOI: https://dx.doi.org/10.1016/j.cose.2018.07.008]
22. Chen, C.-C.; Chuang, M.-C. Integrating the Kano model into a robust design approach to enhance customer satisfaction with product design. Int. J. Prod. Econ.; 2008; 114, pp. 667-681. [DOI: https://dx.doi.org/10.1016/j.ijpe.2008.02.015]
23. Hashim, A.; Dawal, S.Z. Kano Model and QFD integration approach for Ergonomic Design Improvement. Procedia-Soc. Behav. Sci.; 2012; 57, pp. 22-32. [DOI: https://dx.doi.org/10.1016/j.sbspro.2012.09.1153]
24. Materla, T.; Cudney, E.A.; Antony, J. The application of Kano model in the healthcare industry: A systematic literature review. Total Qual. Manag. Bus. Excel.; 2019; 30, pp. 660-681. [DOI: https://dx.doi.org/10.1080/14783363.2017.1328980]
25. Ganz, A.; Schafer, J.; Gandhi, S.; Puleo, E.; Wilson, C.; Robertson, M. PERCEPT Indoor Navigation System for the Blind and Visually Impaired: Architecture and Experimentation. Int. J. Telemed. Appl.; 2012; 2012, 894869. [DOI: https://dx.doi.org/10.1155/2012/894869] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23316225]
26. Giudice, N.A.; Whalen, W.E.; Riehle, T.H.; Anderson, S.M.; Doore, S.A. Evaluation of an accessible, real-time, and infrastructure-free indoor navigation system by users who are blind in the mall of america. J. Vis. Impair. Blind.; 2019; 113, pp. 140-155. [DOI: https://dx.doi.org/10.1177/0145482X19840918]
27. Li, M.; Arning, K.; Sack, O.; Park, J.; Kim, M.-H.; Ziefle, M.; Kobbelt, L. Evaluation of a Mobile Projector-Based Indoor Navigation Interface. Interact. Comput.; 2013; 26, pp. 595-613. [DOI: https://dx.doi.org/10.1093/iwc/iwt053]
28. Zhang, Z.; He, S.; Shu, Y.; Shi, Z. A Self-Evolving WiFi-based Indoor Navigation System Using Smartphones. IEEE Trans. Mob. Comput.; 2019; 19, pp. 1760-1774. [DOI: https://dx.doi.org/10.1109/TMC.2019.2915221]
29. Zhou, Y.; Chen, H.; Zhang, Y.; Huang, Y.; Zhang, P.; Yang, W. Multi-objective indoor path planning method with dynamic environment awareness. J. Southwest Jiaotong Univ.; 2019; 54, pp. 611-618. Available online: https://en.cnki.com.cn/Article_en/CJFDTotal-XNJT201903022.htm (accessed on 11 August 2022).
30. Basu, N.; Haque, M.; King, M.; Kamruzzaman,; Oviedo-Trespalacios, O. A systematic review of the factors associated with pedestrian route choice. Transp. Rev.; 2021; 42, pp. 672-694. [DOI: https://dx.doi.org/10.1080/01441647.2021.2000064]
31. Noriaki, K.; Seraku, N.; Takahashi, F.; Tsuji, S. Attractive Qaulity and Must-Be Quality. J. Jpn. Soc. Qual. Control; 1984; 14, pp. 39-48. [DOI: https://dx.doi.org/10.20684/quality.14.2_147]
32. Ivarsson, L.; Nilsson, A.; Rimfält, T. Customer Satisfaction—An Investigation of Trivselhus; LAP LAMBERT Academic Publishing: Chisinau, Moldova, 2010.
33. Berger, C.; Blauth, R.; Boger, D. Kano’s Methods for Understanding Customer-Defined Quality 1993. Available online: https://www.semanticscholar.org/paper/KANO (accessed on 11 August 2022).
34. Wang, T.; Ji, P. Understanding customer needs through quantitative analysis of Kano’s model. Int. J. Qual. Reliab. Manag.; 2010; 27, pp. 173-184. [DOI: https://dx.doi.org/10.1108/02656711011014294]
35. Lee, M.C.; Newcomb, J.F. Applying the Kano methodology to meet customer requirements: NASA’s microgravity science program. Qual. Manag. J.; 1997; 4, pp. 95-106. [DOI: https://dx.doi.org/10.1080/10686967.1997.11918805]
36. Jang, H.Y.; Song, H.G.; Park, Y.T. Determining the importance values of quality attributes using ASC. J. Korean Soc. Qual. Manag.; 2012; 40, pp. 589-598. [DOI: https://dx.doi.org/10.7469/JKSQM.2012.40.4.589]
37. Wu, C.X. Experience-Based Customer Requirement Study in Emporium. 2008; Available online: https://www.dissertationtopic.net/doc/1398157 (accessed on 11 August 2022).
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
1 School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510335, China
2 Pazhou Lab, Guangzhou 510335, China