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Article

Exploring the Key Priority Development Projects of Smart Transportation for Sustainability: Using Kano Model

by
Ming-Tsang Lu
*,
Hsi-Peng Lu
and
Chiao-Shan Chen
*
Department of Information Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan
*
Authors to whom correspondence should be addressed.
Sustainability 2022, 14(15), 9319; https://doi.org/10.3390/su14159319
Submission received: 16 June 2022 / Revised: 26 July 2022 / Accepted: 26 July 2022 / Published: 29 July 2022
(This article belongs to the Special Issue Latest Developments and Challenges in MCDM Theory, Models, and Applications)
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Abstract

:
Many smart transport programs are being carried out despite the fact that new smart transport programs technologies are not yet mature and people’s needs are not fully understood. As a result, many smart transport projects fall into chaos and fail to operate successfully, and can even impede socioeconomic development for sustainability. Therefore, this study suggests that cities should consider first the perceptions of people toward smart transport before they actively implement smart transport projects; this is an indispensable, key step to the smooth development of smart transport. Based on exploratory research, the study explores the procedure of constructing a kano model of smart transportation. A six-stage procedure is developed as primary collected 50 smart transport cases worldwide and then extracted 24 smart transport items. We designed questionnaire contents within the theoretical framework of the kano model, and eventually collected 369 completed questionnaires to determine how smart transport items can be classified under appropriate need attributes. Additionally, we use the customer satisfaction coefficient method to further prioritize the smart transport items, and four methods to prioritize them. Decision-makers can consider prioritization results from using different rules and methods, and reduce the gap between technologies implementation and actual needs.

1. Introduction

Smart transport has been considered key component in smart city sustainable development, as it has generated novel benefits and conveniences [1]. Smart transport is meant to enhance people’s qualities of life, and data enablement and application analysis are critical to its operation [2]. Smart city, smart transport, and the Internet of Things closely relate to one another, with the three concepts all relying on this growing experience and use of sensor and network technologies; smart transport is the key application scenario for the two other concepts [3]. Transport relates to the hard and technocentric domain of the smart city, both in theory and to a great extent in practice [4]. Given the rapid increase in the number of vehicles, traffic and transport are key components of large cities; therefore, traffic management within smart cities for sustainability poses a challenge.
According to a report issued by the market survey firm, markets, and markets re-search, the global smart transport market is affected by diverse factors (e.g., urban population growth, excessive population density, and interconnection communications and intelligent technologies applied in infrastructure construction); its global market size is expected to reach US$149.21 billion by 2023 [5]. In addition, Navigant research analyzed the global revenue of urban traffic, and estimated it will increase to US$39.8 billion in the coming decade: its compounded annual growth rate between 2018 and 2027 will reach 15.3%, and the cumulative revenue will reach as high as US$240.9 billion. Such revenue growth derives mainly from diverse innovation (e.g., intelligent transport systems (ITSs), infrastructure, electric vehicle charging equipment, and service systems), and smart transportation developments are also showing a trend toward sustainable, highly connected, and multi-mode solutions or services for sustainability.
By 2050, 66% of the global population will live in urban fields. Although population growth can bring to a country prosperity and a rich pool of human resources and, additionally, promote economic sustainable development, it can also impose considerable negative impacts [6]; such impacts can pose considerable environmental challenges, involving waste management, air pollution, human health hazards, and congestion [7]. Nowadays, cities produce 80% of all greenhouse gas emissions, consume approximately 75% of the world’s energy and resources, and impose severe impacts on local, regional, and global environments. Particularly with respect to traffic and transport, research by Zapolskytė et al. [8] shows that in addition to undersupplies arising from resource scarcity and environmental pollution, rapid population growth will increase demand for transport facilities, thus resulting in traffic jams and accidents. While new city concepts are emerging and new policies, laws, and regulations are being implemented, smart cities have emerged as a crucial solution to the aforementioned issues. Intelligent traffic can help build smart cities for sustainability by achieving its specific objectives (e.g., reducing traffic congestion and pollution, reducing noise pollution, increasing human safety, increasing movement speed, and lowering transfer costs) [9,10].
Technologies such as the big data, IOT, and 5G are increasingly maturing, in tandem with looming crises such as global warming, overpopulation, and economic instability. Today, smart transport projects are being actively executed worldwide to address society’s current difficulties and challenges; however, ITSs have a broad range of development orientations, and if ITS projects are not appropriately concentrated, or if resources are not used efficiently, such projects may fail despite governments’ and related sectors’ considerable funding and resource investments [11,12]. Quite frequently, the single greatest obstacle to the development of smart mobility is the complexity of cities themselves. People from dissimilar organizations and groups frequently hold dissimilar viewpoints on how smart mobility projects need to change [13]; they also have dissimilar primacies, and many do not realize these roles played via others in the growth of cities [14].
To develop smart transport for sustainability, it is necessary to apply technologies to daily life and provide services suited to users’ needs. If we do not understand users’ true feelings on this issue, it will be difficult to provide highly targeted services or solve the problems that people encounter in cities, never mind improving their quality of life. The current study focuses on the field of smart transport, especially as it will look in the future. This study utilized the Kano model to investigate quality satisfaction. Due to the fact that the Kano model indicated the presence of the customers’ intuition through the products. This model’s purpose is to maintain the products’ specifications and discussions in order to develop the understanding of team members. Moreover, the model is dedicated to distinguishing the product’s features rather than focusing on the customers’ needs. Moreover, Kano establishes a method to reflect the customers’ responses to the questionnaire into the model. Based on a process framework [15], we use a dual-thinking mode of the Kano model—rather than the conventional and binary Satisfied–Unsatisfied thinking mode—to better understand users’ true feelings and preferences with regard to smart transport items.
Upon undertaking a literature survey and analysis of 50 benchmarking smart transport cases, we extracted some smart transport items to reveal the available applications of smart transport. Within the framework of the Kano model, we designed a questionnaire comprising 50 smart transport items worldwide and classified them in terms of appropriate need attributes (based on the survey’s outcomes) to identify the need attributes of each smart transport item. Finally, we prioritized the classified smart transport items using four methods. Our goal here is to offer governments and other agencies working on smart mobility growth through a comprehensive sympathetic of growth trends relatively crucial technology.
This paper is organized as follows. The next section reviews the key priority development projects of smart transportation issues. Section 3 provides the detailed methods of the integrated kano model. In Section 4, an empirical study with applications is provided and the analysis of the result is discussed. Lastly, conclusions are presented in Section 5.

2. Literature Review

2.1. Smart Transport

Traffic and transport are very important components of modern cities, in that they have significant impacts on cities’ environmental and socioeconomic sustainability [16]. Population growth and a rapid growth in the number of vehicles in large cities have caused various problems, such as traffic congestion and traffic accidents [4]. Smart transport falls within these areas of smart cities. The core goal of smart cities is to enhance people’s quality of life through information technology (IT)—or, to be more specific, (1) reduce noise and other pollution, traffic congestion, and energy consumption, (2) improve people’s health and safety, (3) increase the speed of movement, and (4) reduce transfer costs [10,17]. However, the adverse environmental impacts of traffic and transport (e.g., air pollution, excessive energy consumption, and excessive noise) may impose upon people a low quality of life [18]. In cities where IT is not yet applied to traffic and transport, the overall objectives of smart cities are not being achieved. Previously, many cities expected to improve the speed and convenience of people’s commutation by developing the means of transport, but in doing so inadvertently ignored their quality of life, and this gave rise to frequent problems such as noise and environmental pollution.
To improve the quality of transport services under resource constraints and become “smart”, many cities have for over 10 years continuously invested in ITSs [9]; in so doing they have established a firm foundation for developing smart transport [19]. Both ITSs and smart transport rely on network technologies [3], but while smart transport system conventionally emphasizes system efficiency and vehicular traffic flow, smart transport primarily looks to enhance people’s quality of life and traffic experience [9]. Evidently, smart transport, which aims to satisfy the needs of the people, is an extended ITS application. The concept of “smart transport” has evolved in two stages: (1) technologically improving and optimizing transport planning tools, and (2) considering consumers the key component of smart transport [20,21].
A transport system can become more efficient by leveraging advanced technologies [22]. ICT, if applied to the field of traffic and transport, will provide not only a good solution to urban traffic problems, but also give people diverse value-added services in addition to commutation and create unusual traffic experiences for people [9]. For people who use public transport, ICT can provide during the travel process a variety of services, from the planning of traffic routes and fielding traffic status inquiries to additional services (e.g., wireless internet access, battery-charging stations, and e-payment). For drivers of motor vehicles, ICT provides various convenience and safety services, including navigation, road safety detection, electronic toll collection in parking lots, and electric vehicles [20].
Some of the studies on smart transport mostly focus on development indicators and technological research and development [8,20,23,24,25,26]. Development indicators can reflect the international competitiveness of cities, and city rankings can provide an important experiential basis for cities’ future development strategies [20,27]. The ultimate goal of smart transport centers on humans: Chen, Ardila-Gomez, and Frame [9] argue that smart transport has three characteristics—namely, it is data-oriented, bottom-up innovation-driven, and human-oriented. In a smart transport environment, large quantities of mobile data are generated, and the connectivity between data and people is increasing; all extended application services are processed through data analysis to provide the best solutions [28]. Traditional ITSs are mainly government-dominated, on a top-down basis; in contrast, smart transport allows all agencies concerned to develop application services on a bottom-up basis, and thus achieve diversified innovation. The current study has a hu-man orientation: for any smart transport policy, product, or services, success lies in whether it truly understands people’s needs and their behavior patterns [12]. Hence, the main purpose of the research is to study these perceptions and expectations of people with regard to various smart transport measures for sustainability.

2.2. Kano Model and Its Appliaction

According to the view of Kano [29], users perceive product or service attributes in different ways and are inclined to distinguish product functions. Enterprises can not only meet customer needs but also address these nonlinear relationships between need attributes and user satisfaction [30,31]. It has five attributes, including the attractive need attribute (A), the one-dimensional need attribute (O), the must-be need attribute (M), the indifferent need attribute (I), and the reverse need attribute (R) (Table 1). Each need attribute affects customers in different ways [32]. The Kano model looks to maintain product compliance and facilitate discussion by better developing team understanding [33]. The Kano model is usually used to analyze customer requirements so as to improve product and service quality [34]. Some researchers [29,35,36,37,38,39] use the Kano model to classify service or product functions and maximize customer satisfaction (CS).
Over the years, the Kano model has been mainly used to engineering and the eco-nomics of product development [15,40,41,42,43]. With the vigorous development of IT in various fields, the additional implementation of the Kano model to the fields of computer science and social sciences continues to become more prevalent [44,45,46]. Many publishers about the satisfaction of the service quality of transport also with the use of Kano [47,48,49]. Urban transport will actively promote the growth of smart cities through the application of ICT [50,51]. In terms of “whole city” composition, smart transport is an attribute that will help to fulfill the goal of smart cities. If we use the Kano model to analyze how smart transport items are classified in terms of various need attributes, we can help governments and relevant agencies formulate and optimize layout strategies by which to develop smart transport that satisfies the people’s needs. In this sense, the contributions of the current study are both important and feasible.

3. Methods

According to these conceptions of smart transport and the Kano model, and within the process framework specified by Ko, Lu, and Yu [52], we undertook four research steps: (1) identify a smart transport item, (2) design a Kano questionnaire, (3) distribute online questionnaires, (4) reliability and validity Analysis, (5) Classification results and priority analysis of the Kano model, and (6) Priority analysis for customer satisfaction coefficients.
  • Step 1: Smart transportation generation
This study started through the literature review on smart transportation frameworks and definitions as well as global transportation types. A total of 50 benchmark smart transportation development cases from around the world were collected. The cases mainly came from transportation selected such as the IESE Cities in Motion Index, China’s Smart City Index (CSCI), and the Intelligent Community Forum (ICF). In a separate event, this background is presented and the event is encoded in terms of components to offer a primary picture of the existing global direction in the growth of smart transportation [9,20]. In this stage, six investigators were systematized into three two-person teams, as another two investigators offered third-party confirmation of the primary outcomes of inventory. Cross-checking and inventory are repeated iteratively until a consensus is reached.
  • Step 2: Design a Kano questionnaire
We took an inventory of all of the implementations from the 50 smart transport cases, based on the aforementioned smart transport implementation model. In the step, we de-signed a pair of negative and positive questions for separately smart transport item. The positive question pertains to people’s feelings if the item were available in the city, while the negative questionnaire pertains to people’s feelings if the item were not available in the city. For apiece question, the respondent can select one of five responses: (1) I like it that way, (2) It must be that way, (3) I am neutral, (4) I can live with it that way, or (5) I dislike it that way [29]. Subsequently, the positive and negative question forms were combined (see Table 2) to facilitate comparison with the Kano evaluation table; all smart transport items were then classified under appropriate need attributes. Finally, we undertook statistical analysis based on the response frequency of each respondent, to classify smart transport items under appropriate need attributes [31,53].
  • Step 3: Distribute online questionnaires
We conducted an online questionnaire survey to acquire respondent data. Online questionnaire surveys are frequently used because it is cost-effective; additionally, this format gives respondents more personal space and time to answer the questions, thus making them more willing to fill out the questionnaire.
  • Step 4: Reliability and Validity Analysis
Whenever the Kano model method was previously used, there was no consideration of the reliability and validity of the corresponding dimensions of the extracted items. To verify whether the smart transport items were all explicitly classified under smart transport dimensions, we undertook reliability and validity analysis of the question items and dimensions, both with and without the related need attributes.
  • Step 5: Classification results and priority analysis of the Kano model.
We combined the respondents’ answers to positive and negative questions and compared them to the Kano evaluation table to determine the frequency at which a smart transport item was classified under various attributes (i.e., M, O, A, I, or R). The highest-frequency attribute became the attribute of the smart transport item.
  • Step 6: Priority analysis for customer satisfaction coefficients.
The CS coefficient can not only determine whether the elements of smart transport items help increase or reduce satisfaction: it also reflects the impact of such elements on CS [54,55]. The value of the CS coefficient ranges from −1 to 1, and values from 0 to 1 rep-resent the satisfaction index (SI): the closer a coefficient value is to 1, the more significantly the corresponding item affects people’s satisfaction. Values from −1 to 0 represent the dis-satisfaction index (DI): the closer a coefficient value is to −1, the more significantly the corresponding item affects people’s dissatisfaction. In calculating the CS coefficient, the attractive need attribute (A) can increase people’s satisfaction most significantly without causing people’s dissatisfaction. The one-dimensional need attribute (O) affects both satisfaction and dissatisfaction; if it is available, it will increase people’s satisfaction; if it is not available, it will make people feel dissatisfied. The must-be need attribute (M) can in-crease people’s satisfaction least significantly, because people consider this attribute a matter of course and that it must be perfect. The indifferent need attribute (I) does not affect people’s satisfaction. Evidently, A and O affect people’s satisfaction significantly, and O and M affect people’s dissatisfaction significantly. Therefore, we combined these two value pairs and divided their sum by the sum of all need attributes, thus determining the value of the CS. In particular, the DI should be multiplied by −1, to highlight the negative impact on people’s satisfaction in cases where an attribute is not available. The calculation equation is as follows:
SI = (A + O)/(A + O + M + I),
DI = (O + M)/(A + O + M + I)(−1),
Using the CS coefficient matrix, we can further discuss the prioritization of the need attributes of smart transport items for sustainability.

4. Analysis and Results

4.1. Smart Transportation Prototyping

We extracted 24 smart transport items from the 50 smart transport cases worldwide. The cases mainly came from transport selected for global smart city assessments such as the Intelligent Community Forum (ICF), China’s Smart City Index, and the IESE Cities in Motion Index. Many researchers have contributed to the indicator framework for smart transport [4,8,9,20]. Chen, Ardila-Gomez, and Frame, [9] and Garau, Masala, and Pinna [20] using as references, we created smart transport dimensions. Garau, Masala, and Pinna [20] propose six indicators by which to evaluate smart transport—namely, car sharing, bike lanes, public transport, bike sharing, a public transport support system, and a private mobility support system [9], in investigating how to develop energy-saving smart cities through ITSs for sustainability, mentions the importance of energy-saving cars in smart cities. In the current study, we classify 24 global smart transport items; to ensure consistency, six research team members repeatedly verified the extraction and classification process. Finally, we divided smart transport into four dimensions—namely, a public transport support system, a private mobility support system, a vehicle sharing service, and an energy-saving vehicle service (see Table 3).

4.2. Sampling and Data Collection

Taiwan has the highest rate of web data transmission volume and has the second-highest population density through an average of 639 persons each square kilometer in the world. Taiwan is the one of large smartphone penetration rate in the world which over 75% of Taiwan’s population has a smartphone [50]. This research solicited users in Taiwan to complete a web-based questionnaire. Tan and Teo [51] recommended which online questionnaires have numerous advantages over conventional research-based questionnaires because they are geographically unrestricted, are cheaper to conduct, and elicit faster responses [14,56].
This research solicited users to fulfill the web-based questionnaire in Taiwan. The authors recommended that online questionnaires have several advantages over traditional paper-based questionnaires, owing to they are geographically unrestricted, elicit faster responses, and are cheaper to conduct [14,56,57].
This survey was caused on pertinent pages of Facebook and prevalent Civil IOT publish bulletin board systems. A total of 369 valid and complete replies were collected, involving 158 (42.82%) and 211 (57.18%) male female respondents. About 91% were under the age of 50, and half (45.26%) of respondents were aged among 21–30 years. In terms of educational attainment, 66.40% had some graduate or college degree. Table 4 shows the respondents’ demographic information.

4.3. Variable Measurement

Confirmatory factor analysis (CFA) is a quantity model applied to identify the existence of facets and the application of facet development theory [58]. Cronbach’s alpha and Composite reliability (CR) were applied to assess this model’s internal consistency. The authors suggested a minimum Cronbach’s alpha level of 0.7. CR standards comprise: (1) all measure factor loadings need to exceed 0.5; (2) the AVE need to exceed 0.5; and (3) the CR need to exceed 0.6 [59]. Investigated outcomes are stated in Table 5 and Table 6.

4.4. Classification and Preliminary Prioritization by Kano Model

For each smart transport item, we combined the respondents’ replies to the negative and positive issues, to record this frequency at which each item is classified under each Kano attribute (M, O, A, I, or R). We then classified each smart transport item according to the Kano attribute with the highest occurrence frequency (see Table 7). Six items (i.e., public transport status system, planning of public transport tourist routes (provided by governments’ official websites or mobile apps), payment service for public transport means, real-time traffic information service, traffic and road status warning service, and exclusive bike lane and road indication) were classified most frequently under O; therefore, we recommend that these items be classified under O. Six items (i.e., smart street lights, free charging stations for electronic devices, online interactive traffic map, smart parking service, renewable energy powered vehicle, and solar charging road) were most frequently classified under A; we therefore recommend that they be classified under A. Twelve items (i.e., exclusive bus lanes, optimization of night bus routes, free WiFi networks, expressway sensing toll, car sharing information service, bike sharing service, electric vehicle sharing service, parking planning for shared cars, payment service for car sharing, bike route planning system, electric vehicles, and automatic drive electric vehicles) were most frequently classified under I; we therefore recommend that they be classified under I.
The empirical results show that among the 24 smart transport items, six are classified under O; six, under A; and 12, under I. As such, successful need attributes follow the lifecycle of I→A→O→M The results are highly consistent with the existing literature that the successful quality attributes follow a life cycle: Indifferent Attractive One-dimensional Must-be [60,61]. If I is considered the early stage, A the pre-middle stage, and O the middle and late stage, then M is considered the later stage. In this way, I accounts for the highest proportion of items, followed by O and A. The 24 smart transport items follow the lifecycle of successful need attributes, and thus we see that the development of smart transport is still nascent.
The attractive need attribute (A) is relatively inclined to energy saving devices or services [62]. People today have an increased environmental consciousness and are gradually attaching more importance to power-saving and energy conservation. To increase people’s use of and satisfaction with such smart transport items (e.g., solar charging roads, smart street lights, and renewable energy powered vehicles), more emphasis should be placed on energy conservation [63]. The one-dimensional need attribute (O) is relatively inclined to real-time route inquiry or planning. People are particularly sensitive to whether real-time and accurate traffic services are provided, and will complain when a traffic service system provides inaccurate information due to system failure. To reduce such dissatisfaction, top priority should be assigned to improving the accuracy of traffic information provided by such smart transport items (e.g., public transport status system, planning of public transport tourist routes (provided by governments’ official websites or mobile apps), traffic and road status warning service, and real-time traffic information service) [64,65]. The indifferent need attribute (I) is also inclined to energy-saving devices or services. People are relatively indifferent to such smart transport items (e.g., automatic drive electric vehicles, electric vehicle sharing service, and electric vehicles) [66]. People know that such items are environmentally friendly and energy-saving, but in consideration of cost and safety factors, they take a relatively conservative attitude toward such items so as to mitigate risk.
For the public transport support system, A is first inclined toward smart street lights (40.11%); I, toward free WiFi networks (33.88%); and O, toward the payment service for public transport (48.51%). For the private mobility support system, A is first inclined toward the smart parking service (38.48%); I, toward expressway sensing toll (31.71%); and O, toward the traffic and road status warning service (33.60%). All specific items of the vehicle sharing service are classified under I; specifically, I is first inclined toward the bike sharing service (32.79%). For the energy-saving vehicle service, A is first inclined toward solar charging roads (40.38%), while I and O are first inclined toward the bike route planning system (37.67%) and exclusive bike lane and road indication (31.44%), respectively.
The Kano model can not only preliminarily classify smart transport items, but also prioritize them according to the frequency of occurrence across different need attributes. Such prioritization allows cities to choose those that are most appropriate, especially in times of financial distress or technical constraint [15].
Among the six smart transport items classified under A, the frequency of solar charging roads (40.38%) is higher than that of smart street lights (40.11%), smart parking service (38.48%), renewable energy powered vehicles (36.59%), free charging stations for electronic devices (33.60%), and an online interactive traffic map (30.35%) (see Table 8). According to the definition of A, the solar charging road can bring about more satisfaction than can the other items; however, the absence of this item will not affect people’s feelings. For example, in Amsterdam (in The Netherlands), solar panels are mounted on the ground to charge any electric vehicle that passes by; not only is this convenient, but also it reduces energy consumption.
The empirical findings also address the Kano category measured within each dimension. This study summarizes the compared findings within each dimension in the Table 8.
Among the six smart transport items classified under O, the frequency of the payment service for public transport means (48.51%) is higher than that of the public transport status system (47.97%), planning of public transport tourist routes (provided by governments’ official websites or mobile apps) (35.23%), traffic and road status warning service (33.60%), real-time traffic information service (31.98%), and exclusive bike lane and road indication (31.44%). Thus, in line with the definition of O, the payment service for public transport means can bring greater satisfaction than the other items; however, its absence will create dissatisfaction. Public transport charges user fees, and so people need to contact a wide range of cash flows; in addition, the waiting time for public transport is relatively long. If the payment system is faulty, people will be dissatisfied about the overall traffic experience; they may in turn use private motor vehicles rather than take public transport, which would increase environmental pollution.
Among the 12 smart transport items classified under I, the frequency of exclusive bus lanes (46.61%) is higher than that of automatic drive electric vehicles (45.26%), a payment service for car sharing (42.82%), an electric vehicle sharing service (41.19%), electric vehicles (39.84%), a bike route planning system (37.67%), a car sharing information service (37.13%), parking planning for shared cars (36.04%), the optimization of night bus routes (35.50%), free WiFi networks (33.88%), a bike sharing service (32.79%), and expressway sensing toll (31.71%). In line with the definition of I, people are most indifferent to whether exclusive bus lanes are available in a city, and whether or not they are available makes no difference to satisfaction.
Using the need attribute method based on the Kano model, we can classify each smart transport item, and prioritize them according to the frequency of response to various attributes. Moreover, we can use the CS coefficient matrix to prioritize the same need attributes, as described in subsection.

4.5. Prioritization via the CS Coefficient Matrix

According to the classification of this Kano model, we calculated the CS coefficient of each smart transport item as per the method of [49]. As Figure 1 and Figure 2 show, the SI for smart transport items ranges from 0.38 to 0.68, with an average of 0.57; the DI for smart transport items, on the other hand, ranges from −0.20 to −0.72, with an average of −0.37. Compared to the average DI (−0.37), the average SI (0.57) is closer to 1, thus indicating that people are universally satisfied with smart transport items.
In terms of the SI, the top five smart transport items are free charging stations for electronic devices (0.68), smart parking service (0.66), smart street lights (0.66), traffic and road status warning service (0.65), payment service for public transport (0.64), and a public transport status system (0.64). The closer an SI value is to 1, the more significantly people’s satisfaction is affected by that smart transport item. Therefore, free charging stations for electronic devices, a smart parking service, smart street lights, and a traffic and road status warning service must be given priority and improved by the governments and agencies concerned.
As for the DI, the top five smart transport items are a payment service for public transport (−0.72), a public transport status system (−0.71), planning of public transport tourist routes (provided by governments’ official websites or mobile apps) (−0.50), expressway sensing toll (−0.48), and exclusive bike lane and road indication (−0.45). The closer a DI value is to −1, the more significantly people’s dissatisfaction is affected by that smart transport item. Therefore, a payment service for public transport, a public transport status system, and planning of public transport tourist routes (provided by governments’ official websites or mobile apps) must be given priority and improved by the relevant governments and agencies, as they can very much reduce people’s dissatisfaction with smart transport.

5. Conclusions

To further understand user-centered priorities in the implementation of smart transport items, the current study considers their need attributes by using various rules and methods; it also presents four methods of prioritization (as described in Table 7).
  • First, we used the fundamental Kano model and the most frequently occurring responses to need attributes in our prioritization method and rule, and we classified all smart transport items under appropriate need attributes (as described in Table 9).
  • If the relevant governments or agencies want to provide an attractive need attribute (A) or achieve a better one-dimensional need attribute (O), they should first (and fully) enact must-be need attributes (M) and one-dimensional need attributes (O); this is because M and O attributes affect people’s perceptions more significantly, and thus more markedly affect people’s satisfaction level [30,31,32].
  • Therefore, smart transport items should be prioritized based on this regulation of M→O→A→I, and smart transport items under each need attribute should be prioritized according to response frequency [15] (prioritization 1 in Table 9).
  • We prioritized smart transport items in terms of SI and DI (prioritization 2 in Table 9).
  • Based on SI and DI values combined under the rule of M→O→A→I, we analyzed the attribute classification outcomes of the Kano pattern to gain a more in-depth under-standing of how smart transport items affect people’s feelings (prioritization of Table 9).
  • We prioritized the smart transport items under the rule of M→O→A→I to identify the prioritized items in each dimension (prioritization 4 of Table 9).

5.1. Theoretical Contribution

Past study on the direction of novel markets, products, and technologies are applied to assistance administrations in various fields proposal their development of technology and to offer a tool for prediction of technology. Most prior smart mapping procedures focused on optimization and improvement. In response to fast transformation and industrial development, the study offers a structured and effective approach consisting of six stages.
Unlike previous studies, the research applies the Kano model method to this field of transport; it also argues that the Kano model can be applied to more than engineering and the economics of product development. In terms of city composition, this study also understands that smart transport is an attribute that leads to the goal of smart cities for sustainability.
Using the Kano model method, we collected 50 smart transport cases worldwide, and extracted from them 24 smart transport items. Then, we classified the 24 smart transport items under appropriate need attributes to reveal people’s feelings regarding smart transport worldwide. Empirical results show that among the 24 smart transport items, the largest number of items (N = 12) are classified under I, followed by six under A, and an-other six under O. As the literature states, the development of smart transport obviously follows a certain lifecycle of successful need attributes—namely, I→A→O→M [15]—and is still in an early stage. The term “smart transport” has been mentioned frequently over the years, and many cities worldwide have implemented related policies to attain smart transport. However, due to diverse obstacles (e.g., environmental and re-source constraints), smart transport remains a focus of efforts among many cities world-wide. These circumstances explain why the development of smart transport has, overall, come to a standstill. Based on our findings, we anticipate that smart transport items classified under I can be gradually transformed to smart transport items classified under A and even O and M.

5.2. Recommendations on Policy

In this study, we classified smart transport items under appropriate need attributes, using the Kano model method; we also proposed four methods for the prioritization of smart transport items (see Table 8). Our objective is to provide a reference that informs prioritized resource allocation and service improvements under various scenarios as relevant governments and agencies develop smart transport, thus overcoming the gap between policy implementation and actual needs. According to the results derived from the four prioritization methods, we offer the following five suggestions.
Solely in terms of the characteristics of need attributes, this study recommends that priority be given to smart transport items classified under M and O (e.g., items classified under O such as the payment service for public transport means and a public transport status system), because they can significantly affect people’s dissatisfaction level. To increase people’s satisfaction alone, this study recommends that priority be given to the development or improvement of the smart transport items classified under A (e.g., solar charging roads and smart street lights). People are indifferent as to whether or not such items are available, but are happy when they are.
According to the Kano model and the rule of M > O > A > I (prioritization 1), we recommend that priority be given to the payment service for public transport, a smart transport item classified under O.
According to the CS coefficient (prioritization 2), we make the following recommendations. (1) To increase people’s satisfaction, first priority should be given to free charging stations for electronic devices, a smart transport item classified under A; and (2) to reduce people’s dissatisfaction, first priority should be given to the payment service for public transport, a smart transport item classified under O.
According to the CS coefficient and rule of M > O > A > I (prioritization 3), we make the following recommendations. (1) To increase people’s satisfaction, first priority should be given to the traffic and road status warning service, a smart transport item classified under A; and (2) to reduce people’s dissatisfaction, first priority should be given to the payment service for public transport, a smart transport item classified under O.
In line with the smart transport dimensions and the rule of M > O > A > I (prioritization 4), we make the following recommendations. (1) In the public transport support system, first priority should be given to the payment service for public transport, a smart transport item classified under O; (2) in the private mobility support system, first priority should be given to the traffic and road status warning service, a smart transport item classified under O; (3) in the vehicle sharing service, first priority should be given to the bike sharing service, a smart transport item classified under I; and (4) in the energy-saving vehicle service, first priority should be given to exclusive bike lane and road indication, a smart transport item classified under O.

5.3. Limitations and Future Research

This paper is exploratory research that combines qualitative and quantitative. We have made a complete measurement of reliability and validity. First, the case source is from the international index evaluation: The cases mainly came from transport selected for global smart city assessments such as the Intelligent Community Forum (ICF), China’s Smart City Index, and the IESE Cities in Motion Index. Seconded, the architecture Project Citation basis on literature reviews. Third, it depends on the number of questions in the questionnaire. It is recommended [68,69] that the number of pre-test samples should be 3–5 times or 5–10 times the number of subscale questions that are the most pre-examination questions. There are 24 questions at least 240 samples must be recovered in this research. This research distributed 369 valid questionnaires, which have reached the standard, and the reliability and validity of the questionnaires were analyzed. While the research is sensibly conducted, some present limitations should be mentioned. This study compiles 50 smart transportation cases from about the world to create the existing state of analyze and development the growth trends. Based on this, qualitative data were used to collect cases information; a reliability and validity bias. Thus, the broad base of survey topics recommends that some case subjects would offer more complete information than others, but the huge sample’s sizes can be applied to facilitate the systematic analysis of smart transportation cases. Second, distributing an online questionnaire to a demographically and large diverse samples, therefore, evading bias that could skew outcomes. Nevertheless, this survey alone may be inadequate to broadly assess user perceptions of smart transportation because individual players may place different weighting on the numerous features, and the survey itself may be subject to self-selection bias [50]. Future research must study integrating qualitative analysis approaches such as interviews and observation. Third, the definition of smart transportation quality and terminology applied in the study area may cause ambiguity with respect to how the Kano model can be applied to solve the problems of smart transportation quality. Moreover, the items of questions and the level of comprehension by respondents may cause difficulties with the Kano questionnaire. Thus, avoiding bias which could skew results. Finally, in many of the reviewed studies, the perception of users differs depending on users’ previous experience, expectations, and other factors. Hence, the results of satisfaction with an attribute present in smart transportation will make the most sense when grouped by respondents’ profiles. Thoughtful use of the Kano model is required to obtain results that are representable and help to reach a sustainable goal. This study identified 50 benchmark cases of smart transportation and extracted 24 smart transport items from Kano. We trust that future study can attention in not only the smart transportation for sustainability. Future study can expand this scope of the planned approach in associated fields and industries (e.g., intelligent finance).

Author Contributions

Conceptualization, M.-T.L. and H.-P.L. and C.-S.C.; Methodology, M.-T.L. and C.-S.C.; Formal analysis, M.-T.L. and C.-S.C.; Investigation, M.-T.L. and C.-S.C.; Writing—original draft, M.-T.L. and C.-S.C.; Writing—review & editing, M.-T.L. and C.-S.C.; visualization, M.-T.L. and C.-S.C.; supervision, H.-P.L.; project administration, H.-P.L. and C.-S.C.; funding acquisition, M.-T.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Smart transportation items on satisfaction or dissatisfaction for experienced users.
Figure 1. Smart transportation items on satisfaction or dissatisfaction for experienced users.
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Figure 2. CS coefficients matrix of smart transportation items for users.
Figure 2. CS coefficients matrix of smart transportation items for users.
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Table
Table 1. Definition and relevant characteristics of the five attributes of the model.
Table 1. Definition and relevant characteristics of the five attributes of the model.
AttributesDefinitions and Relevant Characteristics
(A) Attractive need attributeAttractive need is an arc above the horizontal axis, extending from the lower left to the upper right. When the Attract needs to be achieved, the customers would be satisfied; in addition, if the Attract need is not met, the customers would feel indifferent. Moreover, the Attract need in the Two-factors theory is the Motivation-Hygiene factor which is not clearly expressed and without the customers’ expectations. It is often to be used to measure the product differentiation of manufacturers.
(O) One-dimensional need attributeThe one-dimensional need is a straight line extending from the lower left to the upper right, with the slope = 1. Moreover, the customers’ degree of satisfaction and need attributes would become a linear relationship. When the one-one-dimensional need is achieved, the customers would feel satisfied; vice versa.
(M) Must-be need attributeMust-be need is an arc below the horizontal axis extending from the lower left to the upper right. When the must-be need is reached, the customer will take it for granted and will not increase the degree of satisfaction; vice versa. Moreover, the Must-be need is a hygiene factor of the Two-factor theory. Customers will not increase their interest in the product if the attribute is not achieved.
(I) Indifferent need attributeIndifferent need is a straight line overlapping the horizontal axis, which would not affect the customers’ satisfaction degree. Therefore, the sensitivity between the demand for the product and service and the satisfaction degree of the customers is not high.
(R) Reverse need attributeReverse need is a straight line from high left to bottom right with a slope of −1. Achieving the Reverse need would cause customer dissatisfaction; vice versa.
Table
Table 2. Positive and negative question forms in the questionnaire.
Table 2. Positive and negative question forms in the questionnaire.
Positive Question FormMeasures
How would you feel if smart transport provided a public transport status system that displays information such as routes, schedules and waiting time?I like it that way
It must be that way
I am neutral
I can live with it that way
I dislike it that way
Negative Question FormMeasures
How would you feel if smart transport did not provide a public transport status system that displays information such as routes, schedules and waiting time?I like it that way
It must be that way
I am neutral
I can live with it that way
I dislike it that way
Table
Table 3. Dimensions and their functions for smart transport applications.
Table 3. Dimensions and their functions for smart transport applications.
DimensionsDefinitionsItems
Public transport support systemICTs are applied to public transport to create value-added services for the migrant population. In addition to the real-time transmission of traffic information, ICTs play a decisive role in reducing environmental pollution and traffic congestion and facilitating payment transactions.Public transport status system
Planning of public transport tourist routes (provided by governments’ official websites or mobile apps)
Exclusive bus lanes
Optimization of night bus routes
Smart street lights
Free WiFi networks
Free charging stations for electronic devices
Payment service for public transport
Private mobility support systemICTs are applied to increase the scope and efficiency of services in the process of using self-owned vehicles.Real-time traffic information service
Online interactive traffic map
Traffic and road status warning service
Smart parking service
Expressway sensing toll
Vehicle sharing serviceCars are shared by different people, and the ICT is used to improve the efficiency of vehicle movement.Car sharing information service
Bike sharing service
Electric vehicle sharing service
Parking planning for shared cars
Payment service for car sharing
Energy-saving vehicle servicePublic or private transport means can reduce energy consumption and environmental pollution, and ICTs can make driving more convenient.Bike route planning system
Electric vehicles
Automatic drive electric vehicles
Renewable energy powered vehicles
Solar charging roads
Exclusive bike lane and road indication
Table
Table 4. Sample demographics.
Table 4. Sample demographics.
MeasureItemFrequencyPercent (%)
GenderMale21157.18%
Female15842.82%
AgeBelow 2010027.10%
21–3016745.26%
31–403810.30%
41–50349.21%
Above 51308.13%
Education levelHigh School/Secondary61.63%
University/College24566.40%
PhD/Master11831.98%
Table
Table 5. Reliability and validity analysis if items were available.
Table 5. Reliability and validity analysis if items were available.
ItemsFLCRAVEα
Public transport support systemPublic transport status system 0.640.900.630.85
Planning of public transport tourist routes (provided by governments’ official websites or mobile apps)0.65
Exclusive bus lanes 0.76
Optimization of night bus routes 0.71
Smart street lights 0.59
Free WiFi networks 0.85
Free charging stations for electronic devices 0.87
Payment service for public transport0.65
Private mobility support systemReal-time traffic information service0.820.920.700.87
Online interactive traffic map0.85
Traffic and road status warning service0.89
Smart parking service0.79
Expressway sensing toll0.80
Vehicle sharing serviceCar sharing information service0.790.920.740.89
Bike sharing service 0.82
Electric vehicle sharing service0.86
Parking planning for shared cars0.88
Payment service for car sharing0.84
Energy-saving vehicle serviceBike route planning system0.770.900.640.86
Electric vehicles 0.78
Automatic drive electric vehicles 0.69
Renewable energy powered vehicles 0.83
Solar charging roads0.79
Exclusive bike lane and road indication0.78
Table
Table 6. Reliability and validity analysis if items were not available.
Table 6. Reliability and validity analysis if items were not available.
IDItemsFLCRAVEα
Public transport support systemP1Public transport status system 0.800.920.650.90
P2Planning of public transport tourist routes (provided by governments’ official websites or mobile apps)0.76
P3Exclusive bus lanes 0.70
P4Optimization of night bus routes 0.78
P5Smart street lights 0.80
P6Free WiFi networks 0.78
P7Free charging stations for electronic devices 0.79
P8Payment service for public transport 0.81
Private mobility support systemI1Real-time traffic information service 0.910.940.830.92
I2Online interactive traffic map 0.91
I3Traffic and road status warning service 0.92
I4Smart parking service 0.86
I5Expressway sensing toll 0.79
Vehicle sharing serviceS1Car sharing information service 0.890.950.780.93
S2Bike sharing service 0.87
S3Electric vehicle sharing service 0.88
S4Parking planning for shared cars 0.89
S5Payment service for car sharing 0.88
Energy-saving vehicle serviceG1Bike route planning system 0.790.940.780.92
G2Electric vehicles 0.86
G3Automatic drive electric vehicles 0.86
G4Renewable energy-powered vehicles 0.89
G5Solar charging roads 0.90
G6Exclusive bike lane and road indication 0.78
Table
Table 7. Results for smart transport functions.
Table 7. Results for smart transport functions.
DimensionsFunctionsA (%)I (%)M (%)O (%)R (%)Kano Category
Public transport support systemP114.09%13.82%20.60%47.97%0.54%O
P223.31%25.20%13.01%35.23%0.27%O
P316.80%46.61%10.03%17.89%5.69%I
P431.98%35.50%7.05%22.22%1.08%I
P540.11%28.18%4.61%24.39%0.54%A
P625.20%33.88%6.23%30.62%1.90%I
P733.60%24.39%5.96%31.44%2.17%A
P813.55%13.82%20.60%48.51%1.08%O
Private mobility support systemI128.18%25.75%10.57%31.98%0.81%O
I230.35%29.27%8.67%28.18%1.08%A
I329.27%25.75%8.13%33.60%0.81%O
I438.48%28.18%4.07%25.20%2.17%A
I518.16%31.71%17.62%29.00%1.08%I
Vehicle sharing serviceS133.06%37.13%6.50%20.33%0.27%I
S223.58%32.79%9.76%30.35%1.08%I
S331.17%41.19%6.78%16.26%1.63%I
S423.58%36.04%8.94%27.91%1.36%I
S526.02%42.82%5.96%20.33%1.90%I
Energy-saving vehicle serviceG125.20%37.67%8.67%25.75%0.54%I
G234.15%39.84%5.42%16.80%1.08%I
G330.08%45.26%3.79%14.63%3.52%I
G436.59%35.77%4.07%18.97%1.63%A
G540.38%32.79%3.25%18.70%1.63%A
G623.85%29.27%11.65%31.44%1.08%O
Note: A, Attractive; O, One-dimensional; M, Must-be; I, Indifferent; R, Reverse; Q, Questionable.
Table
Table 8. Summarize to compare its findings.
Table 8. Summarize to compare its findings.
DimensionsKano CategoryItemsRef.
Public transport support systemAttractive need attribute (A)Smart street lights (40.11%)[67]
Indifferent need attribute (I)A free WiFi networks (33.88%)
One-dimensional need attribute (O)payment service for public transport (48.51%)
Private mobility support systemAttractive need attribute (A)Smart parking service (38.48%)[64,65]
Indifferent need attribute (I)Expressway sensing toll (31.71%)
One-dimensional need attribute (O)Traffic and road status warning service (33.60%)
Vehicle sharing serviceIndifferent need attribute (I)Bike sharing service (32.79%)[66]
Energy-saving vehicle serviceAttractive need attribute (A)Solar charging roads (40.38%)[62,63]
Indifferent need attribute (I)Bike route planning system (37.67%)
One-dimensional need attribute (O)Exclusive bike lane and road indication (31.44%)
Table
Table 9. Prioritization of smart transport items.
Table 9. Prioritization of smart transport items.
Comparative PerspectiveMethod and RuleResult
Classification of needs attribute(1) Basic Kano model
(2) The most frequent response		One-dimensional: P1, P2, P8, I1, I3, G6
Attractive attribute: P5, P7, I2, I4, G4, G5
Indifferent attribute: P3, P4, P6, I5, S1, S2, S3, S4, S5, G1, G2, G3
Prioritization 1(1) Basic Kano model
(2) The most frequent response
(3) M > O > A > I		One-dimensional: P8 > P1 > P2 > I3 > I1 > G6 > Attractive attribute: G5 > P5 > I4 > G4 > P7 > I2 > Indifferent attribute: I5 > S2 > P6 > P4 > S4 > S1 > G1 > G2 > S3 > S5 > G3 > P3
Prioritization 2Customer satisfaction coefficients(1) For SI:
P7 > I4 ≥ P5 > I3 > P8 ≥ P1 > I1 ≥ G5 > P2 ≥ I2 > G4 ≥ P6 > G6 > P4 ≥ S2 > S1 > S4 ≥ G2 > G1 > S3 > I5 ≥ S5 > G3 > P3
(2) For DI: P8 > P1 > P2 > I5 > G6 > I1 > I3 > S2 > P7 > P6 ≥ I2 ≥ S4 > G1 > P3 ≥ I4 > P4 ≥ P5 > S1 ≥ S5 > S3 ≥ G4 > G2 ≥ G5 > G3
Prioritization 3(1) Customer satisfaction coefficients
(2) M > O > A > I		(1) For SI:
One-dimensional: I3 > P8 ≥ P1 > I1 > P2 > G6 > Attractive attribute: P7 > I4 ≥ P5 > G5 > I2 > G4> Indifferent attribute: P6 > P4 ≥ S2 > S1 > S4 ≥ G2 > G1 > S3 > I5 ≥ S5 > G3 > P3
(2) For DI:
One-dimensional: P8 > P1 > P2 > G6 > I1 > I3 > Attractive attribute: P7 > I2 > I4 > P5 > G4 > G5 > Indifferent attribute: I5 > S2 > P6 ≥ S4 > G1 > P3 > P4 > S1 ≥ S5 > S3 > G2 > G3
Prioritization 4(1) Smart transportation dimensions
(2) M > O > A > I		(1) Public transport support system
One-dimensional: P8 > P1 > P2 > Attractive attribute: P5 > P7 > Indifferent attribute: P6 > P4 > P3
(2) Private mobility support system
One-dimensional: I3 > I1 > Attractive attribute: I4 > I2 > Indifferent attribute: I5
(3) Vehicle sharing service
Indifferent attribute: S2 > S4 > S1 > S3 > S5
(4) Energy-saving vehicle service
One-dimensional: G6> Attractive attribute: G5 > G4 > Indifferent attribute: G1 > G2 > G3
Note: A, Attractive; O, One-dimensional; M, Must-be; I, Indifferent; R, Reverse; Q, Questionable; SI, Satisfaction index; DI, Dissatisfaction index. The name of functions for P1, P2, P 3, P 4, P 5, P6, P7, I1, I2, I3, I4, I5, S1, S2, S3, S4, S5, G1, G2, G3, G4, G5, G6, are listed in Table 6.
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Lu, M.-T.; Lu, H.-P.; Chen, C.-S. Exploring the Key Priority Development Projects of Smart Transportation for Sustainability: Using Kano Model. Sustainability 2022, 14, 9319. https://doi.org/10.3390/su14159319

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Lu M-T, Lu H-P, Chen C-S. Exploring the Key Priority Development Projects of Smart Transportation for Sustainability: Using Kano Model. Sustainability. 2022; 14(15):9319. https://doi.org/10.3390/su14159319

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Lu, Ming-Tsang, Hsi-Peng Lu, and Chiao-Shan Chen. 2022. "Exploring the Key Priority Development Projects of Smart Transportation for Sustainability: Using Kano Model" Sustainability 14, no. 15: 9319. https://doi.org/10.3390/su14159319

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