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Article

Exploring Smart Airports’ Information Service Technology for Sustainability: Integration of the Delphi and Kano Approaches

by
Sooyoung Choi
1,
Chaeyoung Moon
1,
Keunjae Lee
2,
Xinwei Su
3,
Jinsoo Hwang
4,* and
Insin Kim
1,*
1
Department of Tourism and Convention, Pusan National University, Busan 46241, Republic of Korea
2
Department of Economics, Pusan National University, Busan 46241, Republic of Korea
3
School of Tourism, Liming Vocational University, Quanzhou 362046, China
4
The College of Hospitality and Tourism Management, Sejong University, Seoul 143747, Republic of Korea
*
Authors to whom correspondence should be addressed.
Sustainability 2024, 16(20), 8958; https://doi.org/10.3390/su16208958
Submission received: 24 September 2024 / Revised: 13 October 2024 / Accepted: 15 October 2024 / Published: 16 October 2024
(This article belongs to the Special Issue Natural Resource Management and Sustainable Tourism)
Versions Notes

Abstract

:
Airport digitalization has revolutionized service delivery at passenger touchpoints, which leads to sustainable passenger loyalty. However, it is critical to determine whether this rapid transition to digital services genuinely enhances passenger satisfaction with airport services. This study uses a mixed-method approach to identify key traditional and technology-driven information services in smart airports. The specific aim is to determine the optimal balance in which digital technologies can effectively replace human-provided services to establish sustainable passenger loyalty. Two rounds of Delphi surveys were conducted with panels of 23 and 21 experts, followed by an online Kano survey with 401 international passengers. The Delphi analysis identified 16 key information service attributes, while the Kano analysis revealed that the majority of technology-based services were attractive and positively influenced passenger satisfaction. By contrast, human-based services were mostly indifferent, although some were vital for boosting satisfaction and preventing dissatisfaction. These results advance the current airport service research and provide practical insights into optimizing passenger experiences through the strategic integration of technology for sustainable smart airports while maintaining essential human-provided services.

1. Introduction

The airport industry has undergone rapid and significant transformations in recent years, owing to the integration of digital technologies that revolutionize traditional services [1,2]. Faced with an increasingly digital environment, airports have expanded their investments in advanced technologies to maintain competitiveness and sustainable passenger loyalty. Airports utilizing technologies are attempting to enhance passenger services, comfortable circumstances, environmental sustainability, and passenger satisfaction, which are called smart airports [3]. The concept of smart airports has an entailed sustainability aspect. According to SITA [4], 82% of airports increased their expenditures between 2021 and 2022, with over two-thirds expecting IT spending to continue rising through 2023 and 2024. These statistics highlight the growing demand for airports to deliver seamless, efficient, and personalized services that align with the heightened expectations of travelers [5].
The proliferation of various service technologies has fundamentally altered the manner in which passengers interact with service providers at airports [2,6]. Over the past few decades, airports have adopted a wide range of digital technologies to enhance sustainable performance by transforming traditional service delivery methods. For example, in many major airports, service robots now provide real-time information, assist with wayfinding, offer entertainment, reduce the reliance on human staff, and enhance overall operational efficiency [7,8,9]. At Incheon International Airport in South Korea, artificial intelligence (AI)-driven robots interact with passengers in multiple languages, guide them to their destinations, and even escort them to their gates [10]. Similarly, the widespread adoption of self-service kiosks, AI-driven mobile applications, and chatbots has streamlined passenger experiences, allowing passengers to navigate airports more easily and manage their travel plans more efficiently [1,10,11]. The integration of these technologies has proven particularly valuable in high-stress environments such as airports [12,13,14].
Despite these advancements, the impact of technological innovations on passenger satisfaction may not be universally positive. The effectiveness of AI and robotic systems is based heavily on their ability to accurately interpret and respond to diverse passenger requirements, which can vary based on individual preferences and situational factors [15,16,17,18]. The absence of human interaction at critical service points may deteriorate the overall customer experience, particularly for passengers who value personalized service [10,19]. Previous studies also suggest that customer perceptions and subsequent behaviors may differ depending on whether services are provided by technological or human staff [20,21,22,23]. Consequently, significant concerns remain regarding whether automated services can fully replace the personalized touch and empathetic interactions provided by human staff, particularly in situations requiring more nuanced assistance [10,24,25]. However, little attention has been paid to directly comparing the impact of technology-based services with human-provided services on passenger satisfaction. Consequently, despite the widespread adoption of technological innovations in airport service delivery, a significant gap remains in understanding how effectively these technologies enhance passenger satisfaction compared with traditional human services. This gap is particularly relevant in the airport industry as airports are rapidly transitioning from human-provided to technology-driven services, raising critical questions about achieving an optimal balance between technological efficiency and human interaction to maximize passenger satisfaction and sustainable loyalty.
To address this gap, this study aims to identify the key attributes of technology- and human-based information services provided to departing, arriving, and transferring passengers at various airport touchpoints. It also clarifies the prioritization of implementing and improving these service attributes to maximize passenger satisfaction and enhance sustainable passenger loyalty. To achieve these objectives, the study employed a sequential exploratory mixed-method approach, integrating the Delphi technique and the Kano model. This methodology enabled the identification of key airport service attributes from the perspectives of experts, followed by an analysis of passenger satisfaction with these services.
These results provided a comprehensive understanding of how the digital transformation of airport services, incorporating both technology-based and traditional human services, affects passenger satisfaction. In addition, this study provides valuable insights for smart airport management to optimize the integration of digital technologies. By gaining a clearer understanding of passenger perceptions of these innovations, airport managers can make informed decisions on how to best integrate technology and human staff into information services, ultimately enhancing passenger experience.

2. Literature Review

2.1. Integration of Technology in Airport Information Services

Airports have evolved from simple transportation hubs into complex environments to offer a broad array of services and facilities for passengers and establish sustainability goals. Consequently, advancing airport technology for sustainable airport development has become a critical responsibility for airport management [13], which leads to the high level of service quality in smart airports. Airport service quality significantly shapes passengers’ overall experiences, contributing to their satisfaction and the airport’s competitiveness [26,27]. However, interactions with multiple service providers can often cause airport experience stress, leading to inconsistent service quality [28]. To address these issues, technological solutions are increasingly being recognized for their role in enhancing passenger experience at smart airports.
Recent studies have emphasized the impact of technology-based services on airport service quality [1,7,29,30]. Digital technologies such as AI, robotics, Internet of Things, and big data analytics have become integral to daily airport operations [6,10]. These technological solutions are praised for their ability to perform repetitive tasks with high consistency and accuracy, thereby reducing human error and operational costs [24,31]. By optimizing passenger flow, reducing congestion, and improving security protocols, these technologies can enhance convenience and operational efficiency [6,14,32,33].
This study focuses on the key information services that passengers encountered at airports when departing, arriving, and transferring, such as passenger information and wayfinding. Technology-based services at these touchpoints encompass various elements that shape passenger experience within the airport, including the accurate and timely provision of flight and gate information and effective wayfinding throughout the terminal [34,35,36,37]. Interactive wayfinding and passenger assistance systems use AI technologies, such as service robots, chatbots, and augmented reality, to improve passenger access to passenger service support [10,30]. Passengers can inquire directly or scan barcodes and QR codes to obtain information, such as wayfinding help through the use of touchscreen kiosks [38]. Furthermore, airport mobile applications offer personalized navigation from passengers’ current location to their designated gate, along with real-time flight updates. Beyond traditional information desks, these innovative technologies enhance passenger information and communication systems, providing easier and more accessible information and wayfinding services. Previous studies have asserted that the integration of such digital technologies contributes to an improved overall passenger journey within smart airports, with passengers reporting higher satisfaction after interacting with digital services [7,9,37]. Table A1 in Appendix A summarizes previous research on human- and technology-based airport service attributes, focusing on in-airport information services.

2.2. Balancing Technology and Human-Based Services at Airports

The successful implementations of innovative technology in airports, such as AI-assisted robots, is based on understanding passenger perceptions of the differences between human and technology-provided services [20,21,22,23]. Technology-based services ensure consistent service levels, which are particularly valuable in airport environments, where operational efficiency and reliability are crucial. However, customer acceptance and adoption of technology are based on factors such as perceived usefulness, ease of use, and whether the technology meets consumer expectations [39,40]. Research has shown that passengers are more likely to embrace technology-based services when they are perceived as reliable, efficient, and convenient [41,42]. Furthermore, preferences for and intentions to the use of these technologies vary across passenger segments [43,44]. For example, older travelers or those with less familiarity with and high anxiety toward digital interfaces may identify them as intimidating or impersonal [18,19,45], whereas frequent travelers and younger passengers are more likely to choose technological options because of their greater desire for control, independence, and efficiency [17,46].
These factors, combined with individual preferences, can act as barriers to the widespread adoption of technology-based services, making passengers less inclined to choose these services at critical service points. In addition, the emotional and social aspects of service interactions, which are often difficult to replicate in technology-mediated encounters, influence passenger satisfaction. Service encounters are often viewed as social experiences, and many customers value the opportunity to interact with human staff [47,48]. Human interaction remains indispensable in contexts where emotional satisfaction and personalization are crucial [8,14,49]. Human staff excel in offering sympathetic care, friendly service, and superior communication skills, which lead to higher emotional satisfaction and attachment among customers [17,50]. Human interaction enhances the overall service experience by providing warmth and personal connection that technology-based services lack. The preference for human interaction is specifically pronounced in situations where social reciprocity and rapport are important and complex judgments are required [2,51,52,53,54].
Research reveals significant differences in customer perceptions of human–human interactions versus human–robot interactions [55,56,57]. Hwang, Joo, Kim, and Lee [8] claimed that the type of employee (human staff or robot) influenced how passengers perceived airport services, impacting their behavior and attitudes. Studies [8,36,58] also showed that perceptions of service quality and intentions to use a service vary based on whether it is delivered by robots or humans. For example, although customers may appreciate the novelty of robot service in restaurants, they tend to view human staff as more competent and responsive, particularly when handling special requests or unexpected situations [22,42]. Similarly, in luxury hotels, where creating memorable and personalized experiences is essential, human staff prefer to provide nuanced and empathetic service [21,23]. Traditionally, airport services have relied heavily on a human-centered workforce to provide personalized experiences in processes such as check-in, customer service, and security. Although technological solutions excel in performing routine and repetitive tasks [58], the absence of human interaction in technology-based services can negatively affect passenger satisfaction [47]. The challenge for airport management may lie in integrating these technologies in ways that complement rather than replace human touch. As technology continues to advance, maintaining a balance between efficiency and personalization is crucial for ensuring high levels of passenger satisfaction.

3. Methods

3.1. The Delphi Technique

In the first phase, the Delphi technique was used to identify key indicators for human- and technology-based information services at airports and determine their relative importance. The Delphi method is a structured communication process involving multiple rounds of questionnaires aimed at achieving expert consensus on a specific topic [59,60]. After each round, feedback was aggregated and shared with the panelists to refine their views until a consensus was reached [61]. The indicators were evaluated based on the coefficient of variation (CV) and content validity ratio (CVR), both of which serve as measures of validity and stability. A CV value below 0.5 suggests that no further rounds are required [62]. CVR measures the level of agreement among respondents regarding the necessity of an indicator [63,64] and is calculated as follows:
CVR = (Ne − N/2)/(N/2)
where Ne denotes the number of respondents who consider the indicator essential and N denotes the total number of respondents.
Two rounds of Delphi surveys were conducted to gather expert opinions on key airport information service attributes for the departure, arrival, and transfer of international passengers. The first-round questionnaire included 12 questions covering two categories (“information & wayfinding” and “arrivals & transfer”), along with two open-ended questions for adding or removing indicators. Measurement items for airport information service attributes were collated from existing studies (see Table A1 in Appendix A). The expert panel for the first round of the Delphi survey was selected from professionals with experience or current employment at international airports, airline personnel, and academics specializing in research and education related to airport services and aviation operations. Potential panelists were contacted via email or phone to obtain their consent to participate in the study before sending an online survey link via email. A total of 23 panelists participated in the first round.
The questionnaire for the second round was developed based on the results of the first round of analysis: refining, removing, or adding indicators. It comprised 19 questions and two open-ended questions concerning the addition or removal of indicators. The second round of the survey was conducted with 21 panelists from the first round. Through this process, the key indicators for both technology- and human-based airport information services were determined.
All indicators in the first and second Delphi questionnaires were assessed for their importance using a 5-point Likert scale (1 = not at all important, 5 = very important).

3.2. The Kano Model

In the second phase, the Kano model was used to analyze the relationship between the identified airport information service attributes and passenger satisfaction or dissatisfaction, comparing both human-provided and technology-based services from a passenger perspective. The Kano model is a theory for product/service development and customer satisfaction that classifies customer preferences into five Kano classifications: attractive, must-be, one-dimensional, indifferent, and reverse. It helps evaluate how the presence or absence of a service attribute affects passenger satisfaction or dissatisfaction [65,66].
  • Attractive (A): Fulfilling these attributes results in satisfaction. However, if not fulfilled, they do not cause dissatisfaction.
  • One-dimensional (O): Satisfaction increases proportionally with how well these attributes are fulfilled, whereas dissatisfaction occurs if they are not satisfied.
  • Must-be (M): Passengers expect these attributes, and their absence results in dissatisfaction. However, satisfying these requirements does not enhance satisfaction.
  • Indifferent (I): Passengers are indifferent to the presence or absence of these attributes, because they do not affect satisfaction or dissatisfaction.
  • Reverse (R): These attributes are undesirable, and their presence causes dissatisfaction.
  • Questionable (Q): This outcome suggests poor question design, miscommunication, or erroneous responses.
In the Kano survey, 19 airport information service attributes were included. Each service attribute was evaluated using two questions: the functional question assessed passenger perception if the attribute was present, and the dysfunctional question assessed passenger perception if the attribute was absent [66,67]. Respondents were requested to answer these questions using the following options: “I like it”, “It must be that way”, “I am neutral”, “I can live with it”, and “I dislike it”. The survey also included questions on demographics.
The Kano questionnaire was distributed to an online panel of a survey company, the Korea Data Research Center (https://www.k-drc.co.kr, accessed on 23 September 2024). The survey panels consisted of passengers who had traveled abroad and used international airports within the past three years. To verify the participants’ eligibility for the survey, they were asked to respond to two screening questions at the beginning of the questionnaire: “Have you traveled internationally in the past three years?” and “Have you used an international airport abroad for an international flight in the past three years?” Only participants who responded positively to the screening questions proceeded to answer the subsequent questions. A total of 790 panel members participated in the survey, of which 408 completed the survey. After excluding the responses of seven participants who provided insincere answers (e.g., giving the same response to both functional and dysfunctional questions), the remaining 401 responses were used for the analysis.
According to the responses, service attributes were classified into one of the six Kano categories (Table 1). The final Kano classification for each service attribute was determined using the category most frequently identified by all respondents.
Furthermore, customer satisfaction and dissatisfaction coefficients were calculated to evaluate the impacts of key service attributes on passenger satisfaction and dissatisfaction. The following formulas were used [68]:
Customer Satisfaction Coefficient = (A + O)/(A + O + M + I)
Customer Dissatisfaction Coefficient = (O + M)/(A + O + M + I) × (−1)
where A, O, M, and I represent the frequencies of the attractive, one-dimensional, must-be, and indifferent attributes, respectively.

4. Results

4.1. Delphi Results

4.1.1. Sample Profile

The demographic profiles of the respondents in the first and second rounds of the Delphi survey are listed in Table 2.

4.1.2. Findings from the Delphi Technique

Items were refined in the first Delphi round, as listed in Table 3. According to Wilson, Pan, and Schumsky [63], for a sample size of 23, a minimum CVR value of 0.42 at the 0.05 significance level was required. Four indicators failed to meet this threshold and were subsequently eliminated from the 23 panelists who participated in the first round. However, the CV values for all remaining indicators were below 0.5, indicating acceptable stability. In addition, based on expert feedback, 10 indicators were added to the second-round Delphi questionnaire.
In the second round, 21 participants completed a Delphi survey. The CVR values of 16 indicators exceeded the CVR cut-off value of 0.42 at a significance level of 0.05 [63]. Of the 18 indicators, two that did not meet the minimum CVR criteria were deleted, and three were revised based on feedback from experts. Thus, 16 indicators remained after the second round.
Table 4 lists the descriptive statistics of the 16 indicators. The indicator with the highest mean score was “personalized updates on gate changes and delays via mobile app, text, or email” (mean = 4.714), followed by “automated multilingual translation service for navigating airport facilities”, “mobile service providing gate information for transfer passengers”, “multilingual or automatic translation app support”, and “mobile-based e-ticket service for public transportation at the destination” (mean = 4.619).

4.2. Kano Results

4.2.1. Sample Profile

The demographic profile of the Kano survey respondents is listed in Table 5.

4.2.2. Findings from the Kano Model

The Kano survey questionnaire included 19 indicators, consisting of 16 items retained after the Delphi survey and three additional items that were previously removed. Table 6 lists the response frequencies for the Kano classification, which categorizes the service attributes of technology- and human-based airport information services. Of the 19 attributes, 12 attributes were classified as “attractive”, indicating that they significantly increased passenger satisfaction when they were present but did not induce dissatisfaction when they were absent. Notably, AI-powered in-airport wayfinding services, robot porter services, robot-assisted navigation services for arriving and transferring passengers, and 24/7 robot assistance were categorized as “attractive” by more than half of the respondents. Four arrival and transfer attributes (e.g., human staff at the information desk, chatbot service on the airport website, etc.) were classified as “indifferent”, indicating a lack of interest among passengers. Real-time updates through information screens and personalized updates through mobile apps, texts, and emails were classified as “one-dimensional”, enhancing satisfaction when fulfilled and causing dissatisfaction when absent. Finally, the public address (PA) system for announcement was categorized as “must-be”, suggesting it is an essential service to avoid dissatisfaction.

5. Conclusions

5.1. Discussion

The digital transformation of smart airport services has significantly enhanced passenger experience by reducing human interaction and providing faster and more accurate information. However, whether replacing human-based services with technology-based ones can fully maximize the satisfaction and enhance sustainable loyalty of departing, arriving, and transferring passengers remains largely unexplored. This study employed a sequential exploratory mixed-methods approach using the Delphi method and Kano model to identify the most critical technology- and human-based airport information services from expert perspectives and to prioritize the attributes that most effectively enhance passenger satisfaction and foster sustainable passenger loyalty. The comprehensive findings provide a nuanced understanding of how the integration of both human- and technology-based services shapes passenger experiences in contemporary airports.
First, the Delphi method gathered expert opinions on key airport information service indicators. As listed in Table 4, the analysis revealed 16 information service indicators, which partially align with the results of previous studies [10,30]. In the “information & wayfinding” category, two traditional (i.e., airport information screens and public announcements through PA systems) and four technology-based service indicators (i.e., mobile notifications for information updates, AI-based in-airport navigation assistance, porter robots, and multilingual translation services) were identified. Within the “arrival & transfer” category, one human-based service (i.e., telephone staff or ARS services) and nine technology-based services emerged as essential indicators.
Overall, the “information & wayfinding” category exhibited higher importance scores compared with the “arrival & transfer” category. Technology-based services, such as real-time updates through mobile apps, texts, or emails; mobile information notifications for transferring passengers; mobile-based public transportation ticket sales; and 24/7 robot assistance were considered highly important. By contrast, the human-based service indicators such as staff assistance via telephone or ARS received the lowest mean and consensus scores, followed by robot-assisted in-airport navigation services and interactive chatbots. These results reflect the importance that practitioners and experts place on technological platforms such as digital signage and mobile applications to provide passenger information services to departing, arriving, and transferring passengers.
Second, the Kano results provided insights into how passengers perceive different service attributes, revealing distinct patterns in the role of human- and technology-based services in shaping the overall airport experience. Most technology-driven services were classified as “attractive”. Although not essential in preventing dissatisfaction, the implementation of these services significantly enhanced passenger satisfaction [2,6,7,33,54]. Among these, the porter robots received the highest satisfaction coefficient, indicating that passengers found them particularly appealing. Conversely, services such as self-service kiosks and chatbots were largely disregarded by arriving and transferring passengers, with minimal impact on passenger satisfaction and dissatisfaction. Traditional flight and gate information services, such as real-time updates on screens and public announcements via PA systems, were deemed essential, with the former affecting both passenger satisfaction and dissatisfaction, whereas the absence of the latter significantly exacerbated dissatisfaction, thereby causing the most significant complaints from passengers. Transferring and arriving passengers were uninterested in staff-assisted services at information desks and via telephone, indicating that the presence of human staff at certain touchpoints, while once considered essential, may no longer be as critical to passenger satisfaction. Conversely, providing airport and public transportation information through mobile devices, kiosks, and robots increased airport service satisfaction for transferring and arriving passengers. These results reflect the growing reliance on technology-based platforms to provide real-time, accurate information, ensuring that arriving and transferring passengers travel smoothly to their destination.
Third, the results highlight the discrepancies between experts and passengers regarding the priority of smart airport information services. For instance, experts rated self-service information kiosks for arriving and transferring international passengers as relatively important (fifth overall), whereas passengers considered these kiosks indifferent. Similarly, although experts assigned lower importance to terminal-wide PA systems, porter robots, and robot-assisted in-airport navigation services, international passengers evaluated these services as “must-be” or “attractive”, demonstrating their significant influence on passenger satisfaction.

5.2. Theoretical Implications

The results of this study contribute to the air transportation, tourism, and hospitality literature by providing new insights into the evolving nature of services in high-tech environments such as airports.
First, the methodological framework applied in this study contributes significantly to the theoretical understanding of service management in the airport industry. By integrating the Delphi and Kano models, this study offers a comprehensive approach to assessing and improving airport service quality. The Delphi method facilitates gathering expert opinions to ensure that service quality attributes are strategically relevant and important, whereas the Kano model aligns these indicators with passenger expectations and satisfaction levels. This dual approach delivers a holistic perspective [69] on airport service quality, addressing both airport managers’ strategic priorities and passengers’ needs.
Second, this study advances the body of knowledge on airport services by identifying key touchpoints where traditional human services should be retained, integrated with technology-based services, or fully replaced to optimize passenger satisfaction. Kano’s results challenge the conventional view of the role of technology in service delivery. While technological advancements are typically regarded as enhancing efficiency and convenience [1,10,11], the answers emphasize the need for a more nuanced approach that considers how these innovations impact passenger satisfaction. The indifference towards human-based services suggests that passengers generally welcome a shift towards technology; however, this study also raises questions about the effectiveness and appropriateness of technological replacements at specific service touchpoints. For example, self-service kiosks and chatbots rated highly by experts had minimal effect on passenger satisfaction.

5.3. Managerial Implications

First, the methodological framework proposed serves as a practical tool for assessing and improving information services to achieve sustainable smart airports. By incorporating both expert and customer perspectives, this framework provides comprehensive and actionable insights for service quality assessments, offering a solid theoretical foundation for practical applications in service management. This approach allows airport managers to systematically evaluate their current service offerings, identify areas for improvement, and ensure that all critical aspects of sustainable smart airports’ information services are effectively addressed during passenger journeys. Furthermore, this framework has broader applicability beyond airports and provides valuable insights for tourism and hospitality service providers.
Second, this study provides strategic guidance for technology investments and service innovation. By understanding which information service attributes passengers value most, airport managers can make informed decisions regarding where to allocate resources most effectively, ensuring that human resources and technological investments align with passenger needs and expectations. Airports should first ensure that “must-be” attributes, such as PA systems, are consistently available to satisfy basic passenger expectations. These attributes are important for preventing passenger dissatisfaction, as they remain vital despite the growing emphasis on technology. Several “must-be” and “one-dimensional” attributes, primarily related to traditional services, such as real-time flight information updates on screens, also must be prioritized because they play an irreplaceable role in the overall passenger experience.
In addition, the results suggest that airports can enhance passenger satisfaction by focusing on “attractive” and “one-dimensional” attributes of innovative technologies. Investing in services such as mobile technologies, QR codes, AI-based robots, multilingual translation services, and public transport information services has the potential to significantly boost passenger satisfaction and sustainable passenger loyalty. Focusing on these areas aligns with broader service industry trends in which advanced technologies are leveraged to improve efficiency and user experience [9,46]. Therefore, implementing these technology-based services at key information service touchpoints within airports can lead to a more seamless and satisfying airport experience, thereby enhancing both passenger satisfaction and operational efficiency without significantly increasing dissatisfaction [7,10,14].

5.4. Limitations and Future Research Directions

Although this study has significant practical and academic implications, it also has some limitations. First, it focuses on human- and technology-based services applied to airport information services. Expanding the scope of this study to include all stages of passenger journeys, such as check-in, bag tag, bag drop, and security, will provide a more comprehensive understanding of airport service quality. Subsequently, with the rapid pace of technological advancements, the results related to technology-driven services may quickly become outdated. Continuous updates and longitudinal studies are necessary to monitor these changes and assess their long-term impacts on service quality and passenger satisfaction. Finally, human staff in service encounters have undergone emotional exhaustion, leading to anti-social behaviors due to customers’ misbehaviors [70,71], so the adoption of technology-based services should be examined from human staff perspectives. Future studies need to develop not only technologies to respond effectively to dysfunctional customers but also ways to harmoniously collaborate between human staff and technology-based services.

Author Contributions

Conceptualization, I.K., K.L. and S.C.; methodology, I.K., C.M. and X.S.; writing—original draft preparation, I.K., S.C. and C.M.; validation, J.H.; visualization, J.H.; writing—review and editing, K.L., X.S. and J.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the 2023 BK21 FOUR Program of Pusan National University.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Summary of existing studies on airport-specific human- and technology-based service attributes.
Table A1. Summary of existing studies on airport-specific human- and technology-based service attributes.
Author(s)Airport Service DimensionMethodologyStudy OutcomesInformation
Service Attribute Identified
Bezerra and Gomes [72]Check-in, security, convenience, ambience, basic facilities, and mobility EFA and CFA Mobility (walking distance inside terminal, wayfinding, and flight information)
Bogicevic, et al. [73]Self-service technologies and supporting technologies CFA and SEMTravelers’ confidence benefits, enjoyment, and overall satisfactionTouchscreen information kiosks
Free tour guide application provided by the airport
Brida, Moreno-Izquierdo and Zapata-Aguirre [37]Airport information, terminal servicescape, airport sound information system, flight information screenPCA and
a logit model		SatisfactionInformation screens (availability, location, visibility, updating frequency)
Signage (quantity, size, clearness, and orientation ease) and sound information (clearness, volume, accuracy, and timely)
Fodness and Murray [74]Function (effectiveness and efficiency), interaction, and
diversion (productivity, décor, and maintenance)		CFA Function (e.g., signage and information)
Interaction (e.g., staff availability and attentiveness and automated means of obtaining information)
Halpern [7]Surface access, check-in, security, commercial, info. and wayfinding, passport control, departure gate, and arrival Info. and wayfinding (augmented and virtual reality experiences, and personalized notifications such as real-time flight status) and arrival (personalized and customized notifications for transfer or arrivals, such as directions, gate, public transport information, baggage status and reclaim, and context-aware retail offers)
Halpern and Mwesiumo [28]Queuing time, terminal cleanliness, terminal seating, terminal signs and directions, food and beverages, airport shopping, airport wi-fi service, and airport staffPLS-SEMPassengers’ overall satisfaction and intentions to recommend the airportTerminal signs and directions
Halpern, Mwesiumo, Budd, Suau-Sanchez and Bråthen [2] Boarding pass, Bag tag, Bag drop, ID, Security, Payment, and ServiceDescriptive analysis, cluster analysis, and ANOVAPassenger preferencesCustomer information services (staff in person at an information desk or roaming the terminal, staff via telephone or a video link, live online chat service with staff, touchscreen self-service information kiosks, QR codes, assistant robot, hologram, and augmented reality)
Isa, et al. [75]Access, airport environment, airport facilities, arrival services, check-in, finding your way, passport, and securityPLS-SEMOverall satisfactionFinding your way (ease of finding way through airport, flight information screens, walking distance inside the terminal)
Pandey [27]
Pandey, Sahu and Joshi [76]		Access, airport environment, airport facilities, arrival services, check-in, finding your way, passport, and securityFuzzy MCDMImportance and performanceFinding your way (ease of finding way through airport, flight information screen, walking distance inside terminal, ease of making connections with other flights, and courtesy and helpfulness of airport staff)
Pholsook, et al. [77]Access, check-in, security, wayfinding, airport facilities, airport environment, and arrival services SEM, Bayesian networks, and artificial neural networksOverall satisfactionWayfinding (ease of finding directions at the airport, flight information screen, walking distance in the passenger terminal, ease of connecting other flights, and courtesy and helpfulness of airport staff)
Prentice and Kadan [29]Facilities, check-in, servicescape, security, ambienceCFA and SEMPassenger satisfaction with the airport
Airport reuse intention
Destination revisit intention		Servicescape (e.g., signs, physical layout)
Rubio-Andrada, Celemín-Pedroche, Escat-Cortés and Jiménez-Crisóstomo [34] Technologies in smart airports and technologies in the end-to-end travel processEFA and the Student’s t-testPassenger satisfactionDuring baggage collection and transit (messaging to passenger mobile devices about luggage location and transit status) and transportation to and from city/town (GPS info about transport services, app-based transports)
Tseng [78]Ambience, convenience, personnel, empathy, mobility, and baggageIPA and Kano modelImportance and performance
Satisfaction and dissatisfaction		Mobility (wayfinding and terminal signage, clarity of boarding calls and airport PAs, and perception of security and safety standards, and ease of transit through airport)
Wattanacharoensil, et al. [79]Airport information, signage, and layout, terminal ambience, flight information screens, check-in, security, basic facilities, immigration, gate area, baggage, and leisure and entertainmentCFA and SEMSense of place, airport image, and destination imageAirport information, signage, and layout (signage and wayfinding, size of signage, quantity of signage, proper design of the airport’s layout, and easy movement of the crowd within the airport’s layout), flight information (visible flight information screen, updated information on screens, suitable location of information screens, and availability of information screens)
Notes: EFA = Exploratory factor analysis; CFA = confirmatory factor analysis; SEM = structural equation modeling; PCA = principal component analysis; ANOVA = analysis of variance; MCDM = multiple criteria decision marking.

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Table 1. Kano evaluation table.
Table 1. Kano evaluation table.
Response to Dysfunctional Question
LikeMust BeNeutralLive withDislike
Response to Functional QuestionLikeQAAAO
Must beRIIIM
NeutralRIIIM
Live withRIIIM
DislikeRRRRQ
Source: Berger, Blauth, Boger, Bolster, Burchill, DuMouchel, Pouliot, Richter, Rubinoff, Shen, Timko, and Walden [65] (p. 6). Notes: A = attractive; O = one-dimensional; M = must-be; I = indifferent; R = reverse; Q = questionable.
Table 2. Profile of Delphi survey respondents.
Table 2. Profile of Delphi survey respondents.
Demographic InformationRound 1
(N = 23)		Round 2
(N = 21)
SexFemale1817
Male54
PositionAcademics/Research-related1110
Professionals at international airports54
Airline personnel77
TenureLess than 5 years88
5–9 years77
More than 10 years86
Education LevelBachelor’s 22
Master’s or doctorate2119
Table 3. Results of the Delphi survey rounds.
Table 3. Results of the Delphi survey rounds.
CategoryFirst RoundSecond RoundRemaining Items
CVR = 0.402CVR = 0.684
MeanNumber of ItemsDeleted ItemsRevised ItemsAdded ItemsMeanNumber of ItemsDeleted ItemsRevised ItemsAdded ItemsNumber of Items
Information and Wayfinding4.3804--24.4686-1-6
Arrival and Transfer3.77284-84.3251222-10
Total3.975124-104.3731823-16
Note: CVR = Content validity ratio.
Table 4. Descriptive analysis of technology- and human-based airport information services.
Table 4. Descriptive analysis of technology- and human-based airport information services.
Service IndicatorMeanSDCVCVR
Information and Wayfinding1. Real-time gate and flight status updates via information boards (general information service)4.5240.6020.1330.905
2. Public announcements for flight information, gate changes, and flight delays (general notification service)4.2860.8450.1970.524
3. Personalized updates on gate changes and delays via mobile app, text, or email4.7140.7170.1520.905
4. AI-powered service for guiding passengers to the nearest and shortest routes within the airport4.4760.6800.1520.810
5. Automated multilingual translation service for navigating airport facilities4.6190.5900.1280.905
6. Robot service for luggage transport within the airport4.1900.7500.1790.619
Arrival and Transfer1. Mobile service providing gate information for transfer passengers4.6190.5900.1280.905
2. Mobile notifications for baggage arrival and pick-up location4.4760.6020.1340.905
3. Self-service information kiosks with touchscreens4.4760.9280.2070.619
4. QR code service for tourists and transportation information via mobile scanning4.3330.8560.1980.714
5. Interactive chatbot on the airport website for information3.9050.8310.2130.429
6. 24/7 robot assistance for guidance4.3810.8050.1840.810
7. Multilingual or automatic translation app support4.6190.5900.1280.905
8. Public transport routes and real-time traffic updates for travel to the city/destination4.5710.7460.1630.714
9. Self-service kiosks for purchasing public transportation cards or passes at the destination4.5710.7460.1630.714
10. Mobile-based e-ticket service for public transportation at the destination4.6190.6690.1450.810
Note: SD = Standardized deviation; CV = coefficient of variation; CVR = content validity ratio.
Table 5. Profile of Kano survey respondents (N = 401).
Table 5. Profile of Kano survey respondents (N = 401).
VariableN%
SexFemale20150.1
Male20049.9
Age20–298521.2
30–398621.4
40–499222.9
50–598320.7
Over 605513.7
OccupationCompany employee18345.6
Professional 7218.0
Business owner317.7
Student399.7
Housewife5413.5
Retired102.5
Other123.0
Education LevelLess than high school diploma5213.0
Associate’s degree5012.5
Bachelor’s degree24460.8
Graduate degree5513.7
Annual Income (USD)Under 20,0006015.0
20,000–39,99910225.4
40,000–59,99912430.9
60,000–79,9995513.7
80,000 and above6015.0
Marital StatusSingle14937.2
Married24962.1
Other30.7
Table 6. Aggregated Kano classifications and satisfaction/dissatisfaction coefficients for airport technology/human-based information services.
Table 6. Aggregated Kano classifications and satisfaction/dissatisfaction coefficients for airport technology/human-based information services.
CategoryAttributeKano’s Category Distribution (%)Kano
Classification		Satisfaction
Coefficient		Dissatisfaction
Coefficient
AMOIRQ
Information
and Wayfinding		1. Real-time gate and flight status updates via information boards 23.725.332.916.41.70.0O0.575−0.592
2. Public announcements for flight information, gate changes, and flight delays 18.132.031.217.50.80.3M0.498−0.639
3. Personalized updates on gate changes and delays via mobile app, text, or email 28.118.134.817.31.10.6O0.640−0.538
4. AI-powered service for guiding passengers to the nearest and shortest routes within the airport54.63.123.118.70.60.0A0.781−0.263
5. Automated multilingual translation service for navigating airport facilities48.26.727.017.01.10.0A0.760−0.340
6. Robot service for luggage transport within the airport59.20.618.120.91.10.0A0.783−0.188
Arrival and Transfer1. Mobile service providing gate information for transfer passengers32.98.031.925.41.20.5A0.659−0.406
2. Mobile notifications for baggage arrival and pick-up location37.77.028.225.71.00.5A0.660−0.356
3. In-person guidance at the information desk upon airport arrival24.712.219.739.72.21.2I0.462−0.330
4. Information services connected through telephone staff or automated response systems (ARSs)19.013.717.742.15.52.0I0.396−0.339
5. Self-service information kiosks with touchscreens32.27.722.935.40.71.0I0.560−0.312
6. QR code service for tourist and transportation information via mobile scanning40.65.220.231.90.71.2A0.620−0.259
7. Interactive chatbot on the airport website36.25.515.040.12.50.7I0.528−0.211
8. Robot service for in-airport navigation assistance51.41.213.032.91.50.0A0.653−0.144
9. 24/7 robot assistance for guidance55.91.212.929.70.50.0A0.689−0.140
10. Multilingual or automatic translation app support48.62.021.925.21.70.5A0.721−0.244
11. Public transport routes and real-time traffic updates for travel to the city/destination37.27.526.927.20.70.5A0.648−0.348
12. Self-service kiosks for purchasing public transportation cards or passes at the destination38.75.527.925.91.50.5A0.679−0.340
13. Mobile-based e-ticket service for public transportation at the destination44.63.522.727.71.00.5A0.683−0.265
Notes: A = attractive; M = must-be; O = one-dimensional; I = indifferent; R = reverse; Q = questionable.
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Choi, S.; Moon, C.; Lee, K.; Su, X.; Hwang, J.; Kim, I. Exploring Smart Airports’ Information Service Technology for Sustainability: Integration of the Delphi and Kano Approaches. Sustainability 2024, 16, 8958. https://doi.org/10.3390/su16208958

AMA Style

Choi S, Moon C, Lee K, Su X, Hwang J, Kim I. Exploring Smart Airports’ Information Service Technology for Sustainability: Integration of the Delphi and Kano Approaches. Sustainability. 2024; 16(20):8958. https://doi.org/10.3390/su16208958

Chicago/Turabian Style

Choi, Sooyoung, Chaeyoung Moon, Keunjae Lee, Xinwei Su, Jinsoo Hwang, and Insin Kim. 2024. "Exploring Smart Airports’ Information Service Technology for Sustainability: Integration of the Delphi and Kano Approaches" Sustainability 16, no. 20: 8958. https://doi.org/10.3390/su16208958

APA Style

Choi, S., Moon, C., Lee, K., Su, X., Hwang, J., & Kim, I. (2024). Exploring Smart Airports’ Information Service Technology for Sustainability: Integration of the Delphi and Kano Approaches. Sustainability, 16(20), 8958. https://doi.org/10.3390/su16208958

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