Blind indoor navigation app for enhancing accessible tourism in smart cities

Abstract

Purpose

People with visual impairments or blindness (PwVIB) are mostly excluded from tourism activities. Despite the rise of assistive technology (AT) solutions in Tourism, acceptance remains low because of the difficulty of providing the right functionality, effectiveness and usability. Arguably, it can be said that disability-oriented training can affect the latter two and, therefore, an AT solution’s acceptance. This paper aims to contribute to the theory development and conceptualization of technology acceptance of AT solutions in Tourism by studying, in the context of the Unified Theory of Acceptance and Use of Technology (UTAUT), the effects of training PwVIB on using AT solutions. This study presents the effects of training on the tourism behavior of PwVIB and provides valuable information to the stakeholders.

Design/methodology/approach

Questionnaire data collected from 128 PwVIB after evaluating an AT were subjected to exploratory and confirmatory factor analysis and structural equation modeling followed by post-evaluation interviews. The used application, called BlindMuseumTourer, enables high-precision autonomous indoor navigation for PwVIB in tourist places like museums and places of health care.

Findings

The results of this study indicate the partial satisfaction of the extended model validating the importance of performance expectancy and training (the new factor) in predicting the behavioral intention of PwVIB tourists toward using ATs during their tourist activities. This suggests that practitioners have to provide performant technological solutions accompanied by special training sessions for improved engagement and satisfaction.

Originality/value

This study contributes to the UTAUT theory in the context of Tourism for PwVIB by adding a new factor and replacing two moderator variables. To the best of the authors’ knowledge, no similar work is studying AT acceptance by PwVIB in the tourism literature. Furthermore, the validation process used a novel indoor navigation application, demonstrating its effectiveness in the Tactual Museum of Greece.

目的

视障或失明人士（PwVIB）大多被排除在旅游活动之外。尽管辅助技术（AT）解决方案在旅游业中兴起, 但由于难以提供适当的功能、有效性和可用性, 接受度仍然很低。可以说, 以残疾为导向的培训会影响后两者, 从而影响对辅助技术解决方案的接受程度。本文旨在通过在技术接受和使用统一理论（UTAUT）的背景下, 研究培训 PwVIB 对使用 AT 解决方案的影响, 为旅游行业对 AT 解决方案的技术接受的理论发展和概念化做出贡献。本研究将介绍培训对 PwVIB 旅游行为的影响, 并为利益相关者提供有价值的信息。

研究方法

对 128 名无身份者在评估了辅助设施后收集的问卷数据进行了探索性因素分析、确认性因素分析和结构方程模型分析, 然后进行了评估后访谈。所采用的应用程序名为 “BlindMuseumTourer”, 可在博物馆和医疗场所等旅游景点为 PwVIB 提供高精度的自主室内导航。

研究结果

结果表明, 扩展模型的部分结果验证了性能预期和培训（新因素）在预测无障碍游客在旅游活动中使用自动视听设备的行为意向方面的重要性。这表明, 从业人员必须提供性能优越的技术解决方案, 并辅以专门的培训课程, 以提高参与度和满意度。

原创性

本研究通过增加一个新因素和替换两个调节变量, 为UTAUT 理论在无障碍游客旅游方面的应用做出了贡献。据我们所知, 在旅游文献中, 还没有类似的工作是研究 PwVIB 对 AT 的接受程度。此外, 在验证过程中还使用了一个新颖的室内导航应用程序, 并在希腊触觉博物馆展示了其有效性。

Objetivo

Las personas con discapacidad visual o ceguera (PwVIB) están excluidas en su mayoría de las actividades turísticas. A pesar del auge de las soluciones de tecnología de asistencia (TA) en el turismo, su aceptación sigue siendo baja debido a la dificultad de proporcionar la funcionalidad, eficacia y usabilidad adecuadas. Podría decirse que la formación orientada a la discapacidad puede afectar a las dos últimas y, por tanto, a la aceptación de una solución de TA. Este artículo pretende contribuir al desarrollo teórico y a la conceptualización de la aceptación de las soluciones de tecnología de apoyo en el turismo estudiando, en el contexto de la Teoría Unificada de la Aceptación y el Uso de la Tecnología (UTAUT), los efectos de la formación de las PwVIB en la utilización de las soluciones de tecnología de apoyo. El estudio presentará los efectos de la formación en el comportamiento turístico de las PwVIB y proporcionará información valiosa a las partes interesadas.

Diseño/metodología/enfoque

Los datos del cuestionario recogidos de 128 PwVIB tras la evaluación de una TA se sometieron a un Análisis Factorial Exploratorio y Confirmatorio y a un Modelado de Ecuaciones Estructurales, seguidos de entrevistas posteriores a la evaluación. La aplicación empleada, denominada BlindMuseumTourer, permite la navegación autónoma en interiores de alta precisión para PwVIB en lugares turísticos como museos y lugares de asistencia sanitaria.

Resultados

Los resultados indican la satisfacción parcial del modelo ampliado validando la importancia de la expectativa de rendimiento y la formación en la predicción de la intención conductual de los turistas PwVIB por lo que sugieren a los profesionales que las sesiones especiales de formación son esenciales para el compromiso y la satisfacción de los usuarios.

Originalidad/valor

Este estudio contribuye a la teoría UTAUT en el contexto del turismo para PwVIB añadiendo un nuevo factor y sustituyendo dos variables moderadoras. Hasta donde se sabe, no existe ningún trabajo similar que estudie la aceptación de la TA por parte de las PwVIB en la literatura sobre turismo. Además, en el proceso de validación se utilizó una novedosa aplicación de navegación en interiores que ha demostrado su eficacia en el Museo Táctico de Grecia.

Citation

Theodorou, P., Meliones, A., Tsiligkos, K. and Sfakianakis, M. (2024), "Blind indoor navigation app for enhancing accessible tourism in smart cities", Tourism Review, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/TR-02-2024-0123

1. Introduction

Over 2.5 billion people (WHO Report, 2022) are estimated to have visual impairments or blindness, with 330 million experiencing severe visual impairments to blindness according to the International Agency for the Prevention of Blindness (IAPB, 2024). They are often excluded from tourism activities and face numerous challenges while traveling, including, but not limited to, dependence on others for navigation, difficulty identifying and avoiding obstacles, lack of early access to information on Points of Interest (POIs) and difficulty accessing public transportation and visual or textual information (Ceccarini and Prandi, 2019).

Adopting assistive technology (AT) solutions can help overcome the challenges people with visual impairments or blindness (PwVIB) face, enabling the tourism stakeholders to provide experiences comparable to those available to the general population. The benefits from adopting ATs are twofold as, on one hand, the tourism industry will increase its customer base and, on the other hand, PwVIB will actively participate in tourism activities and enjoy high-quality services, thus fostering inclusivity. This is also recognized in the literature, as there is a growing trend toward diversity and inclusion in tourism and hospitality that focuses on people with disabilities (Darcy et al., 2010; Michopoulou et al., 2015; McKercher and Darcy, 2018; Olya et al., 2018; Gillovic and McIntosh, 2020; Darcy et al., 2020).

The field of ATs is under continuous development following an interdisciplinary approach combining Computer Science and Engineering, Design and Ergonomics, Rehabilitation, Psychology and Cognitive Sciences among others. Some of the major research themes at the intersection of technologies and tourism, as demonstrated by Molina-Collado et al. (2022), include technology acceptance models, electronic word-of-mouth, smart tourism and virtual and augmented reality. Despite the wide range and heterogeneous nature of ATs, smartphones are the most preferred solutions because of their proliferation, sufficient computing power, sensing capabilities and extensibility. Complementary technologies, which are also integral for enabling smart city applications, include AI, Bluetooth Low Energy (BLE) beacons, along with a range of sensing and actuating devices that have reached an increased level of maturity, reliability and cost-effectiveness.

Despite the availability of ATs, there are barriers affecting their adoption including low quality, high prices, supply chain challenges along with an overall lack of awareness and insufficient resources within the AT workforce (WHO Report, 2022). Their success and adoption, which is low among PwVIB (Theodorou et al., 2024), depend on the provided functionality, effectiveness and usability, albeit the latter two are often overlooked. We argue that training PwVIB with ATs is an effective way to affect the latter two and increase acceptance that will, subsequently, increase their engagement as more compelling tourism experiences will be available. Furthermore, the introduction of training as a way to approach ATs is also reinforced by the WHO report (WHO Report, 2022). Previous studies in tourism have overlooked the aspect of training people with disabilities on how to use AT solutions and instead focused only on training the staff to be more aware of their special needs (Cengiz, 2016; Chen et al., 2020).

This paper contributes to theory development on acceptance of AT solutions in Tourism by exploring the significance of training PwVIB within the Unified Theory of Acceptance and Use of Technology (UTAUT) theoretical framework, which is an extension of the Technology Acceptance Model (TAM). Its validity is investigated with the help of the BlindMuseumTourer, a novel indoor navigation application that enables PwVIB to tour inside the premises of cultural sites like museums (Meliones and Sampson, 2018; Theodorou et al., 2022a). The application was tested at the Tactual Museum of Athens in Greece. Previously, a modified version of the model was partially validated in the context of outdoor pedestrian navigation for PwVIB (Theodorou et al., 2024). Another goal of this research is to provide insights for both researchers and practitioners on the necessary components to build successful ATs.

This paper is organized as follows: Section 2 reviews the relevant literature on AT solutions in Tourism and their acceptance; Section 3 presents both the rationale for extending the UTAUT model and the methodology by which it was validated; and Section 4 presents the results. Finally, Section 5 concludes the paper with a discussion and future directions.

2. Literature review

2.1 Assistive technologies

The use of ATs to improve tourism experiences for people with disabilities is increasing (Asghar et al., 2019; Vaz et al., 2020; Iftikhar et al., 2023). Unlike traditional technologies that mainly provide access to information and services, such as booking reservations and POI searches, ATs provide functionalities that enable embodied tourism experiences, a goal of accessible tourism as described in Darcy et al. (2020). However, various barriers contribute to the exclusion of people with disabilities including, but not limited to, physical obstacles, communication issues from inaccessible formats and difficulties in navigating and using transportation systems. These are not only limited to outdoor spaces as even simple activities in indoor spaces like adjusting the temperature or the lighting in their homes can be challenging.

ATs can effectively mitigate these difficulties as, for instance, smart home assistants can manage home automation via voice commands, while specialized smartphones applications facilitate access to digital services. Many ATs use both sensory substitution and cross-modal techniques to improve autonomy of people with disabilities by removing physical, navigation and communication barriers. Notable examples include the MANTO project (Manto Blind Navigations Applications, 2023) which developed a suite of applications dedicated to autonomous indoor and outdoor navigation for PwVIB, called BlindMuseumTourer and BlindRouteVision, respectively (Meliones and Sampson, 2018; Theodorou et al., 2022a, 2022b). The former offers real-time autonomous navigation with minimal error margins within touristic places, detects deviations from navigational paths with corrective feedback to prevent injuries or damage and provides POI information, such as help desks and other facilities. The latter provides real-time guidance in outdoor spaces, obstacle detection and avoidance and facilitates the use of various transportation services. Other examples include the applications of Milicchio and Prosperi (2016) targeting indoor and outdoor archaeological sites for people deaf and hard of hearing, mobile applications that present POIs accessibility-wise (Ponciano et al., 2021), AI-powered wearable systems supporting audio-based navigation and obstacle detection and avoidance for individuals with visual and kinetic disabilities (Iakovidis et al., 2022), mobile applications enabling museums remote navigation (Jacobe et al., 2021) as well as virtual and augmented reality mobile applications that allow visitors to explore cultural places both before their visit and physically with enhanced on-site guidance and personalized recommendations (Constantinou et al., 2022). In addition to the aforementioned examples from academia, there are commercially available applications as well. Notable examples are BlindSquare (BlindSquare, 2024), a mobile-based application designed for blind, deafblind and partially sighted offering via the use of GPS and BLE beacons outdoors and indoors location-based information and navigation, respectively, in a user-friendly way; Lazarillo (Lazarillo, 2024), another mobile-based application for the PwBVI that allows outdoor and indoor navigation using the same technologies as BlindSquare; and last but not least, BindiMaps (Bindimaps, 2024), yet another mobile-based application that provides exclusively indoor navigation in complex spaces leveraging BLE beacons. Although all these applications use the same technological foundation, they provide slightly different features.

2.2 Technology acceptance

Technology acceptance targeting general population is extensively studied in Tourism (Molina-Collado et al., 2022). Numerous models have been explored including TAM, UTAUT, Innovation Diffusion Theory and Theory of Planned Behavior among others (Dorcic et al., 2019) incorporating variables such as performance expectancy, perceived usefulness, attitude, social influence, reliability, usability, complexity and curiosity to name a few (Shahab et al., 2018). These studies suggest that acceptance factors vary significantly depending on the context and type of technology. Specifically, factors influencing acceptance of smartphone applications in tourism include, among others, advanced computing capabilities, ubiquity, real-time responsiveness and personalization for dynamic travel decisions (Dickinson et al., 2012). Additional predictors include performance expectancy, social influence, price saving, perceived risk, perceived trust and prior usage habits (Gupta et al., 2018; Kamboj and Joshi, 2020) information quality, source credibility and app functionality (Camilleri et al., 2023).

Conversely, technology acceptance targeting PwVIB and ATs in tourism remains limited and encompasses a small range of technologies and disabilities (Iftikhar et al., 2023). This scarcity is not limited only to this area, as it affects others as well (Noghan et al., 2023). Furthermore, technology acceptance findings pertinent to the general population are not always applicable to PwVIB (Bennett et al., 2019), while models such as TAM may not capture all the key factors (Manis and Choi, 2018). Thus, the following studies on AT acceptance will shed some light for the case of people with disabilities.

Starting from the intellectually disabled (Ali et al., 2022a), traveling experience is affected by ease of use, ease of comprehension, usefulness and reliability, while an overall lack of experience increases the users’ negative feelings, thus impacting their experience.

Regarding PwVIB visiting museums, Vaz et al. (2020) explored the pre-visiting planning and the visitation phase and concluded that while there is some basic accessible information available online, which aids in pre-visiting planning, it is minimal with limited impact and, in addition, the conditions they experience during actual visits are predominantly negative. To improve the situation, the authors suggest the use of ATs.

Iftikhar et al. (2023) addressed the gap in VR technology acceptance for people with disabilities in tourism by proposing a conceptual framework. It tries to explain the features that improve the conditions and includes interpersonal, intrapersonal and structural factors as the main drivers of VR engagement and acceptance, the latter of which is further decomposed into perceived ease of use and perceived usefulness. However, the authors have not conducted experimental validation of this model.

People with dementia share a desire for travel and tourism, and they have better experiences when they navigate independently using ATs without external assistance. Asghar et al. (2019) highlighted the influence of ATs’ usability by four motivational factors, namely, improved travel and tourism experiences, cost-effectiveness, facilitating communication and alignment with user needs, along with improved accomplishments, increased autonomy and heightened safety.

Although the elderly are not always classified among people with disabilities, they frequently encounter challenges that inhibit their participation in tourism activities because of the natural decline in sensory and cognitive functions as they age. VR technology can potentially help them experience tourism activities, and according to Sancho-Esper et al. (2022), perceived usefulness, perceived ease of use and attitude affect VR acceptance. However, perceived ease of use is negatively influenced by technological anxiety.

2.3 Hypotheses formulation

Several observations emerge from the above studies. Firstly, AT solutions are critical for providing engaging tourism experiences. Secondly, their acceptance is important to promote their adoption and the factors affecting it vary significantly depending on the type of disability and the application’s scope. Thirdly, the ever-increasing complexity of theoretical models of technology acceptance complicates both their planning and validation while the difficulty to find adequately sized samples, which in our experience is particularly true for PwVIB, further complicates matters as stable estimates cannot be extracted. Finally, there is a notable gap in the available AT acceptance technology literature in tourism for indoor space navigation targeting PwVIB.

To bridge the gap, we propose a new technology acceptance framework based on UTAUT. Among the available models for assessing technology acceptance, UTAUT stands out as the most eminent. It describes the subjective likelihood that an individual will engage in a particular behavior (Venkatesh et al., 2003) and predicts the actual acceptance of a technological product or service. Previous research works of Dwivedi et al. (2011), Oshlyansky et al. (2007) and Scherer (2016) have empirically validated that UTAUT can explain up to 70% of the variance in intention to use technology (Aggelidis and Chatzoglou, 2009). Furthermore, UTAUT is highly regarded in behavioral research, as it is considered to have a high-prediction capability of the acceptance variance (over 40%).

Thus, to enhance its predictive power and address specific needs such as those of PwVIB, we introduced Training on the use of AT solutions as a novel construct that acts as an antecedent of behavioral intention (BI). This choice was informed by insights gained during our collaboration with PwVIB on the design and implementation of ATs for indoor and outdoor spaces navigation, where the need for specialized training sessions was repeatedly emphasized (Theodorou and Meliones, 2022). Despite this new introduction, it retains the core constructs of performance expectancy (PE), effort expectancy (EE), social influence (SI) and facilitating conditions (FC) of the original UTAUT (Figure 1), as antecedents of BI, the latter of which predicts technology acceptance. Finally, the original model’s moderation variables of experience and voluntariness were replaced by self-efficacy and attitude as the proliferation of smartphones has diminished the role of the former two (Moon et al., 2022). More details on these decisions are provided in the following sub-sections.

2.3.1 Performance expectancy.

PE reflects the extent to which an individual perceives that using a system will result in gains in job performance (Venkatesh et al., 2003). In our case, this translates to the capability of PwVIB to successfully engage in tourism experiences with ATs. Drawing on the tourism technology acceptance literature for people with and without disabilities (Ayeh et al., 2013; Gharibi et al., 2022I; Moon et al., 2022; Ali et al., 2022b; Sia et al., 2023), PE is considered a critical factor. Thus, we propose the following:

H1.

Performance expectancy influences the behavioral intention of people with visual impairments or blindness to adopt assistive technologies positively.

2.3.2 Effort expectancy.

EE reflects the level of the system’s ease of use (Venkatesh et al., 2003). Unlike PE, EE has mixed results. Sia et al. (2023) and Ali et al. (2022b) found, for the general population, no significant effects of EE on technology acceptance when considering smart mobile and broader ICT tourism applications, respectively. Moon et al. (2022) demonstrated similar results about mobile applications adoption by PwVIB. However, Gharibi et al. (2022) highlighted its insignificance in the context of Extended Reality (XR) museums applications targeting people with disabilities. Given these varied findings, we included EE to further investigate its relevance:

H2.

Effort expectancy influences the behavioral intention of people with visual impairments or blindness to adopt assistive technologies positively.

2.3.3 Social influence.

SI reflects the extent to which an individual’s perception is affected by others (Venkatesh et al., 2003). SI has mixed results, as there are tourism-related and broader ICT studies showing both the factors’ significance and insignificance for people with disabilities (Iftikhar et al., 2023; Ali et al., 2022b) and without (Molina-Collado et al., 2022; Moon et al., 2022). This discrepancy in the literature led us to investigate the relevance of SI:

H3.

Social influence influences the behavioral intention of people with visual impairments or blindness to adopt assistive technologies positively.

2.3.4 Facilitating conditions.

FC reflects the extent to which an individual perceives that the organization and technical infrastructure facilitate the adoption of the system (Venkatesh et al., 2003). Despite the study of Ali et al. (2022a) demonstrating the significance of FC, Ali et al. (2022b) and Moon et al. (2022) presented contradictory findings in the context of tourism-related ICTs and mobile applications by PwVIB, respectively. Thus, we included the FC factor to test its relevance:

H4.

Facilitating conditions influences the behavioral intention of people with visual impairments or blindness to adopt assistive technologies positively.

2.3.5 Training.

In the original UTAUT model, training is not a factor directly predicting BI. As mentioned in the introduction of Section 2.3, training was highly requested by PwVIB participating in the development of AT solutions. In response, a specialized version of the outdoor navigation application was developed, providing route simulation with audio and haptic feedback (Theodorou et al., 2023). The reception of the application highlighted its significance and demonstrated a notable improvement in the acceptance of the implemented technology. Given this finding, a search on training in technology acceptance literature followed, including in tourism, revealing no significant results. Despite the lack of technology acceptance results, the literature in inclusive tourism (Cengiz, 2016; Chen et al., 2020; Happ and Bolla, 2022) and other organizations such as the European Network for Accessible Tourism (ENAT, 2024) and EU-led programs (T4proIN, 2024) highlight the importance of training staff member acquiring skills and knowledge to facilitate the special needs of people with disabilities tourism.

Given that our empirical finding was not anticipated by the original UTAUT, we explored the potential of Training as a direct predictor of BI. Further supporting this attempt are the findings of Aggelidis and Chatzoglou (2009) on hospital personnel. Our exploration underscores the importance of integrating training for both staff members and disabled tourists in the use of assistive technologies. Thus, we proposed the following hypothesis:

H5.

Training influences the behavioral intention of people with visual impairments or blindness to adopt assistive technologies positively.

2.3.6 Moderators.

While the model retains the variables of gender and age, it replaced experience and voluntariness of use with self-efficacy and attitude. This decision was a consequence of the diminishing role of the former two because of the proliferation of smartphones (Moon et al., 2022) in combination with numerous studies highlight their importance as constructs fostering technology acceptance (Aggelidis and Chatzoglou, 2009; Kohnke et al., 2014; Moon et al., 2022). These adjustments ensure the model is better suited to today’s technology landscape and user interactions.

Self-efficacy can be described as an individual’s confidence in their ability to create impact. Its importance is generally demonstrated in mobile-based applications studies where it significantly moderates the relationships between PE and BI (Mensah et al., 2022; Liu et al., 2022) and EE with BI (Nikou and Economides, 2017). Attitude refers to either positive or negative sentiments toward engaging in activities like the use of technology to accomplish a goal and has been shown to impact technology adoption (Kohnke et al., 2014). Specifically, Moon et al. (2022) demonstrated a negative moderation effect between PE and BI suggesting that individuals with a lower level of attitude toward mobile apps showed a greater increase in behavioral intention when they perceived the apps to be useful. Finally, based on the results of Theodorou et al. (2024), we anticipate that gender and age will not significantly moderate these relationships. Thus, we propose the following:

H6–H7.

SE moderates the relationship between performance expectancy and behavioral intention as well as effort expectancy and behavioral intention in a positive way.

H8.

ATT negatively moderates the relationship between performance expectancy and behavioral intention in a positive way.

H9–H12.

Gender neither positively nor negatively moderate the relationship between performance expectancy, effort expectancy, social influence, training and behavioral intention.

H13–H17.

Age neither positively nor negatively moderate the relationship between performance expectancy, effort expectancy, facilitating conditions, social influence, training and behavioral intention.

The model is presented in Figure 2 with all the interconnections among the factors and moderation variables.

3. Methodology

3.1 Population characteristics and data collection

The sample included 128 PwVIB with a diverse range of visual impairments, ages and socioeconomic backgrounds. Gender representation was balanced with males accounting for 54.4% and females 45.6%, aged between 34 and 60 years old, 29.8% exhibited total blindness, 35.4% experienced near-total blindness, while the remaining had various visual impairments. The principal cause of visual impairment was congenital blindness, responsible for 63% of cases, followed by cancer, retinopathy and accidents. Furthermore, 47.8% had limited digital skills.

Data collection involved self-administered questionnaires available either through Google Forms or the Lighthouse of the Blind of Greece. All participants provided informed consent; however, written informed consent was not deemed necessary or acquired. After the evaluation, semi-structured interviews followed to gather feedback from PwVIB regarding usability, improvements and the training environment.

3.2 BlindMuseumTourer

BlindMuseumTourer is an indoor Android application enabling PwVIB to autonomously navigate in museums and similar cultural spaces. The app supports the ambient intelligence tourism vision (Buhalis, 2019), one of the pillars of smart hospitality (Buhalis et al., 2022) and smart tourism (Buhalis, 2019) by integrating smart city technologies to enhance user experiences via context awareness, connectedness, personalization and real-time monitoring (Buhalis and Amaranggana, 2015).

BlindMuseumTourer adopted a human-centered design approach, an essential component of Tourism 4.0 (Stankov and Gretzel, 2020). It organizes the museum’s exhibitions into thematic routes for PwVIB to select for navigation and narration, while its advantage lies with the non-requirement for infrastructural changes, as it operates without tactile ground indicators. The application positions users accurately in front of the exhibits at arm’s length by considering the user’s gait characteristics. It maintains navigation accuracy by detecting and correcting any path deviations and monitoring rotational movements (Figure 3).

At the application’s core lies a novel pedestrian dead reckoning algorithm coupled with the gyroscope sensors found on smartphone devices and depending on the spatial characteristics, optionally, BLE technology radio beacons installed in the space to correct the algorithm’s accumulated error. The pedestrian dead reckoning algorithm’s minimal error and ability to navigate the user without the help of any tactile ground indicator in the exhibition rooms is achieved by embedding the special characteristics of each user’s gait into its model. These are introduced at the beginning of the touring experience where users perform a short-duration walk on a special-purpose tactile ground surface indicator following instructions from the application.

The application provides both voice command and graphical user interfaces addressing the needs of those who face moderate to severe visual impairments, while the latter is screen reader compatible. Figure 4 presents trial runs from the Tactual Museum of Athens in Kallithea, Greece (Tactual Museum, 2024). The latter is one of the five museums globally, offering art and cultural heritage experiences through touch for PwVIB and sighted visitors alike.

3.3 Statistical methods

For the analysis, three techniques were used, namely, exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and structural equation modeling (SEM). IBM SPSS was used for both descriptive analysis and EFA, whereas IBM AMOS version 26 facilitated CFA and SEM. This approach was recommended by Anderson and Gerbing (1988).

During the EFA phase, construct validity was assessed using principal component analysis with Varimax with Kaiser Normalization as the rotation method, while in the CFA stage, the robust maximum likelihood method was used with the following parameters:

• item reliability; each item’s loading must have an absolute standardized value of 0.5 or greater.

• discriminant validity, used the Fornell and Larcker (1981) criterion and the heterotrait-monotrait ratio of correlations (HTMT);

• the convergent validity of the measurement model was assessed using the Fornell and Larcker (1981) methodology. Key metrics include:

• Composite reliability (CR): It measures construct internal consistency and require a value above 0.7.

• Average extracted variance (AVE): It quantifies the variance captured by a construct relative to the variance attributable to measurement error and requires a value above 0.5.

Model fit was evaluated using several goodness-of-fit measures, including the Comparative Fit Index, the Tucker–Lewis Index and the root mean square error of approximation. Additionally, we considered suggestions from the modification index output. The finalized model was obtained after removing all non-significant relationships and covariances.

Moreover, before SEM analysis, we rigorously assessed the model’s invariance across age (over and under 40) and gender (Male/Female) groups. We also examined the configural invariance to ensure the measurement model’s structure consistency across these groups, followed by a metric invariance test to verify that the constructs had the same meaning irrespective of gender and age.

Finally, SEM explored how effectively the model’s factors predicted BI and assessed the moderation effects of gender, age, attitude and self-efficacy on these relationships. The SEM analysis used the robust maximum likelihood estimation method.

4. Results

4.1 Exploratory factor analysis and confirmatory factor analysis

EFA as a dimension-reduction technique was used to assess the suitability of our questionnaire’s validity. The principal component analysis effectively reduced the items into eight distinct groups in a way that mirrored our questionnaire’s items organization, all with high factor loadings.

Subsequently, CFA demonstrated that all latent variables had high and statistically significant factor loadings. Table 1 shows our model’s factor loadings.

Regarding model reliability, the CR for each latent variable exceeded the 0.7 threshold, indicating strong internal consistency, and likewise, the AVE exceeded the required threshold of 0.5. The last reliability metric assessed was discriminant validity via the use of HTMT with the measurement model’s HTMT scores being below the required threshold.

The model’s fit was robust, evidenced by a Comparative Fit Index of 0.993, a Tucker–Lewis Index of 0.992 and a root mean square error of approximation of 0.016, indicating excellent fit despite the small sample size. Finally, we established the equivalence of our measurement model across gender and age groups via a thorough examination of model fit and comparison statistics.

4.2 SEM

SEM revealed that H1 (PE) and H5 (Training) were statistically significant, both demonstrating positive effects on BI with standardized regression weights of 0.186 and 0.152 and p-values of 0.002 and 0.016, respectively. These results suggest that an increase in PE and in Training enhances the likelihood of PwVIB using the application in the future.

Regarding the assessment of the moderator variables, the gender and age-related hypotheses are satisfied, as none of them showed any significant impact on the model’s relationships. Furthermore, neither attitude nor self-efficacy relationships had any significant effects (Table 2).

4.3 Semi-structured interviews

The semi-structured interviews that followed the collection of the statistical analysis data set included general observations made by PwVIB and feedback on the application. Firstly, the participants emphasized the sense of independence and ease of indoor space navigation provided by the application and on the drastic impact it had on their perceived tourism experience. Secondly, the role of smartphones as platforms for developing ATs was highlighted, as most PwVIB expressed their familiarity in terms of not only form factor but also basic functionality. This familiarity could significantly reduce the technological barriers, as users can focus on the applications’ accessible user interfaces, features and available customizations instead, via the help of specialized training sessions. Thirdly, closely related to the latter, the staff members need to have skills both for addressing their needs and to train them effectively. Fourthly, it was noted that the proliferation of smartphones could reduce reliance on equipment provided by stakeholders, as smartphone-based applications can be delivered on user-owned devices, thereby reducing associated risks such as cost and the scarcity of the required resources. Fifthly, the participants also highlighted that PwVIB are wary of such applications and prefer to personally experience them rather than relying on feedback given even by their own peers. Finally, the PwVIB provided feedback about the application concerning ease of use, the required learning curve, the user interface’s intuitiveness, the accuracy and reliability of the application and the challenges they faced. Overall, they perceived the application very positively and expressed their outlook for the future.

5. Discussion

Undoubtedly, technology can become the primary enabler of accessibility for people with disabilities in enjoying high-quality and engaging tourism activities, especially when the loss of senses prohibits participation. Smartphone-based applications are the enabling technology, as they possess the necessary characteristics to promote and accelerate the development of smart, privacy-aware ecosystems among tourism stakeholders (Buhalis et al., 2022). These key characteristics include real-time information sharing, responsiveness, analytics and fusion of large volumes of data to get insights on trends, motivations and personalized recommendations among others. By integrating these applications further in the context of Edge/Fog Computing (Bonomi et al., 2012), a paradigm that leverages local computation at the networks’ edge and seamlessly integrates IoT with cloud computing into a single continuum, it can potentially multiply their provided value and bring even closer the realization of smart cities. Furthermore, smartphone-based applications especially the ones that do not require major infrastructural changes, such as our own, make the transition to smart tourism experiences easier, as they reduce the stakeholders’ resistance who often cite the lack of technology standardization and high costs (Ballina, 2022).

While technology can address many of the challenges, it is important for everyone to be aware of its shortcomings as well. According to WHO (WHO Report, 2022), technology can act as a barrier because of its contingency not only on the household’s economic status but also on the ability to use it effectively. Setting aside other issues including privacy and ethical issues (Buhalis, 2019), addressing effectively the financial challenges is crucial. An often-used approach is to trade cost with accuracy of results or processing time. For instance, a museum might opt to decrease the number and density of beacons used, thus reducing the granularity of information that could be leveraged from a navigation system to detect a user’s position or use a less capable smartphone device that might take longer to provide its intended service. Despite some options, ultimately, substantial advancements can only be expected through the natural progression of technology to drive down costs, particularly with the introduction of newer, more advanced technologies.

Setting aside the difficulties, ATs are the most promising solutions for providing people with disabilities accessible tourism experiences (Qiao G et al., 2023; Michopoulou and Buhalis, 2013). Afterall, the limited available options highlight opportunities for significant improvements both in technology and acceptance as this paper’s findings suggest. Specifically, the factor analysis confirmed the reliability and partial validity of the proposed model, revealing statistically significant improvements in BI via PE and Training.

The analysis also highlighted the model’s prediction concerning the negligible moderation impact of gender and age. This can be largely attributed to the type of AT used, as the proliferation of smartphones and the maturity of their native accessibility features have reached a level where users of all ages and genders can effectively use these technologies. Additionally, the analysis showed that EE had no significant effect on the intention to use ATs. SI was also not a significant predictor likely because of the varying degrees of challenges and needs among PwVIB, which can diminish the effect of peer opinions on technology use. Furthermore, FC also showed no significant effect on technology acceptance. According to the post-evaluation interviews with PwVIB, they typically exhibit indifference toward FCs as, in their experience, they fail most often to address their difficulties.

Moderators-wise, attitude and self-efficacy did not affect any of the model’s relationships, suggesting that the critical importance of AT solutions, such as those for navigation, outweighs any personal attitudes toward its use, while the universal use of smartphones may explain the lack of an effect from self-efficacy.

Overall, from a theoretical perspective, the results indicate that PE and Training are the strongest predictors of smartphone-based AT solutions adoption in tourism for PwVIB, while contextual factors such as SI, FC and EE along with the moderator variables of gender, age, attitude and self-efficacy seem to have no effect.

5.1 Theoretical implications

This study makes several theoretical contributions. Firstly, it contributed to the development of technology acceptance theory for PwVIB in the context of tourism. The proposed model expanded UTAUT by introducing training of PwVIB with AT solutions in the original model, and it shed light on its relationship with BI via conducting an empirical validation. The results highlighted the necessity to train PwVIB on the effective usage of smartphone-based AT solutions that a tourist place may have. To the best of our knowledge, there is no other study suggesting this link. Other inclusive tourism (Cengiz, 2016; Chen et al., 2020; Happ and Bolla, 2022) studies, including training in their treatise, instead focus on staff members acquiring skills and knowledge to facilitate the special needs of people with disabilities. Our result compliments the latter, as the AT training sessions cannot be supported without skilled staff members. Secondly, it highlighted the importance of providing training even for smartphone-based AT solutions. Despite being the most common platform of AT tourism applications and thereof users are expected to have a high degree of familiarization with its interface, the provided applications add a new layer of complexity that could impact their usage. Finally, this study expanded the current understanding of tourism behaviors given the unique behavior of PwVIB and provided a guide on how to provide better and more engaging tourism experiences.

5.2 Practical implications

In addition to the theoretical contributions, there are some practical implications for tourism stakeholders offering indoor activities. Firstly, this study demonstrated that to entice PwVIB to participate in indoor space tourism activities, stakeholders need to offer ATs and special training sessions to teach skills required for their efficient use. These could include activities that familiarize PwVIB with accessibility features, user interfaces, available customizations and the main functionality of the provided AT as suggested in the semi-structured interviews. Therefore, the stakeholders need to invest, in addition to ATs, in the creation of appropriate training environments. These should target not only the PwVIB but also the personnel as well as they need to both develop skills to accommodate the needs of the target group and to be sufficiently equipped technically wise to explain the provided ATs. This will boost the confidence of all the involved parties leading to more extensive experimentation with ATs, thus improving their adoption. Secondly, the significance of the PE factor indicates that stakeholders should emphasize on AT solutions with good performance characteristics, and therefore, the training should focus on demonstrating the benefits performance-wise. Thirdly, stakeholders need not invest heavily in improving EE if the performance expectations of the users are met, and finally, the marketing material should focus more on objective results rather than on subjective opinions, as it suggested by both the insignificance of SI and the results from the semi-structured interviews. As PwVIB are not very easily convinced by other people’s opinions and experiences, the marketing strategy should emphasize clearly communicating the performance benefits of the technology. This, however, by no means excludes the use of success stories and the inclusion of other users’ opinions.

In addition to the model’s direct practical implications, the stakeholders need to also consider the broader application context to improve the provided experiences as we have seen from our own experience. Applications targeting PwVIB and in general people with disabilities need to embody specific qualities to yield satisfactory results. The used application is a prime example of an effective indoor space navigation application designed for PwVIB. It adopted a universal design approach integrating PwVIB early in the design process and maintained them throughout the implementation phase. Also, the application adhered to established accessibility guidelines ensuring compatibility with screen readers and included voice-activated controls to facilitate interaction. While these features are vital for acceptance, ultimately the robustness and reliability of the provided functionality determines an AT’s success. In this study, admittedly the precision of the navigation experience greatly influenced the participants’ perceptions.

5.3 Future directions

The tourism literature has limited results on PwVIB. Although there are some results from a technological standpoint, this is not true for the acceptance of ATs. Moreover, tourism literature has no longitudinal studies that would help to understand the acceptance of ATs in the long term. Another research question that needs to be answered in the future is about the impact of AI-powered technologies such as Large Language Models and Generative AI for the inclusion of people with disabilities in tourism activities. Critical also will be to find ways to improve the transparency of decision-making processes, known in the AI literature as Explainable AI, to increase the robustness.

Furthermore, we plan to explore the proposed model’s applicability to other tourism-related applications. One such example is a prototype application our research team developed called the Acropolis Walk app that allows PwVIB to navigate freely around Greece’s most visited monument and universal symbol of democracy. Other future endeavors include enriching BlindMuseumTourer’s indoor coverage to more hospitals and heritage sites as well as adding AR features that offer orientation and mobility via three-dimensional audio. The latter will build on the three-dimensional audio features already included in the BlindRouteVision companion application that trains PwVIB (Theodorou et al., 2022a, 2022c). Finally, we will introduce features providing detailed information about risks, alerts and emergency routes to improve the users’ safety and well-being. This aligns with the growing trend of creating disaster-handling mobile applications, increasing the user’s risk awareness and improving their disaster preparedness (Aliperti and Cruz, 2020; Upadhyay et al., 2023).

6. Conclusion

This paper expanded the technology acceptance literature for PwVIB in tourism and presented the factors influencing the adoption of technologies to provide insights to academic scholars and practitioners. Despite the extensive study of technology acceptance in tourism for the general population to the best of our knowledge, this is the first attempt to study the acceptance of tourism-related ATs for museums targeting PwVIB. The methodology extended the UTAUT model to include Training as a direct determinant of BI and replaced the moderator variables of experience and voluntariness of use with self-efficacy and attitude. The decision to include Training was a consequence of our experience during the development of applications for PwVIB. In addition to enriching the scant literature on the topic, this paper empirically validated via statistical analysis the proposed model with the use of BlindMuseumTourer, an AT that facilitates autonomous navigation for PwVIB in indoor spaces in tourism. It demonstrated the statistical significance of PE and TR in improving technology acceptance.

Figures

Figure 1

Original UTAUT model

Figure 2

Extended UTAUT model

Figure 3

Rotating while touring the premises of the Tactual Museum of Athens

Figure 4

Touring inside the Tactual Museum of Athens

Table 1

Factor loadings

Items ← factor	Factor loadings – Estimate
PE1 ← PE	0.938
PE2 ← PE	0.497
PE3 ← PE	0.628
EE1 ← EE	0.899
EE2 ← EE	0.694
EE3 ← EE	0.629
TR1 ← TR	0.832
TR2 ← TR	0.662
TR3 ← TR	0.905
SI1 ← SI	0.671
SI2 ← SI	0.645
SI3 ← SI	0.883
BI1 ← BI	0.564
BI2 ← BI	0.890
BI3 ← BI	0.624
FC1 ← FC	0.909
FC2 ← FC	0.704
FC3 ← FC	0.894
ATT1 ← ATT	0.766
ATT2 ← ATT	0.844
ATT3 ← ATT	0.816
SE1 ←SE	0.520
SE2 ←SE	0.663
SE3 ←SE	0.903

Source: Table by authors

Table 2

SEM mode parameter estimates and statistical significance

			Estimate	SE	CR	PLabel
BI	←	TR	0.152	0.063	2.418	0.016
BI	←	PE	0.186	0.059	3.132	0.002
BI	←	EE	0.033	0.079	0.419	0.675
BI	←	FC	−0.036	0.046	−0.795	0.427
BI	←	SI	−0.014	0.059	−0.242	0.809
BI	←	SE	0.011	0.120	0.089	0.929
BI	←	ATT	0.032	0.063	0.503	0.615
BI	←	att_x_pe	−0.002	0.034	−0.056	0.955
BI	←	se_x_pe	−0.006	0.036	−0.158	0.875
BI	←	se_x_ee	−0.019	0.040	−0.465	0.642

Source: Table by authors