149Ženka, J. et al. Hungarian Geographical Bulletin 70 (2021) (2) 149–161. Introduction There are several reasons why wayfinding in hospitals requires systematic research. Many authors document increasing difficulties with wayfinding in large complex buildings, in- cluding hospitals (Hölscher, C. et al. 2006; Anagnostopoulos, G.G. et al. 2017). Patients and visitors of healthcare facilities may face considerable anxiety and stress resulting from navigational issues (Wolstenholme, D. et al. 2010; Devlin, A.S. 2014). Staff members also face difficulties in wayfinding and are often asked for advice by hospital patients and vis- itors, which may negatively affect their pro- ductivity (Mollerup, P. 2009). Navigational needs and strategies, spatial and wayfinding skills are associated with gender (Lawton, C.A. 2001), age (Harris, M.A. and Wolberts, T. 2014), education (Ženka, J. et al. 2021), cul- ture (Davies, C. and Pederson, E. 2001), and, most importantly, mental and health (dis) abilities (Souza, R.F. and Martins, L.B. 2019). Wayfinding in a large complex building such as a hospital is also context-dependent, being affected by the specificities and history of the building, its architectural setting, urban de- sign factors, and the connection of the place to other parts of the city. Dealing with the increasing complexity of large buildings requires the adoption of technology-based smart solutions to comple- ment traditional navigational issues, such as maps, plans, signs, arrows, or colour signs. Recently, one of the most progressive so- lutions to navigational issues has been the adoption of smartphone navigation appli- cations for indoor positioning. The devel- opment and/or implementation of a useful smartphone navigation app should be based on a survey of navigational needs among the potential users. 1 Department of Social Geography and Regional Development, Faculty of Science, University of Ostra- va, Chitntussiho 10, 710 00 Ostrava, Czech Republic. E-mails: jan.zenka@osu.cz, jan.machacek@osu.cz, ludek.krticka@osu.cz 2 AGEL a. s., Jungmannova 28/17, Nové Město, 110 00 Praha 1, Czech Republic. 3 AGEL Research and Training Institute, Mathonova 291/1, Krasice, 796 04 Prostějov, Czech Republic. Acceptance of a smartphone navigation application by hospital patients and visitors: the role of gender, age, and education Jan ŽENKA1, Jan MACHÁČEK1, Luděk KRTIČKA1, Pavel MICHNA2 and Pavel KOŘÍZEK3 Abstract This paper analyses the acceptance of a smartphone navigation app in a hospital among its patients/visitors. We tested the effects of socio-demographic factors (gender, age, and education) on technology acceptance and on perceived difficulties with wayfinding in the hospital complex. The empirical research is based on a survey among 928 patients/visitors of the Vítkovice Hospital in Ostrava, Czechia. We found that the accept- ance of smart navigation increases with the level of education and decreases with age. No significant gender differences were observed. Keywords: technology acceptance, hospital, gender, age, education, smartphone navigation Received March 2021, accepted May 2021 DOI: 10.15201/hungeobull.70.2.4 Hungarian Geographical Bulletin 70 2021 (2) 149–161. Ženka, J. et al. Hungarian Geographical Bulletin 70 (2021) (2) 149–161.150 Thus, our first research question is to what extent the respondents (patients and visitors) accept smartphone navigation apps and use them for orientation in the hospital. The second research question is to what extent are the navigational preferences of hospital patients/visitors associated with socio-demo- graphic factors: gender, age, and education. Finally, we also ask to what extent are the perceived wayfinding difficulties associated with gender, age, education, and location. Do men and women, younger and elderly peo- ple, or less educated and more educated peo- ple perceive the same hospital departments as difficult to find, or are there any significant differences? Based on these answers, we aim to provide specific recommendations for de- signing hospital navigation systems that will be based not only on theoretical arguments but also on the empirical identification of the navigational preferences and needs of hospi- tal patients and visitors. The empirical analysis in this paper draws on a case study of the Vítkovice Hospital (official name: ‘Nemocnice AGEL, Ostrava- Vítkovice’) in the city of Ostrava (Czechia). With an area of 4.2 ha and 11 pavilions, it is rather a small hospital. Therefore, patients and visitors are not in urgent need of a more technologically advanced navigational sys- tem, allowing them to cope with the com- plexity of the site (see Ženka, J. et al. 2021 for a more detailed description of the naviga- tional system in the hospital). Nevertheless, our empirical research (see section ‘Results’) revealed that even in a hospital complex of this size, there are frequent issues with way- finding among both patients and visitors. We conducted 928 questionnaires with hospital patients and visitors, focusing on their perception of the current navigational system in the hospital, on the preferences of various types of navigational cues, and, most importantly, on their acceptance of a smart navigation application. While the ex- isting literature on wayfinding in hospitals is abundant, we would like to contribute by connecting two different avenues of re- search: a) Theoretical papers testing the effects of gender, age, and education on wayfind- ing (Lawton, C.A. 2001; Iaria, G. et al. 2009; Anacta, V.J.A. and Schwering, A. 2010; Silber-Varod, V. et al. 2019; Mendez-Lopez, M. et al. 2020); b) Applied studies analysing the techno- logical or management aspects of hospital navigation systems (Balata, J. et al. 2013; Calderoni, L. et al. 2015; Anagnostopoulos, G.G. et al. 2017). In the next section, we discuss the deter- minants of people’s willingness to use smart navigational applications, focusing on the ef- fects of age, gender, education, and their pos- sible interactions. The third section provides the characteristics of data sources and meth- ods, while the fourth presents the selected re- sults of statistical tests. In the fifth section, we compare our empirical findings with those of other authors. The sixth section concludes. Gender, age, education, and their effects on technology acceptance The primary dependent variable in our re- search is technology acceptance: the propen- sity of patients/visitors to use a smart navi- gation app in the hospital. The traditional explanation of the adoption and usage of tel- ecommunication technologies is provided by the digital divide model (Rice, R.E. and Katz, J.E. 2003), based on the effects of gender, age, and education. The adoption of technology improves with the level of education and declines with increasing age. Younger peo- ple and people with a higher level of formal education should adopt technology faster than older people and people with a lower level of education. Older individuals tend to face greater difficulties in processing new or complex information, which affects their use of modern technologies (Custodio, R. et al. 1986; Morris, M.G. et al. 2005). This problem can be attributed to a decline in cognitive and memory skills related to the ageing process (Posner, R.A. 1997; Venkatesh, V. et al. 2012). These authors contend that the adoption and 151Ženka, J. et al. Hungarian Geographical Bulletin 70 (2021) (2) 149–161. use of mobile internet technology lessens with age, as older men in particular rely more on established habits than on mobile internet technology. Harwood, J. (2007) argued that different age groups have different behav- ioural patterns in mobile technology use. Men are generally assumed to adopt tech- nology faster than women, but this applies only in the early phase of the adoption of the technology. Several authors have document- ed small gender differences in the ability and willingness to use smart navigation technolo- gies (Hwang, K.H. et al. 2016; Silber-Varod, V. et al. 2019; Ženka, J. et al. 2021). Custodio, R. et al. (1986) argue that less-educated users may be financially constrained in the use of modern technologies. Educated young us- ers, on the other hand, are increasingly using e-maps and other mobile apps because they are encouraged to do so in school and when using social networks (Lapon, L. et al. 2020). Nevertheless, interactions between gender and education or gender and age may af- fect patterns of individual willingness to use technology more significantly than the isolated effects of these sociodemographic variables. To capture these associations, we discuss briefly two sophisticated theories that are useful for the explanation of indi- vidual technology acceptance. In general terms, our point of departure is the theory of planned behaviour (TPB) (Ajzen, I. 1991). This psychological theory explains an individual intention to perform behaviour by the effects of three variables: at- titude toward a behaviour, subjective norm, and perceived behavioural control (PBC). The attitude toward using technology is de- fined as a respondent’s effective evaluation of the benefits and costs of using the technol- ogy (Morris, M.G. et al. 2005); in our paper it is the perceived usefulness of the smart navigation app. Subjective norm is ‘the per- ceived social pressure to perform or not to perform the behaviour’ (Ajzen, I. 1991, 188): e.g. public/marketing pressure and the rec- ommendations of ‘important people in my life’ to use the smart navigation app in the hospital. PBC is related to the availability of skills, resources, opportunities, and their im- portance in the achievement of the outcome, defined as the ‘perceived ease or difficulty of performing the behaviour’ (Ajzen, I. 1991, 188). In this case, it covers financial costs, necessary knowledge to manage the smart navigation app, own control over the app, and its compatibility with other relevant soft- ware systems and applications. For our re- search, we employ also the unified theory of technology adoption (UTAUT) in the second version (Venkatesh, V. et al. 2012; – see also Venkatesh, V. et al. 2003 for the first version), applying the principles of TPB to explain in- dividual intention (not) to use technology. According to this theory, gender and age moderate the relationship between the ex- planatory variables (performance expectancy, effort expectancy, social influence, facilitat- ing conditions, price value, hedonic moti- vation, and habit) and the intention to use a new technology (Venkatesh, V. et al. 2012).. Gender differences in the importance of facil- itating conditions (training, support) become more distinctive with age (Morris, M.G. et al. 2005). Older women place more emphasis on facilitating conditions and effort expectancy (ease of consumer’s use of the technology) when adopting a new technology, especially in the early stages of adoption. They are more determined to use these technologies when they become aware that these technologies will make their lives easier (Hwang, K.H. et al. 2016). Men, on the other hand, tend to rely less on facilitation conditions and more on performance expectancy (perceived ben- efits associated with the use of technology) when considering the use of a new technolo- gy (Morris, M.G. et al. 2005). This can also be partly explained by the effect of gender roles in society, where men tend to be more task oriented (Lynnot, P.P. and McCandless, J. 2000). For users with less experience of mod- ern technologies, the effect of age and gender on their technology acceptance will be more significant than for more experienced users. Finally, we turn to the geographical aspects of technology acceptance in hospitals, consid- ering the role of gender, age, and education. Ženka, J. et al. Hungarian Geographical Bulletin 70 (2021) (2) 149–161.152 To our best knowledge, no theoretical frame- work linking technology acceptance and spa- tial factors exists. The intention to use a smart navigation app should be higher in large, complex, and uniformly designed buildings. The absence or low visibility of landmarks and other navigational cues (arrows, maps, colour strips) is expected to increase tech- nology acceptance. Women should be more sensitive to these issues for several reasons: a) they exhibit higher levels of uncertainty and spatial anxiety than men (Lawton, C.A. 1994; Mendez-Lopez, M. et al. 2020); b) women perform slightly worse in spatial orientation in the real environment than men (Coluccia, E. and Louse, G. 2004); c) women rely on a ‘route strategy’ in their navigation (Lawton, C.A. 1994; Liao, H. and Dong, W. 2017), following the sequence of landmarks and left-right turns, while men prefer cardinal directions (North–South), global reference points (central square, air- port, sun in the sky) and Euclidean distances (see also Lawton, C.A. et al. 1996, or Ženka, J. et al. 2021). Therefore, men are expected to evalu- ate orientation in the hospital complex as a whole, while women may view some build- ings/parts of the complex as very easy and the others as very difficult to find. They should be more polarized in their evaluations of par- ticular buildings and their perceived way- finding difficulties should be more spatially concentrated at the level of buildings in the hospital complex. Correspondingly, the same differences are expected between younger and older people and between people with elementary, secondary, and tertiary educa- tion. With increasing age, the spatial abilities of elderly people deteriorate (Newman, M.C. and Kaszniak, A.W. 2000; Devlin, A.S. 2014), and elderly people rely more on the route strategy than younger people (Rodgers, M.K. et al. 2012). Less educated people show lower orientation scores (Ulrich, S. et al. 2019) and are likely to follow a route strategy, while empirical evidence for the latter is missing. To sum up, we expect that the acceptance of smart navigation apps will differ among par- ticular hospital pavilions and departments, depending on the perceived difficulties of finding these places. This relationship will be stronger among women and among elderly or less educated people. In the next section we describe the data and methods used to test these relationships. Data and methods To collect data from our respondents (pa- tients and visitors of the hospital), we used structured questionnaires in the paper form. Respondents were asked to evaluate the hospital navigation system and assess their acceptance of a smartphone application for navigation in the Vítkovice Hospital. How- ever, smartphone applications were not highlighted in the questionnaire, as we did not want to influence the decisions of the re- spondents. Focus groups, including doctors, nurses, and other medical and technical staff, were employed to improve the design of the survey (based on the approach of Brown, M. et al. 2016). In the first step, we piloted the survey among 100 respondents to check the ad- equacy, relevance, and comprehensibility of the questions. Based on the results, we adapted or reformulated several questions to improve their comprehensibility. Next, 4,000 questionnaires were distributed to the selected departments of the Vítkovice Hospital (according to the mean weakly number of patients) between 26th June and 3rd July 2019 (a working week). The response rate was 23.2 per cent, as we collected 928 filled questionnaires. Respondents answered 13 questions about their previous experience with wayfinding in the hospital/department, their willingness to use the smartphone ap- plication, or traditional navigational cues. In this paper, we analyse six selected ques- tions (Table 1). Some questions were not an- swered by all respondents or were answered incorrectly. Therefore, the number of valid responses (N) among the various questions varied slightly. 153 Table 1. Variables employed in the statistical analysis Variable name Type Scale Description Wayfinding difficulties dependent ordinal Is it easy to find this department for the new comers?1 = easy; 2 = some difficulties; 3 = difficult. SmartApp acceptance binary Would you use a smartphone for navigation in the hospital? 1 = yes; 2 = no. Smartphone ownership descriptive Do you have a smartphone? 1 = yes; 0 = no. Location explanatory nominal Hospital pavilion, where the respondent filled the questionnaire and evaluated the difficulties with finding a way to the pavilion. Gender binary 0 = male; 1 = female Age ordinal 1 = 0–20; 2 = 21–40; 3 = 41–60; 4 = more than 60 years Education 1 = no or elementary; 2 = secondary; 3 = tertiary Source: Ženka, J. et al. 2021; own survey. Ženka, J. et al. Hungarian Geographical Bulletin 70 (2021) (2) 149–161. To measure the spatial concentration of per- ceived wayfinding difficulties in the hospital complex (based on the answers of respondents to questions concerning the difficulty of find- ing a hospital department), we employed the Herfindahl–Hirschmann index, constructed as: HHI = ∑n s2, i=1 i where si is the share of answers (that the hos- pital department in that pavilion was diffi- cult to find or to be found with some diffi- culty), we collected in pavilion I in the total number of answers in the hospital complex. Apart from basic descriptive statistics, we used two kinds of statistical tests: Crammer V and the binary logistic regression model. In the first step, two dependent variables (SmartApp acceptance and Wayfinding dif- ficulties) were associated with the explana- tory variable Location to test potential geo- graphical effects on technology acceptance and orientation in the hospital complex. To capture the potential effects of gender, age and education, statistical tests were conduct- ed separately for men and women, people in different age groups, and people with el- ementary, secondary and tertiary education. In the next step, we focused on the single most important dependent variable: SmartApp acceptance. A binary logistic regression model has been constructed, with gender, age, educa- tion, and the interaction between gender and age as explanatory variables (see the next sec- tion for details and interpretation). Results Let us start by presenting the selected de- scriptive statistics of the sample (Figure 1). Newcomers and people visiting the hospital after a long time accounted for 44.1 per cent of all respondents. Not surprisingly, the major groups in the sample were middle-aged peo- ple (41–60 years old) and the elderly (over 60 years old) who visit the hospital department regularly or often. These respondents may be Fig. 1. Basic descriptive statistics of the sample. Source: Author’s own survey. Ženka, J. et al. Hungarian Geographical Bulletin 70 (2021) (2) 149–161.154 more reluctant to use smartphone applica- tions and changes in the navigational system of the hospital. While two-thirds of respond- ents answered that wayfinding in the Vítko- vice Hospital is easy, many of them (31.2%) are regular visitors. Correspondingly, this is also one of the reasons why only 35.4 per cent of respondents affirmed that the navigational system in the hospital needed to be improved (14.1% ‘yes’, and 21.3% ‘rather yes’). Despite the high share of regular visitors and the el- derly, 68.9 per cent of respondents are willing to use smartphone navigation in the hospital. The survey (see Ženka, J. et al. 2021 for details) showed that almost 60 per cent of respondents had their smartphones in the hospital (Figure 2). Nevertheless, only 46.8 per cent of them would definitely use their smartphone for navigation in the hospital and another 31.3 per cent would use it only if they got lost. While the ownership of a smartphone is higher among women, men are slightly more willing to use it for naviga- tion. In line with our expectations, the will- ingness to use a smart navigational app was the lowest in the group of 60+ people (below 20%) and highest in the group 21–40 (young respondents below 20 were underrepresent- ed, so their real numbers might be different). More interestingly, the share of patients/ visitors refusing to use a smart navigation- al app is high in all age groups. A positive association between educational level and acceptance of the SmartApp has been found. The most perceived wayfinding difficul- ties differed significantly among particular pavilions and departments of the hospital (Table 2, Figure 3). Almost half of the answers that the hospital department was rather dif- ficult to find were concentrated in pavilion I, the most remote building from the gate- way. Contrary to our expectations, women’s perceived wayfinding difficulties were not more spatially concentrated at the level of buildings compared to men. Gender differ- ences in the perception of particular pavil- ions were rather minor and did not show any systematic pattern. Surprisingly, neither the elderly nor less educated people showed systematically higher perceived wayfinding difficulties than younger and more educated respondents. Perceived wayfinding difficul- ties of people with elementary education were more spatially uneven than in the case of more educated respondents. Most of the abovementioned spatial dif- ferences in perceived way- finding difficulties appear to be statistically significant (Table 3). These findings apply both for men and women, age groups 21–40 and 41–60, and people with secondary and tertiary edu- cation. On the other hand, significant differences in wayfinding among particu- lar pavilions were found neither for people with el- ementary education nor for the age group 60+. The latter can be explained by more frequent visits to the hospital complex by the elderly. However, the ex- pected gender, age, and education differences were Fig. 2. Share of respondents having/willing to use a smartphone for navi- gation in the hospital according to gender, age, and education, in per cent. Source: Author’s own survey. 155 Table 2. Share of the hospital pavilions in the total number of answers*, in per cent Variable name A B D E F H1 H2 H3 I HHI Gender Men 0.0 0.1 6.8 5.1 14.7 0.3 23.8 0.8 48.5 1,984 Women 0.1 0.4 6.9 6.9 19.2 0.8 14.0 1.3 47.4 1,786 Age, years 21–40 – 33.3 75.0 39.1 29.7 50.0 38.7 42.9 35.6 2,437 41–60 7.7 60.0 70.0 25.0 43.5 35.7 24.4 29.4 46.1 1,875 60 + 22.2 25.0 33.3 36.4 28.4 30.0 25.4 22.2 40.4 2,013 Education Elementary 2.4 4.1 2.4 8.9 18.7 0.8 35.0 2.4 25.2 2,322 Secondary 1.8 1.9 5.4 11.0 19.2 3.2 25.4 5.4 26.1 1,894 Tertiary 4.6 1.7 12.1 14.9 14.9 3.4 14.9 2.9 30.5 1,788 *Answers for the whole hospital, that the patients/visitors had difficulty with finding the place. A–I are signs of hospital pavilions, HHI = Herfindahl–Hirschmann index. Source: Authors’ own survey. Ženka, J. et al. Hungarian Geographical Bulletin 70 (2021) (2) 149–161. Fig. 3. Spatial distribution of perceived wayfinding difficulties in the hospital complex. Source: Author’s own survey. not found. Women, elderly and less educated people do not show higher spatial differences in wayfinding than men, younger and more educated respondents. More importantly, substantial spatial differ- ences were found also in the rate of SmartApp acceptance. Significant effects of the variable Location on the intention to use a smartphone Ženka, J. et al. Hungarian Geographical Bulletin 70 (2021) (2) 149–161.156 Table 3. Spatial differences in perceived wayfinding difficulties and the SmartApp acceptance Variable name Location X Wayfinding difficulties SmartApp acceptance N V p N V p Gender Men 384 0.251 < 0.001 382 0.188 0.095 Women 199 0.199 0.020 478 0.200 0.006 Age, years 21–40 185 0.267 0.049 186 0.207 0.461 41–60 341 0.296 < 0.001 342 0.172 0.454 60 + 318 0.136 0.982 317 0.254 0.005 Education Elementary 115 0.271 0.663 117 0.373 0.017 Secondary 588 0.177 0.024 587 0.162 0.062 Tertiary 159 0.424 < 0.001 159 0.276 0.108 Source: Authors’ own survey. for navigation were found in the group of men, women, seniors 60+, and people with elementary education. While not strong, there is a positive association between perceived wayfinding difficulties and SmartApp ac- ceptance at the level of hospital pavilions. Respondents in the difficult-to-find pavilions and departments were generally more keen to use a smartphone for navigation. In the next step, we tested the effects of gender, age, education, and location on SmartApp acceptance (Table 4). We construct- ed many combinations of regression models, including also all other explanatory variables listed in Table 1. These variables, however, did not increase the explanatory power of the model substantially or show a significant effect on the dependent variables. After all pre-tests, we decided to keep the model as simple as possible, including only the vari- ables Gender, Age, Education, Location and interaction of gender, and age (following Venkatesh, V. et al. 2012) as the explanatory variables. The dependent variable was di- chotomous: intention to use a smartphone application for navigation in the hospital: SmartApp acceptance (yes or no). Education showed the strongest positive association with the dependent variable: the intention to use a smart application for navi- gation in the hospital is highest in the group of people with tertiary education. While statistically significant, there were no major differences between men and women: men showed only slightly higher rates of tech- nology acceptance. The model explained a relatively low share of the variability of the dependent variables (16.1%). SmartApp ac- ceptance decreases with rising age; it is sig- nificantly lower in the age group 60+. The spatial variable pavilion showed the second strongest effect, reflecting the high share of the remote pavilion in the total number of perceived wayfinding difficulties. Discussion We found no major gender gap in the inten- tion to use smart apps for navigation in the hospital: in accordance with Hwang, K.H. et al. (2016), or Silber-Varod, V. et al. (2019). This contrasts with the traditional digital di- vide model (Rice, R.E. and Katz, J.E. 2003), which favours males and their higher will- ingness to use new technologies and over- come the associated difficulties with their adoption. While many respondents (especial- ly the elderly ones) may have no experience with the use of navigation smart apps, these 157 Table 4. Correlates of patient and visitor acceptance of a smart navigation app Variables in the Equation Step 1* B S.E. Wald df Sig. Exp(B) N11_Gender(1) N12_Age N12_Age(1) N12_Age(2) N12_Age(3) N13_Education N13_Education(1) N13_Education(2) Age_Gender Age_Gender(1) Location Location(1) Location(2) Location(3) Constant -0.385 – -3.439 -1.859 -0.963 – 0.715 0.017 – -0.096 – 0.137 0.297 0.483 0.637 0.194 – 1.070 0.299 0.194 – 0.032 0.240 – 0.453 – 0.265 0.236 0.229 0.316 3.929 51.456 10.334 38.660 24.702 6.866 4.647 0.005 0.045 0.045 4.812 0.267 1.582 4.472 4.055 1 3 1 1 1 2 1 1 1 1 3 1 1 1 1 0.047 0.000 0.001 0.000 0.000 0.032 0.031 0.942 0.832 0.832 0.186 0.605 0.208 0.034 0.044 0.680 – 0.032 0.156 0.382 – 2.043 1.017 – 0.908 – 1.147 1.346 1.622 1.891 *Variable(s) entered on step 1: N11_Gender, N12_Age, N13_Education, Age-Gender, Section_AGR_reduced. -2 Loglikelihood = 805.946; Cox&Snell R Square = 0.161; Nagelkerke R Square = 0.215. Source: Authors’ own survey. Ženka, J. et al. Hungarian Geographical Bulletin 70 (2021) (2) 149–161. technologies are currently not in the early stage of development. Therefore, perceived ease of use – while generally an important predictor of technology acceptance (Davis, F.D. et al. 1989; Mehra, A. et al. 2020) is prob- ably not a key factor affecting the willingness to use smart navigation apps (Arning, K. et al. 2012). Gender differences in some spatial abilities favouring men (Galea, L.A.M. and Kimura, D. 1993; Lawton, C.A. 2001) and higher spatial anxiety/uncertainty of wom- en in the real environment (Lawton, C.A. et al. 1996; Mendez-Lopez, M. et al. 2020) do not translate into higher demand for smart nav- igation apps among women (Ženka, J. et al. 2021). We, thus, do not confirm the assump- tion that people who are good at navigating themselves are not in an urgent need to use an indoor navigation system and may be less willing to adopt a smart indoor navigation system (Smirnov, M. 2007; Wichmann, J. and Leyer, M. 2021). Age was by far the most important factor of technology acceptance, confirming the findings of Rice, R.E. and Katz, J.E. (2003). Technology acceptance decreases with in- creasing age (Olson, K.E. et al. 2011), which was clearly supported also for the case of smart navigation apps in the hospital. Statistical tests of the interactions between age and gender and education showed non- significant results. This is probably caused by the limited number of answers, when disag- gregated according to the age/education cat- egory and gender, despite using the broadest possible age categories (less than 40 and 40+). Despite nonsignificant results in the re- gression models, the interaction between gender and age has effects on the dependent variable: younger males are the most will- ing to use smart navigation apps, 40+ women the least. Interestingly, gender differences between the younger respondents are much higher than between the 40+ respondents. Therefore, our results contrast sharply with the conclusions of Morris, M.G. et al. (2005, 79), who stated that “…gender differences decline dramatically among the younger cohort of workers and a more unisex pat- tern emerges”. We agree with the statement “mobile service adoption and usage may vary significantly among young users, thus, treating them as a homogeneous group is not appropriate” (Rao, S. and Troshani, I. 2007, 68). Smaller gender differences between the 40+ respondents – more frequent visi- tors to the hospital – may be caused by their higher familiarity with the hospital complex Ženka, J. et al. Hungarian Geographical Bulletin 70 (2021) (2) 149–161.158 and perhaps also by generally higher self- confidence in spatial abilities compared to younger people (De Beni, R. et al. 2006). In addition, elderly men tend to rely more on established habits, so their technology ac- ceptance is lower compared to younger men (Venkatesh, V. et al. 2012). We found positive effects of education on the SmartApp acceptance in the hospi- tal. In this case, the formal educational level should be viewed as a proxy of general cog- nitive abilities (see Elias, M.F. et al. 1997, or Le Carret, N. et al. 2003), spatial abilities (Proust-Lima, C. et al. 2008; Ulrich, S. et al. 2019), and an intention to gain new (techni- cal) skills. Arning, K. et al. (2012) argue that smart navigation technology acceptance is affected by the level of individual technical self-efficacy (TSE), defined as the confidence in one’s own ability to solve technical prob- lems. Wichmann, J. and Leyer, M. (2021), on the other hand, found no significant effects of perceived behavioural control, operation- alized as the individual perception of own control and the ability to use applications for indoor navigation. Even more importantly, these authors also documented the nega- tive effects of individual spatial abilities on the intention of visitors/patients to adopt an indoor navigation and localization system in the hospital. Our results, however, sup- port the assumption that cognitive abilities, represented in our model of formal educa- tion, increase the acceptance of smart naviga- tion technologies. This effect is stronger for younger respondents. We did not find empirical evidence for our initial assumption that perceived wayfind- ing difficulties will be more spatially con- centrated in the case of women, the elderly, and people with elementary education com- pared to men, younger and more educated respondents. Principal differences in spatial abilities and orientation strategies (Lawton, C.A. 1994) between men (cardinal directions) and women (landmarks), younger and old- er (less and more educated) people do not result in significant variations in perceived wayfinding difficulties among the pavilions in the hospital complex. On the other hand, significant spatial differences were found not only in perceived wayfinding difficulties, but also in the intention to use a smart navigation application. The latter finding corresponds with the conclusion of Arning, K. et al. (2012) that disorientation is the most powerful pre- dictor of navigation device acceptance. Conclusions The main aim of this paper was to deter- mine if hospital patients/visitors intend to use a mobile navigation application. To answer these questions, we conducted a questionnaire survey among 928 re- spondents in the Vítkovice Hospital in Ostrava, Czechia. Despite the high share of elderly and regular visitors, almost 70 per cent of respondents answered that they accept a smart navigation app in the hospital. We tested the effects of age, gender, education, and location in the hospital com- plex on individual acceptance of the smart navigation app. Age was the key factor of technology acceptance, which was found to be the lowest in the group of 60+ respond- ents. Education showed a strong and posi- tive effect on SmartApp acceptance, which was highest among respondents with tertiary education. No major gender gap in technology accep- tance was observed, which supports the pre- vious findings of Ženka, J. et al. (2021). The assumptions of the unified theory of tech- nological acceptance that gender differences are more pronounced in older age groups were, thus, not supported by empirical ev- idence. On the contrary, respondents 40+ were more homogeneous in their answers than their younger counterparts, while men below 40 were much more willing to use the smart navigation app than women in the same age group. The higher homogeneity of the 40+ age group might be explained by the familiarity of many (especially elderly) respondents with the hospital complex be- 159Ženka, J. et al. Hungarian Geographical Bulletin 70 (2021) (2) 149–161. cause they are relatively frequent visitors of the Vítkovice Hospital complex. Finally, the location was another major factor of perceived wayfinding difficulties and SmartApp acceptance for navigation in the hospital. We confirmed the finding of Arning, K. et al. (2012) that disorientation is a strong determinant of navigation device acceptance. On the other hand, we did not get sufficient empirical evidence for our assumption that general gender/age/edu- cational differences in spatial abilities and navigational strategies translate into the spe- cific spatial pattern of perceived wayfinding difficulties and technology acceptance in the hospital complex. There were no major and systematic gender/age/educational differ- ences among particular hospital locations (pavilions, departments). Surprisingly, in the group of people 60+ relatively small vari- ations in perceived wayfinding difficulties among the hospital pavilion/departments were found. This could be explained by rel- atively frequent visits and, thus, a high famil- iarity with the hospital complex. Future research on technology accep- tance should develop a further link between the spatial factors and the intention to use a smart navigation device. Empirical evi- dence shows that disorientation in complex buildings and compounds increases people’s acceptance of smart navigational devices. However, little is known about the mecha- nisms and interactions with other nonspatial factors, such as gender, age, and individual cognitive abilities. If we turn to some practical implications of our research, the findings suggest the importance of efficient navigational cues lo- cated near the entrance to the hospital com- plex. The perceived wayfinding difficulties of respondents were spatially concentrated mostly in the most remote pavilion in the hospital complex, which is obscured by oth- er buildings. Therefore, differences among other pavilions/departments in landmarks and other navigational cues were of minor importance. 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