Urban Park Users’ Expectations for Smart Park Applications: An Exploratory Sequential Mixed-Methods Study

Abstract

As smart city approaches increasingly extend to public open spaces, understanding what urban park users expect from digital park applications has become a critical issue for sustainable urban management. This study examines park users’ expectations of smart park applications through an exploratory sequential mixed-methods design. In the first phase (Study I), semi-structured interviews were conducted with 32 purposively selected participants representing four user groups—parents with children, sport-oriented users, older adults, and general adults—in urban parks in Eskişehir, Türkiye. Thematic analysis identified eight user expectation themes, which were subsequently operationalized into a seven-factor quantitative structure. In the second phase (Study II), a seven-factor scale derived from the qualitative findings was administered to 374 participants. Confirmatory factor analysis demonstrated a good overall model fit, and the scale exhibited strong reliability and convergent validity. One-way ANOVA revealed significant between-group differences in six of the seven dimensions, with sport-oriented users consistently reporting higher expectations than older adults. Safety and Activity Diversity was the only dimension showing no significant group differences, indicating a universal expectation across all user profiles. Multiple regression analysis showed that Independent Functionality was the strongest predictor of use intention, followed by Centrality and Communal Function and Safety. Integration of both phases through a joint display revealed that expectations are both universal and user profile-specific, underscoring the need for user-sensitive smart park design. By linking digital park services to user expectations, well-being-oriented park design, and the sustainable use of urban green spaces, these findings contribute to the literatures on smart cities, urban green spaces, and well-being, providing an empirically informed and user-centred framework for digital park applications that may inform efforts toward healthier, more inclusive, and more sustainable urban public spaces in line with SDGs 3 and 11.

1. Introduction

Public open spaces are key components of contemporary urban life because they offer opportunities for recreation, relaxation, socializing, and contact with nature [1,2]. Among these spaces, urban parks are particularly important due to their relatively manageable structure and their characteristics that can be directly linked to health outcomes [3]. Through opportunities for exercise, natural landscape features, and microclimatic comfort, parks contribute to the physical and mental restoration of urban residents and are regarded not merely as recreational venues but also as environmental resources that support social well-being and quality of life [4,5,6].

The relationship between urban parks and well-being is supported by a strong theoretical and empirical literature [7,8]. Spending time in nature is meaningfully associated with health and well-being, and exposure to green space has been shown to have positive effects on a range of physical and psychological health indicators [8,9]. This relationship is explained within the frameworks of Attention Restoration Theory, which argues that natural environments restore directed attention, and Stress Recovery Theory, which posits that contact with nature reduces stress responses [7,10,11]. The benefits parks provide, however, depend not only on the existence of these spaces but also on attributes such as accessibility, safety, attractiveness, environmental comfort, and use quality [12,13]. Indeed, expectations regarding park experience are known to differ across user groups: sport-oriented users tend to prioritize infrastructure and facility quality [13,14], older adults tend to prioritize safety and accessibility [15,16], parents with children tend to prioritize playground safety [17], and general adult users tend to value natural landscape and tranquility [10,18].

Urban parks play a strategic role not only in individual well-being but also in urban sustainability. Within the United Nations Sustainable Development Goals, SDGs 3 and 11 in particular highlight the importance of urban green spaces for healthy, inclusive, and sustainable cities [19]. As nature-based solutions, urban parks have important functions in improving environmental quality, supporting social cohesion, and enhancing quality of life [20,21]. Urban parks should therefore be treated as multifunctional public spaces that bring together the environmental, social, and health dimensions of sustainable cities. Within this framing, smart park applications are not merely a technological add-on but a mechanism through which urban green spaces can more effectively deliver well-being benefits to diverse user groups, thereby reinforcing the contribution of public open spaces to sustainable urban development.

In recent years, digitalization and the smart city approach have been added to these debates. The rapid development of information and communication technologies has led cities to be reconceptualized as data-driven, interactive, and user-centred systems [22,23,24]. Urban parks have also been shaped by this transformation; through tools such as mobile applications, QR codes, location-based services, sensor-based IoT systems, and augmented-reality applications, they are increasingly becoming digitally supported public spaces [25,26,27]. The aim of this transformation is not merely to make parks technologically “smart” but to turn them into more accessible, more effective, more interactive, and more sustainable public spaces [28].

In practice, such applications may include real-time occupancy information for park areas, digital wayfinding, event announcements, facility reservation systems, environmental information, emergency guidance, and feedback mechanisms that allow users to report maintenance or safety problems.

However, the digitalisation of urban parks is not an unequivocally positive development. While smart park applications may improve wayfinding, safety, participation, and facility management, they may also raise concerns related to surveillance, data privacy, technological dependency, and unequal digital access. The integration of sensors, location-based services, and user-generated behavioural data into public open spaces may transform parks from freely experienced civic spaces into digitally monitored environments. This issue is particularly important for older adults, users with low digital literacy, persons with disabilities, and groups with limited access to mobile technologies. Therefore, a user-centred examination of smart park expectations should not only ask which digital services users desire, but also which forms of digitalisation they may find acceptable, inclusive, and trustworthy.

However, the integration of digital solutions into urban parks is not solely a matter of technological capacity. The success and sustainability of these services depend on the extent to which they align with users’ expectations, needs, and experiences [29,30]. According to the Technology Acceptance Model, perceived usefulness and perceived ease of use are decisive factors in users’ adoption of digital systems [31]. Broader acceptance models also indicate that variables such as trust, privacy, and experience quality shape this process [32,33]. Given the voluntary and experiential nature of park use, the success of digital park services depends not only on functionality but also on ease of use, trust, and meaningfulness [34,35]. Yet much of the existing literature focuses on technical systems, management infrastructure, or specific application examples; studies that examine what users expect from these digital services and how these expectations vary across user groups remain limited [25].

These theoretical perspectives are complementary in the smart park context. The Technology Acceptance Model helps explain why users may adopt digital park services when they perceive them as useful and easy to use, whereas broader acceptance models highlight the role of trust, enjoyment, habit, and experience quality in voluntary leisure settings. Together, these perspectives frame smart park expectations as both functional judgments and experiential evaluations.

Previous quantitative studies on urban green space and technology acceptance have shown that perceived usefulness, information quality, accessibility, and perceived safety are associated with satisfaction, intention to use, and continued engagement with public or digital services.

This gap is even more pronounced in the Turkish context. Although smart city and digital municipal applications have visibly increased, empirical research that systematically examines urban park users’ expectations of smart park applications is rather limited. Yet services such as wayfinding, safety, access to physical activity opportunities, facility use, event information, accessibility support, and personalized experience are factors that can determine both user satisfaction and the extent to which parks are used effectively and sustainably [13,36]. Scientifically establishing user expectations is therefore critical for the user-centred design of digital park services.

Addressing this research gap, the present study has a three-layered aim: (i) to identify urban park users’ expectations of smart park applications, (ii) to examine how these expectations differ across user groups, and (iii) to discuss, from a user-centred perspective, the potential contributions of digital park services to the sustainable use of urban parks, user experience, and well-being. To this end, the study was conducted within an exploratory sequential mixed-methods design [37]: in the first phase, the expectations and needs of different user groups were identified qualitatively, and in the second phase, the structure derived from these findings was examined in a larger sample. The study thereby aims to fill an important gap at the intersection of the urban green spaces, well-being, and sustainable cities literatures, and to contribute to the design of digital park services on the basis of user expectations.

2. Materials and Methods

2.1. Research Design

This study employed an exploratory sequential mixed-methods design [37] to identify urban park users’ expectations of smart park applications and to examine the structure of these expectations in a larger sample. In the first phase, users’ experiences, needs, and expectations were explored qualitatively; in the second phase, a construct developed from these findings was tested quantitatively. The design was chosen because no purpose-built instrument exists for capturing the smart park expectations of distinct user groups (general adults, sport-oriented users, parents with children, and older adults), making a qualitative exploratory phase necessary before quantitative validation. The exploratory sequential structure allowed for the themes obtained in the qualitative phase to directly shape the measurement instrument of the second phase, linking the two phases and enabling depth and breadth to be addressed concurrently.

2.2. Research Phases

The research consisted of two principal phases. In the first (qualitative) phase, semi-structured interviews with different park user groups were analyzed using thematic analysis. In the second (quantitative) phase, a questionnaire developed from the qualitative findings was administered to a larger sample and analyzed using descriptive and inferential procedures. The two phases were linked through a building phase in which qualitative themes and codes were operationalized into questionnaire items [37].

Details of Study I (qualitative phase) and Study II (quantitative phase) are presented under separate headings below.

2.3. Study I (Qualitative Phase)

2.3.1. Participants and Sampling

In the qualitative phase, maximum variation sampling—one of the purposive sampling strategies—was used to capture the different characteristics and purposes of urban park users. This approach was chosen because urban park users do not constitute a homogeneous group, and expectations of smart park applications were assumed to vary across user profiles.

Within the qualitative study, participants were selected to represent different user groups, including general adult users, physical activity- or sport-oriented users, older adults, and parents with children. Interviews were conducted with a total of 32 participants representing these four user groups, with eight participants per group who had been using urban parks regularly for at least six months; gender balance was sought within each group as far as possible. Active use of the relevant urban parks and voluntary participation were the main inclusion criteria [38,39].

Participants were recruited on site in selected urban parks in Eskişehir. Members of the research team approached potential participants at different times of the day and on both weekdays and weekends in order to reach users with different park use patterns. Individuals who met the inclusion criteria—regular park use for at least six months, voluntary participation, and belonging to one of the predefined user groups—were informed about the purpose of the study and invited to participate. Recruitment continued until eight participants had been reached in each user group and no new themes emerged in the interviews.

The target population of the qualitative phase consisted of adult urban park users in Eskişehir who regularly used public parks and represented one of the predefined user profiles relevant to the study aims. The sample size of 32 participants was determined through purposive maximum variation sampling and the principle of data saturation. Eight participants were recruited from each user group to ensure balanced representation across parents with children, sport-oriented users, older adults, and general adult users. Data collection was terminated when additional interviews no longer generated new themes within the predefined user groups.

The participant groups were defined as follows:

(1)

General adult users (Adults/A): adults who visit the park for recreation and relaxation and do not focus on a specific sport or special interest;

(2)

Sport-oriented users (Sportive users/S.U.): individuals who visit the park primarily for regular physical activity, sport, or exercise;

(3)

Parents with children (Parents/P): individuals who primarily visit the park with their children and require child-oriented spaces;

(4)

Older adults (Elderly/E): users aged 60 and over who visit the park regularly. Detailed demographic information for the participants is presented in

Table 1. Demographic distribution of qualitative-phase participants.

User Group	Female	Male	Age Range	Main Park Use Purposes	Frequency of Park Visitation
Parents (P, n = 8)	6	2	36–43	Using playgrounds with children, cycling/skating with children, spending time in green areas	From weekends to several times a week; some reported daily use during summer
Sportive users (S.U., n = 8)	4	4	24–51	Exercise, walking, sports participation, socializing	From rarely to very frequent use; commonly 1–4 times per week
Older adults (E, n = 8)	6	2	65–76	Walking, socializing, spending time with family/grandchildren, resting, hosting guests	From event-based/seasonal use to daily use
Adult users (A, n = 8)	4	4	22–55	Walking, resting, spending time with friends/family, attending events	From rare/seasonal use to 1–3 times per week
Total	20	12	22–76	—	—

Note. P = parents, S.U. = sportive users, E = older adults, A = adult users.

.

These four user groups were defined before data collection and served as purposive sampling criteria rather than post hoc categories. Participants were therefore recruited to represent these predefined user profiles.

2.3.2. Qualitative Data Collection Instrument and Procedure

Data in the qualitative phase were collected using a semi-structured interview guide developed by the researchers. The guide was informed by the relevant literature and reviewed by two field experts to strengthen its scope, clarity, and feasibility; it was then pilot-tested with two park users. Following expert feedback and the pilot, the guide was revised and finalized.

The interview guide consisted of an opening section, eight thematic dimensions, and a closing question. In the opening section, participants were asked about their general park use habits, the frequency and purposes of urban park visits, the features that drew their attention in current activities and services, the aspects they found inadequate, and their general expectations regarding digital services that could facilitate park use. This section was designed to obtain a broad understanding of participants’ park experiences and to provide context for the subsequent thematic questions.

In the second section, participants’ expectations of smart park applications were explored across eight park experience dimensions: (1) Accessibility and Flow, (2) Ecological Quality and Flexibility, (3) Comfort and Facility Adequacy, (4) Centrality and Communal Function, (5) Safety and Activity Diversity, (6) Facility Quality and Locational Suitability, (7) Independent Functionality, and (8) Aesthetics and Integration. These dimensions were designed to systematically examine user expectations across in-park mobility, use of natural areas, comfort perception, participation in social activities, safety, reservation options, independent use experience, and interaction with digital content.

Questions for each dimension were tailored to the different park use patterns of the user groups. Questions were organized for four user groups: A = general adult users, P = parents with children, S.U. = sport-oriented users, and E = older adults. Some dimensions were assessed with common questions across all groups, whereas others used group-specific wording. For example, in the Accessibility and Flow dimension, expectations were explored through crowded zones and wayfinding for general adults, playground crowding for parents, sport area occupancy for sport-oriented users, and crowding of physical access areas for older adults. Similarly, in the Facility Quality and Locational Suitability dimension, digital occupancy and reservation features were discussed in relation to facilities prioritized by each group, such as cafeterias, parking, children’s playgrounds, fitness areas, or walking paths. In dimensions with common questions, participants were asked about real-time occupancy, feedback and complaint mechanisms, event announcements, free reservation, safe route suggestions, and emergency guidance. This structure allowed both shared and group-specific aspects of expectations regarding smart park applications to be examined.

In the closing section, participants were asked a general question about how a digital system could be designed to facilitate access to activities and services in parks. This question elicited integrative reflections on previous answers, additional suggestions, and prioritized expectations.

Interviews were conducted face-to-face in urban parks in Eskişehir, Turkey. Before each interview, participants were informed about the purpose of the study, confidentiality principles, and the voluntary nature of participation, and written informed consent was obtained. Interviews were audio-recorded with participants’ permission and lasted approximately 35–45 min each. The structure of this interview guide is summarized in Supplementary Table S1.

2.3.3. Qualitative Data Analysis

Qualitative data were analyzed using Braun and Clarke’s six-phase thematic analysis approach. The process consisted of (1) familiarization with the data, (2) generation of initial codes, (3) searching for themes, (4) reviewing themes, (5) defining and naming themes, and (6) producing the report. Interview recordings were first transcribed verbatim and then read repeatedly by the researchers to ensure familiarity with the data. Meaningful expressions in the transcripts were systematically coded. Similar codes were grouped to form candidate themes, which were reviewed for internal consistency and distinctiveness.

Coding was carried out independently by two researchers, and intercoder agreement was assessed using Cohen’s kappa coefficient (κ = 0.81) [40]. The obtained value indicates a high level of agreement according to Landis and Koch [41]. Disagreements were resolved through discussion among the researchers, and consensus was reached on a final theme structure. NVivo 14 qualitative data analysis software was used throughout the analysis. To enhance trustworthiness, thick description of the context, peer debriefing, reflexive positioning, and member checking were employed [42].

2.4. Study II (Quantitative Phase)

In the quantitative phase, the questionnaire derived from the qualitative findings and designed to measure expectations of smart park applications was administered to a larger sample. The main aim of this phase was to examine the expectation patterns identified in the qualitative phase comparatively and in a generalizable way across user groups.

2.4.1. Building Phase

The two phases were connected through a sequential building logic. The qualitative phase did not function merely as a preliminary descriptive stage; rather, it generated the conceptual categories, codes, and user group-specific expectation patterns that informed the construction of the quantitative instrument. The themes identified in Study I were transformed into item content, refined through expert review and pilot testing, and then examined in Study II through confirmatory factor analysis and group comparisons. In this way, Study II was designed to test and extend the expectation structure that emerged from Study I.

In the exploratory sequential mixed-methods design, the process in which the quantitative measurement instrument is developed from qualitative findings is referred to as the building phase [37]. In this study, the eight dimensions identified through qualitative analysis and their associated sub-codes were operationalized into questionnaire items for the quantitative phase. For each dimension, an item pool was generated based on salient participant statements and meaning patterns, ensuring that the content of the quantitative scale was grounded in the qualitative findings through a data-driven process.

During the building phase, two of the eight qualitative dimensions were consolidated under conceptually related constructs on the basis of theoretical proximity, code-level overlap, expert review, pilot feedback, and subsequent empirical testing in the quantitative phase. First, Ecological Quality and Flexibility was consolidated with Accessibility and Flow, because the codes underlying these dimensions—such as open area information, air quality, nature-based information, area information, and crowding/wayfinding—jointly reflected users’ need for advance, information-based orientation toward the spatial and environmental conditions of the park. Second, Aesthetics and Integration was consolidated with Facility Quality and Locational Suitability, because codes such as perceived digital attractiveness, ease of access to information, 3D experience, time saving, and convenience regarding facilities and resting areas collectively represented the experiential–functional quality of facility use. In users’ accounts, aesthetic and practical evaluations of digital park services were closely intertwined rather than clearly separable. These consolidation decisions were reviewed by two field experts and further supported during the pilot test (n = 25), in which items from the merged dimensions were interpreted by respondents as belonging to the same broader expectation domains.

In addition, a Use Intention dimension reflecting behavioral intention toward smart park applications was added to the scale as a separate construct, because behavioral intention is theoretically and operationally distinct from the antecedent expectation constructs and is a central dependent variable in the Technology Acceptance Model and its extensions [31,32,33]. Following these conceptual and empirical procedures, the eight qualitative-phase dimensions were consolidated into six expectation dimensions; with the addition of Use Intention as a behavioral construct, the final quantitative structure comprised seven dimensions and 21 items.

2.4.2. Participants and Sample

In the quantitative phase, a mixed sampling strategy combining convenience and snowball sampling was used to examine urban park users’ expectations of smart park applications in a larger sample. The questionnaire was administered both face-to-face and online. In the face-to-face procedure, urban park visitors in Eskişehir, Turkey, were contacted directly; in the online procedure, the questionnaire was distributed via social media and institutional networks. Valid responses were obtained from a total of 374 participants.

The target population of the quantitative phase consisted of urban park users in Eskişehir who were potential users of smart park applications. Because the study aimed to test a seven-factor structure with 21 items and to compare expectation scores across user groups, the sample size decision was based on recommendations for factor analysis and multivariate analyses. The final sample of 374 participants exceeded the commonly recommended participant-to-item ratio of 10:1 [43] and provided approximately 17.8 participants per item, which was considered adequate for confirmatory factor analysis, group comparisons, and regression analysis [43].

The demographic and park use characteristics of the quantitative-phase participants are presented in detail in the Section 3.

2.4.3. Quantitative Data Collection Instrument and Procedure

The questionnaire used in the quantitative phase consisted of two sections. The first section included items on participants’ demographic and park use characteristics, including gender, age, user group, park visit purpose, and frequency of visits. The second section comprised 21 items derived from the qualitative findings and designed to measure expectations of smart park applications, organized under seven dimensions: Accessibility and Flow (3 items), Comfort and Facility Adequacy (3 items), Centrality and Communal Function (3 items), Safety and Activity Diversity (3 items), Facility Quality and Locational Suitability (3 items), Independent Functionality (3 items), and Use Intention (3 items). Responses were recorded on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). The item pool was reviewed by two field experts and pilot-tested with 25 park users. Following expert feedback and the pilot, several items were rephrased and the questionnaire was finalized.

2.4.4. Quantitative Data Analysis

Quantitative data were first screened for missing values, outliers, and distributional properties; descriptive statistics for the demographic and study variables were then computed. Confirmatory factor analysis (CFA) was conducted to test the structure of the seven-dimensional measurement model derived from the qualitative findings. Model fit was evaluated using χ²/df, GFI, NFI, IFI, TLI, CFI, RMR, and RMSEA indices, with AMOS used for the CFA. Internal consistency was examined with Cronbach’s alpha; convergent validity and composite reliability were assessed using AVE and CR.

After the dimensional structure was confirmed, composite scores were computed for each dimension and means and standard deviations were calculated. The use of parametric tests was considered appropriate because group comparisons were conducted on multi-item composite dimension scores rather than on single Likert-type items. Each dimension score was calculated as the mean of three items, which provided scale-level composite variables for the subsequent analyses. This approach is consistent with methodological guidance demonstrating that parametric tests such as ANOVA and regression are robust to the ordinal–interval distinction when applied to multi-item composite scores, particularly when distributional properties fall within conventional bounds [44,45]. Before conducting group comparisons, distributional properties of the composite scores were examined using skewness and kurtosis values. Therefore, independent-samples t-tests, one-way ANOVA, and regression analysis were used for the composite dimension scores rather than for individual ordinal items. Independent samples t-tests were additionally conducted to compare expectation scores between female and male participants for each composite dimension. One-way analysis of variance (ANOVA) was used to examine whether expectation dimensions differed by user category; where significant differences emerged, pairwise comparisons were performed using Tukey HSD post hoc tests. Before conducting the multiple regression analysis, multicollinearity among the predictor dimensions was examined. Although some correlations were relatively high, they remained within acceptable limits and did not indicate a serious multicollinearity problem. Finally, multiple linear regression analysis was conducted to determine the predictive effects of the expectation dimensions on use intention. In this analysis, use intention was the dependent variable, and accessibility and flow, comfort and facility adequacy, centrality and communal function, safety and activity diversity, facility quality and locational suitability, and independent functionality were entered as predictors. All analyses were performed with IBM SPSS Statistics 26.0 and IBM SPSS AMOS 26.0, with the level of statistical significance set at p < 0.05.

To improve readability of the main text, tables presenting detailed methodological and analytical support—including the interview guide structure, the per-participant qualitative profile, dimension-level distributional statistics, internal consistency coefficients, dimension-level descriptive statistics, post hoc pairwise comparisons, and gender-based t-test results—have been relocated to Supplementary Materials (Tables S1–S7). All numerical findings essential for interpreting the main analyses are reported inline in the relevant Results sections, with the corresponding Supplementary Table cross-referenced for readers seeking full detail.

2.5. Research Ethics

The research was conducted with the approval of the Ethics Committee of Eskişehir Technical University (Approval No. E-87914409-050.04-127877). In both phases, participants were clearly informed about the purpose of the study, the voluntary nature of participation, confidentiality safeguards, and their right to withdraw at any stage. Written informed consent was obtained from all participants. Audio recordings and transcripts produced in the qualitative phase were stored in secure digital environments accessible only to the research team; in the quantitative phase, all responses were collected anonymously.

3. Results

3.1. Qualitative Findings (Study I)

A total of 32 urban park users in Eskişehir, Turkey, participated in the qualitative phase (Study 1). The participants represented four user groups: parents with children (P, n = 8), sportive users (S.U., n = 8), older adults (E, n = 8), and general adult users (A, n = 8). Their ages ranged from 22 to 76; 62.5% (n = 20) were female and 37.5% (n = 12) were male. Detailed demographic and park use information for the 32 qualitative-phase participants is presented in Supplementary Table S2.

Thematic analysis of the qualitative interviews, conducted within the eight-dimension interview framework, identified user expectation patterns corresponding to eight park experience dimensions: (1) Accessibility and Flow, (2) Ecological Quality and Flexibility, (3) Comfort and Facility Adequacy, (4) Centrality and Communal Function, (5) Safety and Activity Diversity, (6) Facility Quality and Locational Suitability, (7) Independent Functionality, and (8) Aesthetics and Integration. The codes associated with each dimension, their distribution across user groups, and illustrative quotations are presented below.

3.1.1. Accessibility and Flow

Accessibility and Flow occupies a central position among the expectations articulated by participants, reflecting a wish to manage in-park mobility, area choice, and time use in a more controlled way through digital support. Nine codes were identified under this dimension: digital crowding information, area information, information generating revisit intention, short-distance circulation, ability to choose alternative routes, fit with daily routines, family-oriented sense of safety, occupancy rate, and lack of need in familiar parks. Crowding information was raised mainly by parents and adult users; area information was particularly prominent among sportive users; short-distance circulation was specific to older adults; and family-oriented sense of safety was specific to parents.

Digital crowding information was the most frequently emphasized code, especially among parents and adult users. Participants framed advance crowding information as a functional planning tool for park selection, important for choosing quieter areas, using time efficiently, and increasing the benefit gained from the park.

“It would have a very positive effect. As an adult, I sometimes go to a park hoping to be alone, but if I encounter a crowd my mood drops and I can’t really benefit from the park. With such an application I could benefit much more.”

(A-8, male, 24)

Area information was emphasized particularly by sportive users. These participants stated that information on the occupancy and use of different areas within the park could increase their motivation to visit and make their use of the park more efficient. In parallel, the fit-with-daily-routines code shows that sportive users consider park choice alongside their daily plans and training schedules.

“This information would be a direct deciding factor for me. Especially in high-intensity training, not having to stop while running is very important. I would plan my day and routine according to this app.”

(S.U.-1, female, 30)

The code information generating revisit intention, expressed by parents and adult users, indicates that elements such as event calendars, wayfinding, and area information delivered through a digital application can be effective in encouraging repeat visits.

“I could see whether the park is suitable for my children’s age. Following the events organized in these parks would guide my park choice.”

(P-6, female, 40)

Short-distance circulation was emphasized by older adults, while ability to choose alternative routes was raised by adult users. These findings show that digital wayfinding and mapping support performs different functions for different user groups. For older adults this support primarily reduces physical effort and facilitates access, whereas for adult users it is seen as a tool for experiencing the park in a more planned and efficient way.

“Having a map in our hands, with routes that let us reach nicer areas at short distances at our age, would be wonderful. It is a real need.”

(E-4, female, 67)

For parents, the family-oriented sense of safety code links Accessibility and Flow not only to wayfinding and ease of circulation but also to perceived safety.

Meanwhile, the lack of need in familiar parks code raised by sportive users suggests that digital support may be more meaningful for unfamiliar or first-visit parks (e.g., S.U.-4, female, 25).

Overall, the findings on Accessibility and Flow indicate that users perceive digital support as a functional and desirable element in in-park mobility, wayfinding, crowding information, and planning processes. This pattern indicates that the same digital function may serve different experiential purposes across user groups: for parents, crowding information is closely linked to child safety and visit planning; for sport-oriented users, it supports routine, rhythm, and efficient training; and for older adults, it is connected to physical ease and accessible movement.

3.1.2. Ecological Quality and Flexibility

Ecological Quality and Flexibility holds an important place in participants’ accounts: users make sense of the park experience not only through physical use but also through learning about nature, evaluating environmental conditions in advance, and engaging with open-air experience more consciously. Seven codes were identified under this dimension: nature-based information, awareness of announcements and notifications, open area comfort level, open area information, air quality, perception of cleanliness and order, and scenery experience. Nature-based information was the only code expressed across all user groups. Open area comfort level was raised particularly by sportive users, open area information mainly among parents and older adults, and scenery experience specifically by adult users.

The expectation of nature-based information emerged as the most prominent code in this dimension, especially through parents’ emphasis on children’s process of getting to know nature.

“I would prefer it quite often, because our children grow up in an industrialized era and have limited knowledge of life in natural settings. Information on tree species, varieties, and flowering seasons delivered through a digital platform would matter a lot to parents.”

(P-6, female, 40)

The code awareness of announcements and notifications stood out as a shared expectation among parents, sportive users, and adult users. Participants stated that being able to receive timely information about park conditions, maintenance work, or changes in the area could affect their park choice.

“When the application provides information about parks, it shapes my choice. Knowing about renovations or roadworks would help me.”

(S.U.-3, female, 27)

For sportive users, prior information on environmental conditions matters not only for comfort but also for planned use and satisfaction with the activity.

The open area information code raised by parents and older adults indicates that digital access to content about the natural and physical features of parks could positively transform the park experience.

“For example, a QR code could be placed on each tree, and people could scan it through the application to access detailed information. This would let children use digital tools for a useful purpose, and visitors would also gain knowledge.”

(P-8, female, 39)

The scenery experience code expressed by adult users shows that digital content about the visual qualities of parks could increase visit intention.

Overall, the findings on Ecological Quality and Flexibility indicate that users wish to enrich their park experience through information, environmental awareness, and a more conscious relationship with natural areas.

3.1.3. Comfort and Facility Adequacy

Comfort and Facility Adequacy emerges as one of the central expected functions, with users evaluating the park experience not only through ease of physical use but also through the visibility of service quality, the operation of feedback processes, and more planned management of facility use. Six codes were identified under this dimension: planning according to area occupancy, increasing comfort through perceived transparency, intention to choose parks based on feedback display, contribution of map support to park use, reduced preference when complaints and suggestions are ignored, and fostering a sense of voice. Planning according to area occupancy was the most frequently expressed code across all user groups. Both intention to choose parks based on feedback display and increasing comfort through perceived transparency were shared across groups.

Across all user groups, participants linked planning according to area occupancy to making the visit more efficient—advance access to information on resting areas, seating points, and overall crowding.

“If the application shows occupancy rates, we can choose calmer spots with less crowding. This feature lets us spend our time efficiently rather than leaving the park immediately.”

(P-8, female, 39)

The code intention to choose parks based on feedback display was raised by all user groups, indicating that digital applications making user evaluations visible could shape park choice.

“It would have a positive effect, because the comments of those who went before me would influence whether I visit the park. If there are reviews saying the park is unsafe or describing bad experiences, I would not go. We use maps on the road for similar reasons; reviews like ‘this route is more suitable’ end up shaping my preference.”

(S.U.-3, female, 27)

The increasing comfort through perceived transparency code, shared by all groups, is linked to users’ ability to communicate their views and complaints digitally and to the visibility of this process.

The contribution of map support to park use code was raised especially by older adults and sportive users.

More limited codes—reduced preference when complaints and suggestions are ignored and fostering a sense of voice—show that digital applications are evaluated by users not only in terms of utility, but also in terms of functionality and freedom of expression.

“If you push for a response and stay engaged, your concern will eventually reach someone. But what really matters is the response that follows. For example, if I report that the toilets are dirty and nothing changes afterwards, the feedback would not mean much to me.”

(S.U.-8, female, 26)

Overall, the findings on Comfort and Facility Adequacy indicate that users perceive digital park applications as tools that make service quality visible, ease crowd management, and operationalize feedback processes.

3.1.4. Centrality and Communal Function

Centrality and Communal Function brings the social and organizational dimensions of the park experience to the fore: participants treat parks not only as physical use areas but also as public spaces where activities are planned, made visible, and where users feel socially included. Nine codes were identified under this dimension: enabling planning and organization, event visibility, awareness of and engagement with events, reservation system, perceived subjective value, increased event participation, gain of social space, ensuring controlled and frequent participation, and increased interaction. Enabling planning and organization was emphasized strongly by parents; event visibility and engagement were raised mainly by older adults and adult users; and reservation system was observed only among sportive users.

Enabling planning and organization was emphasized strongly by parents. Parents stated that prior announcement of events and the option of digital registration facilitate daily planning.

“I think it would greatly increase the social attractiveness. Registering in advance both helps us be organized and adds a sense of seriousness. Knowing the capacity and registering for the program in advance makes one feel safer. For parents, being planned and organized makes the park more attractive.”

(P-8, female, 39)

The codes event visibility and awareness of and engagement with events were highlighted by older adults, adult users, and sportive users. Participants stated that lack of timely and visible announcement of park events limits participation.

“I think people should be made aware of these things. I believe what people most look for nowadays is leisure activities. When such activities are organized and people see them through the application, they will want to attend.”

(A-4, male, 22)

The reservation system code emphasized by sportive users shows that being able to register digitally for events is perceived not merely as a technical feature for managing participation, but also as something that can create a sense of being valued and increase satisfaction.

“I think it would increase it a lot, and would be very nice. Going through a reservation system would make me feel special. The perception of crowding would also improve.”

(S.U.-3, female, 27)

Overall, the findings on Centrality and Communal Function indicate that participants perceive digital park applications as tools that increase the visibility of events, facilitate participation, and strengthen the social attractiveness of the park. Read analytically, these accounts position the digital layer as a mediator of social interaction in public space—visibility, planning, and participation are produced jointly by the platform and the park, which shifts the locus of community life from spontaneous co-presence toward digitally mediated occasions for being together.

3.1.5. Safety and Activity Diversity

Safety and Activity Diversity occupies a foundational place across participant accounts: users evaluate the park experience not only through recreational activities but also through the need to move safely, receive guidance in risky situations, and anticipate potential threats. Four codes were identified under this dimension: perception of risk management, identification of safe area points for emergencies and health issues, child and area safety, and easy-access route to safe areas. Perception of risk management emerged as a universal code voiced with similar strength across all groups. Child and area safety was specific to parents, easy-access route to safe areas was raised only by older adults, and identification of safe area points was shared by sportive users and adult users.

Across all user groups, participants tied perception of risk management to the ability to view safe areas and support points through the application in case of injury, getting lost, sudden illness, or unexpected emergencies.

“All these are very important issues. Knowing where to gather in emergencies, what to do when needed, and being able to see these areas through the application would be very good. It would also positively affect park choice.”

(P-5, male, 43)

The code identification of safe area points for emergencies and health issues was emphasized particularly by sportive users and adult users. Participants stated that an application providing digital guidance to safe areas in possible disasters or sudden health problems could also serve an important social function.

“I think it would have a 100% effect. After any natural disaster, reaching safe areas in the most accurate way is one of the issues that concerns the public. I believe this would also help raise public awareness about safe areas.”

(A-8, male, 24)

The child- and area safety code raised by parents indicates that a digital application can be a decisive factor in park choice from a parent’s perspective.

“I think safety is the most important thing. When you go somewhere with children, having safety measures in place and knowing where to go in case of a fall or injury is very important. If I had to choose between two parks, I would definitely prefer the one where the emergency procedures are clear.”

(P-8, female, 39)

The easy access route to safe areas code expressed by older adults shows that this group evaluates safety in park use mainly through ease of access and guidance.

“There may be situations such as falling, fainting, or sudden changes in blood pressure. This would be very necessary for us and would make us very happy.”

(E-4, female, 67)

Overall, the expectation of safety emerged as a shared and strong need across all user groups, with a clear expectation that the park experience be reinforced by digital support. Analytically, the universality of this expectation suggests that, in users’ perception, safety is not framed as a value-added feature of smart park applications but as a precondition for legitimate digital intervention in public open spaces; any digital service that fails to address it risks being perceived as cosmetic rather than meaningful.

3.1.6. Facility Quality and Locational Suitability

Facility Quality and Locational Suitability occupies a functional and practical place in participants’ accounts, in which the park experience is evaluated largely through ease of access, time management, the occupancy of facilities, and the suitability of facility use. Four codes were identified under this dimension: ease of access and time saving, planning according to occupancy, training-suitable distance routes, and convenience regarding resting areas. Ease of access and time saving was the only code that resonated across all user groups. Planning according to occupancy was voiced by parents and adult users, while training-suitable distance routes was raised only by sportive users.

Across all user groups, participants tied ease of access and time saving to advance information on the occupancy of parking lots, playgrounds, cafeterias, and similar facilities, which prevents unnecessary loss of time and supports a more planned visit.

“It would have a positive effect. Since I do not like crowded environments, I would not want to lose time going there. Without the application, I might end up going and turning back. To prevent this, I would like to see the occupancy rate and proximity.”

(S.U.-4, female, 25)

The planning according to occupancy code voiced by parents and adult users shows that prior knowledge of facility occupancy directly affects the quality of the park visit and time management.

The training suitable distance routes code voiced in the sportive user sample shows that Facility Quality and Locational Suitability takes on a more specific meaning in sport-oriented use. The convenience regarding resting areas code observed in the older-adult sample indicates that this group evaluates facility suitability mainly through accessible and comfortably usable areas.

Overall, the findings on Facility Quality and Locational Suitability show that users see digital park applications as functional tools that make information on access, occupancy, and use suitability of facilities visible. Analytically, this reframes everyday park use as an increasingly information-dependent practice: the perceived adequacy of facilities is no longer judged solely on site, but in advance, through the data the application makes available—which means that uneven access to such information becomes a new vector of inequality in how parks are used.

3.1.7. Independent Functionality

Independent Functionality forms an expectation domain closely tied to user autonomy: participants want to move without depending on others, use the park in line with their own needs and preferences, and manage the park experience individually. Seven codes were identified under this dimension: time management and individual mobility comfort, comfort of independent participation through freedom to choose activities, area independence in the parent–child relationship, comfort of uninterrupted use, exploration-oriented experience, feedback that encourages participation, and risk of social isolation. Time management and individual mobility comfort was voiced strongly by older adults; comfort of independent participation through freedom to choose activities was emphasized particularly by adult users; area independence in the parent–child relationship was specific to parents; and comfort of uninterrupted use and exploration-oriented experience were observed only among sportive users.

The time management and individual mobility comfort code was raised intensely by older adults. Participants stated that being able to view information on areas and transitions within the park in advance would make it easier to move around without needing help from others.

“If I can access this in the digital application—without asking anyone, see which areas I can use and where I can take a moment for myself—I would act and make decisions accordingly.”

(E-3, male, 74)

The comfort of independent participation through freedom to choose activities code was emphasized strongly by adult users. Participants stated that being able to view park areas and event options in advance makes park use more conscious and based on individual preference.

“It would help us be informed in a positive way. Something I see may catch my attention and positively affect my decision to be there. At the very least, knowing in advance what kind of environment to expect would be nice.”

(A-1, female, 24)

The area independence in the parent–child relationship code raised by parents shows that digital park applications can help parents balance supervision and freedom.

“Being able to see the park in advance is of course important for a safe parent–child relationship and quality time together. So I would prefer such parks more if there were such a digital platform. At least I could see the boundaries within which my child can move.”

(P-6, female, 40)

The codes comfort of uninterrupted use and exploration-oriented experience raised by sportive users show that this group evaluates park use mainly through individual rhythm, route tracking, and a performance-oriented experience.

“Frankly, since it would let me use the park more comfortably without depending on anyone, I could plan entirely according to my own rhythm and preferences. Especially during running, knowing the distance and the course helps me plan my training and makes things much easier.”

(S.U.-1, female, 30)

In addition, more limited codes such as feedback that encourages participation and risk of social isolation indicate that Independent Functionality is not perceived in the same way by every user. While digital wayfinding and information support are seen by some users as a factor that increases participation, a more cautious view also emerges, suggesting that independent use may reduce social interaction.

“I might have bought a training package with a group; that makes more sense to me. I am also going there to socialize. I want to have an independent voice, but that probably accounts for only 25–30% of the experience.”

(S.U.-3, female, 27)

Overall, participants perceive digital park applications as tools that ease in-park mobility, support individual planning, and offer a more autonomous experience. Yet this autonomy can be in tension with the need for social interaction, and participants describe autonomy and sociability as competing organizing principles of digitally mediated park use rather than as opposites—implying that smart park design should treat user autonomy as a calibrated affordance to be balanced against opportunities for shared experience, not as a default optimum.

3.1.8. Aesthetics and Integration

Aesthetics and Integration brings together the visual, informational, and social aspects of the park experience: participants evaluate digital applications not only as a functional tool but also as a component that increases the attractiveness of the park, eases access to information, and makes the experience more holistic. Eight codes were identified under this dimension: perceived digital attractiveness, time saving and information delivery, ease of access to information, the effect of 3D experience on visit perception, technological convenience and increased opportunity for socializing, experiential enrichment grounded in information, increased event participation and opportunity for socializing, and family-based socializing. Perceived digital attractiveness was raised particularly by sportive users; time saving and information delivery was specific to older adults; ease of access to information was shared by parents and adult users; and family-based socializing was raised only by parents.

The perceived digital attractiveness code was emphasized intensely by sportive users. Participants stated that being able to monitor parks digitally and experience them in a more controlled way could strengthen their relationship with the park.

“Seeing events in advance helps me build a connection with the park. Right now, when I have plans related to parks, the park is just a physical place for me; but, as you said, when it also becomes a domain I can follow digitally, my park experience improves substantially because the application can make my park experience smoother and more controlled.”

(S.U.-1, female, 30)

The time saving and information delivery code was voiced strongly by older adults. Participants stated that being able to see park content and events in advance through a digital medium would facilitate planning of their movement.

“If it can be displayed in 3D, I would learn where to go, how to move, which route to choose, and how to reach an event by the shortest path. This would both save me time and become an important factor in my preference.”

(E-3, male, 74)

The ease of access to information and effect of 3D experience on visit perception codes raised by parents and adult users indicate that the digital application makes the park experience more accessible by visualizing it in advance.

“I think it would have a positive effect. Increasing cultural and artistic events and seeing them reflected in the digital medium would, I believe, increase efficiency.”

(A-8, male, 24)

The technological convenience and increased opportunity for socializing code raised by adult users, together with the family-based socializing code observed among parents, shows that the digital application is seen as a tool that supports the park experience socially as well as individually.

“It would have a positive effect on reservation. People generally go to museums and similar art events alone or with friends; so it would be helpful. It would be even better if information about the works on display were also provided.”

(A-5, male, 22)

Overall, the findings on Aesthetics and Integration indicate that participants perceive digital park applications as tools that enrich the park experience visually, informationally, and socially. This dimension shows that digital support is not limited to providing information, but also serves to increase the attractiveness of the park and to ease users’ attachment to it.

3.1.9. Synthesis of the Qualitative Findings

An overall evaluation of the codes that emerged across the eight dimensions reveals three core patterns in urban park users’ expectations of smart park applications. First, information-based functions such as occupancy/crowding information, event announcements, and digital wayfinding emerged as recurring elements across multiple dimensions—accessibility, comfort, centrality, facility quality, and aesthetics—indicating that users primarily position digital applications as tools that reduce uncertainty and ease planning. Second, distinct group-level patterns were observed: parents focused on safety and family-oriented use; sportive users focused on training/performance and distance information; older adults focused on ease of access and solutions that reduce physical effort; and general adult users focused on time management, access to cultural and artistic content, and the visual–experiential quality of the park. This differentiation indicates that smart park applications should be developed through a user-sensitive, modular approach rather than a single design framework. Third, the findings under Independent Functionality suggest that while digital support strengthens individual autonomy, it may stand in tension with the need for social interaction for some users; this finding highlights the need to balance individualization and socialization in smart park design. These three patterns provided the conceptual basis for the scale developed in the second phase of the study; the seven dimensions defined in the qualitative phase were translated into a quantitative measurement instrument to be tested in a larger sample.

3.2. Quantitative Findings (Study II)

In the quantitative phase (Study II), the seven-dimension scale derived from the qualitative findings was administered to 374 participants. This section presents, in turn, participants’ demographic characteristics, the validation of the measurement model, descriptive statistics, comparisons across user groups, gender differences, and the prediction of use intention.

3.2.1. Demographic Characteristics of Participants

A total of 374 participants took part in the quantitative phase. Of the participants, 59.6% were female (n = 223) and 40.4% were male (n = 151). User-group distribution showed that 34.2% of the sample were adult users (n = 128), 25.9% were older adults (n = 97), 21.9% were parents with children (n = 82), and 17.9% were sportive users (n = 67). Participants’ ages ranged from 18 to 84, with a mean age of 49.0 (SD = 16.4). Most participants visited parks for relaxation and socializing, and those visiting parks several times a week or several times a month constituted a substantial portion of the sample. Detailed demographic information is presented in

Table 2. Demographic characteristics of the participants (N = 374).

Variable/Category	n	%
Gender
Female	223	59.6
Male	151	40.4
User group
Parents (P)	82	21.9
Sportive users (S.U.)	67	17.9
Older adults (E)	97	25.9
Adult users (A)	128	34.2
Age group
18–30	70	18.7
31–45	88	23.5
46–60	89	23.8
61 and over	126	33.7
Park visit purpose
Relaxation/socializing	198	52.9
Playground use with children	78	20.9
Sport/exercise	96	25.7
Other	2	0.5
Frequency of park visit
Every day	24	6.4
Several times a week	138	36.9
Several times a month	132	35.3
Very rarely	80	21.4
Total	374	100.0

Note. N = 374. Age: M = 49.0, SD = 16.4, Min = 18, Max = 84.

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3.2.2. Validation of the Measurement Model

The CFA results presented in

Table 3. Fit indices for the confirmatory factor analysis.

Fit Index	Obtained Value	Threshold	Evaluation
χ2	373.263	—	df = 147, p < 0.001
χ2/df	2.539	<5.00	Good
GFI	0.913	>0.900	Good
RMR	0.024	<0.050	Good
NFI	0.943	>0.900	Good
IFI	0.965	>0.950	Good
TLI	0.954	>0.950	Good
CFI	0.965	>0.950	Good
RMSEA	0.064	<0.080	Good

Note. χ²/df = chi-square/degrees of freedom; CFI = comparative fit index; TLI = Tucker–Lewis index; IFI = incremental fit index; NFI = normed fit index; RMSEA = root mean square error of approximation. Thresholds were based on Hair et al. [43] and Hu and Bentler [46].

indicate that the model fits the data well [χ²(147) = 373.263, p < 0.001; χ²/df = 2.539; GFI = 0.913; NFI = 0.943; IFI = 0.965; TLI = 0.954; CFI = 0.965; RMr = 0.024; RMSEA = 0.064]. The fit indices meet the recommended thresholds, confirming that the seven-dimensional measurement model is consistent with the data [43,46].

Standardized factor loadings, composite reliability (CR), and average variance extracted (AVE) values were calculated to evaluate construct validity. As shown in

Table 4. Standardized factor loadings, AVE, and CR values.

Dimension/Item	λ	λ2	AVE	CR
Accessibility and Flow			0.742	0.896
E1	0.862	0.743
E2	0.902	0.814
E3	0.819	0.671
Comfort and Facility Adequacy			0.741	0.895
KT1	0.877	0.769
KT2	0.867	0.752
KT3	0.838	0.702
Centrality and Communal Function			0.752	0.901
MT1	0.870	0.756
MT2	0.884	0.781
MT3	0.848	0.719
Safety and Activity Diversity			0.786	0.917
G1	0.900	0.810
G2	0.899	0.808
G3	0.859	0.738
Facility Quality and Locational Suitability			0.864	0.950
TK1	0.858	0.737
TK2	0.963	0.927
TK3	0.963	0.927
Independent Functionality			0.840	0.940
B1	0.912	0.831
B2	0.921	0.848
B3	0.916	0.840
Use Intention			0.859	0.948
KN1	0.913	0.834
KN2	0.936	0.876
KN3	0.931	0.867

Note. λ = standardized factor loading; AVE = average variance extracted; CR = composite reliability. Criteria: λ ≥ 0.50, AVE ≥ 0.50, and CR ≥ 0.70 [43,47].

, factor loadings ranged from 0.819 to 0.963, satisfying the criterion of λ ≥ 0.50 recommended by Hair et al. [43]. CR values ranged from 0.896 to 0.950, meeting the ≥ 0.70 criterion for all dimensions [47]. AVE values ranged from 0.742 to 0.864, satisfying the ≥ 0.50 criterion for all dimensions. These findings indicate that the convergent validity of the scale is at an adequate level.

Prior to further inferential analyses, the normality of the indicators was examined at the dimension level. As shown in Supplementary Table S3, all skewness values fall within the |skew| < 3 range and all kurtosis values within |kurtosis| < 10, the conventional thresholds for univariate normality in SEM applications [43]. The data therefore satisfy the distributional assumptions required for the parametric analyses reported below.

Discriminant validity was assessed using the Fornell–Larcker criterion [47], which requires the square root of each construct’s AVE to exceed its correlations with all other constructs. As shown in

Table 5. Inter-construct correlations and Fornell–Larcker criterion (√AVE on diagonal).

Dimension	E	KT	MT	G	TK	B	KN
Accessibility and Flow	[0.861]	0.802	0.707	0.713	0.723	0.746	0.668
Comfort and Facility Adequacy		[0.861]	0.738	0.775	0.765	0.781	0.691
Centrality and Communal Function			[0.867]	0.698	0.716	0.721	0.699
Safety and Activity Diversity				[0.887]	0.746	0.811	0.731
Facility Quality and Locational Suitability					[0.930]	0.817	0.730
Independent Functionality						[0.917]	0.797
Use Intention							[0.927]

Note. Diagonal values in brackets are the square root of each dimension’s AVE (√AVE). Off-diagonal values are Pearson correlations between dimensions. Discriminant validity holds when √AVE exceeds the correlations in the corresponding row and column [47]. E = Accessibility and Flow; KT = Comfort and Facility Adequacy; MT = Centrality and Communal Function; G = Safety and Activity Diversity; TK = Facility Quality and Locational Suitability; B = Independent Functionality; KN = Use Intention.

, the √AVE values on the diagonal (in brackets) are larger than the corresponding off-diagonal correlations for every dimension; discriminant validity is therefore established for all seven constructs.

The full inter-construct correlation matrix is provided in Supplementary Table S1. All correlations among the seven dimensions were positive and statistically significant (p < 0.001), with the strongest associations observed between Independent Functionality and Facility Quality and Locational Suitability (r = 0.817), Safety and Activity Diversity (r = 0.811), and Use Intention (r = 0.797).

Cronbach’s alpha (α) was computed for each dimension to assess internal consistency. All seven dimensions satisfied the α ≥ 0.70 criterion [48]. Coefficients ranged from 0.826 to 0.918, indicating that the entire scale exhibits high internal consistency.

3.2.3. Descriptive Statistics

Mean and standard deviation values were calculated for each dimension to describe participants’ expectation levels regarding smart park applications. Mean scores were high across all dimensions. The highest mean was observed in Safety and Activity Diversity, and the lowest in Centrality and Communal Function. These findings indicate that participants generally evaluated their expectations of smart park applications positively and at a high level. Detailed dimension-level descriptive statistics (means and standard deviations across all seven dimensions and Use Intention) are provided in Supplementary Table S5.

3.2.4. Comparisons Across User Groups

One-way analysis of variance (ANOVA) was conducted to determine whether expectations of smart park applications differ across user groups. Results are presented in

Table 6. One-way ANOVA results comparing expectations across user groups.

Dimension	Parents M (SD)	S.U. M (SD)	Older Adults M (SD)	Adult Users M (SD)	F	p	η2
Accessibility and Flow	4.20 (0.85)	4.39 (0.64)	3.83 (0.87)	4.09 (0.76)	7.188	<0.001 ***	0.055
Comfort and Facility Adequacy	4.26 (0.79)	4.44 (0.65)	3.99 (0.88)	4.18 (0.69)	4.891	0.002 **	0.038
Centrality and Communal Function	4.15 (0.78)	4.22 (0.78)	3.87 (0.86)	4.15 (0.81)	3.422	0.017 *	0.027
Safety and Activity Diversity	4.31 (0.77)	4.41 (0.75)	4.21 (0.88)	4.32 (0.66)	0.969	0.408	0.008
Facility Quality and Locational Suitability	4.22 (0.84)	4.49 (0.67)	4.04 (0.77)	4.16 (0.79)	4.733	0.003 **	0.037
Independent Functionality	4.23 (0.79)	4.46 (0.67)	4.12 (0.84)	4.18 (0.73)	2.799	0.040 *	0.022
Use Intention	4.16 (0.82)	4.43 (0.71)	4.09 (0.91)	4.13 (0.77)	2.753	0.042 *	0.022

Note. P = parents with children; S.U. = sport-oriented users; E = older adults; A = general adult users; SD = standard deviation. * p < 0.05; ** p < 0.01; *** p < 0.001.

. Significant group differences emerged in six of the seven dimensions; only Safety and Activity Diversity did not show a significant group difference. This finding indicates that the expectation of safety is perceived as a shared and similar need across all user groups.

Tukey HSD post hoc tests were used to identify the source of group differences in dimensions with significant ANOVA results. Post hoc Tukey HSD comparisons revealed a consistent pattern: sportive users expressed significantly higher expectations than older adults in five of the six dimensions. Full pairwise comparison results are provided in Supplementary Table S6. This finding indicates that the purpose of park use directly shapes the level of expectations regarding digital services.

3.2.5. Gender Differences

Independent samples t-tests were conducted to determine whether expectation levels differ by gender. Results showed that male participants scored significantly higher than female participants in Accessibility and Flow, Comfort and Facility Adequacy, and Facility Quality and Locational Suitability. No significant gender differences were observed in the remaining dimensions. This finding indicates that gender effects do not extend to all expectation domains and are limited to certain functional dimensions. Detailed independent samples t-test results comparing female and male participants on each dimension are provided in Supplementary Table S7.

3.2.6. Prediction of Use Intention

Multiple linear regression analysis was conducted to determine the predictive effects of the expectation dimensions on use intention. Use Intention served as the dependent variable, and the six expectation dimensions were entered as predictors. The regression model was statistically significant [F(6, 367) = 131.157, p < 0.001, R² = 0.682, adjusted R² = 0.677], explaining 68.2% of the variance in use intention. Examination of the coefficients showed that Independent Functionality was the strongest predictor of use intention, followed by Centrality and Communal Function, Safety and Activity Diversity, and Facility Quality and Locational Suitability. Comfort and Facility Adequacy did not reach statistical significance as a predictor in the model. Detailed results are presented in

Table 7. Regression analysis predicting use intention.

Predictor	β	Rank
Independent Functionality	0.451	1
Centrality and Communal Function	0.185	2
Safety and Activity Diversity	0.163	3
Facility Quality and Locational Suitability	0.131	4
Accessibility and Flow	0.039	5
Comfort and Facility Adequacy	−0.027	6

Note. B = unstandardized regression coefficient; SE = standard error; β = standardized regression coefficient; VIF = variance inflation factor. Ranking is based on the absolute magnitude of β. Model summary: R² = 0.682, adjusted R² = 0.677, F(6, 367) = 131.157, p < 0.001.

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3.3. Integration of the Findings

In the exploratory sequential mixed-methods design, the systematic integration of qualitative and quantitative findings is a critical step for conveying the overall meaning of the study. Accordingly, we examined how the eight dimensions identified in the qualitative phase (Study I) corresponded to the quantitative phase (Study II) and what meta-inferences emerged when the two phases were considered together. To this end, the joint display technique recommended in the mixed-methods literature was used [49,50]. The joint display presents qualitative findings (codes and patterns), quantitative findings (group comparisons and predictive effects), and the meta-inferences derived from joint consideration of the two phases side by side, thereby genuinely integrating the two phases.

Table 8. Integration of qualitative and quantitative findings: joint display.

Dimension	Qualitative Finding (Study 1)	Quantitative Finding (Study 2)	Integration Level	Meta-Inference
Accessibility and Flow	Crowding information, area information, and route support were emphasized by different groups; particularly P, A, and S.U. found this dimension functional.	M = 4.10 ANOVA significant (p < 0.001) S.U. > E (p < 0.001) S.U. > A (p = 0.044) P > E (p = 0.026)	Partial overlap	Although this dimension is important across all groups, it carries more pronounced functionality for sportive users. Quantitative findings indicate that the divergence is most marked between sportive users and older adults.
Ecological Quality and Flexibility	Nature-based information emerged as the only shared element across all groups; the other codes remained group-specific.	Consolidated with the Accessibility and Flow dimension in the quantitative scale (building-phase decision).	Complementary	The universal nature of nature-based information was identified in the qualitative phase; this dimension was judged conceptually close to Accessibility and Flow and was therefore subsumed under the same quantitative construct in the building phase.
Comfort and Facility Adequacy	Planning according to area occupancy emerged as the strongest element across all groups; the feedback mechanism was a shared expectation.	M = 4.19 ANOVA significant (p = 0.002) S.U. > E (p = 0.003)	Partial overlap	High expectations regarding facility occupancy are consistent across both phases. The marked divergence of S.U. from E indicates that sportive users associate facility information with training planning.
Centrality and Communal Function	Planning and organization were emphasized by all parents; event visibility stood out among E and A, while the reservation system was salient for S.U.	M = 4.09 ANOVA significant (p = 0.017) S.U. > E (p = 0.046)	Complementary	Although parents most strongly voiced the need for event planning in the qualitative phase, sportive users stood out in the quantitative data. Together, the two phases reveal that different groups evaluate this dimension through different rationales.
Safety and Activity Diversity	Perception of risk management was emphasized equally across all groups; safety expectations did not show group-specific differentiation.	M = 4.30 ANOVA non-significant (p = 0.408) No group differences	Full overlap	This dimension shows the strongest convergence between the two phases. Safety expectations are universal, independent of user profile and purpose of park use.
Facility Quality and Locational Suitability	Ease of access and time saving were shared across all groups; expectations regarding parking, playgrounds, running tracks, and resting areas differed by group.	M = 4.20 ANOVA significant (p = 0.003) S.U. > E (p = 0.001) S.U. > A (p = 0.020)	Partial overlap	The qualitative finding that each group emphasized different facility-related expectations is consistent with the marked elevation of S.U. in the quantitative data. Sportive users link facility suitability directly to performance.
Independent Functionality	All groups emphasized in-park autonomy, but each group defined independence differently: physical convenience for E, performance for S.U., safe-area control for P, and freedom to choose activities for A.	M = 4.23 ANOVA significant (p = 0.040) S.U. > E (p = 0.044) Regression: β = 0.451 (strongest predictor)	Complementary	The autonomy need, given different meanings in the qualitative phase, emerged as the strongest predictor of use intention in the quantitative data—constituting the most striking integrated finding of the two phases.
Aesthetics and Integration– Use Intention link	Perceived digital attractiveness was specific to S.U., time saving to E, and access to information to P and A. 3D experience and family-based socializing were unique codes from the qualitative phase.	Use Intention M = 4.18 ANOVA significant (p = 0.042) S.U. > A (p = 0.044) R2 = 0.682 (B, MT, G strongest predictors)	Complementary	The aesthetic and digital-integration expectations identified in the qualitative phase play a supportive but non-decisive role for use intention in the quantitative data. The use decision is shaped primarily by functional dimensions.

Note. Full overlap: qualitative and quantitative findings show the same direction and a similar pattern. Partial overlap: the general trend is similar, but the prominent user groups differ. Complementary: the two phases reveal different mechanisms that explain each other. M = mean, SD = standard deviation. P = parents, S.U. = sportive users, E = older adults, A = adult users.

presents the qualitative and quantitative findings for each dimension alongside their integrated interpretation.

3.3.1. Universal and Group-Specific Expectations

One of the strongest meta-inferences derived from the joint display is that expectations of smart park applications carry both universal and group-specific dimensions. Safety and Activity Diversity stood out as the dimension showing the strongest convergence between the two phases. Perception of risk management was shared by all user groups in the qualitative phase, and this finding was supported by the absence of significant group differences in the quantitative phase. Together, these results indicate that safety features constitute a fundamental expectation domain for all users, regardless of user profile, age, or purpose of park use.

By contrast, more differentiated patterns emerged in the other dimensions. The quantitative findings show that sportive users in particular reported higher expectation levels than older adults across multiple dimensions. This is consistent with the qualitative finding that sportive users evaluate digital applications as more functional tools for performance planning, route optimization, and time management. Older adults, by contrast, foregrounded themes of safe access, easy wayfinding, and independent movement in the qualitative phase; in the quantitative phase, this group’s expectation levels remained more selective and need-oriented. This integrated pattern indicates that smart park applications should both contain shared core functions for everyone and be designed in a way that is sensitive to different user profiles.

3.3.2. The Central Role of Independent Functionality

One of the most striking integrated findings of the two phases concerns Independent Functionality. In the qualitative phase, this dimension reflected users’ wish to move within the park without depending on others, to access information independently, and to organize the experience at their own pace. At the same time, this autonomy carried different meanings for different user groups: physical convenience and safe access for older adults, performance optimization for sportive users, safe-area control with children for parents, and freedom to choose events and activities for adult users.

In the quantitative phase, the identification of this dimension as the strongest predictor of use intention shows that the autonomy need observed in the qualitative phase is not merely an experiential preference but a central element directly shaping the decision to adopt the application. This finding indicates that one of the most highly valued aspects of smart park applications is their capacity to make the park experience more independent, more personal, and more controllable. In other words, independent functionality is not only a matter of convenience, but also a fundamental motivational domain that shapes user acceptance.

3.3.3. The Contribution of the Mixed-Methods Approach

The joint display structure presented in Table 8 is an analytical tool recommended in the mixed-methods literature for the systematic integration of qualitative and quantitative findings [37,50]. The integration analysis offered a more holistic understanding than either phase could provide on its own. The qualitative phase revealed what kinds of expectations users hold of smart park applications, on which experiential grounds these expectations are formed, and how different user groups make sense of them. The quantitative phase showed in which dimensions these expectations could be generalized, in which areas group-level differences exist, and which dimensions most strongly shape use intention.

Considering the two phases together indicates that the design of smart park applications must take into account not only technical infrastructure but also user experience, group-based differentiation, and shared expectation domains. This study therefore demonstrates that understanding user expectations of smart park applications at both qualitative and quantitative levels provides a strong basis for more user-centred and group-sensitive design decisions.

4. Discussion

This study examined urban park users’ expectations of smart park applications using an exploratory sequential mixed-methods design and showed that these expectations carry both universal and group-specific patterns. When the qualitative and quantitative findings are considered together, smart park applications appear to be perceived not as a purely technical novelty but as tools that make the park experience more predictable, safer, more personalized, and more functional. This overall pattern is consistent with recent studies showing that the digitalization of outdoor recreation is becoming increasingly central to user experience, communication, and management processes [51]. The findings also indicate that extending smart city approaches to public open spaces transforms digital applications into strategic tools for sustainability and quality-of-life goals [52,53].

4.1. Accessibility, Flow, and Ecological Quality

Findings related to Accessibility and Flow indicate that users wish to frame the park experience with information before arriving on site. Crowding information, wayfinding, route planning, and area information were emphasized in the qualitative phase particularly by parents, adult users, and sportive users; in the quantitative phase, this dimension produced the most pronounced differentiation across groups. This suggests that park use is no longer evaluated solely through physical access, but also through the capacity to access information in advance and to organize the experience proactively. Consistent with previous research, the present findings show that real-time information, communication, and wayfinding are valued by both managers and users [51]; the present study extends prior work by demonstrating that these expectations are not uniformly distributed but vary systematically across user groups, with sport-oriented users linking them to training planning and older adults to short-distance circulation. The decisive influence of visitor crowding on park experience and visit intention has likewise been examined in detail in recent work [54].

The higher expectation level among sportive users in this dimension is particularly meaningful. The qualitative findings showed that this group directly linked crowding and route information with training planning, and the quantitative findings supported this pattern. Older adults, by contrast, made sense of the same dimension primarily through short-distance circulation, wayfinding, and ease of access. This differentiation indicates that the same digital function gains value through different rationales of use across user groups [51,55]. Systematic review evidence showing that park use efficiency improves when users can organize their spatial and temporal mobility patterns based on real-time data also supports this interpretation.

The most important finding under Ecological Quality and Flexibility is that nature-based information emerged as a shared expectation across all user groups. This indicates that the park experience is evaluated not only through physical use but also through learning, awareness, and the construction of environmental meaning. Consistent with environmental psychology approaches that emphasize nature’s contribution to cognitive and emotional restoration [56], this finding is also in line with recent work showing that digitally supported green spaces can have multidimensional effects on environmental awareness and quality of life [53].

4.2. Comfort, Facility Adequacy, and Safety

Findings on Comfort and Facility Adequacy indicate that one of the most expected functions from digital applications is making area and facility occupancy visible. The strong emphasis on planning according to occupancy across all groups in the qualitative phase is consistent with the high expectation levels for this dimension in the quantitative phase. Users’ expectations regarding feedback, transparency, and access to other visitors’ experiences suggest that the park experience is becoming a socially validated experience as well as a physical one. Consistent with prior work showing that user feedback and digital information flows influence visit intention and satisfaction [54], the present findings show that participants treat the visibility of occupancy and service quality as a precondition for trust in the park experience rather than as an optional enhancement.

The full convergence of Safety and Activity Diversity between the two phases is one of the strongest findings of the study. In the qualitative phase, perception of risk management emerged as a shared need across all user groups, and no significant group differences were observed for this dimension in the quantitative phase. This clearly indicates that safety is perceived not as a group-dependent preference but as a fundamental condition of the park experience. The decisive role of perceived safety in park use, length of stay, and satisfaction is well documented in the literature [57,58]. Recent reviews indicate that park safety remains one of the main barriers to use, with lighting, wayfinding, visibility, and emergency support directly affecting this perception [59]. In this context, digital features such as safe area mapping, emergency guidance, and risk information should be designed as core functions for all user groups.

4.3. Centrality, Communal Function, and Facility Quality

Centrality and Communal Function indicates that, through digital applications, parks can be transformed into more visible, plannable, and socially accessible spaces. In the qualitative findings, parents most strongly emphasized the need for event planning, while sportive users foregrounded the management of time conflicts. The identification of this dimension as one of the strong predictors of use intention suggests that social visibility and organizational functions play a critical role in user acceptance. This result adds to previous research on technology-supported services [54] by showing that, in users’ own accounts, information provision and participation management are not perceived as separate functions but as two facets of the same expected service layer. Unlike studies that focus mainly on platform-level visibility of events, our findings show that the meaning of that visibility differs across user groups—parents emphasize event planning, while sport-oriented users emphasize reservation and time conflict management.

Facility Quality and Locational Suitability indicates that park use is increasingly becoming a planning-based experience. Prior knowledge of the occupancy and accessibility of facilities such as parking, playgrounds, running tracks, and resting areas is regarded as a fundamental function that reduces wasted time and supports decision-making. The present study extends prior work on the classic determinants of park use—location, access, and facility quality [60,61,62,63]—by showing that, in a digitally mediated context, these determinants are increasingly judged in advance through the data the application makes available rather than only on site; digital systems thus transform the user experience by making these classic determinants visible and manageable: they enable pre-visit planning, reduce spatial uncertainty, give users greater control over time and routes, and support safe movement by increasing the visibility of public events [51].

4.4. Independent Functionality, Aesthetics, and Integration

Independent Functionality forms the theoretical backbone of this study. Users expect smart park services not only to inform them, but also to reduce dependence on the park infrastructure and to increase personal control over the park experience. This expectation framework is the most original contribution of the study to the smart park literature. In the qualitative phase, all user groups emphasized in-park autonomy, but each group defined it differently: physical convenience and safe access for older adults; performance optimization for sportive users; safe area control with children for parents; and freedom to choose events and activities for adult users. Despite these different meanings, the dimension’s emergence as the strongest predictor of use intention indicates that the autonomy need is not merely an experiential preference but a central motivational domain that directly shapes adoption. This finding contributes a distinctive perspective to the conceptual debate by showing that, beyond the ‘usefulness’ and ‘ease’ axis of TAM, autonomy carries decisive weight in the park context. Self-Determination Theory’s framing of autonomy, competence, and relatedness as fundamental motivational needs [64] provides theoretical support for this finding; similarly, UTAUT2 extends classic TAM by including consumer-oriented dimensions such as habit and hedonic motivation [65]. Consistent with prior work on mobile maps and personalized experience features that enable more independent use of public space [25,66], the present study adds that autonomy is not a marginal preference but the strongest predictor of use intention in the smart park context. Autonomy and perceived control are likewise known to play a critical role in adoption among older adults [67].

At the same time, independent functionality should not be equated with social isolation. This dimension does not mean detaching the user from social interaction, but rather granting the power to move at one’s own pace. Some sportive users nevertheless expressed reservations that digital individualization may create tension with group-based recreational experience. Independent functionality should therefore be regarded as a design domain that requires balance between individual control and social interaction. The recent literature, which discusses user acceptance of public digital services in terms of community interaction and sense of belonging, supports this interpretation [53].

Aesthetics and Integration, complementing Independent Functionality, should be regarded as a supportive layer that enriches the experiential quality of smart park applications. The codes of digital attractiveness, 3D preview, and family-based socializing identified in the qualitative phase show that the applications include not only a functional layer but also an experience enriching user layer. The indirect role of this dimension on use intention in the quantitative phase indicates that users primarily adopt the application for safety, independence, and functionality, while aesthetic enrichment functions as a complementary element that reinforces these core expectations. This finding aligns with discussions in the user experience (UX) literature on the complementary nature of functional and hedonic/experiential dimensions [35]. Studies showing that digitalization can strengthen the perceived attractiveness and social meaning of place also support this evaluation [51].

4.5. Measurement Model and Use Intention

The fact that the expectation structure derived from the qualitative phase was also supported quantitatively indicates that the conceptual framework—built up from user experience—provides an interpretable structure that can be transferred to other contexts. The validated dimensional structure points not only to statistical fit but also to the validity of a conceptual framework that systematically represents users’ expectations of smart park applications. In this respect, the study makes an original contribution to the mixed-methods scale-development literature in terms of the theoretical and methodological coherence of mixed-methods integration [25,37].

Although some expectation domains are conceptually related, the seven-factor structure should not be interpreted as an artificial separation of overlapping constructs. The qualitative phase showed that users attached different meanings to different aspects of the smart park experience. For example, Accessibility and Flow mainly concerned mobility, crowding, and spatial orientation, whereas Facility Quality and Locational Suitability referred more directly to the functional suitability and availability of specific facilities. Similarly, Independent Functionality reflected the user’s ability to manage the park experience autonomously, while Use Intention represented a behavioural outcome rather than an expectation domain. The CFA results, reliability coefficients, and convergent/discriminant validity evidence supported this conceptual differentiation. Thus, the final model reflects both qualitative meaning structures and quantitative measurement evidence rather than an aesthetic preference for a seven-factor solution. At the same time, the relatively high correlations among some constructs suggest that these domains should be interpreted as closely related components of a broader positive orientation toward smart park applications, rather than as completely independent psychological constructs.

The high level at which use intention is explained by the identified dimensions indicates that the proposed expectation structure strongly represents the decision to adopt the application. The prominence of Independent Functionality, Centrality and Communal Function, and Safety dimensions suggests that technology acceptance in the park context goes beyond the classic usefulness–ease axis. Users find smart park applications valuable not only because they are ‘easy’ or ‘useful’ but also because they provide more control, visibility, safety, and social order. This interpretation is consistent with foundational approaches to technology acceptance and recent findings on smart facility use [31,32].

An important and somewhat counterintuitive pattern emerges from the regression results. Whereas technology acceptance models typically position perceived usefulness and ease of use as the primary drivers of behavioural intention [31,32], the present findings identify Independent Functionality—the capacity for users to navigate, plan, and engage with the park autonomously and in a self-directed manner—as the strongest predictor of use intention. This finding is consistent with self-determination theory’s emphasis on autonomy as a fundamental psychological need that underlies sustained engagement and well-being in everyday activities [64]. In the context of smart park applications, users do not appear to seek digital mediation primarily for efficiency or convenience, but for the enlargement of their own agency in shaping the park experience. This reframes smart park design from a service delivery problem to an autonomy enabling problem: digital features should expand the user’s capacity to make meaningful choices about how, when, and with what level of guidance to engage with the park, rather than impose a single optimised experience. The parallel observation that Safety constitutes the only universally shared expectation—observed across all four user profiles—further reinforces this reading: safety provides the baseline precondition that makes autonomous engagement possible, while the differentiated dimensions reflect how user groups exercise that autonomy across distinct purposes of use.

4.6. User Groups and Gender Differences

Group-level findings reveal a two-poled profile. Sportive users emerge as the group with the highest expectations across many dimensions; they directly link crowding, route, facility suitability, and time planning functions with performance. Older adults, in contrast, use digital tools more selectively and in a more need-oriented manner; they prioritize concrete needs such as wayfinding, safe access, and physical convenience. This contrast indicates that a one-size-fits-all user assumption is inadequate for smart park applications, and that design decisions sensitive to digital skill, purpose of use, and age group are essential [51,55].

Gender differences are not as decisive as user segments and should be treated as a supplementary finding. Male participants reported higher expectations in functional domains such as accessibility, comfort, and facility quality; however, no significant gender differences were observed in core dimensions such as safety and use intention. This pattern indicates that the logic of acceptance is shaped less by gender than by purpose of use and experiential needs; the TAM literature likewise notes that gender effects are context-specific and vary by dimension [32].

From a critical perspective, the findings also show that smart park design should not be understood only as a matter of adding digital convenience to public green spaces. Features such as real-time crowding information, route guidance, emergency support, feedback systems, and personalized recommendations may improve the usability and perceived safety of parks; however, they also depend on the collection, processing, and interpretation of user-related data. This creates a dual challenge for sustainable smart park design: digital systems should enhance access, autonomy, and well-being without producing new forms of exclusion, surveillance, or technological dependency. In this respect, the user-centred structure identified in this study should be interpreted not as a purely technology-affirmative model, but as a framework for designing smart park applications that are transparent, optional, inclusive, and sensitive to different levels of digital literacy.

4.7. Overall Evaluation and Sustainability Context

When the dimension-based findings of this study are considered together, expectations of smart park applications go beyond a debate on technological novelty and are intertwined with users’ lived park experience, autonomy, and demands for safe public use. Within this framework, the starting point of smart park design is not the capacity of sensor and data infrastructure, but the principle of user-sensitive design; smart city discourse must also be constructed in conjunction with the lived experience of public space, not merely with technological capacity. This interpretation aligns with recent literature emphasizing the role of smart green spaces in environmental awareness, community interaction, and quality of life [52,53]. Smart park applications should therefore be positioned in the context of sustainable cities not merely as managerial infrastructure but as a complementary service layer that supports the use quality and inclusiveness of public open spaces [51].

Taken as a whole, the discussion suggests that expectations of smart parks cannot be read through a single rationale; safety, autonomy, social visibility, and aesthetic integration function as mutually balancing layers. This layered reading separates the urban green space management debate from a narrow “smart infrastructure” discourse and indicates that digital services should be positioned as complementary tools that support the lived use quality of the park.

The contribution of the present study lies not only in identifying expected features such as safety information, crowding data, wayfinding, and facility guidance, but in showing how these expectations are organized into a multidimensional and user-sensitive structure. The exploratory sequential design demonstrates how everyday park experiences can be translated into measurable expectation domains and how these domains differ across user profiles. In this sense, the findings move beyond a list of desired application functions and provide an empirically grounded framework for understanding smart park expectations at the intersection of user experience, sustainable urban management, and digital public space design.

5. Conclusions

This study examined urban park users’ expectations of smart park applications using an exploratory sequential mixed-methods design, tested the multi-dimensional expectation structure identified in the qualitative phase at the quantitative level, and showed that the two phases produce complementary findings. The research demonstrates that user expectations carry both universal and user profile-specific dimensions. This finding indicates that smart park applications should be designed not as a uniform service approach but through a multi-layered design strategy that considers different user needs.

The main findings of the study cluster around three core inferences. First, the expectation of safety constitutes a universal need shared across all user groups, with no group-specific differentiation. This indicates that safety functions must be positioned as a core layer in smart park applications. Second, the purpose of park use—particularly sport-oriented use—markedly differentiates digital service expectations and necessitates user segment-sensitive design decisions. Third, Independent Functionality emerges as the strongest determinant of use intention. This indicates that users are inclined to adopt smart park applications primarily in order to experience the park more autonomously, in a more controlled and personalized way.

Methodologically, the study successfully translated the expectation structure derived from the qualitative findings into a psychometrically robust measurement model. The measurement model was supported by validity and reliability evidence, demonstrating that the conceptual structure developed from user experience is also reflected at the quantitative level. This confirms that the exploratory sequential mixed-methods design provides a methodologically robust framework for user-centred scale development processes [37].

From a practical perspective, the study offers concrete design principles for municipalities, park managers, and smart city practitioners. We recommend positioning safety features as core infrastructure, integrating Independent Functionality into the application’s core functions, and developing content strategies sensitive to user segments. Within this framework, smart park applications should be regarded not merely as technology infrastructure but as potential tools for enhancing the use quality of public open spaces, supporting user-oriented service design, and informing efforts toward healthier, more inclusive, and more sustainable cities in line with SDGs 3 and 11.

5.1. Limitations

Several limitations should be considered when evaluating these findings. The first concerns the sample context. Both phases were conducted in urban parks in Eskişehir, and the findings reflect conditions specific to this urban context. User expectations may show different patterns in cities with different urban scales, climate conditions, and cultural structures, and direct generalization of the findings should be interpreted with caution.

Although the focus on Eskişehir provides contextual depth, it also limits the generalizability of the findings. Users’ expectations of smart park applications may vary according to local park culture, municipal service traditions, digital infrastructure, socio-demographic composition, and levels of digital literacy among park users. For example, in cities with highly mature smart city infrastructures, users may place greater emphasis on advanced functions such as real-time IoT integration, augmented reality navigation, or personalized environmental feedback. By contrast, in cities with lower levels of digital literacy or limited mobile internet access, more foundational functions such as wayfinding, safety information, and basic facility guidance may remain the primary expectations. Future studies should therefore test the proposed structure in different cultural, geographical, and technological contexts.

The second limitation concerns the sampling strategy. Because non-probability sampling was used in the quantitative phase, the extent to which the sample represents the entire population of Eskişehir park users should be interpreted cautiously. In particular, it remains unclear whether less active or less technology-prone user groups were sufficiently represented. Two considerations mitigate this concern. First, the convenience and snowball strategy was a deliberate choice aligned with the study’s exploratory aim of reaching active park users—the segment most likely to adopt smart park applications—and to capture expectation depth across distinct user profiles rather than to estimate population parameters. Second, the achieved sample (n = 374) exceeds conventional thresholds for the planned multivariate analyses [43], supporting the statistical adequacy of the findings within the sampled population.

The third limitation arises from the cross-sectional research design. The study captured expectations at a single point in time and did not allow for examination of how expectations of smart park applications and use intention change over time. Moreover, expectations were measured but actual use behaviour and post-use satisfaction were not assessed. Testing expectation–behaviour consistency is an important next step for future research.

The present study measured users’ expectations and use intention at a single point in time; therefore, it does not allow for conclusions about actual application use behaviour, continuance intention, or post-use satisfaction. This distinction is important because behavioural intention does not always translate directly into actual behaviour. Future research should therefore examine the expectation–behaviour relationship through longitudinal designs, pilot implementations of smart park applications, application use logs, ecological momentary assessment, or follow-up studies tracking users from pre-use expectations to first use and continued use.

Finally, the consolidation of some qualitative dimensions during the building phase may have prevented certain nuances specific to those dimensions from being represented as separate constructs in the quantitative scale. Because the quantitative instrument was developed and initially tested within the same exploratory sequential study, future research should further examine the stability of the factor structure through EFA–CFA procedures on independent or split samples and test measurement invariance across different user groups before making stronger claims about cross-group comparability. In addition, the self-report nature of data collection involves the possibility that participants’ responses may have been influenced by social desirability bias. The high mean scores across dimensions may also partly reflect a generally positive orientation toward digital public services or a tendency to endorse socially desirable expectations such as safety, accessibility, information, and improved service quality. Future studies should therefore combine expectation measures with behavioural, observational, or actual application use data.

5.2. Recommendations for Future Research

The findings provide several directions for future research. The first is the conduct of geographically and culturally comparative studies. Testing the scale developed in this study in different urban contexts would reveal whether expectation patterns are universal or context-specific and would yield more comprehensive implications for smart park design.

The second is research based on actual application use. The present study measured user expectations only; investigating use behaviour, continuance intention, and post-use satisfaction once an actual smart park application has been deployed would reveal the alignment between expectations and experience. Such experimental or field-based designs would fill an important gap in smart park research.

The third is longitudinal research designs. In cities where smart park applications are becoming more widespread, panel studies that track changes over time in user expectations and use patterns may provide critical insights into the long-term sustainability of the digital park experience. Monitoring such changes is a priority research area, particularly for vulnerable user groups such as older adults and parents with children.

The fourth is the extension of the scale’s scope. Developing original items for the dimensions consolidated during the building phase and testing the full-dimensional scale structure quantitatively would contribute to a more complete representation of the expectation structure. Including moderator variables such as accessibility, digital literacy, and prior technology experience could further strengthen explanatory power for use intention.

The fifth is methodological diversification. To overcome the limitations of self-report, complementing the quantitative scale data with location-based data, application use logs, and behavioural methods such as eye tracking would enable a more objective understanding of user behaviour. Such methodological enrichment would allow the human–space–technology triad to be addressed more holistically in smart park research.