Urban Forest Visit Motivation Scale: Development and Validation

Abstract

Due to the importance of the positive effects of urban forests on urban dwellers and the limitations of prior studies, which have mainly dealt with motivations involving rural or tourism-oriented forests, this study aims to develop a scale of visit motivation for an urban forest specifically as a way to deal with more diverse and specific motivations. The first step is to develop a draft version of the Urban Forest Visit Motivation Scale (UFVMS) through experts’ discussions of important factors from prior studies and the analysis of Big Data. Then, to confirm the reliability and validity of these items, 878 valid data of visitors of urban forests living in Seoul and Incheon were collected to conduct EFA and CFA to validate the final version of the scale. As a result of factor analysis, seven dimensions (Experience Activities, Healing and Rest, Health, Environmental Experience, Daily Leisure, Family, Eco-Friendly) with 24 items for the UFVMS were drawn after deleting 5 items that had a low standard regression weight on CFA or were unsuitable for the characteristics of a dimension. The results of this study are expected to provide academic and practical implications for urban forest visit motivation. In particular, unlike common purposes such as healing and health, the results indicate that the purpose of daily leisure-related activities is a notable factor of visiting urban forests, showing that there is also a high need for good-quality facilities such as food suppliers, toilets, and so on. This study will contribute to the academic development of research on UFVMS and the practical development for managers of urban forests in terms of planning a better management of the urban forest with a full understanding of visitors’ motivations. This improving management will contribute to the development of the social sustainability of urban citizens while giving various benefits.

1. Introduction

Green spaces with trees provide humans with many diverse benefits, including economic, ecological, environmental, and social benefits [1]. As the role of forests has come to be considered highly important, some scholars have studied forests through various approaches. In particular, interest in forests near or in cities—which are called “urban forests”—has been increasing worldwide. There are many studies of urban forests that have derived implications for the management of urban forests while focusing on their specific effects or roles for urban areas [2,3,4]. According to Ostoić et al. [5], most papers related to urban forests across the Mediterranean have focused on the subject of efficacy in the environment or human society.

The main reasons for the increased interest in urban forests are environmental factors. The first reason is an increase in the proportion of the urban-dwelling population all over the world. From a publication of UN DESA [6], there will be a 90% increase in urban dwellers in Asia and Africa by 2050. However, there is a huge potential drawback of living in urban areas in that it is possible for urban dwellers to feel a suffocating atmosphere from the high density of tall buildings and the absence of nature. In this case, the unique green space in and around high buildings, which is an impressive characteristic of an urban forest, is drawing attention from urban dwellers. This is because trees in cities improve the quality of life and environment of urban areas and dwellers [7]. Second, the increased general awareness of health with COVID-19 has made people have an interest in wellness destinations, such as urban forests, mountain, spas, and resorts. Under these circumstances, urban residents have gradually shown increasing interest in urban forests. Even if COVID-19 had not occurred, the wellness industry, which is related to urban forests, would still have been developed, because urban dwellers would have still aspired for a healthy life due to stress or feeling tired from their work [8]. According to ART (attention restoration theory) and SRT (stress reduction theory), urban residents want to experience green nature to help their health and care for their mental and physiological functioning [9]. In summary, interest in urban forests has increased drastically due to the benefits they offer to urban areas and dwellers in terms of various environmental factors.

From the point of view of various studies on the benefits of urban forests, e.g., [10,11,12], forests offer many advantages for urban people’s health, local economies, and city ecosystems. Conforming to that research, many studies on urban forests state that local authorities have to manage urban forest to create and maintain those positive effects—e.g., [10,13,14]. Despite all the benefits that urban forests can offer, they have yet to be fully managed like rural forests [15]. In accordance with the fact that the role of urban forests has positive effects in various areas, there is a need to deeply study visitors’ motivations regarding urban forests to develop accurate and efficient management strategies with the full understanding of various visitors. This is also based on research that has evidenced that visitor motivation interacts highly with various aspects of destinations and relates to a positive attitude to attractions [8,16].

Further, there is another reason supporting the importance of studying urban forest visit motivation: Urban forests have different features from rural or common forest tourism destinations, which means that there is a need to research visitor motivation while focusing solely on urban forests, not whole forests. Urban forests have their own characteristics in terms of distance range and tree density. An urban forest is defined as naturally occurring or planted trees within or near urban areas [17,18,19]. Urban forests also differ from rural forests in having a lower cover and density of trees, as well as a closer location to more humans and built structures [12]. This last distinction refers to the fact that an urban forest is substantially different from other wellness tourism destinations such as mountains, national parks, or forests, which are at a far distance. Due to these peculiarities, visitors’ motivations for visiting urban forests are highly likely to differ from those of general forests.

However, there is a limitation to researching urban forest visit motivation from prior studies. Basically, most studies examining forest visitors’ motivations have simply focused on normal/rural forests such as healing forests or national parks that are far from urban areas [20]. Even though there have been a few studies focusing on visitors’ motivations with a main focus on urban forests, the aim of those studies was to find some implications from the results of visitor surveys, not to develop a specific scale—e.g., [2,21,22,23]. This means that those studies did not create their own survey items and just used conventional measurement items of forest visits from prior studies to find practical implications for outcomes such as managing urban forests well. This led to limitations in the ability of these studies to understand the motivations of urban forest visitors specifically, since the scale of those studies was not specifically related to urban forest visits, as they focused on common forests or wellness tourism destinations. Taking this situation into account, this study drew the need for the development and validation of a separate specific scale of urban forest visit motivation.

As the role of urban forests has become highly important in urban areas, and as there is a limitation in the ability of prior studies to capture the visit motivation for urban forests, this study aims to develop the Urban Forest Visit Motivation Scale (UFVMS) by identifying key factors of visit motivations for urban forests. Further, the traditional method of only applying prior studies’ items with the Delphi method to a new research of scale development has some drawbacks in terms of developing detailed and new scales with a generalization. When using only the Delphi technique, there must be a continuous intervention of subjective judgement criteria of experts, which is in some cases inconsistent with the direction of the researcher’s study [24]. It also has a problem of applying high volatility from changes in situations or variables [25]. In this case, the present study applied new methods to develop UFVMS.

First, this study aims to identify key dimensions, meaning the most important factors for composing the motivation measurement scale, by reviewing various prior studies and analyzing Big Data regarding urban forest visitors’ motivations. With this first proposal of the measurement scale’s items, this study will also conduct surveys aimed at urban dwellers who have awareness of urban forests as well as an experience of visiting an urban forest to validate the factors of the measurement scale with EFA and CFA to validate the model. This study will provide academic implications for future studies on urban forests through the presentation of a comprehensive and specific measurement scale related to the motivations of urban forest visitors. This will also provide practical implications such as measures that can be taken to manage urban forests efficiently and satisfy urban forest visitors in relation to various development policies.

2. Literature Review

2.1. Concept of Urban Forest

While many scholars globally have established various definitions for urban forests, many of these definitions emphasize the urban forest’s feature of near range from the city—e.g., [17,18,19,26]. There are slight differences between various studies or policies about the specific distance from the forest to the nearby urban areas, but most studies have dealt with the meaning of urban forest as a nearby green space. Most of the studies on urban forest from the 1990’s dealt with the definition of urban forests as forests in urban areas, but since the 2020’s, many studies have set the boundary of urban forests to also include peri-urban settings [27]. For instance, the Korea Forest Service [28] divided urban forests into two types: forest in living area/non-living area. Accordingly, urban forests are also identified according to the urban green space (UGS) [29]. Prior studies’ definitions of urban forests are listed below in

Table 1. Urban forest definitions from prior studies.

Definition of an Urban Forest	Ref.
Urban parks, planted green places, nature remnants, natural forests and woodlots in urban or peri-urban area (including some kinds of private garden, green roof, farm, and so on)	[30]
The sum of all urban trees, shrubs, lawns, and pervious soils located in highly altered and extremely complex ecosystems where humans are the main drivers of their types, amounts, and distribution	[31]
Networks or systems comprising all woodlands, groups of trees, and individual trees located in urban and peri-urban areas	[32]
Publicly and privately owned trees within an urban area, which include individual trees along streets and in backyards, as well as stands of remnant forests	[27]
Trees and other vegetation growing along streets and waterways, around buildings, in backyards, and parks of our cities and towns	[15]

. Following these definitions, the present study defines “urban forest” as “a type of forest or place with trees and shrubs located in or around urban areas which performing environmental and social function”.

2.2. Prior Studies on Motivations for Visiting Urban Forests

Before developing the UFVMS, prior studies considering the motivations for visiting urban forests had to be analyzed to find key components of the scale. Only a few previous studies have dealt with the visit motivation factors of urban forests when applying the word “urban forest” as is—e.g., [20]. To overcome this theoretical limitation, this study searched previous research using various key words such as urban forest parks/urban green spaces that corresponded to the concept of an urban forest considered by this study, not only research using the word “urban forest” as is. Prior studies of motivations for visit to urban forests are listed below in

Table 2. Prior studies of motivations for visit to urban forest.

Motivating Factors	Type of Urban Forest	Ref.
Time with children, Nordic walking, cycling, walking, dog walking, jogging, reading, picnicking, ball games, passive games	Urban green space	[21]
Physical, space qualities, children, cognitive, social and unstructured time		[2]
Fresh air/scenery, relaxation, exercise, quietness, contacting nature, playing with children, social interaction, accompanying the elderly, walking pets, watching wildlife, cultural activities, adventure, others		[33]
Relaxation and rest, physical exercise, meeting friends, taking children out, walking, nature or clean air, others		[23]
Physical motivation, psychological motivation, social motivation, natural environment, facilities, programs, and experiences and activities	Urban healing forest	[34]
Physical exercise, relaxation and rest, interactions with nature, taking children out, enjoying cool weather, visiting cultural sites, fresh and clean air, reading a book, walking the dog	Urban forest park	[35]
Active recreation, walking, having fun, relaxing, others		[36]
Contact with nature and relax, lay with children, get together with families and friends, jogging/walking, picnic, barbeque, boating, exercise, others		[3]
Café-related walk, daily leisure, clean space, exhibition and photography, healing trip, family, wonderful view	Urban forest	[20]

.

As can be seen in Table 2, many studies have selected the location of the motivation survey as an urban forest park. The table shows that urban forests are mostly managed by the government as a type of green park in cities. Motivating factors of urban forest visits can be explained by the push–pull theory of migration [20]. In push–pull theory, people go or travel to specific destinations because of both their own internal needs and the destination’s attribute [37]. From this point of view, push factors of urban forest visits refer to visitors’ instinctive desires—e.g., relaxing, healing, health, escaping, interest, recreation, socializing. Otherwise, pull factors of urban forest visits include the attributes and type of facilities in urban forests—e.g., space qualities, wildlife, cultural site, nature, clean space, exhibition and performance, scenery.

2.3. Analyzing Big Data of Motivation for Visiting Urban Forests

To surmount the weakness of only applying prior items, this study decided to analyze Big Data related to urban forests to consider the latest trends. In agreement with the usefulness and importance of analyzing Big Data from social media (blogs) to grasp consumers’ information, the amount of research in tourism and hospitality analyzing social media has increased over the last several years [38]. In particular, blogs (web logs) have mostly been used to analyze Big Data and have come to be considered an important resource because of the up-to-date information they offer from the exponential growth of popularity with a high degree of consumer involvement [20,39].

From the importance of the data of blogs, this study crawled data from blog sites from “Naver & Daum”, which are the most famous web portals in South Korea [40]. The three-stage process of analyzing Big Data in this study is shown below in Figure 1.

By using the keywords of “urban forest” and “the name of 12 forests” in South Korea promoted by the Korea Forest Service, which are representative cases of urban forests in South Korea over the periods from 2 July 2019 to 1 July 2020, this study crawled 35,038 data. The cleaning process (text mining) consists of “Noisy and Incomplete Data Cleaning” and “Morpheme Analysis and Stop Word Elimination”. To be more specific, this study eliminated incomplete data and noisy data, as well as useless words. Finally, clean data were applied to perform LDA (latent Dirichlet allocation) topic modeling, and seven topic groups were drawn from consistent words with a high correlation. This study set the name of each topic based on including words from a discussion of various experts. The final result of LDA topic modeling is shown below in

Table 3. Result of LDA topic modeling.

Topic	Events	Date/Stroll	Trip	Scenery of Nature	Cafe	Outing	Family Activities
Ranks	Name	Name	Name	Name	Name	Name	Name
1	Festival	Road	Travel destination	Tourism	Fall	Fine dust	Lake
2	Trip	Tree	Recommendation	Picture	Travel	Museum	Children
3	Region	People	Mountain	River	Tea	Lake	Scenery
4	Fall	Trip	Pine tree	Location	Lake	Dog	Camping site
5	Lake	Street	Travel	Winter	Planning	Nature	Experience
6	Program	Love	Road	Ocean	Café	Convenience store	Trip
7	Attractiveness	Course	Time	People	Mountain	Tree	Camping

. The order of ranks was based on the scores indicating how related words and topics are.

3. Deriving Factors of Urban Forest Visit Motivation Scale

3.1. Drawing Main Factors of UFVMS

According to the results of prior studies examining motivations to visit urban forest and supporting data from analyzing Big Data with LDA topic modeling, this study refined the main motivation factors for urban forest visitors from a discussion of seven experts related to the field of urban forests. As a result, seven factors (Experience Activities, Healing and Rest, Health, Environmental Experience, Daily Leisure, Family, Eco-friendly) were ultimately derived from this research.

3.1.1. Experience Activities

“Experience Activities” refers to visitors’ prepared or recreational experiences in seeing and visiting urban forests. It includes various special activities in urban forests, such as taking pictures of natural scenery or participating in various events in urban forests such as concerts. These days, many countries’ governments have held various events and programs to improve visitors’ satisfaction with urban forests regarding recreational or educational things (e.g., Seoul forest park’s programs: the gardening learning program, insect exhibition, and night forest exploration). Guided educational programs such as nature-related talk concerts or tour programs in urban forests are also provided to enhance the recreational quality of forests for visitors [29]. Shan [33] asked about this motivation with a factor of “cultural activities”, and Nam and Lee [34] included the “forest healing program”, which has a similar characteristic of this factor. Liu et al. [35] dealt with these activities with the factor as “visit cultural sites”.

3.1.2. Healing and Rest

“Healing & Rest” can be described as a visitor’s state of rest for stress relief and mental recharging, which are psychological benefits provided by urban forests. As urban people are tired from their work, they want to escape daily life, relieve their fatigue or stress, and be rejuvenated. For these reasons, people visit urban forests, which provide an optimal environment for promoting their mental well-being [41,42]. Observing nature affects visitors by allowing them to recover concentration and relieve stress [9]. Various studies examining the motivation and benefit of urban forests found this to be the most important factor [21,23,35]

3.1.3. Health

“Health” refers to the motivation of willingness of visitors to prevent diseases and maintain their physical health by engaging in activities such as light exercise. In various scientific studies, green spaces have been proven to provide citizens with many health benefits—e.g., [41,42]. Most urban forest visitors have a motivation of walking for health, and the rest of them visit urban forests to engage in some sport or exercise [2]. Therefore, in the health factor, activities like walking, running, playing sports, exercise, and biking have to be included [42].

3.1.4. Environmental Experience

The “Environmental Experience” factor includes the motivation of experiencing environmental benefit from urban forests that one cannot otherwise experience in urban areas, such as breathing clean air or avoiding noise (feeling a quiet atmosphere). This is slightly related to the “health” factor, but the biggest difference is that it focuses on the functional aspects of a green environment rather than arbitrary activities by visitors. The trees in urban forests help reduce air temperature, which makes air quality better, and they also prevent air pollution and ozone depletion [14]. In this way, urban forests have various positive effects on urban environments. Visitors who have this motivation are seriously concerned about pollution in the urban area and typically have a willingness to visit urban forests to feel those benefits. Nam and Lee [34] named this factor as “natural environment”.

3.1.5. Daily Leisure

The “Daily Leisure” dimension contains leisure activities in daily life such as light walking (stroll), taking a picnic, or dating. It also includes daily activities such as using facilities—e.g., toilets, cafés, restaurants— in or surrounding urban forests. As urban forests are located in/around urban areas, various scholars have studied the leisure or recreation function of urban forest to visitors—e.g., [43,44,45,46]. Urban dwellers spend most of their leisure time in urban areas, which means that they also spend their leisure time in urban forests. Irvine et al. [2] expressed this factor as “unstructured time”, which explained visiting urban forests as a daily routine. “Daily leisure” also indicates a motivation of socializing with others in activities such as dating or meeting friends or co-workers—e.g., [33,34]. Some scholars have referred to this dimension with the term of picnic—e.g., [3,21].

3.1.6. Family

The factor of “Family” means the motivation to do activities with family (children). This study separated it from the “DL” dimension for its independent characteristics. Kim and Lee [47] found that urban forest visitors with their children have the main purpose of improving their relationship or emotional communication with their children. Many urban dwellers visit urban forests with their children to spend time together. Moreover, many families visit urban forests to engage in various activities such as camping or other leisure activities together. The range of these activities contains all activities of the factors that are mentioned above, but the main difference is the purpose of activities with family and whether a family member is accompanied. Many researchers deal with this motivation factor with measurement items including “children” such as “hanging out with children”—e.g., [2,23,33].

3.1.7. Eco-Friendly

The direct meaning of “eco-friendly” in this study is “nature friendly or environmentally friendly”. As the factor of motivation for visiting an urban forest, it refers to ecological experiences that urban forests (green space) provide with the preference of nature. These factors include motivations of seeing or feeling the nature (forest) itself. Urban forests include lots of ecological objects such as green space, trees, birds, insects, and the scenery of nature. Home et al. [21] researched this motivation by asking with items as “interaction with nature: flora and animal appreciation, fishing, feeding fish, and bird watching”. Further, various researchers have also dealt with this factor with the name of “have contact/interaction with nature”—e.g., [23,33,35].

3.2. Urban Forest Visit Motivation Scale (UFVMS): Draft Version

By drawing the main factors of UFVMS, this study developed the draft version presented in

Table 4. First version of measurement items of Urban Forest Visit Motivation Scale (UFVMS).

Dimensions	Measurement Items	Short Word	Ref.
Experience Activities (EA)	To take pictures of the scenery of/around an urban forest	Taking pictures of scenery	[20,33,34,35,36]
	To experience various forest-related experience activities	Forest-related experience
	To watch festivals and performances in an urban forest	Watching performances
	To go to a small exhibition in an urban forest	Going to an exhibition
	To participate in various events in an urban forest	Participating events
Healing and Rest (HR)	To spend time in nature	Spending time in nature	[20,23,33,34,35,36]
	To get out of one’s boring daily life	Out of boring life
	To relieve stress	Relieving stress
	for physical/mental recharging	Recharging
Health	for taking a walk	Taking a walk	[2,20,21,23, 34,35,42]
	To participate in a program for health and healing (e.g., meditation/walking, etc.)	Program for health
	To engage in simple sports	Simple sports
	To stay healthy	Staying healthy
	To prevent diseases	Preventing diseases
	To cure diseases	Curing diseases
Environmental Experience (EE)	To avoid fine dust	Avoiding dust	[14,20,23,33,34,35]
	To breathe clean air	Clean air
	To avoid noise in the city	Avoiding noise
	To feel the cool temperature (To get out of the heat island phenomenon)	Cool temperature
Daily Leisure (DL)	To visit famous cafes and restaurants in the urban forest	Cafes and restaurants	[2,3,20,21,23,33,34]
	To use the convenience facilities within the urban forest	Convenience facilities
	For a date with the opposite sex	Dating
	To use the bike road	Bike road
	To take a walk with one’s dog	Walking with a dog
Family	To go out with children/family	Go out with family	[2,20,21,23,33,35]
	To do various leisure with children/family	Various leisure with family
Eco-Friendly (EF)	To enjoy the natural scenery around the city forest	Enjoy scenery	[23,33,34,35]
	To use the nature and green space in an urban forest	Using green space
	To interact with nature (e.g., animals, trees, plants, etc.)	Interact with nature

. The total number of items is 29 in seven dimensions (Experience Activities (EA), Healing and Rest (HR), Health, Environmental Experience (EE), Daily Leisure (DL), Family, Eco-Friendly). EA has five items related to activities such as taking pictures or participating an event. HR consists of four items that relate to visitors’ motivation of recharging or relieving themselves. Health includes six items related to doing an exercise or a willingness to be healthy. EE has four items with environmental benefit from an urban forest. Next, DL consists of five items related to visiting facilities such as cafés and restaurants around an urban forest or socializing with others. The Family dimension is composed of two items such as doing activities with family. Lastly, EF has three items about the kinds of feeling or interactions with the nature functions of urban forests.

4. Methodology

4.1. A Design of Methodology

The whole research process is shown in Figure 2. First of all, as introduced in a previous chapter, the draft of UFVMS was derived from discussions of experts based on previous studies and analyzing Big Data. With this process, this study derived seven dimensions with twenty-nine items of the visitors’ motivations for visiting urban forests. The questionnaire in this study consisted of a total of thirty-eight questions, including twenty-nine items asking the motivation for the urban forest visit, which is shown in Table 3, and nine demographic questions.

After that, this study conducted a survey of urban forests to verify these items. Specifically, this study applied statistical techniques (exploratory factor analysis and confirmatory factor analysis) to analyze the survey data to confirm reliability and validity, as many researchers have done to develop scales [20,48]. This study used IBM SPSS Statistics for Windows, version 27 (Armonk, NY: IBM Corp) to perform EFA and used Amos, version 27.0 [Computer Program] (Chicago: IBM SPSS) to perform CFA. With the result of factor analysis, the final motivation scale of urban forest visits was derived.

4.2. Data Collection

To verify the validity and reliability of items, this study conducted online surveys administered through a professional Korean research agency from 21 September to 29 September. The respondents were limited to those who visited urban forests at least one time within 2 years and those living in Seoul and Incheon, which are the most representative cities in South Korea. This study uses a 7-point Likert scale (1: strongly disagree~7: strongly agree) to obtain scores about the inclination of visit motivation. In total, 1000 respondents were collected, but 122 questioners were excluded due to the validity of their answers and their ignorance of urban forests.

5. Analysis Result

5.1. Demographic Analysis

This study conducted a frequent analysis with SPSS 26.0 to find demographic characteristics of the survey respondents. Specifically, this study constructed the questionnaire focusing on gender, marital status, age, final education, average monthly income, and occupation. To summarize the important points in demographic results, married people are relatively more present than unmarried people, with a percentage of 59.7, and the proportion of middle-high income earners (equal or greater than 400: 63.1%) is relatively high. The results of the analysis of demographic data from the survey are presented in

Table 5. Demographic analysis.

Characteristic		Frequency (%)	Characteristic		Frequency (%)
Gender	Female	431 (49.1)	Income ($)	<200	82 (9.3)
	Male	447 (50.9)
Marital status	Married	524(59.7)		Equal to or greater than 200, and <400	242 (27.6)
	Unmarried	338 (38.5)
Age	Less than 20	49 (5.6)		Equal to or greater than 400, and <600	271 (30.9)
	20–39	327 (37.2)
	40–59	354 (40.3)
	Above 60	148 (16.9)		Equal to or greater than 600	283 (32.2)
Education level	Below high school graduate	34 (3.9)
	High school graduate	213 (24.3)
	University graduate	519 (59.1)	Job	Office/technical	347 (39.5)
				Professional	90 (10.3)
	Greater than or equal to graduate school	34 (3.9)		Self-owned	43 (4.9)
				Sales/service	56 (6.4)
				Freelancer	14 (1.6)
				Student	119 (13.6)
				Others	19 (2.2)
				Unemployed	190 (21.6)
Total		878 (100.0)	Total		878 (100.0)

.

5.2. Realibility and Validity Analysis

5.2.1. Exploratory Factor Analysis

With 878 data, this study conducted exploratory factor analysis (EFA) using IBM SPSS Statistics for Windows, version 26.0. Exploratory factor analysis was carried out to confirm the suitability and reliability of UFVMS to be developed. EFA, which refers to PAF (principal axis factoring), is conducted to identify the dimensionality of the measurement scale [49]. The EFA process provides researchers with more accurate factors by using multiple measured variables [50].

Some researchers state that factor loading value has to be above 0.5—e.g., [51]; however, many researchers have claimed that the cutoff of good factor loading value is 0.40—e.g., [52,53]. The KMO test has been used to see if the partial correlations within one’s data are close enough to zero, suggesting that there is at least one dormant factor under the variables. Secondly, this study applied the cutoff of the Kaiser–Meyer–Olkin (KMO) value above 0.6 stated by Hair et al. [53] and Hutcheson and Sofromiou [54], implying that an appropriate scale was developed.

As the result of EFA, all of the dimensions and items survived with good results. Hair et al. It is stated by [53] that Cronbach’s alpha values have to be greater than 0.7, and all of the values in this study exceed 0.7 (EA: 0.894, HR: 0.878, Health: 0.826, EE: 0.816, DL: 0.845, Family: 0.794, EF: 0.791), which means that the factors have appropriate reliability with a satisfactory confidence coefficient [55]. The result of EFA is shown in

Table 6. Result of exploratory factor analysis of motivations for visiting urban forest.

Dimensions	Factors (Variables)	Factor Loading	Cronbach’s α (Variance)
Experience Activities (5)	To watch festivals and performances in an urban forest	0.847	0.894 (13.057)
	To go to a small exhibition in an urban forest	0.834
	To participate in various events in an urban forest	0.830
	To experience various forest-related experience activities	0.669
	To take pictures of the scenery of/around an urban forest	0.449
Healing and Rest (5)	To get out of the boring daily life	0.759	0.878 (11.245)
	To relieve stress	0.739
	For physical/mental recharging	0.720
	To spend time in nature	0.631
Health (6)	To stay healthy	0.810	0.826 (10.598)
	To engage in simple sports	0.787
	To prevent diseases	0.745
	To cure diseases	0.609
	To participate in programs for health and healing (e.g., meditation/walking, etc.)	0.501
	For taking a walk	0.485
Environmental Experience (4)	To avoid fine dust	0.764	0.816 (10.063)
	To breathe clean air	0.738
	To avoid noise in the city	0.738
	To feel the cool temperature (to get out of the heat island phenomenon)	0.615
Daily Leisure (5)	For a date with the opposite sex	0.771	0.845 (9.478)
	To use bike roads	0.720
	To take a walk with one’s dog	0.720
	To use convenience facilities within the urban forest	0.599
	To visit famous cafes and restaurants in the urban forest	0.590
Family (2)	To go out with children/family	0.868	0.794 (5.580)
	To do various leisure activities with children/family	0.796
Eco-Friendly (3)	To use the nature and green space in an urban forest	0.765	0.791 (7.179)
	To enjoy the natural scenery around the city forest	0.745
	To interact with nature (e.g., animals, trees, plants, etc.)	0.570

KMO = 0.909, chi-square (χ²) = 147,07.158, df = 435, p = 0.000.

.

5.2.2. Confirmatory Factor Analysis

CFA has been widely used to verify the measurement scale after conducting EFA [56]. In the analysis, researchers have to consider some cut-offs in indicating fit of model [46,57].

At first, this study deleted some measurement items with lower standard regression weight (S.R.W) than 0.5 or 0.7, per [58,59] demonstrating low-related construct of indicators. With two rounds of the CFA process, five items (“taking picture of scenery” in EA, “time in nature” in HR, “health and healing program & taking a walk” in Health, and “walking with dog” in DL) were deleted due to a low value of S.R.W and unsuitability for the characteristics of the dimensions. Consequently, seven dimensions with 24 items verified from CFA are appropriate for factors of UFVMS as shown in

Table 7. Results of confirmatory factor analysis.

Measurement Variables		R.W.	S.E.	C.R.	S.R.W.	C.R.	A.V.E.
Experience Activities	Watching performances	1.000	-	-	0.899	0.917	0.736
	Going to exhibitions	1.019	0.027	37.735	0.904
	Event participation	0.981	0.027	35.985	0.882
	Forest-related activities	0.838	0.031	26.735	0.735
Healing & Rest	Escaping daily life	1.000	-	-	0.566	0.789	0.562
	Stress relief	1.294	0.080	16.259	0.822
	Physical/mental recharging	1.202	0.076	15.746	0.831
Health	Staying healthy	1.000	-	-	0.818	0.795	0.498
	Doing sports	0.834	0.044	19.024	0.610
	Preventing diseases	1.128	0.071	15.858	0.785
	Curing diseases	0.826	0.072	11.424	0.578
Environmental Experience	Avoiding fine dust	1.000	-	-	0.681	0.838	0.566
	Breathing clean air	0.998	0.046	21.936	0.745
	Avoiding noise from a city	1.198	0.059	20.316	0.825
	Feeling cool temperature	0.943	0.051	18.474	0.752
Daily Leisure	Dating	1.000	-	-	0.589	0.814	0.536
	Using bike road	0.834	0.053	15.704	0.503
	Using convenience facilities	1.356	0.074	18.434	0.900
	Visiting cafés and restaurants	1.317	0.072	18.255	0.857
Family	Going out with children/family	1.000	-	-	0.833	0.901	0.821
	Doing leisure activities with children/family	1.087	0.056	19.542	0.973
Eco-Friendly	Using nature and green space	1.000	-	-	0.661	0.754	0.506
	Enjoying natural scenery	1.121	0.053	21.008	0.694
	Interacting with nature	1.470	0.087	16.867	0.775

Chi-square (χ²) = 995.516 (p = 0.000), CMIN/DF: 4.425, RMSEA: 0.062, SRMR: 0.0640, GFI: 0.908, AGFI: 0.878, CFI: 0.937, NFI: 0.920. RFI: 0.902, IFI: 0.937, TLI: 0.923

and Figure 3.

This study considered a universal criteria of CFA from various researchers (RMSEA, SRMR, GFI, AGFI, CFI, NFI, RFI, IFI, TLI) to find an acceptable model fit—e.g., [60,61,62]. This study analyzed CR (construct reliability) and AVE (average variance extracted) values to verify the convergent validity and discriminant validity. If the factor has an AVE value higher than 0.5 and has a CR value higher than 0.6, the validity of the construct is considered to be adequate [63]. As presented in

Table 8. Result of discriminant validity test.

	AVE	EA	EE	HR	DL	Health	Family	EF
EA	0.736	0.858
EE	0.566	0.396 ***	0.752
HR	0.562	0.277 ***	0.588 ***	0.750
DL	0.536	0.685 ***	0.319 ***	0.212 ***	0.732
Health	0.498	0.235 ***	0.600 ***	0.517 ***	0.091 *	0.706
Family	0.821	0.386 ***	0.328 ***	0.309 ***	0.321 ***	0.258 ***	0.906
EF	0.506	0.476 ***	0.756 ***	0.629 ***	0.345 ***	0.485 ***	0.440 ***	0.712

Significance of correlations: †: p < 0.100, *: p < 0.050, **: p < 0.010, ***: p < 0.001.

, all seven of the dimensions have appropriate CR values and AVE values, thus supporting the appropriate convergent validity of the construct. Since the AVE of dimension of health (0.498) is almost close to the cut-off, this study decided to maintain it, considering it to have an appropriate validity. To verify the discriminant validity, this study compared the AVE value with the corresponding inter-construct squared correlation estimates, which was suggested by Fornell and Larker [63] as being the best method to apply [64]. The result is interpreted as almost having relatively good discriminant validity (p < 0.05) as shown in Table 8, except for the relationship between EE and EF with a minute difference.

In conclusion, the values for the seven dimensions’ indices exceed most of the recommended scores of each of the indices, thus suggesting that the model meets the goodness-of-fit requirement. Based on the result, this study finally develops the UFVMS presented in

Table 9. Final measurement scale of motivations for visiting urban forest.

Urban Forest Visit Motivation Scale (UFVMS) (Seven Dimensions with 24 Factors)
Experience Activities	Watching performances
	Going to exhibitions
	Event participation
	Forest-related activities
Healing and Rest	Escaping daily life
	Stress relief
	Physical/mental recharging
Health	Staying healthy
	Doing sports
	Preventing diseases
	Curing diseases
Environmental Experience	Avoiding fine dust
	Breathing clean air
	Avoiding noise from a city
	Feeling cool temperature
Daily Leisure	Dating with opposite sex
	Using bike road
	Using convenience facilities
	Visiting cafés and restaurants
Family	Going out with children/family
	Doing leisure activities with children/family
Eco-Friendly	Using nature and green space
	Enjoying natural scenery
	Interacting with nature

.

6. Conclusion and Implications

6.1. Summary of Results

The UN [65] states that urban areas will occupy 68% of the world’s population by 2050. With the increase in the number of urban dwellers, the scale of urban forests will also be expanded because of the crucial importance of urban forest with the benefits they provide to people living in urban areas. With these urban forests’ various functional values to urban areas, the value of urban forests has increased significantly [66]. Urban forests are different from rural forests or wellness tourism destinations, and they have their own characteristics [12,67]. In this context, it is highly important to analyze specific and multiple motivations of urban forest visitors to research phenomenological and practical studies such as defining visit characteristics of urban dwellers and how to efficiently operate an urban forest.

By contrast, many studies of forest visitors’ motivation have simply dealt with rural forests, which have characteristics of tourism destinations, while focusing on the case of huge forests such as healing forests or national parks. There are a few studies of urban forest visit motivation; however, these just used traditional motivation from other researchers, and they did not develop a motivation scale just for the case of urban forests. This leads to problems in researching specific motivations of urban forest visitors since the research range of motivation is too narrow and cannot objectify the various motives of each urban forest visitor.

By considering various prior studies of urban forest visit motivation and analyzing Big Data of urban forest along with expert discussions, this study deduced important motivation factors. From the discussion among various experts, this study first developed a draft version of UFVMS (seven dimensions with 29 items). By using this scale, 878 data of motivation tendencies obtained from the survey were used to conduct EFA and CFA to verify the UFVMS. With the process of EFA, all seven dimensions with 29 items were satisfactory in terms of suitability and reliability; however, there were some modifications of items in the CFA process. With two rounds of CFA, this study deleted three items with lower S.R.W., and it also erased two items inappropriate for the dimension according to the judgement of experts. In the final analysis, UFVMS, which has a well-accepted model fit, is composed of seven dimensions (Experience Activities, Healing and Rest, Health, Environmental Experience, Daily Leisure, Family, Eco-Friendly) with 24 items. In addition to factors of “health and mental healing”, which have been studied a lot in the past, the results show that urban forest visitors also have specific motivations of experiential activities (EA), environmental experiences (EE, EF), and leisure (DL, Family) characteristics.

6.2. Theoretical Contributions

First of all, this study evidences that urban forest visitors have their own specific motivations to visit urban forests, which are more various than have been stated by many prior studies. It includes a total of seven dimensions, evidencing that visitors visit urban forests for various motives, contrasting with many studies that mainly focus on healing and curing beneficial aspects—e.g., [2,9]. This implies that future studies should deal with various aspects of urban forest visit motivation while not focusing solely on the major factor, which has already been studied a lot. Secondly, this study establishes an objective scale (UFVMS) specifying each visit motivation of urban forests. Previously, many motivational studies have been conducted just using simple surveys based on preliminary studies without using Big Data, and not by developing and validating their own specific measurement scales. By contrast, this study mixes various methods such as reviewing literature, analyzing Big Data, having expert discussions, and conducting surveys on visitors to perform factor analysis to develop a more precise scale. From this specific scale development, this study will serve as academically valuable prior research to future researchers who plan to carry out research into the specific motivations of urban forest visitors. Finally, the result of this study shows that the “recreation & leisure” aspect is a notable dimension of urban forest visit motivation, as has been stated by prior researchers [3,21,68]. Unlike well-known purposes such as healing/health of forest tourism destinations or rural forest visits, the motivation for “recreation & leisure” was derived separately as “Experience Activities”, “Daily Leisure”, and “Family” for visiting urban forests in this research. This study concluded that this is mainly because the urban forest has its own characteristics of having a near distance from the residences of visitors, meaning that the activity of visiting urban forests is considered as part of daily life for urban residents. This result indicates the necessity of research into the leisure and recreation effects and motivations for visiting urban forests along with the healing function of them. In particular, there is a need for in-depth research towards improving the service quality of cafes and restaurants (e.g., facilities, food quality, human service) to lead the satisfaction of visitors, which contributes to the sustainable operation of urban forests.

6.3. Practical Implications

First, DMO or authorities should implement comprehensive management to ensure visitors’ motives evenly. As this result derived, not all visitors visit urban forests for healing motives. Accordingly, detailed plans that can satisfy each motivation should be developed by considering each subdivided motivation derived in this study for management of place. Nevertheless, the result evidences that the important function of urban forest is still improving the physical and mental health of visitors, whose motivators have been continually studied by many researchers—e.g., [9,21,23,35,41,42], meaning that authorities should maintain and enhance these benefits with provision of programs related to the urban dwellers’ health [69]. Next, the administrators of urban forests have to work hard on familiarizing urban forests to urban dwellers’ lives. Urban forests are not just aspects of tourist destinations, but they have also become part of citizens’ lives. This culture has been demonstrated by various motives such as recreation and leisure dimension. For instance, in the DL dimension, measurement factors evidence that visitors for leisure motivation just engage in daily activities such as dating and going out with others. Therefore, in terms of urban forest managers, it is necessary to focus more on the convenience of urban forests and operate various daily programs rather than on the development focusing on tourism factors. Specifically, it is necessary to develop a program that can be enjoyed by family/acquaintances while maximizing recreation and leisure characteristics. It is also important to establish amenities (e.g., toilet, nearby cafes and restaurants, trash cans) for those who visit urban forests as part of their daily lives without specific motives, just for daily activities such as picnics or strolls. For example, it is necessary for the government and administrators to provide a high quality of taste of beverage, desserts, and foods and to supply diverse light snacks with food trucks for continuous daily visits of urban forests as the part of visitors’ lives. In addition, greener food services, which are highly related to the image of urban forests, also could meet needs of urban forest visitors. Finally, the manager of urban forest should develop various events and programs that can improve social relations, which is the main motivator, as shown in the result of “DL & Family” dimensions. Urban forest has been evidenced as the place that provides an environment that benefits the social life of groups with greater social connections [70]. These actions will lead to increased satisfaction with urban forests from visitors and serve as a stepping stone for establishing a higher quality and sustainability of urban forest management of cities. This result of urban forest management will contribute to better urban lives and the development of social resilience with health, social, and environmental benefits.

6.4. Limitations and Future Research Agenda

There are also some limitations to this research. First of all, there are similar characteristics of each measurement item, such as some factors in the “Health” dimension and “Environmental Experience”. This is because the factors in this study were classified to subdivide the most major motivation for visits. This means that, for example, a motivation such as “avoiding fine dust” in an EE dimension can be guessed to be a motivation of maintaining health, but this study was focused more on the environmental effects of urban forests. Secondly, there are dimensions that are not acceptable for finding discriminant validity. As shown in [Table 6], EF (Eco-Friendly) dimension has little discriminant validity with EE (Environment Experience). This may due to the fact that respondents have similar acceptance of environmental experiences and eco-friendly motivation because urban dwellers are sensitive to environmental aspects these days. In future research, this should be combined into one dimension of the environment. Finally, this study does not consider exogenous variables that affect the motivation for visiting urban forests such as weather, crime, distance, and so on. Weather conditions can also play a role in variations of daily use patterns [68]. In future studies, dealing with exogenous variables regarding visit motivations for urban forest can draw academic and practical implications to operate urban forests and develop more specific measurement scales.