Impact of Perceived Value and Community Attachment on Smart Renovation Participation Willingness for Sustainable Development of Old Urban Communities in China

Abstract

The transformation of old neighborhoods involves many important types of livelihood development projects, of which smart transformation is considered one of the most important tasks at present. The smart transformation of old neighborhoods provides an important means to promote sustainable development; however, enhancing the willingness of residents to participate is an important prerequisite for the smart transformation of old neighborhoods. Considering perceptive value acceptance theory and community ownership theory in the field of information management and sociology, we study the intention of residents in old communities to participate in intelligent transformation and its influence mechanisms. Our results show that willingness to participate in the smart renovation of old residential areas is positively affected by the perceived value of smart renovation and the sense of community attachment of residents. The sense of community plays an intermediary role in the relationship between perceived value and willingness to participate in smart renovation. At the same time, the perceived value of intelligent renovation of old residential areas and a sense of community have chain mediating effects between perceived usefulness, perceived enjoyment, perceived risk, perceived cost, and willingness to participate. This paper not only achieves theoretical expansion but also provides good support for accelerating the intelligent transformation of old communities in China.

1. Introduction

Smart city construction is the mainstream trend of urban development throughout the world at present and involves the use of advanced information technology to achieve intelligent management and operation of cities. This, in turn, creates a better life for people and promotes the harmonious and sustainable growth of cities [1]. In the process of promoting urbanization, many cities in China have experienced population expansion, resource shortages, traffic congestion, housing tension, and a series of other problems affecting the sustainable development of life and cities due to population-, resource-, and land-related factors; in response, smart city construction has become an important initiative to resolve these problems. Smart communities are also one of the most important components of smart cities, and accelerating the construction of smart communities is an important strategic initiative to promote the green and sustainable development of cities. Since IBM proposed the concept of a smart community in 2008, countries around the world have started to take it as the goal of information development. Europe, North America, Singapore, and other countries started earlier, and the construction of smart communities in these areas has become more mature. In China, as an innovative model of community management and services, smart communities have also received widespread attention in recent years. China’s 14th Five-Year Plan and the outline of the 2035 Visionary Goals propose accelerating the construction of smart communities, relying on community digital platforms and offline community service organizations, building convenient and beneficial smart service circles, providing online and offline integration of community living services and intelligent community services, and achieving sustainable community development. However, the neighborhoods in cities and towns that were built earlier have objectively become a problem in smart community construction. Therefore, as a key step in the construction of smart communities, the transformation of old neighborhoods comprises a major development project that concerns the fundamental interests of residents. However, as the government has centralized some community-based e-government and e-commerce services, shifting its goal from IT- to citizen-centered, such that citizen participation has been increasingly emphasized as a key operational mode [2]. Therefore, mobilizing and stimulating the initiative and enthusiasm of residents to participate in the transformation and guiding them to participate voluntarily are the basic principles underlying the comprehensive transformation of old neighborhoods, as well as providing an important path to creating an empowering system of “participation in governance”, in order to make up for the inadequacy of the “administrative-led model” in the transformation of old neighborhoods [2,3].

Based on this, clarifying the factors influencing the participation of residents in the renovation of old neighborhoods has also become a key concern in academia. Relevant representative studies are listed below. Based on the theory of planned behavior, social practice theory, and social action theory, Li et al. have analyzed the barriers to resident participation in the process of the spongy transformation of old neighborhoods in Zhenjiang City, Jiangsu Province, and pointed out that a lack of social capital and low community recognition are the main barriers to transforming the willingness to participate of residents into transformation behavior [4]. In a further study, Dezhi Li et al. pointed out that participation in governance attitudes, subjective norms, and perceived behavioral control had significantly positive effects on the willingness to participate of residents, in terms of governance in old neighborhood renovation [5]. Zhang Jiali et al. have analyzed the factors affecting the enthusiasm of residents to participate in the renovation of old neighborhoods in Taocheng District, Hengshui City, and pointed out that factors such as economic interest drive and social capital possessed by residents are important internal factors affecting their participation in the renovation of old neighborhoods, while factors such as imperfect mechanisms and low community governance are important external factors [6].

The above-related work has provided a theoretical means to understand and cognize the influencing factors of resident participation in old neighborhood renovation, in order to promote the smooth implementation of old neighborhood renovation. However, it is not difficult to find that the old neighborhood renovation models mentioned in the existing literature are mainly limited to the basic and improvement types of old neighborhood renovation work, which aim to meet the basic or improved living needs of old neighborhood residents. Further research is needed to investigate the willingness of residents to participate in the renovation of old neighborhoods, including smart renovation, and the associated factors influencing their participation. In fact, in the process of a comprehensive renovation of old neighborhoods, smart renovation of old neighborhoods, as an enhancement type of renovation, focuses on meeting the needs of old neighborhood residents for quality of life improvements; however, compared with the basic and enhancement types of renovation work, the needs of residents are relatively less urgent, and their enthusiasm for participation needs more attention [7]. Therefore, how to mobilize the enthusiasm and initiative of residents to participate in the smart transformation of old neighborhoods, as well as enhancing their willingness to participate, have become prominent issues to be considered in the process of promoting the smart transformation of old neighborhoods.

From the digital life perspective, the smart transformation of old neighborhoods can be deconstructed as replication and re-engineering of the concept of smart community construction in the real field of old neighborhoods which, in essence, is supported by new information technologies such as big data and the Internet of Things, as well as through the construction of digital smart systems and related platforms improving the daily life of residents in old neighborhoods, in order to update and upgrade the community service content and mode [8]. In this way, we can provide more convenient and intelligent community services to the residents of old neighborhoods, thus achieving sustainable community development. Examples of smart transformations include community elevator retrofitting, smart access control, smart logistics, contactless bill payment, community shopping groups, and so on. In addition, research on modern communities has shown that the sense of belonging of community residents has an important influence on community participation behavior [9,10]. Based on this, we analyze the willingness of residents in old neighborhoods to participate in the smart neighborhood renewal, as well as its influencing factors, through perceived value theory, which is used to explain user preferences and usage behaviors in specific situations, as well as the sense of belonging theory, which is used to explain the psychological state of community residents to integrate into a certain area and people. We hope to further expand the theoretical research and provide theoretical guidance for accelerating the smart transformation of old neighborhoods.

2. Theoretical Model and Research Hypothesis

2.1. Perceived Value Acceptance Model and Community Attachment

Perceived value was first proposed by Zeithaml, defined as the overall evaluation of the utility of a product or service by the customer based on the perceived benefits and costs paid [11,12]. Later, Kim et al. introduced the concept of perceived value in the field of information systems management and proposed the Value-based Adoption Model (VAM) to analyze the issue of user willingness to adopt mobile Internet technologies [13]. The model assumes the dual identity of the action subject—as a technology user and a consumer of the service provided by the technology—and advocates revealing the choice problem from the perspective of value maximization of the adopter, which effectively compensates for the shortcomings of the Technology Acceptance Model (TAM), assuming the action subject as a technology user only. In the perceived value acceptance model, the perceived value involves the overall evaluation of the technology and its services by the actor, based on which the actor decides whether to purchase or use the technology and its services. In the empirical measurement, perceived value is mainly measured in terms of the benefits (Benefit) and sacrifices (Sacrifice) that people can obtain from the technology and its services. The benefits perceived by the actors can be classified as perceived usefulness and perceived enjoyment, according to their extrinsic motivation and intrinsic emotion, while the sacrifices perceived by the actors are classified as perceived cost and perceived technicality, according to their monetary and non-monetary nature.

Community attachment (or sense of belonging) is generally defined as the psychological feeling that residents identify with their community members and participate in the collective, but also carries the emotional overtones of individual residents, mainly including feelings of commitment, love, and attachment to the community [9,14,15]. This creates a tangible reflection of an individual’s emotional connection to a place and its residents [16,17]. Researchers have conducted extensive and in-depth studies on the causes and consequences of community belonging. In the analysis of the antecedents of community belonging, the influencing factors have evolved from a linear to a systemic model. In early studies, Hawley et al. and Fischer et al. considered population size and density as important antecedent variables of community affiliation, based on Tennessee and Vos [18,19]. In subsequent studies, factors such as length of residence, community interaction, and community satisfaction were gradually considered important factors of community belonging [20]. In the analysis of the consequences of community belonging, researchers have analyzed the effects of social belonging on community governance [16,21], community participation of tourism residents [9,22], user willingness to participate in knowledge crowdsourcing virtual communities [23], urban integration of landless farmers [24], and emotional well-being [25].

2.2. Research Hypothesis

Cognitive appraisal theory [26] divides the motivation of the subject’s behavior into two dimensions—namely, extrinsic and intrinsic motivation. The perceived value acceptance model further conceptualizes the extrinsic and intrinsic motivations of an actor subject to choosing to accept technology and the services it provides according to the perceived usefulness and perceived enjoyment [13], which together constitute a perceived benefit. Perceived usefulness refers to the total value perceived by the user when using the new technology. Perceived enjoyment refers to the extent to which the process of using the new technology is enjoyable, and portrays the benefits of the action subject from the perspective of individual internal feelings. In the old community smart transformation scenario, the perceived benefits of residents can also be divided into two dimensions; namely, the perceived usefulness and perceived enjoyment of the old community smart transformation. Among them, the perceived usefulness of the smart transformation of the old community by residents mainly refers to the improvement of the convenience and safety of life, as well as the satisfaction of their multi-level needs, through the implementation of the smart transformation; meanwhile, the perceived enjoyment mainly refers to the improvement of the fun, happiness, and satisfaction experienced by the residents through the implementation of the smart transformation of the old community [13,27]. In fact, the implementation of old district smart transformations can effectively improve the old district property and security smart management level, information sharing, and resource integration level; at the same time, the application of new information technology in the community or new smart services will often arouse the interest of residents, while reversing the “old and dilapidated” image of the neighborhood. In this process, the higher the perceived usefulness and enjoyment of residents, the higher the value obtained by renovating old neighborhoods. In addition, in the field of information systems, numerous empirical studies have shown that perceived usefulness and perceived enjoyment positively affect perceived value [28,29,30]. Based on this, we propose the following hypotheses:

H1a. 

Perceived usefulness positively affects the resident’s perceived value of the smart transformation of old neighborhoods.

H1b. 

Perceived enjoyability positively affects the resident’s perceived value of the smart transformation of old neighborhoods.

Perceived sacrifices, in the perceived value acceptance model, describe the monetary and non-monetary costs that actors believe they must pay when adopting technology and services, which have negative impacts on the perceived value [11,31]. The current practice of old neighborhood renovation is mainly funded by the government [32] which, to some extent, conforms to the social public goods attribute of old neighborhood renovation, but also increases the financial burden on local governments. The “General Office of the State Council on comprehensively promoting the work of urban old neighborhood renovation guidance” (2020) clearly proposes establishing old neighborhood renovation funds through a reasonably shared mechanism, in accordance with the principle of “who benefits, who finances”, and actively promotes the residents to participate in the transformation. In addition, the smart transformation of old neighborhoods is a community function optimization activity, intended to enhance the intelligence of public and people-friendly services in old neighborhoods by constructing a smart community integrated information service platform through the comprehensive use of modern science and technology and corresponding infrastructure [33]. However, the overall age of the residents in old neighborhoods makes it difficult for them to quickly learn and adapt to the operation of relevant smart platforms, as the use of smart community-related information platforms often requires residents to upload information such as ID cards, account books, house books, and educational qualifications, which increases their concerns regarding identity information leakage. In view of this, we divide the perceived sacrifices of residents in the old neighborhood smart transformation scenario into two aspects—namely, the perceived cost and perceived risk. The former mainly refers to their perception of the monetary cost related to carrying out the smart transformation, including the pre-investment costs and subsequent operation and maintenance costs. The latter mainly reflects a potential concern of residents regarding the risk of loss that may be brought about by operational maladjustment and personal privacy leakage in the process of using the relevant platform after the smart transformation. Therefore, we propose the following hypotheses.

H2a. 

The perceived cost negatively affects the resident’s perceived value of the smart transformation of old neighborhoods.

H2b. 

The perceived risk negatively affects the resident’s perceived value of the smart transformation of old neighborhoods.

Perceived value is the core construct in the perceived acceptance model, which is the holistic value assessment of the product or service by the actor after considering their gains and losses [11], which is generally measured according to the actor’s evaluation between the perceived benefits and perceived sacrifices [34]. The smart transformation of old neighborhoods is a transformation activity based on the actual conditions of the neighborhoods, with the main purpose of enriching the supply of community services and improving the quality of life of residents in such areas; thus, the perceived value of the smart transformation of old neighborhoods by residents reflects their overall value assessment. Compared with the original state of the old neighborhoods, the smart transformation can realize online bill payments, online warranties, and advanced problem feedback, thus effectively improving the quality and efficiency of property services in old neighborhoods. At the same time, the application of new information technologies, such as the Internet of Things and artificial intelligence, can facilitate the intelligent monitoring of old neighborhood buildings, pipelines, access control, garbage classification, and so on, as well as enhance the security and convenience of community life. In addition, based on smart community-related information service platforms, the upgrading of convenient living services can be effectively realized, such as community platforms with built-in online stores, which can provide residents with more convenient goods and consumer services. These potentially significant changes can effectively enhance the self-concept capabilities or utility of residents in the socialization context of older neighborhoods; which, in turn, can have a positive impact on their behaviors and attitudes [34]. Related studies have shown that perceived value has a positive effect on willingness to participate [27,35]. Based on the above, we propose the following hypothesis:

H3. 

The perceived value of the smart transformation of old neighborhoods by residents positively influences their willingness to participate in the smart transformation process.

A sense of community attachment is an important expression of the emotional commitment of individual residents to the community, representing an emotion-based resident–community relationship and acceptance of community value goals [36,37]. In the perspective of community commitment theory, when residents are satisfied with their living environment and the experienced community public services and property services, they become emotionally committed to the community as a whole [37,38]. In this scenario, individuals are more willing to participate in community-building activities in return, through supportive behaviors, according to the social exchange principle. Theodori and McCunn et al. have demonstrated, through empirical analysis, that such community affective commitment has a significant positive effect on the engagement behaviors of residents in the community [37,39]. In addition, a sense of community belonging, as an important manifestation of high community affective commitment, has been studied to show that residents with a sense of community attachment are more inclined to support new initiatives or new construction plans in their communities [10,40]. The smart transformation of old neighborhoods involves all aspects of the community life of residents, involving a community renewal initiative through which communities may improve the quality of life of neighborhood residents. According to community commitment theory, residents of old neighborhoods with a sense of community attachment will be more likely to understand the goals and characteristics of the smart transformation of the old neighborhood, and, at the same time, they will more clearly understand and experience the improvement of community services and quality of life brought about by the smart transformation of the old neighborhoods. Thus, they will more likely support the smart transformation initiative if there are practical actions to increase their willingness to participate. Based on this, we propose the following hypothesis:

H4. 

A resident’s sense of belonging positively affects their willingness to participate in the smart transformation of old neighborhoods.

According to the division of perceived value by Swenny et al., the perceived value of residents can be further divided into the perceived emotional and social value and the perceived functional value, in the case of the smart transformation of old neighborhoods [41]. Among these, perceived emotional and social values can be translated into good neighborhood relations, while perceived functional values can be translated into the improvement of community services and the community’s physical environment. In the process of promoting the smart transformation of old neighborhoods, the work related to the smart transformation will often become a hot topic for the original (formal and informal) organizations in the community, which not only enhances the activity of community organizations but also further expands the community social network (e.g., offline social relationships or online WeChat groups) and close neighborhood relations. In the infrastructure dimension, carrying out the smart transformation of old neighborhoods will, on one hand, build up new information infrastructure, such as smart community public service platforms, linked community security platforms, and comprehensive community information management platforms, among others. On the other hand, it can effectively activate the inherent infrastructure of the old neighborhood, improving its management level, enhancing utilization rates, and improving the physical environment. In addition, the smart transformation of old neighborhoods can also effectively optimize the community’s public services, elderly services, and emergency management services, thus improving the community service level. Meanwhile, studies have shown that a resident’s sense of belonging is positively influenced by community neighborhood relations, community services, and the community’s physical environment [42,43,44]. Based on this, we propose the following hypothesis:

H5. 

The perceived value of the smart transformation of old neighborhoods positively affects the sense of community belonging of residents.

Based on the above analysis of the influence mechanisms between perceived benefits, perceived sacrifices, perceived values, sense of belonging, and willingness of residents to participate in the smart transformation of old neighborhoods, we further propose the following hypotheses:

H6. 

Community belongingness plays a mediating role between perceived value and willingness to participate of residents in the smart transformation of old neighborhoods (i.e., the perceived value of the smart transformation by residents will improve their sense of belonging, thus enhancing their willingness to participate in the smart transformation of the old neighborhood).

H7a. 

The perceived usefulness of the smart transformation of old neighborhoods positively affects the sense of belonging of residents, through its effect on the perceived value of the smart transformation, thus positively affecting their willingness to participate.

H7b. 

The perceived enjoyment of residents with the smart transformation of old neighborhoods positively influences their sense of belonging, through its effect on the perceived value of the smart transformation, thus positively influencing their willingness to participate.

H8a. 

The perceived cost of the smart transformation in old neighborhoods negatively influences the sense of belonging of residents, through its effect on the perceived value of the smart transformation, thus negatively influencing their willingness to participate.

H8b. 

The perceived risk of the smart transformation in old neighborhoods negatively affects the sense of belonging of residents in old neighborhoods, through its effect on the perceived value of the smart transformation, thus negatively affecting their willingness to participate.

In addition to the aforementioned assumptions and influencing factors, gender, age, education, occupation, residential attributes, length of residence, and nature of community property rights of residents in old communities may also have an impact on their participation in smart renewal construction. Therefore, we constructed a theoretical model regarding the willingness to participate in the smart renewal construction of residents in old neighborhoods, as shown in Figure 1.

3. Methodology

3.1. Sample and Data Collection

The sampling in this study was conducted mainly by questionnaire, and data were collected in China to test our hypotheses. In China, the community is the basic unit of the city, comprising the most important living space for people [45]. With the popularization and application of new-generation information technology, leading to the foundation of smart cities, government departments have taken a series of measures to accelerate the construction of smart cities, making great progress. However, in the process of continuous promotion, problems such as low motivation and low initiative of residents to participate in construction have been gradually exposed. Therefore, we take China as the object of the research. The sample of this research was selected mainly from old neighborhoods (neighborhoods built in 2000) in Beijing, and the research subjects included owners and tenants living in these old neighborhoods. The questionnaire used in this study is divided into three main parts, among which the first part includes the research guidelines, explaining the definition and functions of a smart community; the second part involves personal information, including gender, age, education level, work unit, area of residence, housing properties of residence, and length of residence; the third part includes the main questions relating to the relevant variables, including willingness to participate, the perceived value of smart transformation in old neighborhoods, perceived usefulness of smart transformation in old neighborhoods, perceived enjoyment of smart transformation in old neighborhoods, perceived risk of smart transformation in old neighborhoods, perceived cost of smart transformation in old neighborhoods, and sense of belonging to the community.

The research was conducted in two phases. The first phase was a pre-study phase, in which 50 questionnaires were randomly distributed to the residents of the old neighborhoods under the jurisdiction of Beixiaoguan Street in Haidian District, Beijing, through which the reliability and validity of the questionnaire were initially checked. The second phase was the formal research phase, in which the questionnaire was first improved and modified, based on the results of the analysis and testing of the questionnaire data in the pre-study stage and the feedback from the street staff. On this basis, the formal research work was carried out through a combination of online and offline methods, mainly using the Seeing Numbers questionnaire platform for precise delivery, in order to motivate the subjects to actively fill out the questionnaire and improve the validity of the online questionnaire, as the subjects who filled out the questionnaire completely and submitted it effectively within the specified time were given a certain bonus. Offline delivery was conducted mainly through the old neighborhood residents, who filled out the questionnaire directly. In the formal research stage, 300 questionnaires were distributed, 286 were collected, 44 invalid questionnaires were excluded, and thus, 242 valid questionnaires were collected, with an effective recovery rate of 81%.

Table 1. Sample demographics (n = 242).

	Percentage
Gender
Male	36%
Female	64%
Age
≤25	10%
26–49	65%
≥50	25%
Properties
Owners	53%
Tenants	47%
Nature of housing ownership
Commercial housing	41%
Reformed housing and affordable housing	12%
Shared ownership housing	14%
Rental housing	28%
Other properties	5%
Education
College and below	48%
Undergraduate and above	52%
Career
Employees of enterprises	55%
Government organizations, people’s groups, military	4%
Institutions/organizations	24%
Self-employed	4%
Other jobs	13%
Living Area
Within the third ring	27%
Within the fourth ring, outside the third ring	28%
Within the fifth ring, outside the fourth ring	31%
Other areas	14%
Length of residence
≤1 year	15%
2–4 years	29%
5–9 years	16%
10–19 years	25%
≥20 years	15%

describes the demographic data of our respondents.

3.2. Measurement of Variables

The items set in the formal research questionnaire of this paper were taken from established scales in the foreign literature, and the initial translation standards were all in English. The English was translated into Chinese, according to back-translation procedures, and was reviewed and revised by experts in the field to modify the nuances of the measurement items and ensure the applicability of the scale in the Chinese context.

In the scale design process, we based the questionnaire on scales related to willingness to participate, perceived value, perceived usefulness, perceived enjoyment, perceived cost, and perceived technicality developed by Kim et al. [13], and combined with the willingness to participate, perceived usefulness, and perceived risk scales developed by Davis et al. [46,47,48]; the perceived value scale developed by Sirdeshmukh et al. [49]; the perceived enjoyability scale and the perceived cost scale developed by Agarwal et al. and Voss [49,50]; the resident community scale designed by Chavis et al. and Ruizhi Li [10,51]. In the adapted scale, perceived usefulness regarding the smart transformation of old neighborhoods contains four questions, perceived enjoyment contains four questions, perceived risk contains five questions, perceived cost contains two questions, perceived value contains three questions, and the willingness to participate includes four items. The specific questionnaire items are detailed in

Table 2. Questionnaire item design for model variables.

Variables	Title Item	Description	References
Old neighborhood smart transformation perceived usefulness	Use1	I think the transformation of the community into a “smart community” will improve the security of the community.	[13]
	Use2	I think the transformation of my neighborhood into a “smart community” will improve the convenience of my daily life.	[47]
	Use3	I think the transformation of my neighborhood into a “smart community” will improve the efficiency of my daily life.	[47]
	Use4	Overall, I think that the transformation of this community into a “smart community” will be useful in my daily life.	[47]
Perceived enjoyability	Fun1	In my opinion, the transformation of the neighborhood into a “smart community” will bring me a lot of fun.	[13]
	Fun2	In my opinion, I would like the services offered by the “Smart Community”.	[49]
	Fun3	I think the transformation of the neighborhood into a “smart community” will make me feel more comfortable than the current situation of the neighborhood.	[13]
	Fun4	Compared with the current situation of the community, I think the transformation of the community into a “smart community” can make me feel happier.	[13]
Perceived risk	Risk1	I don’t think it’s particularly easy to use some of the services offered in the “Smart Community”.	[47]
	Risk2	After the district is transformed into a “smart community”, it may take me some time and effort to get familiar with the relevant smart community service platform.	[48]
	Risk3	The transformation of my neighborhood into a “smart community” may have an impact on my lifestyle that makes me feel uncomfortable.	[13]
	Risk4	My personal privacy may be compromised by the use of services after the district is converted into a “smart community”.	[13]
	Risk5	The “smart community” service platform may have technical shortcomings that affect my life.	[13]
Perceived cost	Fee1	I am willing to provide financial support for the transformation of the district into a “smart community”.	[13]
	Fee2	I think it is reasonable to pay to use the services of the community after it is transformed into a “smart community”.	[50]
Perceived value	Value1	I think it’s worth the effort to participate in the “smart community” transformation of my neighborhood compared to the services it can provide me.	[13]
	Value2	Compared to the services that the “Smart Community” can provide me, I think it is worthwhile for me to spend some time in participating in the construction of the “Smart Community” in my community.	[13]
	Value3	I think it’s a good deal to pay a certain amount of money compared to the services I can get after the “smart community” transformation.	[52]
Willingness to participate	Join1	I plan to participate in the “smart community” transformation of my neighborhood in the future.	[46]
	Join2	In the future, I would like to participate in the promotion of the “smart community” transformation of our community.	[52]
	Join3	In the future, if I am required to cooperate, I am willing to participate in the maintenance of the terminal equipment related to the “smart community” of this district.	[13]
	Join4	In general, I would like to participate in the “smart community” transformation of our community.	[13]
Community Sense of Belonging	Emo1	I would feel very attached if I moved out of the neighborhood.	[51]
	Emo2	I will like my neighborhood even more if I carry out smart transformation of the neighborhood.	[51]
	Emo3	I would like to live in this community for a long time if conditions permit after carrying out the wisdom transformation of the community.	[51]
	Emo4	Carrying out smart transformation of the community will enable me to better integrate into the community.	[53]

. The Cronbach’s alpha values for the scales in this study were 0.700, 0.789, 0.770, 0.791, 0.784, 0.829, and 0.858, respectively. In addition, gender, age, educational background, residential area, residential housing property attributes, length of residence, and workplace, which may affect the study variables, were used as control variables.

4. Data Analysis and Results

4.1. Common Method Bias and Validation Factor Analysis

A common method test was performed using the Harman one-way method. The single-factor test indicated that the unrotated exploratory factor analysis analyzed a total of five main factors, and the degree of variation in the first factor did not exceed half of the total variation (34.885%), so there was no serious effect from the common method bias in this study.

Confirmatory factor analysis (CFA) was conducted using the Mplus 7.0 software, in order to examine the differential validity among the factors, for which one- to seven-factor models were tested (as shown in

Table 3. Results of confirmatory factor analysis.

Models	χ²	df	χ²/df	RMSEA	CFI	TLI	SRMR
Single factor (US + FU + SK + FE + VA + JO + EM)	1352.291	299	4.523	0.120	0.613	0.580	0.107
Two factors (US + SK + FE + VA + JO + EM, FU)	1258.916	298	4.225	0.115	0.647	0.615	0.106
Three factors (US + FE + VA + JO + EM, FU, SK)	1051.801	296	3.553	0.103	0.722	0.695	0.095
Four factors (US + VA + JO + EM, FU, SK, FE)	929.815	293	3.173	0.095	0.766	0.741	0.089
Five factors (US + JO + EM, FU, SK, FE, VA)	889.479	289	3.078	0.092	0.780	0.752	0.089
Six Factors (US + EM, FU, SK, FE, VA, JO)	759.739	284	2.675	0.083	0.825	0.800	0.110
Seven factors (US, FU, SK, FE, VA, JO, EM)	480.587	278	1.729	0.055	0.926	0.913	0.062

Note: US denotes perceived usefulness; FU denotes perceived enjoyment; SK denotes perceived risk; FE denotes perceived cost; VA denotes perceived value; JO denotes perceived willingness; EM denotes indicates the sense of belonging to the community.

). The results demonstrated that the hypothesized seven-factor benchmark model (including perceived usefulness, perceived enjoyment, perceived risk, perceived cost, perceived value, willingness to participate, and sense of belonging) compared to the other competing models, had better fit (χ² = 480.587, df = 278, χ²/df = 1.729, CFI = 0.926, TLI = 0.913, RMSEA = 0.055, SRMR = 0.062, N = 244); these results support the discriminatory validity of the measurement model.

4.2. Descriptive Statistics and Correlation Analysis

The mean, standard deviation, and correlation coefficients between the variables were analyzed by descriptive statistics using the SPSS 22.0 software. The correlation coefficients and descriptive statistics among the variables are provided in

Table 4. Descriptive statistics and correlation coefficients between variables.

Variables	M	SD	1	2	3	4	5	6	7
1 Perceived usefulness	4.3361	0.46289	1
2 Perceived enjoyability	4.0799	0.56270	0.669 **	1
3 Perceived risk	2.9057	0.72655	−0.263 **	−0.312 **	1
4 Perceived cost	2.7029	0.98488	−0.227 **	−0.363 **	0.460 **	1
5 Perceived value	3.7309	0.72513	0.431 **	0.568 **	−0.297 **	−0.542 **	1
6 Sense of community attachment	3.9047	0.74424	0.214 **	0.277 **	−0.213 **	−0.346 **	0.467 **	1
7 Willingness to participate	3.7264	0.75748	0.365 **	0.506 **	−0.298 **	−0.419 **	0.693 **	0.481 **	1

Note: **, p < 0.01.

. The results of the preliminary analysis showed that perceived usefulness, perceived enjoyment, perceived risk, perceived cost, and perceived value of smart transformation in old neighborhoods by residents were significantly correlated (r = 0.431, p < 0.01; r = 0.568, p < 0.01; r = −0.297, p < 0.01; r = −0.542, p < 0.01). The perceived value of smart transformation in old neighborhoods correlated significantly with the willingness to participate in smart transformation (r = 0.693, p < 0.01), and the sense of community attachment of residents correlated significantly with their willingness to participate in smart transformation (r = 0.481, p < 0.01). The results of the simple correlation analysis were basically consistent with the study predictions, indicating suitability for further regression analysis tests.

4.3. Hypothesis Validation

The main effects test was conducted first. As shown in the hierarchical regression results in

Table 5. Hierarchical regression results: main effects.

Variables	Perceived Value
	M1	M2	M3	M4	M5
Gender	0.168	0.128	0.072	0.118	0.045
Age	0.143	0.170 *	0.148 *	0.163 *	0.057 *
Education	−0.011	−0.008	−0.037	−0.015	−0.068
Occupation	−0.006	0.019	0.005	0.036	0.016
Residence property	−0.082	−0.117	−0.079	−0.201 *	−0.199 **
Area of residence	0.012	0.018	−0.014	0.001	0.022
Length of residence	0.060	0.044	0.018	0.048	0.051
Nature of housing ownership	−0.121 ***	−0.109 ***	−0.090 ***	−0.126 ***	−0.105 ***
Perceived usefulness		0.573 ***
Perceived enjoyment			0.630 ***
Perceived risk				−0.251 ***
Perceived cost					−0.356 ***
Adjusted R2	0.151	0.286	0.373	0.211	0.364
ΔR2	0.180	0.128	0.213	0.056	0.204
F	6.378 ***	11.789 ***	17.091 ***	8.234 ***	16.465 ***

Note: *, p < 0.05; **, p < 0.01; ***, p < 0.001.

, age and the nature of property rights of housing had significant effects on perceived value. After controlling for gender, age, education, occupation, residential attributes, residential area, length of residence, and nature of property rights in the neighborhood, the perceived usefulness (β = 0.573, p < 0.001) and perceived enjoyment (β = 0.630, p < 0.001) of smart transformation in old neighborhoods had significant positive effects, while perceived risk (β = −0.251, p < 0.001) and perceived cost (β = −0.356, p < 0.001) had significant negative effects on resident’s perceived value of smart transformation in old neighborhoods. Therefore, H1a, H1b, H2a, and H2b were supported.

The mediating effect of a sense of community attachment was tested, according to the mediation test proposed by Baron and Kenny [53]. It can be verified, from

Table 6. Hierarchical regression results: mediating effects.

Variables	Sense of Community Belonging	Willingness to Participate
	M6	M7	M8
Gender	0.050	0.104	0.098
Age	−0.007	−0.025	−0.025
Education	−0.025	−0.020	−0.016
Occupation	0.013	−0.037	−0.045
Residence properties	−0.003	−0.059	−0.051
Perceived value	0.433 ***	0.661 ***	0.541 ***
Sense of belonging			0.217 ***
Adjusted R2	0.298	0.484	0.475
ΔR2	0.324	0.327	0.033
F	12.359 ***	26.320 ***	22.798 ***

Note: ***, p < 0.001.

, that the perceived value of smart transformation of old neighborhoods had a significant positive effect on the willingness to participate in the smart transformation (β = 0.661, p < 0.001); thus, H3 was verified. The sense of community attachment had a significant positive effect on willingness to participate in the smart transformation (β = 0.217, p < 0.001), and perceived value (β = 0.433, p < 0.001) had a significant positive effect on the sense of community belonging, verifying H4 and H5. When the perceived value of smart transformation of old neighborhoods and sense of community attachment were entered into the equation at the same time, the effect of perceived value on willingness to participate was significantly lower (β = 0.541, p < 0.001), which correspondingly indicated that sense of community attachment has a partially mediating role between perceived value and willingness to participate.

Furthermore, the Bootstrap method was used to further test the mediating role of the sense of community belonging, as shown in

Table 7. Decomposition of total, direct, and mediating effects.

Variables		Effect Value	Boot Standard Error	Boot CI Lower Limit	Boot CI Upper Limit	Relative Efficacy Value
Community Sense of Belonging	Total effect	0.706	0.063	0.105	0.351
	Direct effect	0.5843	0.0730	0.4463	0.7331	82.76%
	Intermediary effect	0.1217	0.0345	0.0576	0.1938	17.24%

. After 5000 samples, the 95% confidence interval of perceived value was [0.0576, 0.1938]; this excluded 0, further indicating that the mediating effect of the sense of community attachment between perceived value and willingness to participate holds. Thus, H6 was verified.

Finally, we used model 6 in the SPSS plug-in PROCESS provided by Hayes [54], with perceived usefulness, perceived enjoyment, perceived risk, and perceived cost of smart transformation in old neighborhoods as independent variables; perceived value and sense of community attachment as chain mediating variables; gender, education background, residential area, residential housing property attributes, length of residence, and work unit as control variables. The results are shown in

Table 8. Decomposition of chain-mediated path effects.

Variables	Intermediary Path	Effect Value	Boot CI Lower Limit	Boot CI Upper Limit	Relative Efficacy Value
Perceived usefulness	Perceived value—sense of community belonging	0.0443	0.0136	0.0888	13.56%
Perceived enjoyability	Perceived value—sense of community belonging	0.0515	0.0149	0.1011	14.93%
Perceived risk	Perceived value—sense of community belonging	−0.0194	−0.0406	−0.0052	12.32%
Perceived cost	Perceived value—sense of community belonging	−0.0261	−0.0503	−0.0066	10.91%

.

In terms of the perceived benefits, the whole regression equation of perceived usefulness was significant, with R² = 0.2376, F(4, 244) = 8.0321, and p < 0.001. The mediating effect was tested through Bootstrap sampling. The results showed that, in the regression equation of perceived usefulness, the path with perceived value as the mediating variable was significant with an indirect effect of 0.2631 (95% CI = [0.1542, 0.3886]). Furthermore, the path with perceived value and sense of community attachment as the mediating variables was also significant, with an indirect effect of 0.0443 (95% CI = [0.0136, 0.0888]). The overall mediating effect of the chain was 0.3074, accounting for 94.09% of the total effect (0.3267). The whole regression equation for perceived enjoyment was significant, with R² = 0.3152, F(4, 244) = 11.8662, and p < 0.001. The mediating effect was tested using the Bootstrap sampling method. The results showed that, in the regression equation of perceived enjoyment, the path with perceived value as the mediating variable was significant, with an indirect effect of 0.2797 (95% CI = [0.1842, 0.3854]). The path with perceived value and sense of community attachment as the mediating variables was also significant, with an indirect effect of 0.0515 (95% CI = [0.0149, 0.1011]). Thus, the overall mediating effect of these two mediating variables was 0.3312, accounting for 96.05% of the total effect (0.3448). In summary, H7a and H7b were verified; that is, the perceived usefulness and perceived enjoyment regarding the smart transformation of old neighborhoods can be influenced by the perceived value of the smart transformation by residents, as well as their sense of community attachment on the willingness, in terms of their willingness to participate in the smart transformation.

The whole regression equation of perceived risk was significant, with R² = 0.2217, F(5, 244) = 7.3434, and p < 0.001. The mediating effect was tested by Bootstrap sampling, and the results showed that, in terms of the perceived risk, the path with perceived value as a mediating variable was significant, with an indirect effect of −0.1192 (95% CI = [−0.2042, −0.0481]). Furthermore, the path with perceived value and sense of community attachment as mediating variables was also significant, with an indirect effect of −0.0194 (95% CI = [−0.0406, −0.0052]). The overall mediating effect of these two mediating chains was −0.1386, accounting for 88% of the total effect (−0.1575). The entire regression equation for the perceived cost of smart transformation in old neighborhoods was significant, with R² = 0.2863, F(2, 244) = 10.3394, and p < 0.001. The mediating effects were tested using Bootstrap sampling, which showed that the path with perceived value as a mediating variable in the regression equation for perceived cost was significant, with an indirect effect of −0.1885 (95% CI = [−0.2653, −0.1232]). Furthermore, the path with perceived value and sense of residential community attachment as mediating variables was significant, with an indirect effect of −0.0261 (95% CI = [−0.0503, −0.0066]). In summary, H8a and H8b were verified; that is, the perceived risk and perceived cost of smart transformation of old neighborhoods have an impact on the willingness of residents to participate in the smart transformation, mediated through the perceived value and sense of community belonging.

5. Conclusions and Discussion

5.1. Discussion

Based on the perceived value acceptance theory and community attachment theory, we explored the factors influencing the willingness to participate of community residents and analyzed questionnaire data through the use of hierarchical regression methods. Most of the hypotheses of this study were supported, suggesting that residents’ perceived benefits, perceived sacrifices, and community attachment all have important effects on their willingness to participate.

Our results indicated that perceived benefits (i.e., perceived enjoyability and perceived usefulness) positively affect the perceived value, while perceived sacrifices (i.e., perceived risk and perceived cost) negatively affect the perceived value. This validates the previous statement that residents will perceive the renovation process as valuable if they experience a general improvement in the convenience and safety of their lives and an increase in their sense of well-being and satisfaction with the smart renovation of the old neighborhood they belong to. In contrast, if a resident believes that the preliminary investment costs and subsequent operation and maintenance costs in the smart transformation are too high, as well as the risk of loss due to the leakage of personal privacy when using the relevant service platform, then they will consider the value of the smart transformation to be low. In addition, we not only verified that sense of community attachment has a positive effect on residents’ willingness to participate, but the results also suggested that perceived value and social belonging play chain mediating roles between perceived usefulness, perceived enjoyment, perceived risk, and perceived cost. This demonstrates that a sense of community attachment can be translated into good neighborhood relations, and perceived functional values can be translated into the improvement of community services and the physical environment. Therefore, residents can enhance their willingness to participate in the process of smart transformation of old neighborhoods through the relative benefits perceived between the two.

5.2. Theoretical Implications

This study has the following theoretical implications. First, according to previous research, it is well known that the success of smart community construction cannot be achieved without the participation of residents, and whether residents use the smart community service platform will affect the long-term development of smart communities [2,55]. However, residents may not be willing to actively participate in the construction of smart communities, due to technological, social, humanistic, and customary influences [56]. In the existing literature, research on smart communities has focused more on its conceptual and construction path levels, while empirical studies exploring the barriers to resident participation in construction are relatively limited. Therefore, in this study, we empirically analyzed the factors influencing the participation of residents in the renovation of old neighborhoods from a technical perspective (i.e., perceived value theory).

Second, based on perceived value theory, we emphasize the important role of community attachment. In previous studies, it has been demonstrated that people’s behavioral habits are influenced by their social environment as well as subjective norms; therefore, scholars have called for the introduction of a social perspective in future studies for further investigation [10,57]. This study answers that call by analyzing the mediating role of the sense of community attachment on the willingness of residents to participate in the smart transformation.

Finally, we not only identified that the willingness of residents to participate in the smart transformation of old neighborhoods is directly influenced by their perceived value and community belonging, but also identified the roles of perceived value and community attachment in mediating between perceived usefulness, perceived enjoyment, perceived risk, perceived cost, and willingness to participate. In previous studies, scholars have focused more on the direct roles between affective factors and willingness to participate [57], or have used affective factors as moderating variables to determine an interaction term and investigate the complex relationship between the two [10]. In this study, the data confirmed that community attachment also plays a chain mediating role, enriching the original findings.

In summary, we analyzed and discussed the factors influencing residents’ willingness to participate in the smart transformation of old neighborhoods from the microscopic perspective of individual residents, considering “smart communities” as community information services provided to residents supported by the integration of multiple information technologies. This research logic not only makes up for the shortcomings of previous theoretical studies on the renovation of old neighborhoods from the macro perspective of government or social progress but also fills the gap in academic research regarding the smart renovation of old neighborhoods.

5.3. Practical Implications

This study analyzed the willingness of residents to participate in the smart transformation of old neighborhoods and its influencing factors, through the use of perceived value acceptance theory and community belonging. We proposed and verified theoretical hypotheses, providing practical insights for accelerating the smart transformation of old neighborhoods, achieving sustainable community development, and solving the difficult problems in the associated construction process. Specifically, the following suggestions can be made:

(1)

Take the smart transformation of old neighborhoods as an opportunity to enhance the sense of belonging of residents in the community, thus solving the problem of “stranger” communities in the process of renewal of old neighborhoods. The community should actively build a communication platform between residents, the community, and neighbors in the process of transformation of old neighborhoods, eliminating information barriers which, on one hand, allows both sides to communicate and interact without barriers and, on the other hand, allows residents to enhance their knowledge of the new services and technologies through communication, thus enhancing their acceptance of the smart community, increasing their emotional dependence on the community, and making them feel a part of the community.

(2)

From the perspective of improving the quality of the community and creating a better digital life, we can actively mobilize residents to participate in the transformation of old neighborhoods into smart communities by means of initiatives, rather than administrative orders. The community service staff should release information regarding the process of smart community construction as soon as possible, such that residents can express their views on the current progress in a timely manner, increasing their sense of participation in the smart transformation, improving their autonomy, truly putting the sense of participation into practice, and enhancing the value perceived by the residents.

(3)

Technical barriers pose a major difficulty affecting the smart transformation of old neighborhoods. To lower these barriers, we must improve and optimize the design of smart community-related service platforms, reduce the difficulty of technical operations with a focus on aging and fun, and pay great attention to ensuring that private information is kept secure. While improving and reducing the difficulty of technical operation, the community should guide the current service concerns of residents through publicity, as well as use digital technology to train residents in digital operation skills, in order to stimulate demand for smart community transformation.

(4)

Appropriately attract social capital into the old community smart transformation work and actively optimize the relevant cost-sharing approach. Some residents of the community may invest a certain amount of financial support or agree with the smart transformation, and so, residents should be encouraged to complete part of the smart construction of the community through self-financing, if feasible.

(5)

Government departments and related communities promoting smart old district transformation should adhere to the “people-oriented” and “life-oriented” construction ideas, rather than “Formal” and “Performance” concepts. Ideally, the smart transformation of old districts should improve the quality of life of its residents.

5.4. Limitations and Future Research

Finally, there were some limitations to this study. First, although the data collected supported the hypotheses presented in this paper, we did not reduce the measurement error by obtaining data in nodes. Therefore, we also suggest that future studies should obtain data in nodes and track respondents until all data are completed. Second, this paper was based on a combination of perceived value theory and community attachment theory, in order to explore the influences on the willingness to participate of residents; however, there may be further latent variables that can be included in the model. Thus, in future research, more influencing factors can be explored from other theoretical perspectives and further integrated with other disciplines. Additionally, the findings related to this paper can be further investigated and verified through case studies, in-depth interviews, and other methods. Again, our data collection was limited to China, which may limit the generalizability of our findings. Although China’s large population provides an ideal context for research on sustainable community development, the study of willingness to participate in the smart transformation of old neighborhoods is a worldwide phenomenon. Therefore, future research should be conducted in different cultural, geographic, economic, and political contexts. Finally, each community is unique and the path to achieving smart transformation may vary, depending on its population, geography, and customs, which may also limit the generalizability of our findings, which will hopefully be further investigated in future research.