EWM-FCE-ODM-Based Evaluation of Smart Community Construction: From the Perspective of Residents’ Sense of Gain

Abstract

As a crucial paradigm for addressing urbanization-related problems, smart community construction is in full swing, and its goal is to enhance residents’ sense of gain. Prior studies have not been able to account for all aspects of smart community construction, especially the evaluation tools from the perspective of residents’ sense of gain. Therefore, this paper seeks to establish a comprehensive evaluation framework for residents’ sense of gain in the smart community through the integrated method, which includes the entropy weight method (EWM), the fuzzy comprehensive evaluation (FCE), and the obstacle degree model (ODM). For the purpose of verifying the feasibility of the evaluation framework, 31 smart communities in 6 Chinese cities (Shenzhen City, Putian City, Huizhou City, Dongguan City, Zhengzhou City, and Luoyang City) were selected. The results indicated that the weight of “Cultural activities for the elderly” indicator is the highest while the “Overall design” indicator is the lowest. In addition, Putian City had the best performance, but Shenzhen City ranked last among the six cities. Moreover, among the 31 communities, the Fengshan community in Putian City performed the best while the Xinglong community in Luoyang City performed the worst. Several suggestions are proposed to improve residents’ sense of gain in smart communities, such as enhancing the quality of healthcare services, meeting the needs of the elderly through multiple channels, and enriching business services. This study not only innovates the evaluation method of smart community construction from the perspective of residents’ sense of gain but also provides suggestions for promoting the sustainable development of the smart community and enabling residents to feel more satisfied.

1. Introduction

Increasing attention is being paid to the smart community as a new paradigm that applies advanced information and communication technology (ICT) to supplement traditional community tools [1,2]. This enables the community to satisfy the needs of residents for a variety of services such as management, business, entertainment, and interpersonal communication in a more efficient, convenient, accurate, and proactive manner [3,4]. The implementation of smart communities has been widely carried out in the United States, Europe, Britain, Japan, Singapore, and other places [5]. For example, Japan combined information technology (IT) with community energy management systems (CEMSs) to develop smart communities through smart grids, microgrids, and smart homes for the sake of helping residents solve energy problems [6]. Smart communities in Singapore were built with a “one-stop” service port to provide residents with a safe, comfortable, convenient, and modern living environment [7]. In the United States, smart communities were implemented and improved using the open network data obtained by sensors [6]. The IT, network, and data were integrated into the “Smart Country 2015” plan adopted in Singapore to build the smart community [8,9]. The “Digital Britain” program in Britain was proposed with the aim of improving infrastructure, promoting digital applications for all, and enhancing the life satisfaction of residents [5]. However, compared with the traditional community, the smart communities in the world are more inclined to focus on the connection of people and things using modern information technology [5]. There is a lack of attention to residents’ feelings in these national guidelines for the construction of smart communities.

In order to change this situation and attract attention to the needs of residents, a concept of “sense of gain” was put forward by the Chinese leaders during the Speech at the Tenth Meeting of the Central Leading Group for Comprehensively Deepening Reform in 2015 [10,11,12]. It refers to the security and satisfaction of residents from the existing material and a spiritual perspective. Whether or not a smart community succeeds is determined by residents’ sense of gain (RSG) [13]. For example, Zhiyuan community in Wuhan City provided an AI robot language service module in the elderly care service, which greatly improved the RSG and was greatly appreciated by them [14]. Nevertheless, residents’ dissatisfaction and the serious distress of the community staff were caused by the lack of attention to the RSG during the management of Jiufengmingchangli Community in Wuhan City [14]. It is important to evaluate smart communities not only for construction but also for finding out what residents need and how satisfied they are with them. Thus, this paper focuses on the evaluation of smart community residents’ sense of gain.

Studies on the assessment of smart communities are underway. Existing research covers many aspects, such as sustainable development [15,16], service quality of smart communities [17,18,19], intelligent transportation [20], smart home technologies [3,21], and community safety [22,23]. Similarly, research on the sense of gain is now gradually increasing. There are two main parts of these studies that can be divided into theoretical and empirical studies. In terms of theoretical research, some scholars have studied the sense of gain from different perspectives. First, on the psychological side, from the perspective of individuals, a questionnaire survey of college students was used to explore the relationship between the sense of gain, well-being, and safety [10]. Moreover, the role of community identity in mediating the relationship between economic status and the RSG has been explored through mediation analysis [24]. Then, from the management point of view, enhancing employees’ sense of gain can positively contribute to the development of the company [25]. With regard to empirical studies, different kinds of literature have studied residents from different regions. For city residents, data from residents of Nanjing City were used to identify the factors that affect citizens’ sense of gain for smart city services [13]. For rural residents, farmers’ sense of gain was evaluated through the development of a comprehensive model after exploring the influence mechanism for farmers’ sense of gain [26]. Moreover, structural equation modeling (SEM) [26], factor mixture models [27], regression models [27,28,29], fuzzy comprehensive evaluation [26], analytic hierarchy process (AHP) [30], and other methods were used to evaluate the sense of gain. Most attention was paid to the aspect of technology while less attention was paid to the humanistic aspects in existing studies about the evaluation of smart communities, especially the RSG. As a result, there are three main research gaps: (1) Most evaluation systems only pay attention to a certain field in smart communities. There are few studies focusing on the evaluation of smart communities from the perspective of the RSG. (2) Few studies have been reported on indicators for evaluating the sense of gain of residents in smart communities. (3) There is a lack of an objective and quantitative method to determine the sense of gain of residents in smart communities.

Therefore, the performance evaluation of the RSG can provide a new perspective on the construction in smart communities and improve the development level for smart communities. This paper aims (1) to explore the evaluation indicator system of the RSG in smart communities; (2) to establish an evaluation framework of the RSG in smart communities through the EWM-FCE-ODM method; and (3) to provide strategies for promoting the RSG level in the smart community.

The remainder of the research paper is organized as follows: Section 2 shows the research method in the study and establishes the evaluation indicator system of the RSG for the smart community. Section 3 explains some reasons for selecting empirical research cases and describes the process of raw data obtained. Section 4 represents the total result of the indicator weight and community overall score, as well as the ranking results of cities and communities. Section 5 provides an insightful discussion of the calculation results and some suggestions for improving the RSG in smart communities. Section 6 describes the findings and future research in this paper.

2. Method

To objectively quantify the level of the RSG in smart communities, assessment indicators for the RSG were developed; moreover, an assessment model was proposed based on the EWM (entropy weight method) and the FCE (fuzzy comprehensive evaluation method). Moreover, the ODM (obstacle degree model) method was used to determine the obstacle degree that affects community evaluation results. The three methods overcome the disadvantages of relying on subjective awareness to analyze the assessment results, and it is helpful to put forward targeted strategies to improve the RSG. The combination of the three methods can make the evaluation result more accurate. At the same time, the main obstacle factors for the RSG are identified by the ODM method, and the evaluation results can be discussed more objectively and quantitatively. Figure 1 shows the evaluation procedure of the RSG. The first step is to identify the preliminary assessment indicator system of the RSG, which is the basis of the final indicator; The second step is to determine the final evaluation indicators of the RSG, which lays the foundation for the follow-up study. The third step is to obtain each indicator weight through the EWM method, which is an important part to determine the level and obstacles of the RSG. The fourth step is to calculate the score of the RSG in smart communities through the FCE method. The fifth step is to analyze the obstacle factors of the RSG through the ODM method. The results of the fourth and fifth steps lay a foundation for in-depth analysis of the RSG in smart communities.

2.1. Selecting the Preliminary Evaluation Indicator for the RSG in Smart Communities

Selecting scientific and effective indicators is the basis for establishing an evaluation system for the RSG in smart communities. There are a number of factors to consider along with certain principles, which are helpful to determine the systematic and accurate indicator system for the RSG in smart communities. Then, a systematic literature review (SLR) was used to identify evaluation indicators for the RSG, and the detailed process is depicted in Figure 2. In order to evaluate smart community residents’ sense of gain, a primary indicator system was developed, as shown in

Table 1. The initial evaluation indicators of the RSG.

Dimensions	Indicators	References
Smart property services	Smart payment, Online butler, Building intercoms, Smart access control, Vehicle management, Garbage classification, Visitor registration	[31,32,33,34,35,36]
Smart logistics services	Express delivery, Cargo handling, Merchandise delivery	[37,38,39]
Smart housekeeping services	Household cleaning, Babysitting, School-age childcare	[40,41,42]
Smart medical services	Physical examination, Health records, Telemedicine, Appointment registration, Vaccination, Two-way referrals, Emergency assistance alarm	[43,44,45,46,47,48,49]
Smart business services	Unmanned stores, Real estate rental, Car maintenance, Recycling of old items	[50,51,52,53]
Smart emergency services	Fire emergency, Community flood prevention, Epidemic prevention and control	[54,55,56]
Smart communication services	Neighborhood communication, Community entertainment, Community forums	[57,58,59]
Smart home services	Smart appliance control, Smart home environment, Remote monitoring	[60,61,62]
Smart government services	Government inquiries, Government services, Government affairs, Social safety, Complaint suggestions	[63,64,65,66,67]
Smart elderly care services	Geriatric health management, Senior dining services, Daily care for seniors, Cultural activities for seniors, Emergency services for the elderly	[68,69,70,71,72]

. The 10 dimensions and 43 indicators are included in assessment indicators for the RSG in smart communities.

2.2. Selecting the Evaluation Indicators for the Sense of Gain for Residents of Smart Communities

The evaluation indicators of the RSG in smart communities are mainly obtained from the literature. In order to better represent the current situation for the RSG, it would be wise to optimize the assessment indicator system. Then, experts who have researched smart communities were selected, which shows that they are knowledgeable about smart community development and have a wealth of experience in the field [23]. Interviews were conducted with 27 experts and scholars in the field of smart communities through the VooV Meeting in June and July 2022. Detailed information on effective experts can be found in Supplementary File S1. The interview questions are detailed in Supplementary File S2. Through expert interviews, the importance of the preliminary indicators to the sense of gain of residents of smart communities was discussed, and the indicator system was optimized through the experts’ suggestions. If more than 80% of the experts considered the indicator to be very important, then the indicator was accepted. In the end, the final evaluation indicators of the RSG in smart communities had 8 dimensions and 32 indicators, as shown in

Table 2. The final evaluation indicators for the RSG in smart communities.

Dimensions	Code	Indicators
Smart community assurance system (SAS)	SAS1	Safeguard mechanism
	SAS2	Construction guarantees
Smart community software and hardware facilities (SHF)	SHF1	Service station
	SHF2	Convenience service mobile terminal
	SHF3	Communication network
	SHF4	Platform function
Smart community medical services (SMS)	SMS1	Physical examination
	SMS2	Appointment registration
	SMS3	Vaccination
	SMS4	Two-way referrals
	SMS5	Emergency assistance alarm
Smart community elderly care services (SES)	SES1	Senior daily care
	SES2	Emergency services for the elderly
	SES3	Cultural activities for elderly
Smart community business services (SBS)	SBS1	Real estate rental and sale
	SBS2	Recycling
	SBS3	Store service
	SBS4	Electric vehicle charging piles
	SBS5	Smart catering service
Smart community property services (SPS)	SPS1	Smart access control
	SPS2	Vehicles management
	SPS3	Complaints and suggestions
	SPS4	Environmental improvement
Smart community safety services (SSS)	SSS1	Natural disaster response
	SSS2	Responding to social safety incidents
	SSS3	Public health event response
	SSS4	Emergency rescue alarm
Smart community governance services (SGS)	SGS1	Government affairs
	SGS2	Social safety
	SGS3	Online communication
	SGS4	Legal advocacy and assistance services
	SGS5	Community culture

. The detailed evaluation indicator system is described in Supplementary File S3.

2.3. Assessing the Level of the RSG in Smart Communities

The EWM-FCE-ODM method is used to develop a system to evaluate residents’ sense of gain in smart communities, and the comprehensive level of the sense of gain of smart community residents is ranked and analyzed. Specifically, the EWM method is an important information weighting model [73]. Compared with other subjective weighting models, the main benefit of the EWM is the elimination of human influence on indicator weights, which enhances the overall evaluation’s objectivity [74,75]. In this regard, the EWM method was chosen to calculate the weights of each smart community resident's sense of gain evaluation indicator [76,77]. Based on the EWM method, a new method was needed to calculate the sense of gain performance of smart community residents. The fuzzy comprehensive evaluation method (FCE) is a comprehensive evaluation method based on fuzzy mathematics to evaluate objects affected by multiple factors [78,79]. Through the calculation of the fuzzy information of indicators, the evaluation objects can be classified. Compared with other methods, the FCE method is a more comprehensive evaluation method [80]. Hence, this method can be used to calculate the sense of gain score of smart community residents. Then, according to the scores of community residents’ sense of gain, The obstacle degree model (ODM) is used to analyze the reasons that hinder the RSG in smart communities. It can determine the obstacle degree of assessment indicators in the comprehensive evaluation and investigate the factors that prevent things from developing further [81,82]. Thus, the ODM method can be used to identify the main obstacle factors that affect the RSG of smart communities. The following are the specific steps of smart community residents’ sense of gain evaluation system.

2.3.1. Calculating the Indicator Weights of the RSG through the EWM Method

(a)

The evaluation data are standardized.

$$m_{\mathit{ij}}=\frac{n_{\mathit{ij}}-\mathit{min}\left(n_{\mathit{ij}}\right)}{\mathit{max}\left(n_{\mathit{ij}}\right)- \mathit{min}\left(n_{\mathit{ij}}\right)}$$

(1)

where $m_{ij}$ is the normalized decision matrix, $\mathit{min}\left(n_{\mathit{ij}}\right)$ is the minimum value, $\mathit{max}\left(n_{\mathit{ij}}\right)$ is the maximum value. And $n_{\mathit{ij}}$ is the score of residents’ sense of gain in smart communities.

(b)

The probability of each $n_{ij}$ is determined.

$$W_{\mathit{ij}}={{(m}_{\mathit{ij}}/\sum _{j=1}^y{m}_{\mathit{ij}}) }_{xy}$$

(2)

where $W_{\mathit{ij}}$ is the probability of each indicator $n_{\mathit{ij}}$ for residents’ sense of gain.

(c)

Each indicator’s entropy is calculated.

$$E_j= -\mathit{ln}{\left(y\right)}^{-1}\sum _{i=1}^x{(W}_{\mathit{ij}}{\mathit{lnW}}_{\mathit{ij}}),(0 <{ W}_{\mathit{ij}} \le 1)$$

(3)

where $E_j$ is the information entropy value of the jth indicator of smart community residents’ sense of gain.

(d)

The weight of evaluation indicator is determined through the information entropy.

$$G_j=\frac{1 -{ E}_j}{{\sum }_{j=1}^y(1 - E_j)}$$

(4)

where $G_j$ is the weight for assessment indicators of the residents’ sense of gain in smart communities.

2.3.2. Evaluating the Performance Level of the RSG through the FCE Method

(a)

The evaluation set is determined.

There are five scoring levels for the RSG including lowest, low, medium, high, and highest.

$$P =\left\{P_1,P_2,\cdots ,{ P}_x\right\}=\left\{n_1,n_2,\cdots ,{ n}_i,\cdots ,{ n}_x\right\}$$

(5)

(b)

The evaluation indicators are determined.

The evaluation set includes 32 evaluation indicators for the RSG.

$$Q =\left\{Q_1,Q_2,\cdots ,Q_y\right\}=\left\{n_1,n_2,\cdots ,{ n}_j,\cdots {, n}_y\right\}$$

(6)

(c)

The degree of affiliation of each indicator is determined.

The single factor is evaluated, and then the vector $C_j$ is obtained. The single-factor evaluation matrix C is a combination of the numbers of the single-factor evaluation vectors. There are several frequently used single-factor membership degree computation methods.

$$C =\left[\begin{array}{c}\begin{array}{c}{C}_1\\ C_2\end{array}\\ \begin{array}{c}⋮\\ C_j\\ \begin{array}{c}⋮\\ C_n\end{array}\end{array}\end{array}\right]=\left[\begin{array}{cc}\begin{array}{cc}{c}_{11}& c_{12}\\ c_{21}& c_{22}\end{array}& \begin{array}{cc}\cdots & c_{1m}\\ \cdots & c_{2m}\end{array}\\ \begin{array}{cc}⋮& ⋮\\ c_{n1}& c_{n2}\end{array}& \begin{array}{cc}{c}_{\mathit{ji}}& ⋮\\ \cdots & c_{\mathit{nm}}\end{array}\end{array}\right]$$

(7)

where C is a matrix of fuzzy relationships made up of the evaluation set P and the evaluation indicator set Q in which $c_{ji}$ denotes the ratio of the number of people scoring with nji to the total number of people, called affiliation. C₁ contains $c_{11},c_{12},\cdots ,c_{1i}$.

(d)

The weight of the evaluation factors is calculated.

The relative weights of each evaluation criterion ($Q_1,\cdots {, Q}_y$) to the overall rating of residents’ sense of gain should be measured in order to obtain a thorough assessment of residents’ sense of gain. The weight vector can be denoted by G and can be formulated using the EWM method. A vector W is used to denote the evaluation indicator weights.

$$G=\left\{G_1,G_2,\cdots ,{ G }_j,\cdots {, G}_y\right\}$$

(8)

(e)

The affiliations of the weighted indicators are determined.

$$A=G\times C=\left\{G_1,G_2,\cdots ,{ G }_j,\cdots {, G}_y\right\}\times \left[\begin{array}{cc}\begin{array}{cc}{c}_{11}& c_{12}\\ c_{21}& c_{22}\end{array}& \begin{array}{cc}\cdots & c_{1m}\\ \cdots & c_{2m}\end{array}\\ \begin{array}{cc}⋮& ⋮\\ c_{n1}& c_{n2}\end{array}& \begin{array}{cc}{c}_{\mathit{ji}}& ⋮\\ \cdots & c_{\mathit{nm}}\end{array}\end{array}\right]$$

(9)

where A is the overall evaluation result of residents’ sense of gain by smart community residents.

(f)

The results of the evaluation are obtained.

To better quantify the results, the values of P₁ to P₅ are 10, 30, 50, 70, and 90, respectively, which are multiplied by the results of A to obtain the final scores of the 32 evaluation indicators, and the scores for each dimension can be calculated.

$$T=A\times P$$

(10)

where T means the final evaluation results for the RSG.

2.3.3. Analyzing the Obstacle Factors of the Residents’ Sense of Gain through the ODM Method

(a)

The factor contribution is calculated.

$$R_j=G_j\times B_j$$

(11)

where $R_{j }$ is the contribution of the jth evaluation indicator to the total target, $G_{j }$ is the weight for each indicator of the RSG, and $B_j$ is the weight of each dimension to which indicator j belongs.

(b)

The degree of deviation of the indicator $D_j$ is calculated.

$$D_j=1 -m_{\mathit{ij}}$$

(12)

where $D_j$ is the difference between the actual value of the jth evaluation indicator and the best target value and $m_{\mathit{ij}}$ is the standardized value of the jth evaluation indicator.

(c)

The degree of obstacle $H_j$ of indicators is determined.

$$H_j=D_j{R}_j/\sum _{j=1}^n{D}_j{R}_j$$

(13)

where $H_j$ is the obstacle degree of the jth evaluation indicator on the residents’ sense of gain.

Finally, in order to evaluate the rationality and effectiveness of the EWM-FCE-ODM model, the evaluation model was assessed by a team of 13 experts.

3. Empirical Study

3.1. Study Area

In 2013, General Secretary Xi proposed the “One Belt, One Road” project to promote economic and cultural communications between China and the rest of the world and to build a new regional economic cooperation framework [83]. In this paper, six important cities in the “Belt and Road” route are selected for the study: Shenzhen City, Putian City, Dongguan City, Huizhou City, Zhengzhou City, and Luoyang City. Among these cities, Shenzhen City, Dongguan City, and Huizhou City are located in Guangdong Province, Zhengzhou City and Luoyang City are located in Henan Province, and only Putian City is located in Fujian Province. There are several reasons for choosing these survey sites. First of all, since these cities have been chosen as pilot cities for smart community implementation, experience has been accumulated. Moreover, these cities are economically stronger than other cities in China and can provide financial support for smart community development. What is more, the residents’ perception of smart community construction in these cities is generally high, and data on the RSG of these smart communities are easy to collect.

Among the chosen cities, 31 communities were selected in this research. The main reason for this is that these communities are more prominent in the construction of smart communities. In particular, some communities have established comprehensive smart information platforms. These platforms greatly improve the effectiveness and convenience of community services. The remaining communities use the Internet of Things (IoT), cloud computing, and other technologies to further promote the development of smart communities and also make the life of residents more convenient. In Figure 3, smart communities are highlighted by their locations.

3.2. Data Collection

Questionnaires were distributed uniformly to the above six cities—Shenzhen City, Putian City, Zhengzhou City, Luoyang City, Dongguan City, and Huizhou City—to ensure data consistency. The detailed information of residents interviewed includes gender, age, home ownership, years of residence, and monthly income. Detailed questionnaire information can be found in Supplementary File S4. During data collection, residents were invited to score the level of the RSG, which mainly includes two aspects. On the one hand, the importance of indicators of the RSG in smart communities was collected from 1 (extremely unimportant) to 5 (extremely important), which lays the foundation for determining the weight of indicators. On the other hand, the data for residents’ sense of gain level from each indicator were obtained on a scale from 1 (low level) to 5 (high level), which is the basis for calculating the acquisition level and main obstacle factors of different smart communities. This survey was conducted in June 2022. A total of 2442 questionnaires were collected through the Wenjuanxing platform (https://www.wjx.cn/vm/r5oDXct.aspx#, accessed on 30 June 2022) [29], of which 2128 were valid. This is a professional online questionnaire, examination, assessment, and voting platform. Moreover, according to the information from the questionnaire, the indicator weights were determined by the EWM method. Additionally, the results of the calculation of the FCE score and the ODM analysis were obtained from the weighting results using the EWM method and the raw data acquired by the questionnaire.

4. Result

4.1. Descriptive Statistics of the Respondents

The survey collected data about respondents’ demographic and socioeconomic characteristics, duration of residence, and the sense of gain with the smart community.

Table 3. Descriptive statistics of individual characteristics.

Characteristics		Percentage	Characteristics		Percentage
		(N = 2128)			(N = 2128)
Gender	Male	42.20%	Age	Less than 20	9.54%
	Female	57.80%		21–35	66.49%
Monthly income	<CNY 3000 (about USD 430.46)	13.58%		36–49	19.74%
	CNY 3000–5000 (about USD 430.46–717.43)	19.64%		50–64	3.76%
	CNY 5000–7000 (about USD 717.43–1004.40)	17.76%		More than 65	0.47%
	CNY 7000–9000 (about USD 1004.40–1291.37)	17.86%	Home ownership	Rent	27.96%
	>CNY 9000 (about USD 1291.37)	31.16%		Own	66.82%
Educational	Primary school or below	1.50%		Other	5.22%
	Middle school	10.15%	Duration of residence	1 year or less	9.96%
	High school and technical	12.36%		1–3 years	24.77%
	College and undergraduate	68.84%		3–5 years	19.92%
	Master or above	7.14%		Above 5 years	45.35%

presents the respondents’ individual characteristics. The survey sample was composed of 42.2% of men and 57.8% of women. More than 80% of the respondents were between the ages of 21 and 49 (86.23%). As the survey data show, the number of people with university and higher education accounted for 75.98%. Over half of the people lived in their own houses. With regard to the duration of residence, 65.27% of them lived there for more than 3 years. Moreover, from the perspective of economic status, respondents were divided into five levels by their monthly income, among them, 31.16% of the respondents had a monthly income of more than CNY 9000 (about USD 1291.37). Moreover, the questionnaire’s reliability and validity were analyzed. The reliability value of the questionnaire (Cronbach’s alpha) was obtained as 0.745, which satisfied the requirements of the analysis. Hence, this suggests that the survey data can be further analyzed.

4.2. The Results of Evaluation Indicator Weights for the RSG through the EWM Method

The scoring data of all evaluation indicators were obtained through a questionnaire survey. There is high reliability with the scoring data for the evaluation indicator, which has a confidence level of 0.745. The weights of the evaluation indicator for the RSG in smart communities were calculated through the entropy weighting method (EWM). The weight results of the assessment indicators are shown in Figure 4. The “smart community governance service (SGS)” has the highest weight, followed by the “smart community medical services (SMS)”. This indicates that SGS and SMS are important bases for judging the RSG. In addition, the three evaluation indicators weighted most highly are SES3, SES2, and SBS5, while the three of the lowest indicators are SAS1, SMS3, and SBS1.

4.3. The Results of Evaluation of the RSG through the FCE Method

According to the weight of evaluation indicators for the RSG, the FCE method was used to calculate the level of the RSG for different smart community residents’ sense of gain in different communities and cities.

Table 4. The result of the level and ranking for the RSG.

City	Community	SAS	SHF	SMS	SES	SBS	SPS	SSS	SGS	T	N1	N2
Shenzhen	M1	65.52	62.70	65.91	64.60	67.94	67.74	67.23	66.95	66.30	8	6
	M2	63.00	61.02	62.26	59.42	63.68	63.15	64.06	63.45	62.56	27
	M3	61.96	59.40	62.26	60.92	64.93	65.46	63.66	63.56	63.00	26
	M4	64.78	60.14	60.96	59.68	63.93	60.42	64.33	63.39	62.10	29
	M5	63.27	59.81	63.57	61.70	65.26	65.38	66.25	64.12	63.80	23
Dongguan	M6	73.97	67.78	63.72	64.22	65.48	69.60	69.07	68.82	67.17	4	2
	M7	70.88	69.20	67.14	61.74	68.12	66.95	65.27	65.52	66.59	7
	M8	65.46	64.03	64.38	62.48	62.66	64.01	64.83	63.53	63.76	24
	M9	68.90	68.24	66.08	63.60	65.08	65.54	66.61	65.41	65.90	10
	M10	69.70	68.14	64.60	64.04	65.08	65.83	66.22	65.08	65.70	12
Huizhou	M11	69.53	68.23	68.49	64.39	65.34	67.06	70.58	64.02	66.90	5	4
	M12	66.70	68.13	64.53	61.58	62.33	63.67	66.07	62.30	64.12	20
	M13	66.81	68.00	66.86	62.98	63.79	63.41	65.24	62.67	64.76	17
	M14	67.75	67.39	64.93	62.65	63.64	64.42	67.00	64.14	64.94	16
	M15	64.43	64.72	56.90	58.08	62.09	67.22	67.25	60.71	62.31	28
Putian	M16	67.93	68.28	66.50	62.32	64.98	67.44	66.74	65.63	66.01	9	1
	M17	72.32	70.16	71.68	64.36	67.68	66.77	71.36	69.57	69.08	1
	M18	68.86	69.33	68.56	63.83	66.56	66.32	68.58	66.40	67.18	3
	M19	64.78	60.14	60.96	59.68	63.93	60.42	64.33	63.39	62.10	29
	M20	68.70	67.89	64.44	60.78	63.54	69.40	67.74	65.50	65.63	13
Zhengzhou	M21	69.20	69.13	61.64	59.92	67.86	70.37	66.24	63.95	65.77	11	3
	M22	64.54	68.37	61.74	63.64	64.20	64.75	61.82	64.16	64.04	21
	M23	69.06	68.48	63.08	60.95	64.49	68.60	65.87	64.86	65.32	15
	M24	69.51	65.59	63.51	62.32	65.02	67.72	66.56	65.87	65.42	14
	M25	67.41	63.36	68.36	63.22	61.12	66.62	63.85	61.67	64.18	19
	M26	65.76	65.88	62.61	61.14	61.50	63.88	64.71	63.50	63.39	25
Luoyang	M27	68.36	68.70	64.42	60.70	62.73	64.79	64.25	63.74	64.38	18	5
	M28	69.54	73.29	65.73	63.53	67.51	69.23	64.30	69.48	67.74	2
	M29	65.20	66.20	64.87	62.84	61.14	64.26	65.07	63.22	63.90	22
	M30	70.05	68.19	65.88	63.46	64.12	68.42	68.52	66.84	66.65	6
	M31	57.90	55.76	55.92	57.75	56.85	57.83	56.53	57.44	56.92	31

Note: T is the final evaluation result through the FCE method.

illustrates the results of the level and ranking for the RSG. Each smart community score is calculated by the FCE method. Each smart community ranking (noted as N₁) can be derived from T. The city ranking (noted as N₂) is calculated through the average T of each city.

In terms of the six cities, Putian City has the best performance in the RSG for smart communities, followed by Dongguan City, while Shenzhen ranks the lowest. As for the RSG in smart communities, based on the results, the highest score for 31 sample communities was 69.08, while the lowest score was 56.92. Considering the range of values of performance, it can be divided into 5 levels of the total score, namely, highest level (80~100), high level (60~80), medium level (40~60), low level (20~40), and lowest level (0~20) [23]. Then, it can be concluded that the level of the RSG was high for M1 to M30, and M31 has medium performance. The results represented that no smart community was at the low and lowest level, which means that good performance was achieved by 31 smart communities in terms of the RSG in smart communities [23].

4.4. The Results of Obstacle Factors Obtained through the ODM Method

The ODM method was used mainly to determine the main obstacle factors of the RSG in smart communities. According to the weights of assessment indicators (as illustrated in Figure 4), the indicator obstacle degree of the RSG is calculated.

Table 5. Calculation of the obstacle degree factors for major communities.

Community	Indicator	${\mathit{H}}_{\mathit{j}}$	Indicator	${\mathit{H}}_{\mathit{j}}$	Indicator	${\mathit{H}}_{\mathit{j}}$	Indicator	${\mathit{H}}_{\mathit{j}}$	Indicator	${\mathit{H}}_{\mathit{j}}$
M4	SPS3	5.79%	SBS2	5.59%	SES1	5.21%	SMS1	5.08%	SHF2	5.08%
M17	SES3	10.95%	SPS1	9.66%	SPS2	7.44%	SGS5	7.34%	SHF3	6.75%
M18	SES3	6.61%	SGS5	6.60%	SBS1	5.63%	SGS2	5.04%	SPS2	4.65%
M19	SGS5	7.31%	SES3	6.84%	SBS1	6.00%	SGS2	5.24%	SGS3	4.71%
M28	SSS1	10.15%	SES2	9.35%	SBS1	7.36%	SES1	7.15%	SMS1	6.33%
M31	SES3	4.10%	SES2	4.02%	SBS5	3.96%	SMS2	3.86%	SGS4	3.73%

Note: The $H_j$ in the table represents the five main obstacle degree factors for each community.

shows five main obstacle factors in the three best- and worst-performing smart communities. A detailed indicator obstacle score is shown in Supplementary File S5. $H_j$ in the table represents the five main obstacle degree factors for each community. SPS3 (complaints and suggestions), SBS2 (recycling), and SES1 (senior daily care) were the main indicators in M4. Similarly, SES3 (cultural activities for elderly), SPS1 (smart access control), and SPS2 (vehicles management) were the major obstacle factors in M17, and the highest SES3 of $H_j$ was 10.95%. The main obstacle factors in M18 were SES3 at 6.61%, SGS5 (community culture) at 6.60%, and SBS1 (real estate rental and sale) at 5.63%. For M19, the obstacle factors over 6% were SGS5 and SES3. The main obstacle factors in M28 were relatively high for SSS1 (natural disaster response), SES2 (emergency services for the elderly), and SBS1 with 10.15%, 9.35%, and 7.36%, respectively. The main obstacle factors in M31 were relatively low, with SES3 at 4.10%, SES2 at 4.02%, and SBS5 (smart catering service) at 3.96%.

5. Discussion

5.1. Differences in Weighting of the RSG Evaluation Indicators

The RSG was significantly influenced by differences in weights. On the one hand, the three dimensions of the higher weight were SMS, SBS, and SGS. There is one main reason that the three dimensions are an important part of smart community development, which can improve the sense of gain for residents. Specifically, smart community medical services can directly impact the quality of residents’ life and sense of gain [84,85]. Simultaneously, the smart community business service makes residents’ life more convenient, which helps to enhance their sense of acquisition [86]. More importantly, there is a close relationship between the governance service level and the development level of the smart community, and a higher governance level can positively contribute to community development [87]. On the other hand, SAS had the lowest percentage of all dimensions. The main reason is that residents cannot accurately grasp the direction of community construction due to the continuous updating of smart community policies [88]. According to Figure 4, the indicator with the highest weight of the sense of gain for residents of smart communities was SES3. Community cultural activities for the elderly (SES3) can promote the physical and mental health of residents [89]. It indicates that residents could obtain more sense of gain from the cultural activities of the elderly. Furthermore, the three indicators with higher weights are SES2, SBS5, and SMS2. It shows that the factors that have more influence on the sense of gain for smart community residents are emergency services for the elderly, smart catering services, and hospital appointment registration services. The safeguard mechanism (SAS1) has the lowest weight. The reason for the result is that residents have a low understanding of the safety mechanism, and they think that the safety mechanism cannot directly improve their RSG. Therefore, the weight of SAS1 is low.

5.2. Differences in Overall Level of the RSG

5.2.1. Differences in Overall Level of the City

The overall scores of the sample cities are significantly different. In detail, Putian City ranked highest among the six cities and four-fifths of the communities ranked higher, which indicates that residents in Putian City have a greater sense of gain from their communities. Because the level of economic development in Putian City has not been high among the six cities, the living expenses of residents have also been low, and the pressure of life for the residents would be comparatively low, which can make them feel a greater sense of gain. However, scores of SBS, SGS, and SES obtained were not very high in each community in Putian City by the FCE method, and among them, the SES score was the lowest. This indicated that the main problems affecting the sense of gain of Putian City residents were commercial services, community governance services, and elderly services, which reduced the sense of gain of residents. According to the resident interviews, some of the selected communities were old communities. The old community has insufficient smart business services, a low property level, and outdated infrastructure, which cannot achieve the standards of smart community services [90,91]. The quality and stability of elderly care services in smart communities were uncertain due to the limitation of the pension infrastructure [92,93]. The results of the ODM show that the main obstacle factor of the smart community in Putian City was SES3. By interviewing the community residents, we found that the spiritual needs of the elderly were neglected in the smart community construction, and cultural activities about the elderly were rarely held. Therefore, this community should pay attention to SBS, SGS, and SES.

Meanwhile, Shenzhen City was the worst performer among the six cities. A total of 80% of the communities surveyed in Shenzhen City generally ranked low. The reasons for this are that Shenzhen is an important city for smart community pilots, it has a high economic level, and residents have higher requirements for all aspects of life [94]. However, in terms of the five communities in Shenzhen City, all dimensions scored poorly except for SES and SBS. First, the safeguard system of the community needs to be improved. It mainly involves the safeguarding of talents, technology, capital, and innovation mechanisms [66]. A shortage of professional talent exists in smart communities, which hinders their development. Second, with the rapid development of society, a better communication network and convenient mobile terminals and infrastructure terminals are demanded by residents [57,95], which cannot all be achieved in existing smart communities. Third, the SMS score had not yet reached the total community score. It means that there is still plenty of room for improvement in smart community healthcare services, such as improving the convenience of booking registration [96]. Fourth, the three dimensions of SPS, SSS, and SGS scored higher than the total community score. All of these demonstrate that the community has better property services, safety services, and governance services, which can bring more sense of gain for residents.

In addition, Dongguan City was ranked second, followed by Zhengzhou City, and the main reason for this result is that the score of elderly care service is lower than other cities. Huizhou City ranked fourth. The scores of SES, SBS, and SGS of all five communities were lower than the overall community scores. It suggests that the community problems in Huizhou are mainly focused on elderly care services, commercial services, and governance services. Luoyang City ranked fifth, and the main reason is that the Xinglong community had the lowest scores in all indicators, which seriously affects the score of Luoyang City.

5.2.2. Differences in Overall Level of Smart Communities

According to the ranking result of smart communities, the three communities with higher rankings are M17, M28, and M18. In detail, M17 and M18 are located in Putian City. The overall level of smart community construction in Putian City was high, and the scores of all dimensions were also generally high. The reason may be that the supporting facilities of the smart community in Putian City are relatively perfect and the governance model is more obvious, which makes residents achieve a greater sense of gain in all aspects of the communities [10]. For example, M28 is an advanced smart community in Luoyang City, which has relatively better smart infrastructure. Thus, community residents have a higher sense of gain. However, these smart communities did not reach the highest standard in sense of gain, and there are a number of factors that can affect the sense of gain for residents. Combined with the results of the ODM method, the main obstacle factors of the three smart communities are SES and SPS. “Smart community elderly care services” and the “Smart community property services” can directly or indirectly enhance the RSG. Hence, this smart community should focus on SES and SPS.

The three communities with lower rankings are M31 in Luoyang City, M19 in Putian City, and M4 in Shenzhen City. M31 was the lowest scoring of all communities and scored below 60 on all dimensions, which indicates that the community had many problems. First, Luoyang City has the lowest level of economic development of the six cities. The economic level of the city directly affects the development level of a smart community [97,98]. Moreover, the existing property management level of M31 is low, which leads to the poor living environment of residents and reduces their sense of gain. The results of the ODM method indicated that the main obstacle factors of M31 were SES2, SES3, and SBS5. Based on scores and interviews with community residents, there were more elderly people in M31, and the limited medical conditions in the smart community could not meet the emergency needs of the elderly in time [84]. At the same time, the spiritual needs of the elderly are also the focus of smart community development [99]. Meanwhile, according to the residents’ interviews, there are large shopping malls around M31, and residents prefer to go shopping in the malls, which hinders the development of community commercial services. Therefore, providing comprehensive community services that are tailored to residents’ needs can enhance the RSG. M19 and M4 were two communities with low scores, which has the same evaluation score and ranking. M19 is located in Putian City, and M4 is located in Shenzhen City. According to the results of the FCE method, the score of SES was the lowest in the two smart communities. This shows that SES needs to be focused on these two communities, which is a common issue in smart communities. The results are consistent with the ODM of the community [100]. Combined with the results of the ODM and the FCE, the scores of SPS, SGS, and SBS are low in the two smart communities, and these three dimensions are also the main obstacle factors. One reason is that the two communities are old communities. Generally, old communities have not kept up with the development practices of the times [101]. These communities cannot provide better property services, commercial services, and governance services for residents, which affects the RSG. In addition, as for the medical services, residents’ health data cannot be shared with various hospitals, and residents’ data are difficult to integrate [48]. At the same time, the safety factor of residents’ health data is not high. SPS, SGS, and SBS are critical to the community residents’ sense of gain. Over time, problems could be discovered by examining the associated services of these communities in detail. The RSG may be improved through continuous infrastructure upgrades and improved service levels.

5.3. Strategies to Enhance the Sense of Gain of Residents

According to the above research results, there are some obstacle factors in the development of smart communities, including medical services, elderly care services, business services, and governance services. Some suggestions for promoting the RSG in smart communities can be put forward to the decision-makers.

First, the medical service of smart communities is an important part of the resident service, and it is the basis for smart community development [102]. To improve the medical service, community hospitals can strengthen cooperation with specialized hospitals, which can speed up the basic processes such as referral, registration, and appointment for medical examination [85]. The treatment of basic mild diseases can be completed in the community hospital, which can save time for medical treatment [84]. Moreover, it is required to strengthen the management of community medical service records, which can promote the development of community medical services and improve the RSG in smart communities [96].

Second, the level of elderly care services has gradually become one of the criteria for measuring the development of smart communities. Elderly care is becoming an increasingly concerning issue for residents, which can directly affect the sense of gain for every family. In the development of smart communities, housing designs more suitable for the elderly can be built to meet their basic living needs and appropriately reduce the burden of families [21]. Moreover, the community can provide life assistance services for the elderly, such as health services, pick-up services, medication management, and other services [103]. The living problems of the elderly can be solved efficiently, and their sense of gain can be improved. In addition, medical care, healthcare, and nursing care can be integrated into a medical service system for the elderly to improve their physical condition [48].

Third, according to the needs of community residents, a smart business service platform should be established to meet the basic needs of community residents, including food, catering, recreation, and other services [104]. At the same time, a new mode of community business service should be explored to realize the diversified development of community business subjects, which provides comprehensive and convenient business services for residents and improves the RSG [105]. In addition, community residents are users of business services in smart communities, so the needs of the resident must be considered when planning smart community services. Therefore, it is suggested to comprehensively consider various influencing factors and formulate detailed development plans for smart community services [106].

Finally, the level of community governance is closely related to the RSG. It is also a basic indicator that directly affects the lives of residents. The improvement of smart community governance services is the basis for achieving good operation of smart communities. The goal is to build an all-round, three-dimensional, and deeply integrated community governance wisdom platform [107]. The platform provides efficient solutions for community governance and builds new patterns of community governance [108]. The construction of a smart management platform can not only improve the positivity of residents to participate in governance but also enable smart early warning and rapid disposal of IoT sensing [1]. Then, a community data standard system can be established to realize community data resource management [109]. Big data mining of community events can reduce the occurrence of risky events and improve the effectiveness of community governance [110].

5.4. Verification of the Survey Results through Expert Evaluation Collection

In order to verify the results of this study, the feedback collected the opinions of experts in the fields of “smart community” and “sense of gain”. The evaluation results of 13 respondents were collected through a questionnaire survey. Interviewees from academia and industry were invited, with 53.85% of them having 3–5 years of work experience. Therefore, the interviewees’ ability to provide professional feedback is credible.

In the questionnaire, respondents were asked to provide an assessment of the three outcomes of this study by scoring from 1 to 5, including the (1) completeness of identified RSG indicators, (2) reliability of the EWM-FCE-ODM model, and (3) applicability of the proposed methods. According to the results shown in Figure 5, 84.62% of the respondents agreed with the applicability of the EWM-FCE-ODM model. The results from the interviews with experts were that experts’ opinions were feasible. It also proved the correctness of the EWM-FCE-ODM evaluation model to study the sense of gain of residents in smart communities.

6. Conclusions

An evaluation framework of smart community residents’ sense of gain is developed in this paper to discuss the development level of 31 smart communities in 6 cities. There are several key findings from this study. First, a framework for assessing the level of the residents’ sense of gain was developed through the EWM-FCE method, and the main obstacle factors affecting evaluation results were identified through the ODM method. Second, according to the weighting results, SES3 has the highest weight while the lowest weight is SAS1. Third, the evaluation results showed that Putian City ranked first among the six cities, and Shenzhen City ranked last. As for M17, it performed the best among all communities, and M31 performed the worst. As a result of these findings, an innovative evaluation model has been developed for assessing the RSG within smart communities as well as enriching the content of smart community evaluations. It was possible in practice to evaluate the level of the RSG in smart communities in different communities based on the framework for evaluation presented in the paper. According to the evaluation results, there is potential for improving smart community development, and the RSG can be increased.

Nevertheless, this research has some limitations. First, the data sample size of this study is small. Second, the data sample is from residents’ questionnaires, which is highly subjective, and part of the survey is inefficient. Third, an evaluation model based on EWM-FCE-ODM is static, but measuring the RSG is a dynamic process in reality. Therefore, a more complex evaluation model will be needed. To validate whether the evaluation model can be applied to other communities in China or abroad, more data will need to be collected in future studies. Moreover, with the development of smart communities, the RSG will change, and a dynamic evaluation model should be established to improve the sustainable development of smart communities.