Research Article
Service Quality Evaluation of Urban Parks Based on AHP Method and SD Software
School of Urban Design, Wuhan University, 430072, Wuhan, China
Yichuan Zhang
School of Urban Design, Wuhan University, 430072, Wuhan, China
Urban park is the most important part of urban green land system and the main place of public recreational activities as well. Urban parks have many integrated functions (Millward and Sabir, 2011): Improving the eco-environment (Lu et al., 2012) and recreational environment (Jim and Chen, 2006), protecting biodiversity and providing a place for cultural activities. Because of the development of economy and public awareness, the green land coverage of China urban parks is low and the green land area per capita is lower than the average level stipulated by United Nations. Therefore, the service quality of urban park needs to be improved. However, it is obvious that the satisfaction degree of the citizens, the objects of service for urban parks, is the most suitable measure of the service quality of urban park. Therefore, public satisfaction (Wang et al., 2012; Arnberger, 2012) is the main basis to measure the service quality of urban park. Three problems need to be addressed if evaluate the public satisfaction: Indexes, methods to perform statistics and the standard to evaluate. Concerning the first problem, it is of no question that parks target at the public service as it is a public investment. Therefore, indexes should be determined in accordance with the standpoint of citizens. As to the second problem, there are many indexes to influence the service quality of urban parks, but the importance of them varies, so a scientific method needs to be proposed. AHP (Li et al., 2005; Nekhay et al., 2009), as a flexible and practical multi-criteria decision-making method to quantitatively analyze the issue of interest, is very suitable for this study. Fuzzy evaluation method which converts the quantitative evaluation into qualitative evaluation in accordance with fuzzy mathematics theory, can be used in solving the problem of public satisfaction evaluation criteria. It solves problems which are fuzzy, non-linear and hard to quantify. The research aim is to evaluate the service quality of urban parks combining AHP method and SD Software.
Step 1: | To build a sample set: |
S = {P1, P2, P3} | (1) |
Three urban parks of Zhengzhou in Henan Province, namely, Zhengzhou Peoples Park (P1), CBD Park (P2) and Zijinshan Park (P3), were selected to be the evaluation samples
Step 2: | To build AHP model: According to the result of previous researches and the combination of expert interviews and public interviews, 6 factors influencing the service quality of urban parks were screened, namely, place environment, landscape environment, culture environment and eco-environment, traffic environment and facilities environment which were subdivided into 18 impact indexes. Three-hierarchy AHP model was established based on these indexes (Table 1) |
Step 3: | To use SD software to calculate the weight: Although, the calculation of AHP method is complicated, programmed solution is enabled with SD software (Sun et al., 2007; Kizilkaya et al., 2011) to simplify the calculation. Model was established in the SD software (Fig. 1). Weight of each element in the layer relative to the element in the previous layer WC-B and WB-A the sequencing of total weight WC-A were obtained. The results passed the consistency check after the pairwise comparison of elements on a 1-9 scale by three experts |
Step 4: | To build the comment set: Comment set D was built. According to the Likert scale (Li, 2013) method, comments were divided into 5 degrees: Very dissatisfied, dissatisfied, mediocre, satisfied and very satisfied. Comment set D is obtained after the fuzzy value assignment was performed to comment set and the intermediate value was Med.dt: |
D = {80<d1≤100, 60<d2≤80, 40<d3≤60, 20<d4≤40, 0<d5≤20} = {Excellent, Good, Mediocre, Bad, Very bad} | (2) |
Table 1: | Three-hierarchy AHP model of service quality evaluation of urban parks |
Fig. 1: | Three-hierarchy AHP model of service quality evaluation of urban parks in super decisions software |
Med.dt = {90, 70, 50, 30, 10} | (3) |
Step 5: | To build the fuzzy relation matrix: Each index was quantified according to the comment set which means that the fuzzy membership to each evaluation degree of each factor was determined to obtain the fuzzy relation matrix R: |
(4) |
Step 6: | To calculate the result vector of the combined weight matrix and survey data matrix: |
(5) |
(6) |
Step 7: | To calculate the score M of all factors and comprehensive score of evaluation samples: |
MB = EB.Med.vt | (7) |
MA = EA.Med.vt | (8) |
M represents the score of evaluation samples.
Weight results: The weights obtained by SD software are seen in Table 2.
It can be seen from Table 2 that in factor layer, eco-environment, place environment, landscape environment, facilities environment, traffic environment and culture environment are arranged in decreasing weight. For the total weight of index layer relative to target layer, the rationality of place distribution, green quantity, general coordination of landscape, rationality of green land distribution and safety of facilities have high weight, with a great influence on the service quality of parks.
Results of public satisfaction survey: Three parks of Zhengzhou, Henan Province (Peoples Park, CBD Park and Zijinshan Park) were evaluated and 150 visitors of each park were surveyed about their satisfaction with service quality. The result of public satisfaction survey is seen in Table 3.
Table 2: | Weight results obtained by SD software |
Table 3: | Results made by visitors of each park scoring to the 18 impact indexes of index layer (%) |
Pi(i = 1, 2, 3): Investigated park, Bi(i = 1, 2, , 6): Six factors in factor layer, Ci(i = 1, 2, , 18): Eighteen indexes in index layer, Di(i = 1, 2, , 5): Satisfaction degree according to the 5-point likert scale |
Score and comprehensive score of all factors: The factor score and comprehensive score of service quality of three parks were calculated. The calculation of Peoples Park is illustrated as an example and the result is shown in Table 4.
The result vector E of service quality evaluation was calculated in accordance with step 5-6:
Table 4: | Result vector (E) combining the weight matrix and survey data matrix |
EBi(i = 1, 2, , 6): Factors result vector of factor layer; EA: Result vector of target layer |
The factor score and score M of comprehensive service quality were calculated in accordance with step 7-8 and the result is presented in Table 5.
MB1 = 90x0.6717+70x0.3287+50x0.0367+30x0.0112+
10x0.0207 = 85.840
MB2 = 90x0.6063+70x0.3349+50x0.0255+30x0.0318+
10x0.0012 = 80.251
MB3 = 90x0.6646+70x0.2989+50x0.0286+30x0.0036+
10x0.0044 = 82.319
MB4 = 90x0.6717+70x0.2586+50x0.0592+30x0.0068+
10x0.0037 = 81.756
MB5 = 90x0.7327+70x0.1628+50x0.0530+30x0.0437+
10x0.0086 = 81.386
MB6 = 90x0.6683+70x0.2027+50x0.0870+30x0.0309+
10x0.0117 = 79.730
MA = 90x0.6654+70x0.2653+50x0.0527+30x0.0222+
10x0.0096 = 81.854
It can be seen from Table 4 that eco-environment, place environment and facilities environment of CBD Park have high service quality as well as high comprehensive service quality, showing that the Park with integrated function can provide services of high quality to the public.
Table 5: | Scores (M) of all factors and comprehensive score of each park |
MBi(i = 1, 2, , 6): Factor score of factor layer; MA: Comprehensive score of target layer |
Although, the scores of some factors of Peoples Park and Zijinshan Park are high, the total score is lower than that of CBD Park. The reason is that Peoples Park and Zijinshan Park have been built for a long time. Peoples Park is the earliest one and has not been innovated since it was built. It cannot meet the requirements of the public in the aspect of facilities environment. Zijinshan Park was renovated in the 90's which improved the overall quality. CBD Park was built in 2005 and the process of planning, designing, construction, management and maintenance drew attention from the government. It has become a central green land of Zhengdong new district of Zhengzhou, playing an important role in improving urban environment. In order for management to be responsive to the public, park professionals need to understand public attitudes about urban parks (Baur et al., 2013). Service quality of urban parks appears to be correlated with landscape planning and design (Golicnik and Thompson, 2010). This study can be of benefit for future landscape design and renewal of urban parks.
The service quality of urban parks can be evaluated from factors of place environment, landscape environment, culture environment, eco-environment, traffic environment and facilities environment and these factors were subdivided into 18 indexes.
Three-hierarchy AHP model was established based on these factors and indexes, with SD software to calculate the weight of each factor relative to the factor of the previous layer and sequencing of total weight.
Weight was combined with the factor score and comprehensive score of service quality of all parks from the data of public satisfaction survey. The results of evaluation also can be the reference for the renovation and new parks construction.