This study aims to utilize the fuzzy analytical/network process (FAHP/FANP) and decision-making trial and evaluation laboratory (DEMATEL) approach to recognize the influential indicators of sport centre business management in Taipei city’s sports centre. Twenty-three of sports centres with six-dimensions were identified from the literature review and interview with twelve experts (academic and practical experience). By considering the interrelationships among the indices, DEMATEL was used to deal with the importance and causal relationships among the evaluation indices of sports centre. Then, we employ the FAHP/FANP to determine the weight of each management criterion. Our empirical results provide two main insights: first, sports centre business management strategies comprise six-dimensions and 23 indexes; second, the FANP analysis shows that the six key factors are (in order of priority) service price, site conditions, operations management, traffic conditions, sports products, and staff quality. This study uses the FANP and DEMATEL along with mathematical computing in order to provide sports centre managers with a reliable decision-making reference and to assist them in formulating the most effective business strategy possible.
Taiwan has seen rapid economic growth recently, with its gross national income reaching US $17,590. The Taiwanese have switched to two-day weekends and are becoming more health conscious; they are also eager to pursue leisure activities. Taiwan’s government is attempting to provide a variety of leisure activities suitable for all ages, genders, habits, employment backgrounds, and physical abilities. The government is promoting sports in order to help people switch from an indoor to an outdoor lifestyle and acquire a lifetime sport habit. Because of the increasing demand for leisure activities, Taiwan’s current number of public leisure facilities has become inadequate. Therefore, Taipei needs more public sports centres to enable the Taiwanese to more easily attend sports activities, perform community service, pursue lifetime learning courses, and enjoy artistic activities. The vision of Taiwan’s government is to implement “sports anytime, sports anywhere, and sports for everybody” in its capital, Taipei. However, Taipei’s government has frozen hiring and budgets and has thus had to use an operate-transfer (OT) strategy, by which nongovernment organizations are called on to manage the centres. The selected nongovernment organizations must support a variety of sports activities, courses, exhibitions, and community programs in the sports centres while helping city residents improve their health and quality of life.
According to the Department of Sports and Taipei’s statistical data, Taipei transferred 12 sport centres to nongovernmental organizations from 2003 to 2012. The usage of the centres increased each year, reaching a record high in 2011 with 12,000,000 person-times, indicating that Taipei residents enjoy accessing the centres’ leisure activities and that the government’s goal matched the people’s requirements. As market competitors have now emerged, sports centre performance and management are top priority for each of Taiwan’s 12 administrative areas. This study’s objective is to identify the indicators that can help sports centre managers formulate an ideal management strategy and derive profits.
The analytic hierarchy/network process (AHP/ANP) is a well-known multicriteria decision-making (MCDM) technique widely used in sport management, business, economics, and industry [
The hybrid sports centre performance evaluation model presented in this study structures the problem of evaluating 23 indices for sports centre performance in a fuzzy hierarchical/network form and links the determinants, dimensions, and enablers of performance with the various indices. This study aims to use indicators from the relevant literature and expert questionnaires to construct a successful sports centre management strategy. The indicator analysis combines qualitative and quantitative data. In this study also decision-making trial and evaluation laboratory (DEMATEL) method [
The rest of this study is organized as follows. In Section
This section briefly reviews the previous research on sports management via MCDM [
Sinuany-Stern [
Gou [
Thus, a review of the literature on the measurement of sports management clearly shows that various MCDM methods can be applied in this field (sport management). In the literature, there is few using DEMATEL method with FANP aimed at evaluating the performances of sport management [
Following our review of the relevant literature and based on discussions with both professional and academic sports management experts, some of the most important selection criteria for sports centre operations can be identified. Our evaluation procedure consists of several steps, as shown in Figure
Proposed evaluation model of Taipei’s sports centres.
Twelve professional and academic experts were selected. Since the important weights for both types of experts are considered to be equal, to avoid the occurrence of bias, all experts consulted in this study had both academic and practical experience and met the following conditions: (a) they held a managerial position in either a sport club or sport management company; (b) they had research backgrounds related to sports management.
The participating experts comprised eight managers (working in either the sports centre or a sports club) and four academics (whose principal research area was sports management). Background information about these experts is presented in Table
Background information of sports center experts.
Category/classification | Number |
---|---|
Working background | |
Industry field | 8 |
Academia | 4 |
Education level | |
Bachelor | 3 |
Master | 5 |
Ph.D. | 4 |
Work classification | |
Sports centre | 5 |
Sports club | 3 |
Scholars | 4 |
From the six perspectives (i.e., service prices, place conditions, staff qualify, sports products, traffic conditions, and operations management) and based on our review of the literature, 45 evaluation indexes for sports centre performance were summarized. Then, expert questionnaires were used to screen the indexes’ fit for the sports centre performance evaluation. Twenty-three evaluation indexes were selected by the committee of experts (i.e., the group of twelve comprising professionals and academics). The criteria for the sports centre performance evaluation are described in Table
Descriptions of the evaluation indices for sports centre.
Evaluation criteria | Evaluation subcriteria | Descriptions |
---|---|---|
Service prices (SP) | Charging methods (SP1) | Charge by times, monthly, quarterly, and yearly membership fee for courses and facilities rental. |
Special discounts (SP2) | Occasional marketing projects like coupons, free gifts, and anniversary discount for consumers. | |
Membership benefits (SP3) | Members have birthday coupons, personal lockers, member publications, and free fitness coach lessons. | |
Ads and activities (SP4) | Sports centre ads for sport games competition, public service activities, and health advocacy to match consumers’ needs and attention. | |
| ||
Site conditions (SC) | Environment atmosphere (SC1) | Sport centre creates health and sport atmosphere. |
Professional facilities (SC2) | Complete sports facilities and equipment. | |
Internal planning (SC3) | Different sport facilities plan for suitable space. | |
Facilities safety (SC4) | Regular facility safety inspection and monitoring of facility installations according to legal instructions. | |
| ||
Staff qualifies (SQ) | Communication and coordination (SQ1) | Staff should service and communicate with consumers quickly and correctly. |
Professional skills (SQ2) | Coach should have professional knowledge and instruction skills. | |
Service attitude (SQ3) | Service staff (not including coach) should properly handle consumers’ complaints and provide best service satisfaction. | |
| ||
Sport products (SD) | Sports types (SD1) | Sport centre can provide sports type. |
Sports features (SD2) | Sport centre facilities have special features and attractions. | |
Renew plan (SD3) | Sport centre facilities and lessons are periodically renewed. | |
Leisure facilities (SD4) | Sport centre has leisure facilities such as a coffee shop, convenient store, message service, and kids’ playground. | |
| ||
Traffic conditions (TC) | Transport facilities (TC1) | Sport centre location can be reached by multiple public transportation modes. |
Parking space (TC2) | Plenty of parking space for customers’ cars and motorcycles. | |
Distance (TC3) | The distance between customers’ homes or work places to sports centre. | |
Neighbourhoods (TC4) | Sports centre is convenient to most neighbourhoods. | |
| ||
Operations management (OM) | Management systems (QM1) | Financial and operational departments have effective management systems to prevent financial crises or downtimes. |
Safety maintenance (QM2) | Consumers’ safety should be the first priority of centre management. | |
Business hours (QM3) | Daily business hours from daytime (open) to night time (close). | |
Emergency response capabilities (QM4) | Proper handling of emergency issues and regular emergency response practice. |
Hierarchal evaluation structure of sports centre.
The experts who have practical experience of extension education have been consulted (as shown in Table
Through studying a literature review or brainstorming, inducting and defining the elements of the systems and then the relation between elements are judged by professionals subjectively via the design of questionnaires. The professional questionnaire is based on comparing criteria form of each pair of elements and it is represented by numbers from 0 to 4 to present “no influence” to “very high influence.”
After comparing the influential degree between one element and another, an
Consider
Make
Using
The total amount of each row is presented by
Consider
The casual diagram uses (
The fuzzy set theory, introduced by Zadeh [
The AHP, first proposed by Saaty [
This study obtained the local weights of the sports centre performance indicators by using Chang’s [
The value of the fuzzy synthetic extent with respect to the
To obtain
As
The intersection between
To compare
The degree possibility for a convex fuzzy number to be greater than
Assume that
Via normalization, the normalized weight vectors are
The analytic network process (ANP) is a comprehensive decision-making technique that captures the outcome of the dependence and feedback within and between the clusters of elements [
After the previous stage which utilizes DEMATEL to analyze the mutual influential causality of the evaluation dimensions and setting up networked level evaluation structure is done, now current stage, FANP questionnaire is developed based on the previous stage. The FANP model of sports centre is defined in the following steps.
Structure the FANP model hierarchically.
Determine the local weights of the six factors and evaluation indicators by using pairwise comparison matrices (assume that there is no dependence among the five factors). The fuzzy scale regarding relative importance to calculate the relative weights is presented in Table
Membership function of linguistic scale.
Fuzzy number | Linguistic scale | Triangular fuzzy scale | Inverse of triangular fuzzy scale |
---|---|---|---|
|
Equal | (1, 1, 1) | (1, 1, 1) |
|
Weak advantage | (1, 2, 3) | (1/3, 1/2, 1) |
|
Not bad | (2, 3, 4) | (1/4, 1/3, 1/2) |
|
Preferable | (3, 4, 5) | (1/5, 1/4, 1/3) |
|
Good | (4, 5, 6) | (1/6, 1/5, 1/4) |
|
Fairly good | (5, 6, 7) | (1/7, 1/6, 1/5) |
|
Very good | (6, 7, 8) | (1/8, 1/7, 1/6) |
|
Absolute | (7, 8, 9) | (1/9, 1/8, 1/7) |
|
Perfect | (8, 9, 10) | (1/10, 1/9, 1/8) |
Determine, with fuzzy scale (Table
Compute the global weights for the evaluation indicators. The global weights of evaluation criteria are calculated by multiplying local weight of the evaluation indicators with the interdependent weights of the factors to which it belongs.
The aim of this study is to evaluate the performance of Taipei’s sports centres by using hybrid fuzzy MCDM. The study employs six-dimensions as the framework for establishing its performance evaluation indices, while FAHP/FANP is introduced within this framework to obtain the indices’ weights. (We adopted the DEMATEL to address the complex and interdependent relationships among the variables and thereby construct a relation structure among the measurement factors for evaluation purposes.) The study’s comprehensive analysis is illustrated in the steps described below.
Using the literature review and experts’ suggestions, an evaluation framework is established, as shown in Figure
We asked 12 experts to indicate the critical level of relationships for sports centre measurement dimensions based on their experience. Summarizing their responses, we derive the average initial direct-relation
Calculating the normalized initial direct-relation matrix
We calculated matrix
Table
The sum of influences on measurement dimensions.
SP | SC | SQ | SD | TC | OM |
|
|
|
|
---|---|---|---|---|---|---|---|---|---|
SP |
|
|
1.102 |
|
0.889 |
|
6.944 | 14.379 | −0.492 |
SC |
|
0.952 | 1.064 |
|
0.896 |
|
6.839 | 13.130 | 0.548 |
SQ |
|
1.072 | 0.976 |
|
0.838 |
|
6.910 | 13.265 | 0.556 |
TC |
|
1.061 | 1.112 | 1.080 | 0.857 |
|
6.729 | 13.981 | −0.523 |
OM | 0.980 | 0.847 | 0.837 | 0.941 | 0.584 | 1.083 | 5.271 | Threshold value = 1.121 | |
|
7.435 | 6.291 | 6.354 | 7.252 | 5.020 | 8.008 |
Any value greater than Threshold value (1.121) is presented in bold.
The digraph showing causal relations among six-dimensions.
Based on Table
Based on the hierarchical framework of the study’s evaluation criteria and subcriteria, a FAHP questionnaire using the geometric mean method (GMM) and TFNs was distributed to the 12 experts in order to solicit their professional opinions. In FAHP, the main criteria and subcriteria must first be compared using linguistic terms and their equivalent fuzzy numbers denoting comparison measures. The linguistic comparison terms and the equivalent fuzzy numbers used in this study are shown in Table
The local weights of the criteria and subcriteria were determined by the FAHP. The pairwise comparisons were scored by the 12 participating experts. The fuzzy pairwise comparison matrices for the goal and the subcriteria are shown in Tables
Fuzzy pairwise comparison matrix of six criteria with respect to the goal.
SP | SC | SQ | SD | TC | OM | |
---|---|---|---|---|---|---|
SP | (1, 1, 1) | (0.95, 1.178, 1.742) | (1.644, 2.074, 2.898) | (1.165, 1.484, 2.207) | (1.328, 1.567, 2.229) | (1.057, 1.271, 1.720) |
BC | (0.574, 0.849, 1.050) | (1, 1, 1) | (1.366, 1.831, 2.549) | (1.616, 2.078, 2.853) | (0.844, 1.134, 1.595) | (0.500, 0.638, 0.927) |
SQ | (0.345, 0.482, 0.608) | (0.392, 0.546, 0.732) | (1, 1, 1) | (1.179, 1.565, 2.166) | (0.515, 0.635, 0.862) | (0.583, 0.700, 0.945) |
SD | (0.453, 0.674, 0.858) | (0.351, 0.481, 0.619) | (0.462, 0.639, 0.848) | (1, 1, 1) | (0.961, 1.200, 1.693) | (1.005, 1.296, 1.898) |
TC | (0.449, 0.638, 0.753) | (0.627, 0.882, 1.184) | (1.160, 1.575, 1.944) | (0.591, 0.833, 1.041) | (1, 1, 1) | (0.742, 0.996, 1.414) |
OM | (0.581, 0.787, 0.946) | (1.078, 1.568, 1.998) | (1.058, 1.428, 1.714) | (0.527, 0.771, 0.995) | (0.707, 1.004, 1.347) | (1, 1, 1) |
Fuzzy pairwise comparison matrix of twenty-three subcriteria with respect to the criteria.
SP | SP1 | SP2 | SP3 | SP4 |
---|---|---|---|---|
SP1 | (1, 1, 1) | (0.565, 0.683, 0.924) | (0.462, 0.595, 0.852) | (1.070, 1.367, 2.053) |
SP2 | (1.083, 1.465, 1.771) | (1, 1, 1) | (1.581, 1.976, 2.790) | (1.532, 2.086, 3.024) |
SP3 | (1.173, 1.677, 2.166) | (0.358, 0.506, 0.633) | (1, 1, 1) | (1.860, 2.414, 3.353) |
SP4 | (0.487, 0.731, 0.935) | (0.331, 0.479, 0.653) | (0.298, 0.414, 0.537) | (1, 1, 1) |
| ||||
SC | SC1 | SC2 | SC3 | SC4 |
| ||||
SC1 | (1, 1, 1) | (0.595, 0.750, 1.102) | (0.937, 1.204, 1.789) | (0.671, 0.808, 1.139) |
SC2 | (0.907, 1.333, 1.681) | (1, 1, 1) | (2.135, 2.804, 3.783) | (1.211, 1.521, 2.390) |
SC3 | (0.559, 0.830, 1.067) | (0.264, 0.357, 0.468) | (1, 1, 1) | (0.431, 0.536, 0.737) |
SC4 | (0.878, 1.238, 1.491) | (0.418, 0.657, 0.826) | (1.356, 1.865, 2.321) | (1, 1, 1) |
| ||||
SQ | SQ1 | SQ2 | SQ3 | |
| ||||
SQ1 | (1, 1, 1) | (1.238, 1.538, 2.245) | (0.801, 0.943, 1.390) | |
SQ2 | (0.445, 0.650, 0.808) | (1, 1, 1) | (0.969, 1.198, 1.810) | |
SQ3 | (0.720, 1.060, 1.249) | (0.552, 0.835, 1.032) | (1, 1, 1) | |
| ||||
SD | SD1 | SD2 | SD3 | SD4 |
| ||||
SD1 | (1, 1, 1) | (0.692, 0.922, 1.377) | (1.929, 2.627, 3.739) | (1.845, 2.523, 3.527) |
SD2 | (0.726, 1.084, 1.445) | (1, 1, 1) | (1.354, 1.895, 2.804) | (1.055, 1.466, 2.118) |
SD3 | (0.267, 0.381, 0.518) | (0.357, 0.528, 0.739) | (1, 1, 1) | (0.704, 0.979, 1.458) |
SD4 | (0.284, 0.396, 0.542) | (0.472, 0.682, 0.948) | (0.686, 1.021, 1.421) | (1, 1, 1) |
| ||||
TC | TC1 | TC2 | TC3 | TC4 |
| ||||
TC1 | (1, 1, 1) | (1.292, 1.537, 2.508) | (1.771, 2.319, 3.215) | (1.249, 1.604, 2.357) |
TC2 | (0.399, 0.651, 0.774) | (1, 1, 1) | (1.829, 2.654, 3.720) | (1.277, 1.636, 2.486) |
TC3 | (0.311, 0.431, 0.565) | (0.269, 0.377, 0.547) | (1, 1, 1) | (0.742, 0.877, 1.347) |
TC4 | (0.424, 0.623, 0.801) | (0.402, 0.611, 0.783) | (0.742, 1.140, 1.347) | (1, 1, 1) |
| ||||
OM | OM1 | OM2 | OM3 | OM4 |
| ||||
OM1 | (1, 1, 1) | (0.838, 1.088, 1.581) | (1.078, 1.498, 2.198) | (0.694, 0.835, 1.333) |
OM2 | (0.633, 0.919, 1.194) | (1, 1, 1) | (1.758, 2.434, 3.426) | (1.421, 1.766, 2.740) |
OM3 | (0.455, 0.667, 0.927) | (0.292, 0.411, 0.569) | (1, 1, 1) | (0.534, 0.646, 0.948) |
OM4 | (0.750, 1.198, 1.441) | (0.365, 0.566, 0.704) | (1.055, 1.548, 1.872) | (1, 1, 1) |
The values of the fuzzy synthetic extents relative to the goal were computed using (
The synthetic values obtained were compared using (
Values of
|
Value |
|
Value |
|
Value |
|
1 |
|
0.871 |
|
0.401 |
|
1 |
|
1 |
|
0.567 |
|
1 |
|
1 |
|
0.928 |
|
1 |
|
1 |
|
0.816 |
|
1 |
|
1 |
|
0.717 |
| |||||
min value | 1 | min value | 0.871 | min value | 0.401 |
| |||||
|
Value |
|
Value |
|
Value |
|
0.494 |
|
0.585 |
|
0.694 |
|
0.650 |
|
0.745 |
|
0.850 |
|
1 |
|
1 |
|
1 |
|
0.891 |
|
1 |
|
1 |
|
0.794 |
|
0.895 |
|
1 |
| |||||
min value | 0.494 | min value | 0.585 | min value | 0.694 |
The weights vector is
In this step, the dependencies among the six perspectives are taken into account and interdependent weights of the six perspectives are computed. In order to determine the dependence among the six factors, the impact of each factor on every other factor using fuzzy pairwise comparisons is evaluated. Based on fuzzy pairwise comparison matrices, Table
Using the calculated relative importance weights, the dependence matrix of the six factors is constructed. Interdependent weights of the six factors are computed by multiplying the dependence matrix of the six factors with the local weights of the six factors obtained in the previous step. The interdependent weights of the six factors are computed in the following part:
As shown above, the results are significantly different from when the interdependent weights and dependencies are not taken into account. The final results change from 0.247 to 0.294, 0.215 to 0.209, 0.099 to 0.076, 0.122 to 0.130, 0.145 to 0.078, and 0.172 to 0.212 for the priority values of factors SP, SC, SQ, SD, TC, and OM, respectively.
The innerdependence matrix of the factors with respect to six main criteria.
The innerdependence matrix of the factors with respect to “SP” | |||||
SC | SQ | OM | Local weights | ||
| |||||
SC | (1, 1, 1) | (1.831, 2.521, 3.369) | (0.485, 0.613, 0.879) | 0.473 | |
SQ | (0.297, 0.397, 0.546) | (1, 1, 1) | (0.589, 0.707, 0.953) | 0.067 | |
OM | (1.137, 1.631, 2.062) | (1.050, 1.415, 1.699) | (1, 1, 1) | 0.460 | |
| |||||
The innerdependence matrix of the factors with respect to “SC” | |||||
SP | SD | OM | Local weights | ||
| |||||
SP | (1, 1, 1) | (1.354, 1.703, 2.635) | (0.818, 0.979, 1.324) | 0.437 | |
SD | (0.380, 0.587, 0.739) | (1, 1, 1) | (1.060, 1.337, 1.940) | 0.311 | |
OM | (0.755, 1.022, 1.222) | (0.515, 0.748, 0.943) | (1, 1, 1) | 0.252 | |
| |||||
The innerdependence matrix of the factors with respect to “SQ” | |||||
SP | SD | OM | Local weights | ||
| |||||
SP | (1, 1, 1) | (1.165, 1.484, 2.207) | (1.336, 1.624, 2.203) | 0.553 | |
SD | (0.453, 0.674, 0.858) | (1, 1, 1) | (0.953, 1.191, 1.799) | 0.309 | |
OM | (0.454, 0.616, 0.749) | (0.556, 0.839, 1.050) | (1, 1, 1) | 0.138 | |
| |||||
The innerdependence matrix of the factors with respect to “SD” | |||||
SP | OM | Local weights | |||
| |||||
SP | (1, 1, 1) | (1.035, 1.226, 1.625) | 0.674 | ||
OM | (0.615, 0.815, 0.966) | (1, 1, 1) | 0.326 | ||
| |||||
The innerdependence matrix of the factors with respect to “OM” | |||||
SP | SC | SQ | SD | Local weights | |
| |||||
SP | (1, 1, 1) | (0.932, 1.142, 1.652) | (1.829, 2.347, 3.153) | (1.165, 1.552, 2.256) | 0.396 |
SC | (0.605, 0.876, 1.073) | (1, 1, 1) | (1.324, 1.791, 2.506) | (1.532, 2.015, 2.790) | 0.328 |
SQ | (0.317, 0.426, 0.547) | (0.399, 0.559, 0.755) | (1, 1, 1) | (1.179, 1.565, 2.166) | 0.145 |
SD | (0.443, 0.644, 0.858) | (0.358, 0.496, 0.653) | (0.462, 0.639, 0.848) | (1, 1, 1) | 0.131 |
In this step, the overall weights of the evaluation indices are calculated by multiplying the interdependent weights of six factors found in previous step with the local weights of evaluation indices obtained in Step
Weights of the main evaluation indices by FAHP.
Criteria | Weights | Subcriteria | Local weights | Global weights |
---|---|---|---|---|
SP | 0.294 (1) | SP1 | 0.189 | 0.047 (10) |
SP2 | 0.434 | 0.107 (1) | ||
SP3 | 0.367 | 0.091 (3) | ||
SP4 | 0.010 | 0.002 (22-23) | ||
| ||||
SC | 0.209 (3) | SC1 | 0.212 | 0.046 (11) |
SC2 | 0.481 | 0.104 (2) | ||
SC3 | 0.010 | 0.002 (22-23) | ||
SC4 | 0.296 | 0.064 (6) | ||
| ||||
SQ | 0.076 (6) | SQ1 | 0.405 | 0.040 (14) |
SQ2 | 0.304 | 0.030 (15) | ||
SQ3 | 0.291 | 0.029 (16) | ||
| ||||
SD | 0.130 (4) | SD1 | 0.477 | 0.058 (8) |
SD2 | 0.366 | 0.045 (12) | ||
SD3 | 0.067 | 0.008 (20) | ||
SD4 | 0.090 | 0.011 (19) | ||
| ||||
TC | 0.078 (5) | TC1 | 0.448 | 0.065 (5) |
TC2 | 0.413 | 0.060 (7) | ||
TC3 | 0.036 | 0.005 (21) | ||
TC4 | 0.103 | 0.015 (17) | ||
| ||||
OM | 0.212 (2) | OM1 | 0.286 | 0.049 (9) |
OM2 | 0.405 | 0.070 (4) | ||
OM3 | 0.067 | 0.012 (18) | ||
OM4 | 0.242 | 0.042 (13) |
Note: () denotes ranking order.
The results show that the critical order for the six-dimensions of the sports centre evaluation is “SP: service prices (0.294),” “SC: site conditions (0.209),” “SQ: staff qualifies (0.076),” “SD: sport products (0.130),” “TC: traffic conditions (0.078),” and “OM: operations management (0.212).” Table
Thus, the synthesis values (global weights) of the sports centre performance evaluation model under the 23 indicators are as follows: SP1 (0.047), SP2 (0.107), SP3 (0.091), SP4 (0.002), SC1 (0.046), SC2 (0.104), SC3 (0.002), SC4 (0.064), SQ1 (0.040), SQ2 (0.030), SQ3 (0.029), SD1 (0.058), SD2 (0.045), SD3 (0.008), SD4 (0.011), TC1 (0.065), TC2 (0.060), TC3 (0.005), TC4 (0.015), OM1 (0.049), OM2 (0.070), OM3 (0.012), and OM4 (0.042).
Based on the overall weights listed in Table
This study adopted the quantitative evaluation indices developed by the Taipei city sports centres to establish a fuzzy network hierarchical structure. Expert opinions were compiled using the FANP to perform a weight analysis on the evaluation indices. We then conducted an empirical analysis using the FANP. The important research conclusions, practical management implications, limitations, and suggestions for future research are summarized as follows.
This study conducted a performance analysis on Taipei’s sports centres through the DEMATEL and FANP approach using six dimensions. The empirical findings can be summarised as follows. First, after integrating all the relevant literature reviews and expert opinions, 23 indices were identified as being relevant to the centre’s performance in terms of the six-dimensions. Second, the empirical results of the FANP provided the order of importance for the six centre performance dimensions: service prices, site conditions, operations management, traffic conditions, sport products, and staff quality. Therefore, the service prices, site conditions, and operations management are the three most important factors in any evaluation of the performance of sports centre managers. Third, the empirical results of the FANP provided the order of importance for the 23 sports centre performance evaluation indices: special discounts, professional facilities, membership benefits, safety maintenance, transport facilities, facilities safety, parking space, sports types, management systems, and charging methods. Thus, the special discounts, professional facilities, membership benefits, safety maintenance, and transport facilities are the five most important evaluation indices in any evaluation of the performance of sports centre managers. Thus, this study’s use of FANP and statistics has provided sports centre managers with a reliable decision-making reference with which they can formulate a business strategy that optimally meets their customer’s expectations.
The empirical results indicate that “service prices” (0.247) has the highest value among the model’s six main criteria, while “site conditions” (0.215) has the second highest value, “operations management,” (0.172) the third highest, and “staff quality,” (0.099) the least.
Although our study contributes to the evaluation of sports centre performance and helps sports centre managers create the best business strategies, it still has limitations. Previous research has found that staff quality is important, while our results indicate that staff quality is the least important indicator; thus, our results cannot be generalised widely. Moreover, although the study’s criteria and features were edited according to suggestions from academics and sports centre managers in Taipei, we may have overlooked some important criteria and features. However, sports management practitioners may still adopt our analytic approach to improve their centre’s performance.
We hope that our sports centre performance evaluation model will help sports centre managers and other decision makers formulate ideal business strategies. A future study could utilise the DEMATEL to examine the interactive and feedback relationships among the subcriteria of the six main perspectives and thus enrich the research on sports centres.
The authors are grateful to the editor Kim-Hua Tan and three anonymous referees for their valuable comments and suggestions which helped in improving the quality of this paper.