CHAPTER 4 RESULTS
4.3 Descriptive Statistics
4.3.4 CFA Model Parameter
The CFA measurement model was identified on the 7 factors. Table 12 presented the standardized CFA model results. All items loaded significantly (p < .001) on the 7 theorized latent variables. The values of the factor loadings ranged from .550 to .910.
Most values of the factor loadings were greater than .70; while only 3 values were smaller than .70. The results provided evidence that the indicators are good measures of their
39
conceptual constructs. In addition, each item only loaded on one factor, which suggests convergent and discriminant validity. Figure 4 depicts the CFA diagrams for the 7 factors, with factor loadings and error terms.
Table 12
Standardized CFA measurement model results
Constructs Indicators β p-value
Service employees
40
Figure 5. CFA diagrams for the 7 factors with factor loadings
41 4.3.5. Confirmatory Factor Analysis
A confirmatory factor analysis was conducted using AMOS for measurement model of determinants of customer satisfactions and behavior intension. Confirmatory factor analysis (CFA) serves as a measurement model for a structural equation model.
Anderson and Gerbing (1988) argue that, in customer behavior research it is common to analyze the measurement model before the structural models. CFA allows the researcher to assess the contribution of each observed variable and determine how well the observed variable measures its underlying latent construct. The purpose of CFA is to specify the relationship between each scale item and its underlying latent constructs (factors).
After the data were screened, the CFA was run for the measurement model in Figure 5 including the seven latent constructs: service employees, access, opponent characteristics, player performance, game atmosphere, customer satisfaction and behavior intension. The results are presented and discussed for the model fit indices, factor loadings (the correlation between the latent variable and the observer variable). The results of the CFA measurement model indicated a good fit of the data to the hypothesized structure. The value of CFI was .952, the value of GFI was .902, and the value of RMSEA was .60. All model indices exceeded the suggested criteria indicating a good fit. Table 14 provided the results of fit indices for the CFA measurement model and the recommended value of the good-of-fit indices.
Table 13
Results of model fit indices for CFA measurement model
Absolute fit indices Obtained Recommendations on fit
indices
CFI .952 > .90
GFI .902 > .90
RMSEA .060 < .08 or < .1
2.906 < 5
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Absolute fit indices Obtained Recommendations on fit
indices 677.025 P < 0.05(N > 250)
d.f .000
Figure 6. Confirmatory factor analysis a measurement model
43 Table 14
Correlation matrix between the CFA measurement model
Correlations Estimates
Service employees Experiences .629
Service employees Opponent characteristics .665
Service employees Player performance .335
Service employees Game atmosphere .544
Service employees Customer satisfaction .619
Service employees Behavior intension .640
Experiences Opponent characteristics .611
Experiences Player performance .622
Experiences Game atmosphere .868
Experiences Customer satisfaction .773
Experiences Behavior intension .738
Opponent characteristics Player performance .385
Opponent characteristics Game atmosphere .539
Opponent characteristics Customer satisfaction .640
Opponent characteristics Behavior intension .684
Player performance Game atmosphere .635
Player performance Customer satisfaction .436
Player performance Behavior intension .473
Game atmosphere Customer satisfaction .795
Game atmosphere Behavior intension .750
Customer satisfaction Behavior intension .880
4.3.6 Estimated Correlations of Latent Variables for CFA
The model had 67 parameters to be estimated and 233 degrees of freedom. The correlations among 7 latent variables were estimated. Table 15 was presents the estimated correlation matrix for the 7 latent variables. The results show that all correlations among 7 latent variables were statistically significant at the .001 level and ranged from .334 to .948.
According Green & Salkind (2008) correlation coefficients of .10, .30, and .50 are usually interpreted as small, medium, and large coefficients respectively. Only 5 of 21 correlations were smaller .50 – the correlations between stadium employees and player performance (.309); between stadium employees and opponent characteristics (.476); between opponent
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characteristics and player performances (.334); between opponent characteristics and game atmosphere (.389); and between player performance and customer satisfaction (.434). The rest of the correlations had high coefficient values (greater than .05). The results suggest that all latent variables were highly correlated to each other in the CFA measurement model.
Table 15
Estimated Correlation Matrix of Latent Variables for CFA
Measure Correlation Matrix
1 2 3 4 5 6 7
1. Stadium employees 1.00
2. Experiences .521** 1.00
3. Opponent characteristics .529** .476** 1.00
4. Player performance .309** .563** .334** 1.00
5. Game atmosphere .417** .653** .389** .533** 1.00
6. Customer satisfactions .564** .690** .547** .434** .658** 1.00
7. Behavioral intentions .639** .723** .642** .517** .681** .948** 1.00
4.3.7 The Second-order-factor Model
The second-order model represents the hypothesis that these seemingly distinct, but related constructs can be accounted for by one or more (Chen, 2005). CFA was used to confirm the expected relationship of service quality latent variable and game quality latent variable and their corresponding dimensions. As a two latent factor of service quality and three latent factor of game quality were significantly correlated, second order-factor-model was tested. Figure 7 & Figure 8 was showed that all the items loaded significant on their respective factors (p < .01) and the factor loading ranking from .56 to .89. The overall, the fit of the model to the data were moderate as following. For the service quality model (CFI = .981; GF I= .981; RMSEA = .075) and for the game quality model (CFI
= .964; GFI = .946; RMSEA = .076).
45 Table 16
Results of model fit indices for CFA second-order-model Service Quality
Model
Game Quality
Model Recommended Value
CFI .981 .964 > .90
GFI .981 .946 > .90
RMSEA .075 .076 < .08 or < .1
4.026 4.077 < 5
32.206 167.172 P < 0.05
d.f 8 41
Figure 7. The second-order-factor model for service quality
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Figure 8. The second-order-factor model for game quality
4.3.8. Structure Equation Model
In this study formulated a SEM to analyze the proposed model using AMOS to test the relationship among the proposed model. According to many scholars, the first issue to consider in examining a structural model is to examine the goodness of fit (GOF) of the model (Hair, et al.2006; Patrick 1997). Benchmarks for recommendable values for an overall fit have been suggested in previous studies (Table 17).
The results of the standardized path coefficients (β) indicated that service quality had a significant influence on customer satisfaction (β = .43, p > 0.001) and behavior intensions (β = .22, p > 0.001); game quality had a significant on customer satisfaction (β
= .67, p > 0.001) and behavior intensions (β = .20, p > 0.001); customer satisfaction had a
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direct significant influence on behavior intensions (β = .61, p > 0.001). The structural equation model was illustrated in Figure 8.
Table 17
Results of Model Fit Indices for SEM
Absolute fit indices Obtained Recommendations on fit
indices proposed service quality has a direct positive relationship with customer satisfaction and behavior intension. The results of structural equations model estimates in Figure 8 show that service quality is a significant predictor of customer satisfaction (H1: β = .43, p < .001) and behavior intensions (H2: β = .22, p < .001). Game quality has a direct significant influence on customer satisfaction (H3: β = .67, p < .001) and behavior intensions (H4: β
= .20, p < .001). Finally, in the hypothesis 5, customer satisfaction has a direct significant influence on behavioral intentions.
Table 18
Results of research hypothesis testing
Hypothesis β
Satisfaction .434 *** Direct Supported
H2: Service Quality Behavior intension .222 *** Direct Supported H3: Game Quality Customer Satisfaction .666 *** Direct Supported H4: Game Quality Behavior intension .198 *** Direct Supported
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Hypothesis β
p-value
Path Pattern
Test Outcome H5: Customer satisfaction Behavior
intension .610 *** Direct Supported
Figure 9. The structural equation model
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CHAPTER 5 CONCLUSION
This chapter summarizes the purpose of the study, the major findings and limitation. This chapter is divided into several sections. This information is presented in the following sections: a) overview of the study, b) implication of findings, c) limitations and future research.
5.1 Overview of the Study
The primary purpose of this study was to investigate sport spectators’ perceptions of the service evaluation variables: service quality, game quality and satisfaction in relation to their behavioral intentions while attending a college sporting event. A conceptual model with four dimensions was proposed, which were spectators’ perceptions of service quality, game quality, customer satisfaction, and behavioral intentions. The final scale generated a total of 37 items. All constructs were measured using seven-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree).
The final scope of the study was to test the proposed conceptual model by using service quality, game quality, customer satisfaction and behavior intension questionnaire.
536 usable surveys were collected from spectators who was participated the 2014 Vietnam University Games held in Ho Chi Minh City.
The first part, the study was to understanding the satisfaction of spectators’
perceptions service quality and game quality during the game. Based on the evaluation of spectator indicated that they are most satisfied with (1) “player try to do best”, (2) “home team plays hard”, (3) “home team give 100% effort”, (4) “score board entertaining”, (5)
“home player associated with player”. And the spectator was most unsatisfied with (1)
“the food in arena”, (2) “guest team have start player”; (3) “quality of sound in arena”; (4)
“guest team have win/lose records”; (5) “attitude of employees”.
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The second part, the data was analyzed using by structural equation modeling, consisting of two parts: measurement model and structural model. Confirmatory factor analysis was first assessed to determine the appropriateness of the measurement model which generated 4 factors. Finally, the structural model was assessed to identify the causal link among the 4 latent variables. The results reveal that the proposed conceptual model explained 63.1 % in customer satisfaction, 73.9% variance in behavioral intentions to the sporting event. In addition, the results indicated the model fits the data well. For the hypothesis testing, all other hypothesized paths were significantly with p-value small than .01.
5.1.1 Service Quality
Service quality refers to the element of the service quality in the sporting events content. The current study reveals that service quality had a positive influence on both customer satisfaction and behavior intension. Thus this findings support hypothesis 1 (β
= .434, p < .001) and hypothesis 2 (β = .222, p < .001). That implies which is high quality service is a key determinant of spectator’s satisfaction and behavior intension. This also consisted with the previous studies (Theodorakis et. al., 2013) the findings in this study suggest service quality lead to the customer satisfaction; customer satisfaction was a mediated factor influence on behavior intension. In this current study found service quality also has a direct effect on behavior intension; therefor, the results were suggested the path from service quality to customer satisfaction was significantly than the path from service quality to behavior intension. The above results suggested that the relationship between service quality dimension and behavior intension was partially mediated by customer satisfaction.
51 5.1.2 Game quality
Game quality in the spectator industry refers to the entertainment of the competition based on the game outcome, associated of player with excitement of the sporting event. The findings indicate that the relationship between game quality customer satisfaction and behavior intension was significantly different support hypothesis 3 (β
= .666, p < .001) and support hypothesis 4 (β = .198, p < .001). The game quality has been widely discussed by pervious researches (Yoshida & Jame, 2010; Theodorakis et. al., 2013), the results findings game quality also has a direct effect on customer satisfaction on behavior intension, and however, game quality was strongest effect among customer satisfaction and behavior intension. Thus, customer satisfaction also was a partially mediated factor to behavior intension. The results of game quality in this current study consisted with the previous research.
5.1.3 Customer Satisfaction and Behavior Intensions
A positive relationship between satisfaction and behavioral intentions has been confirmed by many researchers (Caro & Garcia, 2007; Hightower et al., 2002; Kaplanidou
& Gibson, 2010; Shonk, 2006; Shonk & Chelladurai, 2008; Yoshida &James, 2010;
Theodorakis et. al., 2013). The path between customer satisfaction and sports spectator behavioral intentions toward the sporting event was statistically significant. The findings support hypothesis 5 (β = .610, p < .01), indicating that when satisfied with the service and game will be more likely to recommend the sporting event to others and revisit the sporting event.
52 5.2 Implication of Findings
The study aimed to investigated customer satisfaction and behavior intension in the context of college games. Both dimensions of service quality and game quality were included in the model and were tested in the relationship to customer satisfaction and behavior intensions. Studies have provided on the measurement of the previous research and its influence on spectators’ satisfaction and behavior intensions (Yoshida &James, 2010; Theodorakis et. al., 2013).
The first theoretical contribution of our study was proposed two clear dimension service quality (service employees and experiences environment). These two dimensions were incorporated within an integrated model of service quality, as the experiences environment (sensorycape), which was proposed by Lee, Lee, Seo, and Green (2012) in the general service marketing literature. The both of service employees and experiences environment dimensions were found to be reliable, valid and applicable in this context. As previously research discussed, in this present study the service quality covered the sub-dimensions of employees and environment experiences. There are typical sub-dimensions that had been used in the previously research in a spectator sporting event and describe the process part of service quality (Lee, Lee, Seo, and Green, 2012; Yoshida & James, 2010).
A second theoretical contribution of the study was to get clarification the relationship between the three dimensions of game quality (opponent characteristics, player performance and game atmosphere). The results indicated that the game atmosphere was strongest significant of the game quality in the content of college games.
In the previously research such as Yoshida & James (2010), found the game atmosphere was significantly influence on game satisfaction with one sample in Japan and the other in United States. Thus, in the game quality dimension, the game atmosphere is an important factor in the context of college games; therefore, it should not be overlooked in the future studies and organizations.
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A third theoretical contribution of our study is to support all theoretical models proposed before; service quality and game quality has direct influence on customer satisfactions and behavior intensions. The results clarified that game quality has a stronger influence on customer satisfaction than service quality; furthermore, customer satisfaction partially mediates the relationship among service quality, game quality and behavior intension. As a previously research discussed, the results had been reported so far for above relationship. Regarding our hypothesis, we found that customer satisfaction was a partially mediated the relationship between service quality, game quality and behavior intensions in the context of college games in Vietnam.
Finally, hypothesized model provides a starting point of service quality and game quality for sporting events. The management of college games should understand and recognize that the game quality and the service quality, the quality of the players, the performance of the university team, are important element quality of the games; those influence spectators’ satisfaction and loyalty toward the games. In addition, the history of the team, the quality of the players, the personality of the coach, the personality of the referee and the brand of the university are issues that should be communicated.
Furthermore, perceptions about fairness and equal treatment of the teams from referees and the league administration bodies play an important role in the development of the outcome dimension of service quality (Theodorakis &
Alexandris, 2008).
The service quality dimension has been widely discussed by previous researcher (Theodorakis & Alexandris, 2008, 2009, 2013; Yoshida & James, 2010). Issues related to the stadium such as the design, space, safety, and atmospherics, as well as the supportive services, such as parking, accessibility, concessions and in game competitions are important aspects of the functional dimension of service quality (Wakefield et al., 1996). It also note that by Yoshida and James (2011) provided service
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quality which as the interaction between service employees and service environment. In conclusion, the present study provided empirical evidence that game quality, as measured by the opponent characteristics, player performances, game atmosphere; is an important element of quality construct in the context of college games. From a marketing standpoint, it is implied that marketing professional sport teams should include efforts to foster customers’ game and service quality.
5.3 Limitations and Future Research
Several limitation of the study should be influenced the study results. The first limitation might be omission of the important variable. For example, this study did not include various ancillary entertainment activities such as cheerleaders, halftime shows, halftime games, mascots design, and giveaways in the research model, because our model was largely based on the traditional definition of service quality and game quality which was a customer’s perception of the quality of the customer-service environment interaction and the customer-frontline employee interaction.
The second limitation, this study did not exam the relationship between the service quality and game quality. According Zhang et al. (1997) was defined ancillary service as the set of the items supporting the core product, that given the definition supplementary service may be affect a customer’ perceptions of the game quality. In the future research, the relationship of service quality and game quality should be examined.
The third limitation is in relation in data collection, the data were collected from spectators of the college game, which the mean that results are only indicative cannot be generalized. For the future studies should be use lager samples, including spectator of more kind of the sporting event in Vietnam such as championship, international open, professional games, young championship, and mega-event, to allow results to be generalized with more confidence.
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Finally, the differences between spectators’ perception of service and game quality on different sport such as beach games, motor sport, indoor games and outdoor games is also an issue that should be investigated in future studies. This request the testing of the customer satisfaction and behavior intensions while the spectator’s perception in the different sports and different environment. That could understand evaluations of spectators make an effort to enhance of service quality and game quality in the difference sports context.
5.4 Managerial Implication
The scope of this study was to understand the perceptions of spectator’s about the service quality and game quality in sporting events. The study examined service performance during the final round in 2014 Vietnam University Games. Thus, based on the evaluation of spectator’s viewpoint, some suggestions need to carry out for organization. First point, 34.5% spectator was evaluated the food outside is better than the ones in the arena. Second, the quality of sound in arena is important. Third, the attitudes of employees while interacting with spectators are critical. Four, the professional knowledge of font line employees is necessary. Finally, the player performance including the home team and guest team play an important role on the games itself. For the future organizations, the manager should pay attention to improve such as providing training sections for employees, offering high quality food and sound system in the arena, and preparing high quality technology scoreboards and decorations in the arena.
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