CHAPTER 3 METHODOLOGY
3.3 Research Framework
The review of the literature in service quality, game quality in sporting event that constructs of perceived service quality, game quality, customer satisfaction, and behavioral intentions. The relationship among these constructs will be test by this current study. The research framework is shown in Figure 4.
H1: The customer’s perceptions of the services quality have a positive impact on customer satisfaction.
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H5: Customer satisfaction has a positive impact on behavior intentions.
Figure 4. The research framework 3.4 Data Collection
After the pilot study, the data collection was set to survey on Tan Binh arena, Vietnam in the final round of the Vietnam University Games. Question was distributed around the arena after the game over during two weekends of the event from 19th to 26th April, 2014. In our study was distributed randomly to spectator who willing respondents the questionnaires. It was collected 600 respondents for this study.
3.5 Data Analysis
All of the return questionnaires would be review, and the research sorted out the invalid questionnaires. The Statistical Package for Social Science (SPSS) 20.0 was used to analysis the descriptive statistic; Exploratory Factor Analysis (EFA); Confirmatory Factor Analysis (CFA); Structural Equation Modeling (SEM) and Cronbach’s Alpha were employed to ensure the construct validity and reliability. Cronbach’s Alpha will test to ensure internal consistency of the scales. At least it should meet the minimum acceptable level of 0.7 or above (Nunnally & Bernstein, 1994).
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Descriptive statistics will use to analyze subject’s demographic profile including gender, age, marital status, educational level and occupation of the spectator’s Vietnam University Games.
Exploratory Factor Analysis (EFA) is used to determine the number of continuous latent variables that are needed to explain the correlations among a set of observed variables. EFA helps researchers define the construct based on the theoretical framework, which indicates the direction of the measure (DeVon et al. 2007) and identifies the greatest variance in scores with the smallest number of factors (Delaney 2005; Munro 2005). This is statistical approaches used to examine the internal reliability of a measurement.
Structural Equation Modeling (SEM) is a stage to examine if the model can be useful and identify whether the scope of dimension fit or not. SEM encompass an entire family of the model by names, among them covariance, structure analysis, latent variables analysis, confirmatory factor analysis (Hair et al., 2006). It includes one or more linear regression equations that describe how the endogenous constructs depend upon the exogenous constructs. Their coefficients are called path coefficients, or sometimes regression weights (Reisinger & Turnes, 1999).
CFA and SEM will use to analysis by AMOS Statistical package software to testing hypothesis in this study. By convenience, the value of overall fit of a hypothesis model can test to evaluate significant when criteria Chi-square (P value > 0.05), fit indices such as the ratio of Chi-square to degrees of freedom (Chi-square/df 5), goodness of fit index (GFI > 0.9), goodness of fit index (GFI > 0.9), root mean square error of approximation (RMSEA < 0.08) (Hair et al, 2006 an Patrick, 1997).
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CHAPTER 4 RESULTS
This chapter provided analysis of the collected data and explained the results of the statistical analyses in this study. SPSS was used to initially screen data for statistical assumptions, to estimate Cronbach’s alpha coefficient (α), bivariate correlation and descriptive statistics. Then, AMOS was employed to test all structural models, Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM). There are three sections included in this chapter. The first section contained the demographic characteristic data of the study sample. The second section provided descriptive statistics of the variable in this main study. Finally, the relationship among the hypothesis model by CFA and SEM was presented.
4.1 Demographic Characteristics
A frequency analysis was run on respondents’ demographic information of spectator in 2014 Vietnam University Games. Demographic characteristics are provided in table 2. A total of six hundred surveys were distributed and there were a total of 536 usable surveys in this study for statistical data analysis. Among the respondent participants, the gender of female was 58.6% (n = 314) which was a higher than rate of male 41.4% (n = 222). In the term of age, the most participants were 18-24 years old with the rate of 99.8%. Almost of respondents in attendance at this sporting event were single or never married (99.8%), those who were divorced or separated only one of the sample size. In the review of participants’ education background, majority respondents were undergraduate 99.4%, only 0.4% of respondents were graduated or above. The occupation of respondents approximately 100% were student, 0.4% were business and others.
27 Table 2
Demographic Characteristics (N = 536)
Demographic Variables Frequency Percentage%
Gender
Male 222 41.4
Female 314 58.6
Marital Status
Single/Never married 535 99.8
Divorced/Separated 1 0.2
Age
18-24 years old 535 99.8
25-30 years old 1 0.2
Education
High school/technical school 1 0.2
Undergraduate 533 99.4
Graduated or above 2 0.4 presented in (Appendix 5) included 37 variables to measurement which the perception of spectators’ service quality dimension, game quality dimension, customer satisfaction dimension and behavior intensions dimensions.
Service quality dimension a score equal 1 disagree to 7 strongly agree. In the service employees, the respondents were most agree with “professional knowledge” of employees (M = 4.89, SD = 1.174). The respondents least agree with “attitude shows understand” (M = 4.66, SD = 1.128). Overall, respondents were evaluated neither agree or disagree ranking from 29.1% to 36% in employees service. For the experiences, the respondent evaluation “scoreboards are entertaining” were the most agree with (M = 5.34,
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SD = 1.131). The spectators evaluation lowest “food better than outside” (M = 4.28, SD = 1.332). In general, spectators’ somewhere agree with the experiences in the arena.
Game quality dimension was evaluated by spectators’ equal one is disagree to seven is strongly agree. For opponent characteristics the highest agree with “opposing teams have a good history” (M = 4.95, SD = 1.160); player performance most agree with
“players try to do their best” (M = 5.44, SD = 1.131) and game atmosphere most agree with “the team understand atmosphere is important to you” (M = 5.29, SD = 1.107). For the lowest evaluation of game quality dimensions as following “opposing teams have star players” (M = 4.47, SD = 1.081), “players on your team have superior skills” (M = 4.96, SD = 1.027) and “the excitement surrounding the performance of the players” (M = 5.11, SD = 1.105). In the overall, the perceptions of spectators’ of game majority was examined somewhere agree to agree, as the items “music exciting” 56.7% spectators strongly agree.
Customer satisfaction and behavior intension dimension was indicated the level satisfaction and behavior intension of spectators’ equal one is very low to seven is very high. For the customer satisfaction after perception the quality of the service and the game, the spectators’ were most satisfied “my experience very satisfied” with (M = 5.06, SD = 1.125). Where in satisfaction dimension “outcome of this game” was lowest satisfied with (M = 4.98, SD = 1.223). For the behavior intension dimension, the spectators highest
“would like to visit this sporting event in the future” with (M = 5.18, SD = 1.186) and second was “recommend this sporting event to others” with (M = 5.14, SD = 1.239), finally, the lowest in behavior intension was “I would like to attend this sporting events in future” with (M = 5.07, SD = 1.219). According the presented in the table 3, spectators’
was evaluated somewhat high to high in the satisfaction and behavior intension ranking from 28% to 35%. (See Appendix 5)
Table 3 indicated the mean values, standard deviation and percentage of frequencies were obtained from top 5 highest and top 5 lowest mean values which the
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evaluation of spectators’ perception the service quality and game quality. According the evaluation of spectators’, the most highest agree in the service was following as (1)
“players on your team always try to do their best” (M = 5.44, SD = 1.131); (2) “your team plays hard all the time” (M = 5.39, SD = 1.090); (3) “your team gives 100% effort every game” (M = 5.38, SD = 1.095); (4) “the arena scoreboards are entertaining to watch” (M = 5.34, SD = 1.131); (5) “you like the excitement associated with player performance” (M = 5.3, SD = 1.061). The top five highest items percentage of frequency over 30% the spectators’ at agree level with the service during the game, ranking from 31.2% to 36.2%.
Following in the table 4 the results clearly indicated the top five lowest in the performance of the service included (1) “the arena provides better food than outside” (M = 4.28, SD = 1.332); (2) “opposing teams have star players” (M = 4.47, SD = 1.081); (3)
“the arena has a quality sound system” (M = 4.62, SD = 1.419); (4) “opposing teams have good win/loss records” (M = 4.66, SD = 1.186); (5) “the attitude of the employees at this arena shows that they understand your needs” (M = 4.66, SD = 1.128). The evaluation of spectators’ majority 26.7% to 40% at neither agree or disagree level in the top five lowest of mean values.
30 Table 3
Mean, Standard Deviation and Percentage of Frequencies of Top 5 highest
Percentage of Frequencies (%)
4. The arena scoreboards are
entertaining to watch 5.34 1.131 0.0 0.9 4.3 19.8 24.4 36.2 14.4
5. You like the excitement Associated with player performance
5.3 1.061 0.2 0.9 1.1 22.6 29.3 33.6 12.3
30
31 Table 4
Mean, Standard Deviation and Percentage of Frequencies of Top 5 lowest
Percentage of Frequencies (%) 1 The arena provides better
food than outside 4.28 1.332 2.4 7.6 13.8 34.5 23.1 13.8 4.5
2. Opposing teams have star
players 4.47 1.081 0.2 4.9 8.2 40.3 30.0 13.6 2.8
3. The arena has a quality
sound system 4.62 1.419 3.2 4.5 11.8 26.7 22.2 25.4 6.3
4. Opposing teams have good
win/loss records 4.66 1.186 0.6 4.5 7.1 34.5 28.0 20.5 4.9
32 4.3 Descriptive Statistics
4.3.1 Reliability test
Reliability refers to the extent to which a scale produces consistent results if repeated measurements are made (Malhotra, Hall, Shaw, & Crisp, 1996). The precise measurement of variables is an important step in the process of research. The reliability of the scales is measured in order to determine if the scales consistently reflect the construct it is measuring. Scale reliability was measured by calculating Cronbach’s alpha (α), the most common measurement for scale reliability. Table 5 showed the reliability estimation for the four constructs. Service quality dimension included fifteen items with a Cronbach’s Alpha of .93. Game quality dimension included fourteen items with a Cronbach’s Alpha of .91. Customer satisfaction dimension included four items (α = .93) and behavior intension included four items (α = .93). Thus, the Cronbach’s Alpha values for all scales were above .80, which is generally the accepted value indicating good reliability of scales (Nunnaly & Bernstein, 1994).
Table 5
Reliability Estimates for the Constructs
Constructs Mean SD Cronbach’s Alpha
Service Quality (15 Items) 4.83 0.86 .93
Game Quality (14 Items) 5.09 0.76 .91
Customer satisfaction (4 Items) 5.03 1.02 .93
Behavior intension (4 Items) 5.12 1.09 .93
Reliability estimates for the constructs between Pilot-test (n = 100) and Post-test (n
= 536) was presented in the table 6. The results of Cronbach’s Alpha are the correlation between scores from the same subjects test at two different seasons. The correlation coefficients for the variables ranged from .86 to .93. The closer the two seasons sets of
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score are to each other and the greater in the Pilot test reliability. That refers to the correlations between two season consistent with the subjects is spectator in the content of college games.
Table 6
Reliability Estimates for Pilot-test and Post-test
Constructs
Pilot-test Season 2013 VUG
(n = 100)
Post-test Season 2014 VUG
(n = 536) Cronbach’s Alpha Cronbach’s Alpha
Service Quality (15 Items) .91 .93
Game Quality (14 Items) 92 .91
Customer satisfaction (4 Items) .93 .93
Behavior intension (4 Items) .86 .93
4.3.2 Factor loading analysis
Factor validity is construct validity technique used in assessing the quality of questionnaire and it is obtained by means of factors analysis. The main measures used to test the validity of an instrument in factor analysis include:
Extraction communalities are estimates of the variance in each variable accounted by the components. The communalities values indicate that the extracted components represent the variables well. Thus, small values indicate variables that do not fit well with the factor solution, and should possibly be dropped from the analysis.
aiser-Meyer-Olkin Measure of Sampling Adequacy is a statistic that indicates the proportion of variance in the variables that might be caused by underlying factors. High values (close to 1.0) generally indicate that a factor analysis may be useful with the data.
If the value is less than 0.50, the results of the factor analysis probably won't be very useful.
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matrix, which would indicate that the variables are unrelated and therefore unsuitable for structure detection. Small values (less than 0.05) of the significance level indicate that a factor analysis may be useful with the data.
Base on the original dimension of service quality, game quality, customer satisfaction and behavior intensions, the current study made on effort to examine underlying of four dimension. In this section we discuss the results of the exploratory factor analysis to assess the suitability of the instruments.
According the results analyze of factor loadings, the table 7 indicated the service quality dimension rotated component matrix, it was recognized that were two factor component to be correlated with all variable. The KMO for the service quality dimension of this study was .800. Bartlett's Test of Sphericity test ( The factor loadings of service employees factor (3 items) was ranking from (.864 - .841) with Cronbach’s Alpha of .853. For the experiences factor (3 items) the factor loadings was higher than .7, ranking from (.701 – .859) and the Cronbach’s Alpha at .733. In this dimension, nine items were deleted by the value is less than 0.50, the results of the factor analysis probably won't be very useful.
Table 7
Factor Analysis of the Service Quality Dimension
Factors Items Cronbach’s
Alpha The attitude of the employees at this
arena shows you that the understand
.841 You can rely on the arena employees
taking actions to address your needs
.843 The employees at this arena respond
quickly to your needs
.864 Factor 2
Experiences
The arena’s scoreboards are entertaining to watch
.733 .758
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The arena’s provides good sightlines to watch the game
.701
The smell of the crowd is exciting .859
Kaiser-Meyer-Olkin Measure of Sampling Adequacy .800
Bartlett's Test of Sphericity
Approx. Chi-Square 1253.806
df 15
Sig. .000
% of Total Cumulative 71.71
From the table 8 indicated the factor analysis of the game quality dimension. The results showed factor loadings of eleven items were higher than .5 ranking from (.552 – .892), the factor loading of three items were less than .5. The KMO for the game quality dimension was .882. Bartlett's Test of Sphericity test ( After rotated component matrix, that was rotated 3 factor including opponent characteristics, player performance and game atmosphere. In the opponent characteristics factor (3 items) the results of factor loadings ranking from (.552-.892) with Cronbach’s alpha was .786. For the player performance factor (4 items) with the factor loading ranking from (.713-.850) and Cronbach’s alpha was .889. In the third factor of game quality dimension, game atmosphere (4 items) with the factor loadings ranking from (.746-.840), the Cronbach’s alpha also larger than .7 that was .884 of game atmosphere.
Table 8
Factor Analysis of the Game Quality Dimension
Factors Items Cronbach’s
Alpha
Opposing teams are high quality teams .552
Opposing teams have star players .892
Opposing teams have good win/loss records
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Factors Items Cronbach’s
Alpha
Factor loadings
Your team gives 100% every game .850
Your team plays hard all the time .871
Players on your team always try to do their best At this stadium, you can rely on there
being a good atmosphere.
.811 This stadium’s ambiance is what you
want at a game.
.840 The (team name) understand that
atmosphere is important to you.
.820 You enjoy the excitement surrounding the
performance of the players
.746 Kaiser-Meyer-Olkin Measure of Sampling Adequacy
.882
% of Total Cumulative 74.84
The results showed in table 9 & table 10 present the detail the factor loadings of customer satisfaction dimension and behavior intension dimension. In the customer satisfactions dimension the results factor loading of all items higher than .8, it ranges from .918-.903. “I am very satisfied” (.895), “I am satisfied with my decision” (.903), “I am satisfied with the outcome” (.849), “I truly enjoy to going this event” (.818) and the Cronbach’s alpha was .889, “I would go on in the future” (.920). The KMO for the customer satisfaction dimension was .813. Bartlett's Test of Sphericity test (
For the behavior intension dimension, Cronbach’s alpha was highest with .932 and the factor loadings ranking from .904-.920. “I intend to visit this sporting event in the future” (.904), “I would say positive things” (.916), “I would go on in the future” (.920),
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“I would recommend to other people” (.908). The KMO for the behavior intensions dimension was .855. Bartlett's Test of Sphericity test (
Table 9
Factor Analysis of Customer Satisfaction Dimension
Factors Items Cronbach’s
Alpha Based on all my experience in this arena,
I am very satisfied
.895 I am satisfied with my decision to attend
this games
.903 I am satisfied with the outcome of this
game
.849 I truly enjoy going to this sporting event .818
Kaiser-Meyer-Olkin Measure of Sampling Adequacy .813
Bartlett's Test of
Factor Analysis of Behavior Intensions Dimension
Factors Items Cronbach’s
Alpha
Factor loadings Behavior
Intensions
I intend to visit this sporting event in the future
.932
.904 I would say positive things about going
to this sporting event to others .916
I would go on sporting events in future .920 I would recommend going to this
sporting event to other people .908
Kaiser-Meyer-Olkin Measure of Sampling Adequacy .855
Bartlett's Test of Sphericity
Approx. Chi-Square 1767.142
df 6
Sig. .000
% of Total Cumulative 83.16
38 4.3.3. Correlation among Variables
A bivariate correlation was run between all continuous variables. The correlation coefficients (r) ranged from .683 to .833. No correlations were higher than .90 or lower than .10. The majority had a correlation greater than .6, a medium strength of relationship.
The correlation among the variable in the contrast are presented in table 11, all the variables were significant correlated and the listed as following. Service quality was positively correlated with game quality (r = .722, p < .01), customer satisfaction (r = .692, p < .01), behavior intensions (r = .683, p < .01). Game quality variables were positively correlated with customer satisfactions (r = .711, p < .01), behavior intension (r = .692, p
< .01). Finally, customer satisfactions was also positively correlated with behavior intensions (r = .833, p < .01).
Table 11
Correlation among the variable in the contrast
Measure Correlation Matrix
1 2 3 4
1. Service quality 1.00
2. Game quality .722** 1.00
3. Customer satisfactions .692** .711** 1.00
4. Behavior intensions .683** .692** .833** 1.00
**. Correlation is significant at the 0.01 level
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
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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
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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
4.3.6 Estimated Correlations of Latent Variables for CFA