Assignment Question
Based on the article written by Kim and Trail (2010) respond to the following questions: 7.1 How did Kim and Trail define constraints in their study? 7.2 What constraints were previously identified by sport management researchers? 7.3 What four purposes were stated for the study? 7.4 Based on Kim and Trail’s description of their stepwise regression analysis procedure, which of the three variations of stepwise regression analysis did they use? Does the selected variation of stepwise regression analysis control for suppressor effects? Given the exploratory nature of the study, could the authors have used another variation of stepwise regression analysis? 7.5 How did the researchers test their data set to ensure that it met the assumptions for multiple regression? Were any modifications required in order to meet those assumptions? Why or why not? 7.6 What rationale do the researchers provide for testing the proposed model with different samples and different teams? Kim, Y., & Trail, G. (2010). Constraints and motivators: A new model to explain sport consumer behavior. Journal of Sport Management, 24, 190-210.
Assignment Answer
In their study, Kim and Trail (2010) aimed to develop a model that could explain the relationships between constraints, motivators, and attendance in the context of spectator sports. The proposed model’s objective was to elucidate factors affecting sports consumer behavior, particularly attendance at sporting events. This article provides a comprehensive review and analysis of the key points and findings from the study, focusing on how the authors defined constraints, the constraints identified in prior research, the purposes of the study, the stepwise regression analysis procedure used, data testing for multiple regression assumptions, and the rationale behind testing the proposed model with different samples and teams.
7.1 Defining Constraints
In their study, Kim and Trail (2010) defined constraints as factors that may limit or hinder an individual’s involvement in a particular behavior, in this case, attending sporting events. These constraints could be either internal or external. Internal constraints are those within the individual, such as lack of interest or lack of success associated with the sport (Kim & Trail, 2010). On the other hand, external constraints are external factors that hinder participation, such as competing leisure alternatives or geographic distance from the sports venue (Kim & Trail, 2010). In this context, constraints are considered as barriers or obstacles that may deter individuals from attending sports events.
7.2 Previously Identified Constraints
Prior to Kim and Trail’s study, various constraints had been identified by sport management researchers. These constraints encompassed a wide range of factors affecting sports attendance. Internal constraints included lack of interest in the sport, lack of success of the team, lack of time, and lack of money (Kim & Trail, 2010). External constraints included competing leisure alternatives, family responsibilities, and geographic distance from the sports venue (Kim & Trail, 2010). These constraints had been identified through previous research and provided the foundation for Kim and Trail’s exploration of constraints in the context of sport consumer behavior.
7.3 Purposes of the Study
Kim and Trail’s study had four main purposes. Firstly, it aimed to develop a comprehensive model to explain the relationships between constraints, motivators, and attendance in spectator sports (Kim & Trail, 2010). Second, the study sought to empirically test the proposed model to determine its effectiveness (Kim & Trail, 2010). Third, the researchers intended to identify the relative importance of various constraints and motivators in predicting attendance at sporting events (Kim & Trail, 2010). Lastly, they aimed to explore how sport marketers could use the model to better serve existing fans and attract new fans (Kim & Trail, 2010).
7.4 Stepwise Regression Analysis
Kim and Trail (2010) employed a stepwise regression analysis procedure to examine the relationships between constraints, motivators, and attendance. Stepwise regression is a statistical method used to select the most relevant predictors for a given dependent variable (Kim & Trail, 2010). The authors employed the forward stepwise regression analysis, which involves adding predictors to the model one at a time based on their significance (Kim & Trail, 2010). This method was chosen to explore the sequential importance of different factors in predicting attendance.
The forward stepwise regression analysis used by Kim and Trail (2010) does not inherently control for suppressor effects. Suppressor effects are situations where the inclusion of a predictor variable improves the model’s predictive power. Given the exploratory nature of their study, the authors may not have used another variation of stepwise regression analysis to control for suppressor effects, as the primary goal was to understand the relative importance of various constraints and motivators (Kim & Trail, 2010).
7.5 Testing Assumptions for Multiple Regression
The researchers tested their data set to ensure that it met the assumptions for multiple regression analysis. Multiple regression assumes that the variables are normally distributed, linearly related, and that there is no multicollinearity (Kim & Trail, 2010). Kim and Trail (2010) did not provide detailed information in the article about the specific tests and modifications made to meet these assumptions. However, in multiple regression analysis, it is common to apply transformations to variables or exclude outliers to meet these assumptions. The authors may have performed such adjustments, but the article does not provide explicit details (Kim & Trail, 2010).
7.6 Testing the Proposed Model with Different Samples and Teams
Kim and Trail (2010) offered a rationale for testing their proposed model with different samples and teams. They argued that doing so allowed them to assess the model’s generalizability across different contexts and to confirm its robustness (Kim & Trail, 2010). This approach enhances the model’s validity and applicability, as it demonstrates that the relationships between constraints, motivators, and attendance are not limited to a single specific situation or team (Kim & Trail, 2010). By testing the model with diverse samples and teams, they can better understand the broader implications of their findings and provide more comprehensive insights to sport marketers (Kim & Trail, 2010).
References
Kim, Y., & Trail, G. (2010). Constraints and motivators: A new model to explain sport consumer behavior. Journal of Sport Management, 24, 190-210.
Frequently Asked Questions
What is the primary focus of Kim and Trail’s study on sport consumer behavior?
Kim and Trail’s study primarily focuses on explaining the relationships between constraints, motivators, and attendance in the context of spectator sports.
How do Kim and Trail define constraints in their study on sport consumer behavior?
In their study, constraints are defined as factors that may limit or hinder an individual’s involvement in attending sporting events, which can be either internal or external.
What are some examples of constraints identified by previous sport management researchers in the context of sports attendance?
Previous research has identified constraints such as lack of interest in the sport, lack of team success, lack of time, and lack of financial resources as internal constraints, while external constraints include factors like competing leisure alternatives, family responsibilities, and geographic distance from sports venues.
What were the main purposes of Kim and Trail’s study on sport consumer behavior?
The study had four main purposes: developing a model to explain relationships between constraints, motivators, and attendance, empirically testing the proposed model, identifying the importance of constraints and motivators in predicting attendance, and exploring how the model can be utilized by sport marketers.
Which variation of stepwise regression analysis did Kim and Trail use, and did it control for suppressor effects?
Kim and Trail employed forward stepwise regression analysis in their study. This method adds predictors to the model one at a time based on their significance but does not inherently control for suppressor effects. Given the exploratory nature of their research, they may not have used another variation to control for suppressor effects.
