Thesis Proposal: Behavioural economics: Behavioural insights in sports betting
In the market of sports betting, the subjects often have an opportunity of choosing standard lotteries, which is applicable in behavioural economics (Andrikogiannopoulou & Papakonstantinou, 2011; Anthony & Norman, 2013; Birău, 2012; Carpenter, 2013; Credit Suisse AG., 2015; Mitroi & Oproiu, 2014; Stekler, Sendor & Verlander 2010). These events in betting are often defined as “Team 1 beats Team 2, ‘Red card for defender” since the outcome is either Y for win or X for otherwise (Bruno & Olivier, 2012; Chesir, 2013; Croxson, & James, 2014; Feddersen, Humphreys & Soebbing, 2017). The availability of a wide range of data and events as well as implied possibilities enables the testing of various behavioural theories (Lai, Gross & Shen, 2011).
The ‘favourite-longshot bias’ has been one of the most popular phenomenon in sports betting over the years (Abinzano, Muga, & Santamaria, 2016; Feess, Muller & Schumacher, 2014; Davis et al., 2015; Jansa, 2012; Lahvicka, 2014; Palmer, 2013; Paul, Weinbach, & Humphreys, 2011; Paul & Weinbach, 2012; Tekçe, 2011). According to the concept, participants often have a tendency to overrate “long-shots” and underrate the favourites (Franck, Verbeek & Nüesch, 2010; Gainsbury, & Russell, 2015; Gneiting & Katzfuss, 2014; Kevin, 2013). The result of the development causes a higher expected value of a bet on the favourite (Kelly et al., 2012; Lahiri, & Yang, 2013; Lyócsa, & Fedorko, 2016; Onsomu, 2014; Štrumbelj, 2014a; Štrumbelj, 2014b). The ‘favourite-longshot bias’ has an influence on the decision making of bettors around the world.
- What behavioural traits does a bettor display when placing their bets?
- What behavioural patterns are demonstrated by each gender of the betting community?
- To examine the behavioural sentiments and biases that impact the decision of bettors.
Significance of the study
There is extremely little studies conducted in the field of betting globally from a behavioural economics viewpoint. As a result, this area demands more research within the betting market.
The proposal will utilise survey questionnaires to collect relevant data to support the examination of the phenomenon of human behaviour in betting (Al Marzooqi, 2015; Archibald et al., 2015). Questionnaires will enable the collection of essential and relevant data to the completion of the exploration. The research design was appropriate because the study seeks to understand human behaviour in regards to betting. Therefore, data needs to be collected from the population under study (Onen, 2016).
Questionnaires will be distributed to a sample population identified (Post-graduate students in the University of economics in Greece). The sample size will entail approximately 170 participants picked from the sample frame. The sample size will be sufficient to represent the general population enabling the capturing of behavioural traits needed in the model. The research sample size will be attainable through stratified random sampling from the sample frame in a bid to eliminate any bias associated with other sampling techniques (Nadia, Noor & Muhammad, 2017) and ensure the realisation of a representative population (Abdelfatah & Mazloum, 2016; Zahid & Shabbir, 2018).
Type of data
The study will utilise quantitative research to examine the phenomenon of human behaviour (Boeren, 2018; Brunsdon, 2018; Chandler et al., 2015; Ellerston et al., 2016; Gilad, & Elnekave, 2006; Goertzen, 2017; Jianhua et al., 2019; Kim & Cuskelly, 2017). The methodology was appropriate because the study needs information with high degree of accurateness and precision (Ahn et al., 2018; Barnham, 2015; Bentahar & Cameron, 2015; Crişan (Mitra) & Borza, 2015; Guetterman, Fetters & Creswell, 2015; Lund, 2012). Accordingly, it will have binary data that encompasses simple yes/no reactions from the participants, which take the form of 1 or 0 respectively accompanied by variable testing (Bosman et al., 2018; Johnston et al., 2014; McCusker, & Gunaydin, 2015; Onen, 2016). The queries will be set in a manner capturing the independent variables within the models of human behaviour and behavioural economics (Tominc et al., 2018).
Data collection method
Every question in the questionnaires will be tailored to meet the research aims and objectives (Dey et al., 2016; Schilling et al., 2018). The selection of the method was apposite because it is the most efficient technique to understand the behaviour of bettors (Tavakol & Sandars, 2014; Zur & Carmeli, 2013). Most bettors use the internet and other social media platforms as their primary technique of accessing the betting markets (Vicens et al., 2018; Weinbach & Paul, 2009). As a result, the questionnaires will be dispersed on social media platforms, e-mails as well as physical collection of data (Xerri, 2017; Zyphur & Pierides, 2017).
Some of the limitations associated with the methodology selected. Firstly, it is not appropriate where the investigator may require spontaneous answer. Secondly, it has no mechanism that will allow the researcher to check misinterpretations and unintelligible answers given by the respondents. Finally, the method lacks flexibility when compared to interview because respondents can skip questions.
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