The role of social media and gaming activity in procrastination behavior

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The Role of Social Media and Gaming

Activity in Procrastination Behavior

Master’s thesis

Name: Timo Pleus Student number: 6050166 Master’s track: Behavioral Economics and Game Theory Supervisor: Jan Engelmann Second reader: Joël van der Weele

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This document is written by Student Timo Pleus who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Summary

This study analyzes the relationship between self-reported procrastination, self-esteem and Social Media and gaming activity among high school students. The purpose is to investigate which factors can serve as an indicator for procrastination behavior, which is associated with poorer academic performance and health issues. 86 subjects participated in a survey where the filled out questions on procrastination, Social Media and gaming activity, self-esteem, gender and age. Procrastination and self-esteem were measured via tests that are broadly used in the field. Results yield no support for significant relationships between Social Media activity and procrastination, nor for gaming activity and procrastination. Self-esteem, however, has a significant negative relation with self-reported procrastination on a 5% level. Small sample size and omitted variable bias limitations are taken into consideration and further research in the form of fMRI investigation is suggested.

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“Lost time is never found again; and what we call time enough always proves little enough.” (Franklin, 1821, p. 80), as one of the Founding Fathers of the United States, dr. Benjamin Franklin, wrote in his essays that were published thirty years after his passing. It shows that time has been evaluated as a scarce and precious commodity by mankind over centuries. As a species, we constantly struggle with our assessment of time. Franklin does point this out in the later part of the quote; he rephrases the problem of procrastination many of us face on a regular basis. Where some individuals have little problem to appropriately distribute time between mandatory and leisure activities, others constantly struggle with their time management. Whether it is to uphold certain deadlines, filing a tax return or writing a thesis, doing it later rather than doing it now seems acceptable in many cases.

But is procrastination in itself a troublesome trait? Solomon and Rothblum (1984) define procrastination as the act of needlessly postponing tasks to the point that subjective discomfort is experienced.

This subjective discomfort establishes itself in different ways, a study by Tice & Baumeister (1997) shows. Procrastinators experience significantly more stress and illness than non-procrastinators and score poorer grades at the end of a semester (Tice &

Baumeister, 1997). Ferrari (1994) shows that self-esteem has a significant negative relation with procrastination. This is just one example that procrastination might not just be a characteristic of an individual, but might be the product of underlying influences. It is, of course, questionable if these problems lead to procrastination, the other way around or if these attributes are a consequence of something else.

The question that arises is if the causality that some individuals suffer of

procrastinating tendencies, while others do hardly, is of an endogenous or exogenous kind. If it is embedded into our genes, an attribute that we inherit from our parents, procrastination might be something that starts in the younger years of a person and sprouts as he or she goes on about their life. In that case, behavioral therapy at an early age might lead to good results if procrastination habits can be identified at an early age.

If the cause is of an exogenous kind however, it is interesting to find out which inputs or experiences lead to procrastinating behavior. Are our lucrative ‘outside options’, that give us an immediate reward, a cause to postpone mandatory, ‘costly’ work? Alternatively, procrastination is something we can pick up from our social frame of reference. If we are

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surrounded by people that tend to procrastinate, do we tend to pick up this type of behavior from our direct social network just as is the case with smoking (Green, West & Ecob, 1991)?

In contemporary society, there are some aspects that have drastically changed compared to the period (the mid 1990’s) in which most of the referenced studies were conducted. Internet and computer usage have surged as the accessibility has greatly been increased and the importance to social and economic aspects of life has strengthened. A more recent study showed that time spent online is related to self-reported procrastination (Lavoie & Pychyl, 2001). Internet and computers are constantly accessible due to the wide

availability of smartphones; in 2017 of all Dutch citizens 89 percent have internet access via a mobile phone, 98.2 percent of all citizens between the ages of 12 and 25 (Internet; toegang, gebruik en facaliteiten, 2018).

How is the latter relevant to the topic at hand? The author of this thesis works as a coordinator and tutor at a tutoring institute, where high school children receive after-school support. It is noted, here and at the high schools they go to, that the children tend to postpone their mandatory activities by using their phones to play games or use social media

applications.

This leads to the main topic of this thesis; to analyze potential relationships between social media and gaming behavior and procrastination. To do this, a research is conducted among 85 high school children. At the time of the research, these children received support on study subjects and were taught planning and homework skills at the tutoring institute. Through a self-report questionnaire (to be found in the Appendix), these children answered questions on procrastination, self-esteem, social media and gaming activity and some other topics that will be touched upon in the methodology section of this thesis.

The main research question of this thesis is: Is there a significant relationship between the level of gaming and social media behavior and procrastination among high school

students? Subsequently, as will follow out of the upcoming literature review, the relationship between self-esteem and procrastination is researched.

In the first upcoming part, the hypotheses are discussed. Following is a review of important related literature. Economic relevance will be further discussed, and findings and insights of other studies are evaluated.

Thereafter, the methodology of the conducted research is discussed. A more precise description of the research is given and the specifications of the model and all used variables will be examined.

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Following the methodology, the results of the data analysis are discussed. This is accompanied by graphs to give visual insights into differences and similarities within the gathered data. To close this segment, limitations of the conducted research are touched upon.

Finally, the findings are discussed. In this segment it is examined how to interpret these findings. The main conclusions of this study are summarized and potential steps for further research are suggested.

Hypotheses

As was previously mentioned, this research aims to test relationships between social media behavior, gaming behavior, self-esteem and procrastination. Based on the literature review and own intuition, the following hypotheses are established; it is expected that both time spent on social media and time spent on gaming relate positively with self-reported

procrastination. These beliefs are supported by King & Delfabbro (2014). As will be more extensively addressed in the literature review, they establish the idea that individuals tend to overestimate the gratification of video games. This overestimation may lead individuals to postpone the costly act of studying more, where the gratification of good grades and academic success is not as immediate.

The last hypothesis concerns the expected relationship between esteem and self-reported procrastination. This is expected to be a negative relationship. Ferrari (1994), Solomon & Rothblum (1984) and Senécal, Koestner & Vallerand all find a significant

negative relationship between self-esteem and procrastination. These studies might have used sample groups of a different composition, but the differences do not seem that large that similar expectations seem to be unreasonable.

Literature Review

First of all, a closer look is taken at what procrastination exactly embodies. Procrastination is described as a self-control problem (Ariely & Wertenbroch, 2002). For an economic

approach of this problem, we turn to the paper by O’Donoghue and Rabin (1999): ‘Doing it now or later’. In their article it is explained that the discount factor , which is regularly used

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in economics to discount future utilities to a present-day value to explain intertemporal preferences, implies time-consistency. For visual support, the standard model is given:

𝑈𝑡_(𝑢

𝑡, 𝑢𝑡+1, … , 𝑢𝑇) = ∑ 𝑇𝑢𝑇 𝑇

𝑇=𝑡

0 <  ≤ 1

The value of  gives information about the ‘impatience’ of the individual. Low values indicate that someone is very impatient, high values correspond to a patient person.

This model doesn’t hold for many cases, however (O’Donoghue & Rabin, 1999). People tend to show time-inconsistent preferences and these conflict with the model above, which

assumes a constant discount factor. These time-inconsistent preferences are formulated as present-biased preferences. As O’Donoghue and Rabin (1999) explain it: “When considering trade-offs between two future moments, present-biased preferences give stronger relative weight to the earlier moment as it gets closer.” (p. 106). An example of this would be that an individual might prefer $ 1,500 in two years’ time over $ 1,000 in one year’s time, but prefers $ 1,000 now over $ 1,500 in a year’s time due to the fact that (close to) immediate

gratification has a relative stronger importance. To explain this further, the researchers refer to a present-biased preferences model by Phelps and Pollak (1968).

The introduction of a new parameter , which represents the bias for the present, helps to capture the inconsistency. The range for  is the same as , it est  ∈ (0,1]. For clarification: high values of  imply that there is little bias towards the present, low values, obviously, imply a large bias. As stated in the article: “The person gives more relative weight to period 𝑇 in period 𝑇 than she did in any period prior to period 𝑇.” (O’Donoghue & Rabin, 1999, p. 106).

How is this precisely connected to procrastination? Procrastinators are described as naïve (O’Donoghue & Rabin, 1999). These procrastinators assess their future preferences to be exact as they assess them now, whereas in practice, their preferences will change to put more emphasis on utility that is close to that future moment in time. Even if naïve

procrastinators realize that they might be biased, there is a chance that they underestimate the size of the bias (O’Donoghue & Rabin, 1999).

This seems to be a reasonable explanation of the authors of how the thought process of procrastinators work. It should be noted however that the model does not account for learning; when individuals experience that their perception of their future self is incorrect,

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they should adjust the evaluation of their bias. Over time, it is to be expected that even naïve procrastinators are able to reduce their procrastinating behavior by self-correction.

In the field of neuroeconomics there also exists research that supports the idea of - and -parameters. For a study conducted by McClure & Al. (2004) functional magnetic resonance imaging (fMRI) is used to record brain activity of participants that choose between rewards at different points in time. Parts that are associated with the dopamine system show a higher fMRI-activation when rewards are considered in a close time-proximity. These

regions are strongly related to impulsive behavior (McClure & Al., 2004). The lateral prefrontal cortex and posterior parietal cortex on the other hand show uniform activation across all decisions, but tend to be more active when options are considered that are further away in time. These brain areas are mostly associated with planned decision-making and cognitive control (McClure & Al., 2004).

Without delving too deep in the neuroscientific jargon, these insights help to

understand how individuals can come to time-inconsistent evaluations; when different parts of our brain are involved in the decision-making process at different points in time, it gives an initial explanation for the fact that the actual behavior can differ from the planned behavior.

Now that there exists more clarification on the concept of procrastination and its possible origin, similar research as the one at hand is examined. First, some important studies regarding procrastination and relating variables are explored.

A paper referenced earlier, by Ferrari (1994), studies the relation between

procrastination, interpersonal dependency, self-esteem and self-defeating behavior. The 263 college students were to fill out scales on all previously named topics. The Self-Esteem Scale used in the study is a creation of Rosenberg (1979), which is the same scale that is used for the assessment of self-esteem in the study of this thesis. In his results, Ferrari (1994) finds significant negative relationships – at a one percent level – between self-esteem and both decisional and behavioral procrastination. This implies that, based on the data, individuals with higher self-esteem are expected to show less procrastination behavior than those with lower self-esteem. At the same level of insecurity, significant positive relationships are found between interpersonal dependency, self-defeating personality and both types of

procrastination (Ferrari, 1994). No significant differences were to be found between males and females.

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These results seem solid, but it should be noted that the external validity is questionable. The group composition of the participants is heavily skewed toward being female. 202 of 263 were female, implying only 61 males to be part of the researched group (Ferrari, 1994). Furthermore, all participants were students of the same psychology course, and their ages ranged from 18 to 21 years old. It should be kept in mind that these results can’t be generalized too much, as the group is not even remotely randomly generated. On the other hand, the study is helpful on developing variables to be used in the study of this thesis.

The paper by Solomon & Rothblum (1984) yields some interesting results that supports the findings of Ferrari (1994). In their subject pool, they find significant relations between self-reported levels of depression, low self-esteem and procrastination.

The factor of low self-esteem is interconnected with some other aspects, as

participants claimed topics like performance anxiety and the fear of failure as driving factors of their procrastination behavior (Solomon & Rothblum, 1984). It is suggested by Haycock, McCarthy & Skay (1998) that individuals try to protect their esteem from being lowered further by refraining from confrontations that test their capabilities, thus postponing whatever they have to do.

A large study among 498 junior college students shows similar results (Senécal, Koestner and Vallerand, 1995). Through the means of the Academic Motivation Scale, combined with a measurement of procrastination, it is confirmed that anxiety, self-esteem and depression are related to fear of failure and procrastination.

Another resourceful insight that is gathered from this study, is that the level of academic procrastination correlates with the reasons students have for studying. Students that have more intrinsic arguments for completing academic tasks, tend to procrastinate less than students that are driven by less autonomous reasons (Senécal, Koestner and Vallerand, 1995). Arguments for studying and procrastinating is not something that is taking into account in the study of this thesis, but would proof useful in future studies.

Of similar interest is the study by Tice & Baumeister (1997), which was shortly mentioned in the introductory segment. The most particular findings from this study are the time-specific differences in relationships between health variables and procrastination. Whereas, at the end of semesters investigated participants that tended to procrastinate more exhibited higher stress levels and more cases of illness (Tice & Baumeister, 1997). However, during the early parts of the semester, procrastination significant related with lower levels of stress and illness.

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It also impacts academic achievement; procrastinators score significant lower grades. Even students claiming that last-minute pressure is beneficial to their performance, end up performing poorer and tend to be sick more (Tice & Baumeister, 1997). An implication of this is that procrastinator indeed suffer of a type of bias that withholds them from making proper judgements about their own performance and future-self estimations.

As the literature shows some important factors concerning procrastination, an adequate way to assess procrastination itself is needed to conduct this study. The paper by Solomon & Rothblum (1984) mentioned earlier provides a useful tool. They collected information on self-reported levels of procrastination by letting the students fill out the Procrastination Assessment Scale-Students (PASS), which the authors of the article developed themselves. In the first part of the PASS, students had to fill out the level of procrastination and the level of trouble they experienced from procrastinating on each of six tasks (Solomon & Rothblum, 1984). These six topics concern:

1: Writing a term paper 2: Studying for an exam 3: Keeping up with weekly readings

4: Performing administrative tasks 5: Attending meetings

6: Performing academic tasks in general

The second part concerns a potential procrastination scenario, listing possible reasons to procrastinate. As this is a well-suited way of measuring self-reported procrastination, an adjusted, simplified version of the PASS is used for the questionnaire in this thesis.

Concerning the study of this paper, where the relation between social media and gaming behavior and procrastination is to be examined, it is notably hard to find literature on this precise topic. A paper that states something that offers a grasp, is by King & Delfabbro. In their paper, a review of 36 studies on Internet gaming disorder is done (King & Delfabbro, 2014). Even though it does not compute any relationships between variables that are of use for this research, it is stated that a gaming addiction could potentially involve a constant overvaluation of gratification that is experienced in video games (King & Delfabbro, 2014). Some insights of the review of the neuroeconomic paper of Mclure & Al. (2004) can give some connection here. Recall that immediate rewards that can be experienced are processed by a different part of the brain than rewards that are to be in the, relative, distance. The easy

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access and potential addiction to these immediate rewards that video games can hand to the player could be another reason that individuals might think they will do mandatory tasks later, as they naively estimate they’ll have a realistic interpretation of their emotional state at that time. It is a debatable subject whether or not video games can be addicting by definition, but as of 2018 the World Health Organization (WHO) classifies gaming addiction as a disorder (WHO, 2018). It even is added to the International Classification of Diseases, which, as the WHO defines it: “…is the basis for identification of health trends and statistics globally and the international standard for reporting diseases and health conditions.” (WHO, 2018).

Addition to existing literature

The research of procrastination in itself is not unique. As seen in the literature review, procrastination is a topic that is broadly investigated. The research of this thesis is unique because it combines a set of variables that is hardly found to nonexistent in the current

literature. Gaming and social media behavior are relatively new phenomena that are yet to be picked up by the larger research institutes and studies as the one by King & Delfabbro (2014) show that, the opportunity of immediate gratification through these types of media may lead to procrastinating behavior. As even a renowned institute as the World Health Organization acknowledges the issue at hand with gaming disorders, this cannot be something to be left unexplored.

Through the means of having individuals give an indication of the level of their gaming and social media activity, it can be studied if there exists relationships between procrastination, social media and gaming. Even though individuals report their activity through a questionnaire and are not actually observed and this could lead to a bias or

misjudgment, this research could show some indication that further research in this direction is useful.

Many important studies in the field of procrastination could not have taken these aspects into account, simply due to the fact that the phenomenon of social media was nonexistent during the 1990’s and early 2000’s. Whereas video games have been around since the 1980’s, it has never been as widely integrated into everyday life as in contemporary society.

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The research of this thesis can be seen as an initial exploration of potential

relationships between the earlier mentioned variables, while controlling for some variables that are shown by previous, larger studies to be relatable to procrastination behavior.

Additionally, a studied group consisting out of students in the age range of 13 to 18 (the age range of Dutch high school students) is hardly found in existing literature. In most articles, the participants are college students that are at least of the age of 18, but in most cases even older. Whereas this teenager age is a period in which many of the behavioral aspects of an individual are shaped and defined. Gaviria & Raphael (2001) explain this in their research by stating that through social interaction, which becomes more dominant in high school,

teenagers tend to develop and pick up on habits to fit in with their peers, even when these habits seem counterintuitive for one’s performance.

All in all, this new combination of factors may result in different insights out of a relatively unique research.

Methodology

The discussion of the methodology is divided into three separate segments. First of all, an elucidation of the data set takes place, to give more clarity on the composition of participants and the setting in which the research took place. Secondly, the self-report questionnaire the participants received is examined. Thirdly, the regression model used to analyze relationship is revealed, and a motivation for the used variables is given.

The Data Set

The research was conducted during the week of the 25th_{of June 2018. The group of}

participants consists out of 85 high school students from the area the Gooi, which resides in the center of the Netherlands. They are all enrolled at PIOS, which is an institute in the Gooi that supports children in their academic achievements and esteem by teaching them planning and study skills, while giving in-depth explanation on subjects they experience difficulties with. Depending on their program, they receive one-on-one teachings or are supported during their studying in a group setting.

Students are enrolled at the institute for various reasons. Some students are signed up by their parents because of bad grades, while others seek a safe haven from distractions and experience PIOS as a place where they can concentrate on their homework and study

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assignments. Among these students, many of them are staying throughout the college year. However, a few of them only enroll during their preparation for exam periods.

Age Female Male Total

13 8 9 17 14 7 10 17 15 7 8 15 16 7 6 13 17 3 13 16 18 5 2 7 37 48 85

Out of the 85 students, 37 of the participants are female and the other 48 are male. Their ages range from 13 to 18 years, with an average age of slightly over 15 years.

As parents pay for the time that their children receive education at the institute, time for the survey was rather limited. Due to the time limit, not all variables that are found to be interesting in the existing literature could be measured. There is a focus on some important control variables, as will follow out of the examination of the self-report questionnaire. It should be kept in mind that this most likely leads to an omitted-variable bias, because the literature mentions the importance of some of these variables.

The Questionnaire

The questionnaire was to be filled out paper and anonymously. The latter is of importance, because if participants would give their name, they would potentially realize that their

personal information on procrastination, social media and gaming behavior and self-esteem is retraceable. This could potentially lead to participants being reluctant to give genuine

answers to the questions.

At the start of the survey, participants were clearly instructed to only fill out one answer for every question. Subsequently, they filled out their gender and their age, which are used as control variables for the regression model.

Following, participants answered questions on procrastination following the structure of the Procrastination Assessment Scale-Students (PASS) designed by Solomon & Rothblum (1984). Because the PASS is manufactured to fit situations that college students can associate with, the topics for high school students are adjusted. The six topics that the participants answered questions on are as follows:

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1: Working on group assignments 2: Working on individual assignments

3: Studying for tests 4: Keeping up with homework

5: Working during class 6: Doing household chores

These are all topics that Dutch high school students can relate to. The last topic might not have anything to do with school activity, but children often are assigned to do chores in and around the house. This is something they can procrastinate on as well, and as it is something they experience on a daily basis, this is part of the questionnaire.

On every topic, subjects had to answer the same two questions. Firstly, to what degree they procrastinate on that task, secondly to what degree procrastination on that task a problem is to them. To answer these questions, participants chose from options on a five point Likert scale, ranging from ‘Never’ to ‘Always’. Every answer was pre-coded with a numerical value. For evaluation, these points are all added together to get a grasp of the level of self-reported behavioral procrastination. For further reference, the questionnaire can be found in the appendix.

After this set of questions, participants had to pick from a list of alternative activities they do while procrastinating. This question is part of the questionnaire, to see if there is a relationship between the level of self-reported procrastination and the type of activity they do while procrastinating.

The last two questions of this segment are about the average weekly hours spent on gaming and social media. For the hours on gaming, they were asked to make an estimation of the hours. For the hours of social media, participants were instructed to look at their battery information on their mobile phones. In this section, the average time spent on each

application is given for the past seven days. The addition of the time spent on all social media applications (which are Instagram, Facebook, WhatsApp and Snapchat) served as an estimate of the weekly average hours spent on social media. It has to be noted that many participants reported that these total hours exceeded their own expectations.

The idea behind ordering these questions in such a manner that procrastination is assessed before asking information on social media and gaming behavior, is to prevent individuals to go into the procrastination assessment with a kind of bias, knowing that their social media and gaming activity will be related with procrastinating behavior.

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The questionnaire concludes with an assessment of the level of self-esteem of the participants. This self-esteem scale is the Rosenberg (1979) Self-Esteem Scale. This scale is divided into ten statements, that try to capture positive and negative feelings about the self. Every statement is answered using a four point Likert scale ranging from ‘Strongly Agree’ to ‘Strongly Disagree’. These answers are pre-coded with scores from 1 through 4. Some questions are reverse scored, as ‘Strongly Agree’ for a positive statement obviously has a reverse relationship to self-esteem than ‘Strongly Agree’ for a negative statement.

The Model

To be able to state any insights retrieved from the data, a multiple linear regression model is used. As the gathered data is of cross-sectional origin, this regression model type is suitable (Stock & Watson, 2015). The model is estimated through the use of STATA v.15. The structure of the model is as follows:

𝑃𝑟𝑜𝑐𝑟𝑎𝑠𝑡𝑖𝑛𝑎𝑡𝑖𝑜𝑛 = β₀+ β₁ 𝐴𝑔𝑒 + β₂ 𝐹𝑒𝑚𝑎𝑙𝑒

+ β3𝑆𝑒𝑙𝑓𝐸𝑠𝑡𝑒𝑒𝑚 + β4𝐺𝑎𝑚𝑖𝑛𝑔𝐴𝑙𝑡𝑒𝑟𝑛𝑎𝑡𝑖𝑣𝑒

+ β₅𝑉𝑖𝑑𝑒𝑜𝐴𝑙𝑡𝑒𝑟𝑛𝑎𝑡𝑖𝑣𝑒 + β₆𝑂𝑢𝑡𝐴𝑙𝑡𝑒𝑟𝑛𝑎𝑡𝑖𝑣𝑒 + β₇𝐴𝑣𝑒𝑟𝑎𝑔𝑒𝑆𝑜𝑐𝑖𝑎𝑙𝑀𝑒𝑑𝑖𝑎 + β₈𝐴𝑣𝑒𝑟𝑎𝑔𝑒𝐺𝑎𝑚𝑖𝑛𝑔 + β₉𝐴𝑣𝑒𝑟𝑎𝑔𝑒𝐺𝑎𝑚𝑖𝑛𝑔 ∗ 𝐺𝑎𝑚𝑖𝑛𝑔𝐴𝑙𝑡𝑒𝑟𝑛𝑎𝑡𝑖𝑣𝑒 + β10𝐴𝑣𝑒𝑟𝑎𝑔𝑒𝐺𝑎𝑚𝑖𝑛𝑔 ∗ 𝐴𝑔𝑒 + β11 𝐴𝑣𝑒𝑟𝑎𝑔𝑒𝐺𝑎𝑚𝑖𝑛𝑔 ∗ 𝐹𝑒𝑚𝑎𝑙𝑒

The model is tested for heteroscedasticity of the error terms. This is conducted by computing the Breusch-Pegan test. If heteroscedasticity is present, robust standard errors will be used for the regression model to increase the trustworthiness of the results. With a p-value of 0.1781, this test is non-significant at acceptable scientific levels of 0.05 and 0.01. We will maintain the assumption of homoscedasticity.

Some of these variables enclosed in the model are based on literature. A closer look is taken at each variable and how they are mathematically implemented.

Procrastination: This variable is a measure of the self-reported procrastination. This can range from 12 through 60. If a participant answers ‘Always’ on every question the maximum score of 60 is received. At the very least, 12 points are accumulated if ‘Never’ is answered on every question. Higher values indicate a higher self-reported level of procrastination.

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Age: This value of this variable is the age of the participant in years. These variables range from 13 through 18 in this data set.

Female: This is a dummy variable based on the genre of the participant. This variable takes on the value 1 if the participant is female, and 0 if male. The Male variable is not

implemented into the model to prevent multicollinearity. The coefficient β₂will give the estimated difference in self-reported procrastination levels compared to male.

Self-Esteem: This variable is a measure of the self-reported self-esteem. The values can range from 10 through 40. Higher values indicate a higher level of self-reported self-esteem.

The Alternative variables: These three dummy variables correspond with the alternative activity participants report at question 13 (see the questionnaire in the appendix). Even though five options were given at this question, only three are implemented in the model. To prevent multicollinearity, normally only one of the options is left out as a variable.

Interestingly, the last ‘other’ option was never chosen by any of the 85 participants. Due to this given, the option of ‘Social Media’ is left out of the model to prevent multicollinearity. The alternative activities they could choose from are:

a. Social Media (the likes of Snapchat, Instagram, WhatsApp) b. Gaming (on mobile, console or computer)

c. Watching video’s or series (through, for example, Netflix and YouTube) d. Going outside (for example: playing sports, meeting up with friends) e. Other, namely _______

The last option was given for students that resort to other activities than the ones listed before. They could fill out any reason. As stated before, this option was never chosen.

Average Social Media: This variable is the self-reported weekly average hours spent on Social Media. For data simplicity, reported hours were rounded down to whole and half hours.

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Average Gaming: This variable is, just as the previous discussed variable, the self-reported weekly average hours spent on gaming. For data simplicity, these reported hours were rounded down to whole and half hours as well.

An interaction between Average Gaming and Gaming Alternative is included to see if the effect of Average Gaming on Procrastination is dependent on if the individual mainly procrastinates through gaming. Interactions between Average Gaming and both Age and Female are implemented to see if the effect of Average Gaming on Procrastination is dependent on the level of Age and Female.

Results

This segment is split into three parts. At first a look is taken at some data figures that describe differences between male and female participants. With the use of t-tests, it is examined if these differences are of a significant type. Some implications of these outcomes will be discussed.

Following these initial results are the results of the regression model. The initial interpretation and significance of coefficients is discussed. A more extensive discussion of these results will be conducted in the discussion section of the thesis.

In the third and final part, we discuss limitations of the model and the research itself. This is a relative important section, as studies involving human participants are prone to issues with both internal and external validity.

Results – Gender differences

The group of 85 participants consists out of 37 females and 48 males, as mentioned before. There are some peculiar differences found within their the surveyed data. As a visual support, graphs are provided to help explain the insights gathered from the data.

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Self-reported Procrastination

General procrastination test scores average at 35.09. Scores for females have a mean of 36.57 and males average at 33.96. The scores for males tend to be a bit more volatile than for the opposite sex; having a sample standard error of mean of 0.93 compared to the 0.81 for females.

As becomes obvious from the graph, the data shows a difference between average self-reported levels of procrastination between males and females. Whether or not this is significant, is tested with the help of a two mean t-test. The outcome of this test (2.05) suggests that there is a significant difference on a 5% significance level, but not a 1% level. Does this lead to believe that females tend to procrastinate more? That would be a rather naïve statement. It should be kept in mind that these are self-reported levels. This could potentially mean that females at least have the perception that their procrastinating behavior is of a higher level than males. When compared to existing literature, a study by Özer, Demir & Ferrari (2009) shows that males tend to report more frequent procrastination than females. This somewhat conflicts with the above results. Özer, However, Demir & Ferrari (2009) continue by stating that females tend to procrastinate significantly more due to fear of failure and laziness, while males procrastinate more due to risky behavior and rebellion against

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authority. That fear of failure is a stronger factor for procrastination for females than males is further confirmed through the results of Solomon & Rothblum (1984). Recall that the studied group of students in this thesis are individuals that are all enrolled at a tutoring institute. These students tend to have several reasons to be at the institute, which are registered during the intake procedure. Among them are indeed a large fear of failure and laziness. If this aspect is more prevalent in the studied group of students, it could explain the found results.

Self-Esteem

For the scores of the genders on the Self-Esteem Scale, a general average is found of 31.39. Females average at 30.41 and the male participants have a mean of 32.15. Volatility seems to be comparable for both genders; scores of females have a standard error of mean of 0.69, while mean score standard error for males are 0.66.

Again, we see a difference between the averages of males and females, as the latter score lower. This complies with literature in this field, where it is found that females tend to exhibit lower self-esteem on dimensions like self-regard and academic, social and physical self-esteem than males (Rentzsch, Wenzler & Schütz, 2016). Again, the difference of means

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score of 1.799 a significance is found at a 10% and 5% level if we test for the mean of males being higher than females. More precise levels do not yield significant differences, most likely due to the sample size.

Average Hours on Social Media

Looking at the weekly average hours spent on Social Media, we find an average of 11.02 hours for the group as a whole. Females tend to spend more time, i.e. 12.68 hours weekly, while males spend 9.74 hours on a weekly basis. For males, the sample is slightly more volatile, as the mean standard error is 0.92 compared to the female mean standard error of 0.78.

As in the other cases, the difference between the male and female average weekly hours are tested for significant difference. The t-test score is 2.32, which leads to assuming a significant difference at the 5% level, but not at the 1% level.

Within the current literature, it is hard to find convincing scientific evidence that might support significant differences. Even though it is not a prime focus of this study to examine differences on social media use between the genders, it is still a resourceful insight that might give reason for further investigation in the future.

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Average Hours on Gaming

The surveyed average weekly hours yield a much larger difference than previous cases. The group as a whole averages at 5.02 hours, while females only spend 0.76 hours, which comes down to just over 45 minutes, on games in a week. Males, on the other hand average at 8.3 hours. The standard error of mean for females is 0.22, for males 0.92.

Out of the 37 females, 19 reported that they didn’t play any games at all. For males the reported hours were much more divers; there were cases of no game activity at all, but a few individuals reported spending over 20 weekly hours on gaming.

Similarly, these differences were tested through t-test. The score of 7.07 leads to believe that even at a 0.1% level, these means are significantly different. In everyday society, video gaming is seen as a hobby mostly dominated by males. These results seem to support that idea, even despite the small sample size.

Alternative Activity

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exogenous factors might impact procrastinating behavior. A pie chart is provided to give information on what participants indicate to do while procrastinating.

Recall that none of the participants declared any other activities while procrastinating than either spending time on social media, gaming, watching video content or going out.

The largest share of the students indicate that they mostly spend time on Social Media; 39 of the 85 students do so. This is followed by watching video content (27), then gaming (12) and lastly 7 students reported mostly going out of the house.

From this graph, it can be picked up that time spent watching video content, mostly through the means of Netflix and YouTube, could provide an additional measurement that might relate to procrastination. This is something that should be kept in mind, as a plausible cause for omitted variable bias.

Relationship Self-Esteem and Procrastination

Now that some differences are observed between the two sexes within the gathered data, a careful, closer look is taken at the relationship between the independent self-esteem test score and the procrastination test score variable. For visual guidance, a plotted graph is provided of the combination of both scores of all participants.

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Before trying to uncover certain trends, it would provide helpful to give an interpretation to what this graph unveils about the two test scores.

Scores of participants on the self-reported Procrastination Scale, which can range from 12 to 60, are in between 20 and 50. Most scores seem to be clustered between the value of 30 and 40.

The Self-Esteem Scale scores of the participants, that theoretically can range from 10 through 40, are between the values of 15 and 38. One of the participants that reported the lowest score on Self-Esteem at 15, has the highest self-reported level of procrastination as well. This is quite an outlier, as appears visually from the corresponding graph. With a small sample size of 86, this outlier has a nontrivial effect on the trend line.

To say that these scores are relative high, low or median is not something stateable from this set of data. To be able to, a random comparison sample would be required to control for significant differences of procrastinating and self-esteem levels.

Even though, if the data is evaluated as it is found through the survey, it appears that there is a slight downward trend; as self-esteem scores increase, self-reported procrastination levels seem to decline. Whether or not this is of any significance, will follow out of the regression of the model in the upcoming segment.

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Relationship Social Media, Gaming and Procrastination

Additionally, an examination of the relationships between the average time spent on Social Media, gaming and self-reported procrastination can provide some intuition on how these variables seem to relate. Just as in the previous case, a plotted graph is provided for an easier understanding of the relationship. First for Social Media and procrastination, then for gaming and procrastination.

As appears from the graph, most reported average hours spent on Social Media are between the five and fifteen hours. Some higher values are found, with eight observations of twenty hours or more spent on Social Media.

There seems to be a positive correlation (r = 0.258) between the hours spent on Social Media and procrastination. As individuals spend more time on Social Media, they tend to procrastinate more.

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Looking at the reported average weekly hours spent on gaming, there are only three individuals that report spending twenty or more hours on this activity. There are many observations at or near zero hours spent on gaming. As seen in the gender comparison section, these were mostly reported by female individuals. Again, it should be taking into account that these are all self-reported hours, and not observed hours. Individuals might have a biased estimation of how much time they spent on gaming or social media.

For the relationship between average weekly hours spent on gaming, there seems to be no clear connection based on the correlation. This is supported by the low value of r (-0.041) and the high p-value (0.713). A more precise analysis of these relations will follow out of the upcoming regression results.

Regression results

The use of a regression model as an additional mean of analysis is a necessary tool, because it helps to separate related effects to designated independent variables. Purely based on a

comparison of means, this is not achievable.

As was earlier touched upon, through the means of a link test and the Breusch-Pagan test, the model was already tested on misspecification and heteroscedasticity respectively.

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Table I

Determinants of Self-Reported Procrastination

(1) (2) (3) (4) (5) Age 0.963* (0.38) 0.950* (0.39) 0.919* (0.38) 0.466 (0.50) Female 2.151 (1.25) 2.910 (1.62) 4.037* (1.64) 4.892* (1.89) Self-Esteem -0.304* (0.14) -0.243 (0.14) -0.277* (0.14) -0.260 (0.14) Average Social Media 0.222 (0.17) 0.160 (0.14) 0.040 (0.13) 0.063 (0.13) Average Gaming -0.060 (0.22) 0.149 (0.13) 0.242 (0.16) -1.031 (0.99) Game Alternative -1.476 (2.62) 1.124 (5.11) Video Alternative -3.173 (1.61) -2.958 (1.60) Out Alternative 3.801 (2.32) 4.49 (2.37) Average Gaming * Game Alternative -0.173 (0.34) Average Gaming * Age 0.088 (0.06) Average Gaming * Female -0.890 (0.72) Average Gaming * Average Social Media 0.004 (0.015) N 85 85 85 85 85 R2 _0.067 _0.142 _0.181 _0.274 _0.510 AIC 537.6 537.7 533.5 534.6 BIC 547.4 552.3 555.5 563.9 * = p < 0.05, ** = p < 0.01

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Table I (above) shows the regression results that were computed with the data through the use of the application STATA. Five different models were estimated, of which (2), (3) and (4) are nested models of (5). It is clear that the significance of close to all variables as determinants of self-reported procrastination is rather poor.

In itself, that isn’t a bad thing. The goal of a research is not to prove that certain aspects relate to each other. Rather, it is to discover if a relationship exists between variables, and progressively try to figure out potential causes for this.

There are some issues to take into account considering these results. For example, the small sample size decreases the statistical power. The variance is most likely higher than in a larger sample, which has a deteriorating effect on the trustworthiness.

However, if a closer look is taken at the coefficients that are significant at a 5% level, an interpretation of them can be given.

Taking a look at the most expansive model, it appears that only the gender has a significant relationship to self-reported procrastination. It is estimated that females score 4.892 points higher on the procrastination test than their male counterparts. Other variables do not seem to be of any significance within the model. This includes the three interaction terms between average gaming and gaming alternative, age and female, suggesting that there is no dependency of the relation between average hours spent on gaming and procrastination on the level of any of these variables.

As derived from the AIC and BIC assessment, both values suggest that this model is not the best fitting model to use. AIC and BIC values of the other models do not seem to point towards a single model to be the best fit. However, for further inspection model four is examined.

In this model, some more significant relationships between procrastination and independent variables are found. The positive coefficient of Age, (0.919) implies that an additional year of age is associated with an increase of the self-reported procrastination score of 0.919. The implication of this positive significant effect is that as individuals get older, this is associated with a higher level of self-reported procrastination.

For the dummy variable Female, there is another positive significant coefficient found at the 5% level. The value of this coefficient is 4.037, which denotes that the female gender is associated with about a 4-point higher score on the procrastination scale. This was shortly discussed before, but is important to be recalled: this does not prove in any form that females

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tend to procrastinate more, but does show that the perception of their level of self-perceived procrastination is higher than their male counterparts.

Taking a closer look at the estimated coefficient of self-esteem, a confirmation is found of the findings of the literature study. Just as the findings of Ferrari (1994) and Solomon & Rothblum (1984), a significant negative relationship is found between self-esteem and procrastination. In the case of these results, for every point increase on the Rosenberg’s (1979) Self-Esteem Scale, it is estimated that an individual scores over a fourth point (-0.277) lower on the procrastination test. The insinuation here is that individuals with a higher self-esteem tend to procrastinate less.

Other estimates aren’t significant for at least a 5% level, but an interpretation of some will be give nonetheless. Part of the reason for this research is to figure out if there is a relationship between social media and video game behavior and procrastination. Even though both estimated coefficients are nonsignificant even at the 10% level, it is found that

individuals that spend more time on social media hardly experience that they procrastinate more than their peers that spend less time on this activity. For gaming the same holds, though the coefficient is estimated quite a bit higher.

The dummy variables for alternative activity are all nonsignificant. Though, the estimated coefficient for Video Alternative, which implies that the respondent mostly spends his or her time while procrastinating on watching video content, is close to being significant at a 5% level (p-value of 0.052). With an estimated size of -3.173, this implies that

individuals that tend to mostly watch videos while procrastinating, tend to score over three points lower on the procrastination scale than their peers do that tend to spend time on Social Media.

Research Limitations

To every research there are certain limitations to take into account, and in this case that is not any different. There are a few to name that are obvious and have been touched upon before. For example the small sample size, which has been mentioned several times. A small sample bias decreases statistical power and leads to a lesser likelihood of finding trustworthy

relationship between the studied independent and dependent variables.

Another significant issue could be the omitted variable bias. From the literature review, there were some variables that seem to be very much relevant. For example an evaluation of depression levels, fear of failure, anxiety and a measure of - and -factors,

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elements that seem to be associated with procrastination, could help to better explain the data. In the case of this research, the time available with the students was the main cause that restricted the gatherable amount of information.

Additionally, external validity is another issue that looms large. The researched sample consists only out of students from a tutoring institute. These students will

undoubtedly share some characteristics which are not generalizable. As for one, tuition fees at the institute are relatively high. Most children at the institute come from families that are up in the wealthier segments of society. For example, this could have implications for the level of self-esteem.

Another attribute that could impact self-esteem levels, is the fact that students are enrolled at an tutoring institute. These children tend to have low average grades, which could have a strong relationship with their self-esteem, at least on an academic level.

Next to that, the age range of the participants is quite narrow, with only about 5 years in between the youngest and the oldest respondent. Relationships between age and

procrastination are hardly generalizable when the research has been conducted among an age category where many factors are present that do not hold for adult people.

Lastly, the issue of reversed causality should be addressed. Behavior and the driving factors behind this are very hard to capture within a quantitative model. Is low self-esteem a result of procrastination, the other way around or do both of these aspects merely correlate? Due to the complexity of the human brain and the behavior coming forth out of it, delicacy is advised for interpreting results from something as ‘crude’ as a regression model.

Discussion

It is clear that the results need to be carefully interpreted. From here on, the issue of a small sample size will be seen as a given, and not further given as an argument for the weak statistical power of the information that the regression provides.

Procrastination seems to be a type of behavior that does not easily let itself be

captured in a model. The main findings of data analysis give nonsignificant relationships for all variables, except for age, gender and self-esteem levels at a 5% level.

The fact that an increase of age is related to a higher level of procrastination gives some support to the idea that procrastination is a habit that establishes itself during the adolescence. This leads to believe that if this type of behavior is identified at a young age,

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doing this, an individual will be better to sustain him- or herself in both an academic and professional career. Not only would that be beneficial to the individual, businesses and society could benefit from a higher average educational level of employees. It should not be forgotten that some studies find that procrastinators tend to perform poorer (Tice &

Baumeister, 1997). If the level of this type of behavior can be reduced, there are most likely only winners.

The connection between gender and self-reported procrastination hands food for thought as well. Even though perception of one’s procrastination behavior and actual

procrastination itself are not the same, it is a result that gives some ideas for potential further studies. If an experiment can be developed that measures actual procrastination and perceived procrastination, some insights could be gathered on a form of over- or underestimation bias. Yielding potential interesting results, as an underestimation bias of the tendency to

procrastinate could be at the root of the problem itself.

There were no clear relationships found between average weekly social media and gaming activity and procrastination. An idea that can be established from this, is that it might not necessarily be about what the available outside options are to procrastinate with. The aversion to the mandatory task might be so large, that it essentially does not matter what the alternative activity is, as long as the thoughts can be taken away from the mandatory

assignment. From this, it is assumable that ‘task aversiveness’ is a very relevant factor for the likelihood to procrastinate on a task. That, in itself, is not very surprising. A prime example of this is a young child that leave the food that it likes the least for the last, and start with the food that it likes more when consuming a meal. This example might be somewhat banal, but can be considered as a form of short-term procrastination.

The way that this research is set up, might have been a rather crude way to measure aspects like procrastination and self-esteem. In the way that it is measured in this research, only self-reported procrastination can be taken into account. The big issue with this, is that individuals might have some idealized idea about their own behavior concerning this topic, but in reality act very differently. As was touched upon earlier, this forms a so-called estimation bias, which is more likely to be a type of underestimation than overestimation bias, as can be concluded from the study by Tice & Baumeister (1997).

It is mostly due to financial and technological constraints that this research was not able to test for actual procrastination, but merely self-reported levels. Problems that might not be the case for larger research institutes.

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Conclusions

All in all, this research of this thesis obviously suffers from several limitations that impact the trustworthiness of the findings. However, based on the results from the regression of the linear model there are some conclusions we can make considering the hypotheses of this thesis.

The first and second hypotheses; that Social Media and gaming activity of students has a significant relationship with self-reported procrastination, is not supported by data analysis. For further analysis of this concept, it is suggested that an experiment is developed that is able to capture the estimation bias of procrastination. An estimation of the relationship between the level of this bias and social media and gaming activity could help to better explain the association between Social Media, gaming activity and self-reported procrastination.

For the third and final hypothesis; that self-esteem is significant negatively related to self-reported procrastination, there is confirmation found at the 5% level. This confirms conclusions of other studies in this field. For a more precise analysis, it is suggested that self-esteem should not be seen as a uniform concept, but that the assessment should be split for different fields. For example, social self-esteem and academic self-esteem might be of a different category, and should be distinguished in a model.

For future studies on procrastination, a study that is more in the direction of the McClure & Al. (2004) is suggested. One of the potential ideas fabricated during this study, is one through the use of fMRI. If two groups of participants can be established, one

composited out of procrastinators and a control group, brain activity can be used to see how and if activation of brain regions differ between the two groups as participants consider time-differentiated options of gains and costs. This idea needs quite a bit of thought, as it’s still ambiguous, but offers an opportunity for further thoughts.

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Appendix

Questionnaire

Omcirkel bij elke multiple choice vraag het getal onder het antwoord dat voor jou het meest van toepassing is.

Geslacht: MAN VROUW

Leeftijd: _____ JAAR

I. WERKEN AAN GROEPSOPDRACHTEN

Nooit Bijna Nooit

Soms Bijna Altijd

Altijd

1. In welke mate stel je deze

taak uit? 1 2 3 4 5

2. In welke mate is

uitstelgedrag bij deze taak een probleem voor je?

1 2 3 4 5

II. WERKEN AAN INDIVIDUELE OPDRACHTEN Nooit Bijna Nooit

Altijd

3. In welke mate stel je deze taak uit?

1 2 3 4 5

III. LEREN VOOR TOETSEN

Altijd

taak uit? 1 2 3 4 5

1 2 3 4 5

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Altijd

taak uit? 1 2 3 4 5

1 2 3 4 5

V. WERKEN TIJDENS DE LES

Altijd

taak uit? 1 2 3 4 5

1 2 3 4 5

VI. HUISHOUDELIJKE TAKEN

Altijd

taak uit? 1 2 3 4 5

1 2 3 4 5

VII. BEZIGHEDEN TIJDENS UITSTELLEN

13. Op momenten dat je verplichtingen uitstelt, waar ben je voornamelijk mee bezig? Geef één antwoord aan.

a) Social Media (Zoals Instagram, Snapchat of WhatsApp)

b) Gamen (mobiel, computer of console)

c) Video’s of series kijken (zoals Netflix en Youtube)

d) Naar buiten gaan (Zoals sporten of met vrienden afspreken)

e) Anders, namelijk ________________________________

14. Hoeveel uur spendeer jij gemiddeld wekelijks aan gamen?

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15. Hoeveel uur spendeer jij gemiddeld wekelijks aan Social Media?

_____ uur

Omcirkel bij elke stelling het antwoord dat voor jou het meest van toepassing is.

Helemaal Mee Eens

Mee Eens Niet Mee Eens

Helemaal Niet Mee

Eens 1. Over het algemeen ben ik

tevreden met mezelf.

1 2 3 4

2. Soms denk ik dat ik helemaal niets waard ben.

1 2 3 4

3. Ik vind dat ik een aantal sterke kwaliteiten heb.

1 2 3 4

4. Ik kan dingen net zo goed doen als de meeste andere mensen.

1 2 3 4

5. Ik heb het gevoel dat ik niet veel heb om trots op te zijn.

1 2 3 4

6. Ik voel me echt waardeloos op bepaalde momenten.

1 2 3 4

7. Ik voel dat ik een persoon van waarde ben, tenminste op een gelijk niveau met anderen.

1 2 3 4

8. Ik zou willen dat ik meer respect voor mezelf kon hebben.

1 2 3 4

9. Al met al ben ik geneigd te voelen dat ik een mislukkeling ben.

1 2 3 4

10. Ik heb een positieve houding tegenover mezelf.

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References

Ariely, D., & Wertenbroch, K. (2002), Procrastination, Deadlines, and Performance: Self-Control by Precommitment. Psychological Science, 13(3), 219-224.

Elster, J. (1998). Emotions and Economic Theory. Journal of Economic Literature, 36(1), 47-74. Ferrari, J. R. (1994) Dysfunctional procrastination and its relationship with self-esteem, interpersonal dependency, and self-defeating behaviors. Personality and Individual Differences, 17(5), 673-679.

Franklin, B. (1821), Essays & letters. R. & W. A. Bartow & Co., Retrieved from

https://babel.hathitrust.org/cgi/pt?id=mdp.39015062282655

Gaming disorder (2018), World Health Organization. Retrieved from http://www.who.int/features/qa/gaming-disorder/en/

Gaviria, A. & Raphael, S., (2001), School-Based Peer Effects and Juvenile Behavior. Review of Economics and Statistics, 83(2) 257-68.

Green, G., MacIntyre, S., West, P., & Ecob, R. (1991). Like parent like child? Associations between drinking and smoking behaviour of parents and their children. British Journal Of Addiction, 86(6), 745-758.

Haycock, L. A., McCarthy, P., Skay, C. L. (2011). Procrastination in College Students: The Role of Self-Efficacy and Anxiety. Journal of Counseling & Development, 76(3), 317-324.

Internet; toegang, gebruik en faciliteiten (2018). In CBS StatLine. Retrieved from http://statline.cbs.nl/

King, D.L. & Delfabbro P.H., (2014), The cognitive psychology of Internet gaming disorder. Clinical Psychology Review, 34(4), 298-308.

Lavoie, J. & Pychyl, T. A. (2001), Cyber-slacking and the procrastination superhighway: A web-based survey of online procrastination, attitudes, and emotion. Social Science Computer Review, 19, 431–444.

McClure SM, Laibson D, Loewenstein G, Cohen JD. (2004). Separate Neural Systems Value Immediate and Delayed Monetary Rewards. Science, 306, 503-507.

O'Donoghue, T. & Matthew R.. 1999, Doing It Now or Later. American Economic Review, 89 (1), 103-124.

Owens, A. M., & Newbegin, I. (1997). Procrastination in high school achievement: A causal structural model. Journal of Social Behavior and Personality, 12(4), 869.

Özer, B. U. , Demir, A. & Ferrari, J. R. (2009) Exploring Academic Procrastination Among Turkish Students: Possible Gender Differences in Prevalence and Reasons, The Journal of Social Psychology, 149(2), 241-257.

Phelps, E., & Pollak, R. (1968). On Second-Best National Saving and Game-Equilibrium Growth. The Review

of Economic Studies,35(2), 185-199.

Rentzsch, K., Wenzler, M. P. & Schütz, A. (2016). The structure of multidimensional self-esteem across age and gender. Personality and Individual Differences, 88, 139-147.

Rosenberg, M. (1979). Conceiving the Self. New York: Basic Books.

Senécal, C., Koestner, R. & Vallerand, R. J., (1995). Self-Regulation and Academic Procrastination. The Journal of Social Psychology, 135(5), 607-619.

Solomon, L. J., & Rothblum, E. D. (1984). Academic procrastination: Frequency and cognitive-behavioral correlates. Journal of Counseling Psychology, 31(4), 503-509.

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Stock, J. H., & Watson, M. W. (2015). Regression Analysis of Economic Time Series Data. In J. H. Stock, & M. W. Watson, Introduction to Econometrics (pp. 568-717). Essex: Pearson Education Limited.

Tice, D., & Baumeister, R. (1997). Longitudinal Study of Procrastination, Performance, Stress, and Health: The Costs and Benefits of Dawdling. Psychological Science, 8(6), 454-458.

The role of social media and gaming activity in procrastination behavior