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Deconstructing the psychology of cheating in AIT gym: “Cheating is a choice, not a mistake”

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AIT offers gym service at a concessional charge of 20 Baht per day. Standard protocol mandates the users to drop 20 Baht as soon as they enter the gym. Regardless of the presence or absence of the gym volunteer, whose job is to collect the fees, users are expected to chip in the designated amount every time they use gym facilities. The entire payment system is based on trust and benevolence. On the contrary, our normal human instinct manipulates us from paying under different paradigms. We tend to demonstrate cheating habits (by not paying 20 Baht) when nobody is watching, for instance. In this research, we will try to find out under what underlining exemplars users are most likely or least likely to demonstrate such ‘Cheating behavior’. An interesting fact is that irrespective of a person’s nature, cheating behavior becomes transparent when we inject (external) catalysts. These external catalysts may be gym volunteer’s observation, religious affiliation etc. (These external variables are further described in the report).

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There was a sneaky suspicion of cheating due to a sudden influx of gym users. As some of us are regular gym users ourselves, we could sniff comprehensive disarray and inconsistency on the unusual rise of number of users in some days and deflated numbers on the other days. Conspicuously, there was a free-rider problem. Taking this as the main problem, we lay out some utilitarian questions that tap into the psychology of cheating.

  1. Are students most likely to cheat when there are no gym volunteers and less likely to cheat when there are?
  2. Does mob behavior influence cheating?
  3. Does the affiliation to ‘God’ discourage cheating?
  4. Is there a difference in the paying behavior of male and female?
  5. Does cheating behavior depend on the region from where specific users come from?

Meanwhile, other relevant influencing factors have been scrutinized through keen observation and sensible assumptions.

Major assumptions

Assumptions Reasons

All gym users make the payment in the beginning. Those who don’t are automatically tagged for cheating. The customary gym norm mandates users to pay in the beginning. Volunteers can’t differentiate among students, staffs and non-AIT members. There are different volunteers at different timings and it’s a Herculean task for different volunteers to know who are students and who are AIT staffs. Most of the users right now are non-members. Since the membership from Jan-June has expired, most haven’t renewed their membership. All gym users saw the Bible quote (a behavior altering external factor). Bible quotes were pasted in strategic areas of the gym. Hence, we assume that the users saw the quotes before paying.

Sample Selection

The AIT gymnasium was selected as the place of our observation and the samples were randomly drawn from the population of gym users. Students, staffs, alumni and non-AIT residents, approximately 20-50 years of age were observed to understand their payment behavior under different external parameters. The focus of our observation was both male and female from different regions (South Asia and other regions). Parameters such as mob behavior, religious emotions and volunteer surveillance were studied to identify their tendency to pay (More specifically, understanding cheating behavior).

Data Collection

AIT Gym opens daily for three time periods, morning (6:30- 10:30), evening (16:00- 21:00) and ladies’ time (21:00-22:00). The observation was carried out for painfully long 11 days, randomly in all the three time periods. Observers disguised themselves as normal gym users during the observation period. Initially, our observation in the morning, evening and ladies’ time were from 7:00- 8:00, 17:30- 19:00 and 21:00-22:00 respectively. Prior to our observation, it was found that ladies preferred using gym during the ladies’ time, which is why the ladies’ time was taken into consideration. These time frames were chosen because most of the students and staffs have their commitments starting 8 in the morning to 5 in the evening. As the study progressed, we observed that very few ladies used the gym (9 in total). Most importantly, the data was collected in different time frames to observe any variations in the payment of gym fee and the propensity to cheat. So, limited sample during the ladies’ time made us to adjust our observation to 19:00-20:00. Adopting a more holistic approach, we also decided to study the moral behavior of the sample during the last four days of the observation. Bible quotes were placed at different gym locations, which implied not to cheat and steal. After reading the bible quotes, we tried to identify whether the gym users were influenced morally to pay the gym fees or not.

Data Analysis Method

We used Descriptive Statistics method to study the relationship between Independent variables namely: presence of volunteers, gender, and religious affiliation with the Dependent variable i.e. payment behavior. This analysis will help to present an overview of the sample through tools like Mean, Median, and Standard Deviation that depicts the percentage of people that do not pay to use the gym. Indeed, we wanted to see the different effect of these variables in the payment behavior. Therefore, we conducted a two-tailed A/B testing to verify our hypothesis and eventually offer a utilitarian suggestion to the gym management.

Challenges

As it is the case with any other study, our research is constrained by a set of challenges and limitations. A major challenge lied in differentiating the volunteers from the non-volunteers. The gym volunteers are also avid users of the gym who have the benefit of using the gym facilities at no cost, for their service. Hence, in a few cases (where we could not identify the volunteers) they might have been categorized as users who did not pay. Furthermore, to reduce error, we categorized gym users into regions (South Asia, Other) rather than nationality. This was done as many people from these particular regions have strikingly similar features. It was also a daunting task for us to track each individual user during the peak gym hours. This is especially during 5:00 P.M to 7:00 P.M. The data for peak hours are subject to our limited ability in monitoring everyone. Likewise, some of us who are not regular gym users had to blend in perfectly to avoid raising suspicion from our sample. We also had to learn and adopt a gym routine (resulting in sore muscle and joints for some). Also, it was found that during rainy days less people went to the gym that affected our data collection. Finally, there was a very distinct, pungent smell in gym that made our time in the gym painful.

Limitations

The data from our observations are limited by a few constraints. First of all, all data were collected at 3 distinct times of the day (morning, evening, ladies’ time). Though we covered a majority of the period when the gym was open, we could not observe gym users throughout the entire operating time of the gym. There was also a major limitation in terms of the number of students currently at AIT (because of inter-semester and new August batch yet to enroll). A research during a full semester would have yielded better results. Finally, the gym users are mostly student and staff who follow a daily routine. But for the purpose of this research students who repeatedly use the AIT gym on a daily basis are considered as a new sample every day.

Findings

Most gym users are South Asian males, who use the gyms during the evenings. We will now delve deeper into the behavioral aspects of the gym users. The data collected shows that only 29.27% of all males from the South Asian region make payments, whereas 63.41% of their counterparts from other regions of the globe uphold this. Similarly, this chart informs about the gym proportion of time consumed in the gym. Weekdays provide a more diversified mix of users in comparison to weekends. During weekends, no users could be found in the gym during the morning shift (maybe they sleep-in during the weekends). Now for the most interesting part, A/B testing! We had proposed the following hypothesis upon which we stumbled upon exciting findings. (Also, we’ve tried to stick with the jargon ‘failing to reject’ rather than ‘accept’).

We have classified the variable under A/B testing as with and without external influence. The variables with the influence are mainly 1) the use of volunteers, 2) religious affiliation and 3) mob influence. Likewise, for no external influence, we’ve used simple variables like 4) gender and 5) region. (Let’s start with gender and region as we’ve saved interesting findings in the end.

Hypothesis 1: Region Ho: There is no difference in the paying behavior of South Asian students and students from other nationalities. H1: The paying behavior differs among the students from South Asian nations and students from other nationalities. At the outset, two-third of the gym users are from South Asian nations like Sri-Lanka, Nepal, India, Pakistan and Bangladesh. As per our observation, irrespective to whether there was a gym volunteer or not, monitoring payment, an alarming rate of 71.09% of South Asian users didn’t pay as compared to other nationalities whose cheating rate was minimal of 37.78%. (These numbers fall under our confidence interval of 69% to 79% for the South Asian users who paid and 23% to 52% for the others who paid). In addition, since the p-value is astonishingly low (0.005541%), we are subjected to reject the null hypothesis. As per our personal analysis, as South Asia is heavily dominated by lower per capita income compared to South East Asia, their tendency to ‘not pay’ and demonstrate cheating-like behavior inflates considerably.

Hypothesis 2: Gender Ho: There is no difference in the paying behavior of male and female H1: There is difference in the paying behavior of male and female We desired to see if the payment pattern across genders was the same or different. Since the sample size for female gym users was ridiculously small, we conducted t-tests to draw a conclusion. Nevertheless, the payment pattern between both the genders was strikingly similar. Almost 37.8% of the female paid in assessment to the 33% of male who paid. We obtained a p-value of 0.65, which gave every reason not to reject the null hypothesis.

Hypothesis 3: Presence of volunteer Ho: There is no difference in the paying behavior with and without the presence of volunteers. H1: Paying behavior differs in the presence and absence of the volunteers. There were 87 samples in the presence of gym volunteers and 86 in the absence of the volunteers. During such a period, a whopping 50.57% of the users paid when there was a volunteer and 24.42% of the users paid in the absence of a volunteer. (These figures fall under our confidence interval (95%) of 49% and 61% for those who paid in the presence of volunteers and 16% to 34% for those who paid in the absence of volunteers). After crunching some numbers, we came up with a p-value of 0.022254%, asserting evidence to reject the null hypothesis. We can draw conclusion that cheating happens more often when there is no higher authority watching you pay.

Hypothesis 4: Mob behavior Ho: Mob behavior has no influence in the paying pattern H1: Mob behavior influences gym users paying pattern Normally people tend to behave differently when they are in groups compared to when they are alone. (This is not split-personality disorder) Mob behavior influences people to adopt certain behaviors on a largely emotional, rather than rational basis. So, could students’ decision to pay in the gym be associated with mob influence? i.e. either all the students coming in groups pay or they don’t. After running some observations, we witnessed that both the categories of students, (coming to gym in groups and coming alone) had similar paying patterns. Contradictory to the mob influence, we came up with a p-value of 35.14%, which strongly mandates us in failing to reject the null hypothesis. Hence, we do not have enough information to reject that mob behavior has no influence in the paying pattern. Hypothesis 5: religious sentiments Ho: Adding religious sentiments have no effect in the paying pattern H1: Religious sentiments affect the paying pattern To make things more enthralling, we decided to put a quote from the Bible in various strategic places in the gym to see if there was any dramatic change in the paying pattern. (It was intimidating to be stared by people doing bicep curls while pasting Bible quotes around the gym). Astoundingly, 89% of the users paid, out of a total of 46. This was the lowest level of cheating tendency ever. Furthermore, as the p-value is 0.0000516%, we can reject the null hypothesis, proving a claim that associating God can help to reduce negative behavior.

Regression analysis from the three major cheating behavior-inducing factors.

  1. Presence and absence of volunteers
  2. Mob behavior
  3. Use of religious affiliation.

The most effective external parameter was using bible quotes. The co-relation was positive and strong (0.875), meaning that more gym users were likely to pay when they saw the bible quotes. (Now, we will advise the gym management to post Bible quotes everywhere to reduce cheating). The second most effective tool was the presence of volunteers. Most users were probable to pay and not engage in cheating when they felt being watched by the volunteer. The correlation of 0.27, pretty significant enough, speaks for itself. Lastly, but sadly, though we hoped to find some correlation between mob- behavior and payment patterns (cheating patterns), we remained unsuccessful, as there was no significant correlation. Particulars Presence of Volunteers Mob behavior Religious Affiliation Payment behavior 0.27003087 0.0448667 0.875528

All in all, we’ve come to some fascinating conclusions based on our observation regarding cheating. It is likely that there is difference between behavior of men and women when they pay. Nevertheless, it is most unlikely that there is no difference in the paying patterns between the South Asians and others. This brings us to the adjacent issue of mob behavior. We hypothesized that there is no difference between paying in groups and paying individually and yet we fail to reject that claim. Though we expected people coming in groups to influence paying behavior, i.e. if one person doesn’t pay, the others follow the suit and vice versa, it didn’t happen. So cheating behavior was irrelevant whether students came individually or in groups. Correspondingly, gym users were more likely to cheat (by not paying) when there were no volunteers. Obviously, it’s hard to put 20 Baht when nobody is watching. On a curious note, we witnessed that 4% of those who did not pay in the beginning paid towards the end of the gym. (Maybe volunteers shot suspicious looks at them).

We’ve come up with a handful of recommendations based on our findings. Let’s start with gender. As per now, boys in AIT gym outnumber the girls. So, girls should be further encouraged to use the gym because their greater number can help reduce cheating. (Because boys wouldn’t want to get caught cheating by the volunteer in presence of girls). Further, there should be a CCTV in the gym so that people feel observed. Hence, cheating can be reduced, and volunteers’ time could be saved. Moreover, more enlightening quotes from different Holy Books (alongside posters of Arnold and Sylvester Stallone) should be kept around the gym as this will inject positive vibe and reduce moral bankruptcy. We highly recommend the gym owner use the doctrines to encourage the people to do the right thing. Finally, the gym could offer other various benefits to the volunteers to encourage payment receipts. A volunteer appreciation party or some prize (best volunteer of the month) could be given from the funds collected by the volunteers.

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