ANALYSIS OF TIPPING BEHAVIOUR TOWARDS ONLINE TRANSPORTATION SERVICE PARTNER BEFORE AND DURING COVID-19 OUTBREAK

Online Transportation Services are growing significantly in Indonesia from 2015 to 2020. With their proposed value propositions, this service has become one of the most popular services in Indonesia. Due to the high customer satisfaction of this service, many customers give tips to the driver or riders as a reward for the service. However, since the COVID-19 outbreak in Indonesia, the user traffic of this service has been decreasing significantly. As a result, many partners of this service cannot earn adequate income for their families. In response to this, to help alleviate the life of these service partners during the COVID-19 outbreak, a campaign has been started through social media to encourage customers to give more tips to these service partners. Based on this phenomenon background, this study was conducted to analyse the customer tipping behaviour change on this service before and during the COVID-19 outbreak in South Sulawesi and South East Sulawesi province. Wilcoxon Signed-Rank Test Analysis Method was used to analyse 74 respondent data. There are eight customer categories in this study. The results show significant changes in the tip size for Customers with No Income and Customers from South East Sulawesi category. Another interesting finding is that although most respondent customers as a mean to reward the service, the number of customers who give a tip for almsgiving is increasing significantly during the COVID-19 outbreak.


INTRODUCTION
Online Transportation Service has become one of the most highly demanded service in Indonesia for the last 5 years since its entrance. Wallsten (2015) stated that Online Transportation Service is a ride-sharing service where a car/vehicle that is owned by a driver/rider is shared with customers. With simple human interface apps, customers can order the service easily and therefore increase the number of apps users rapidly (Silalahi, Handayani, & Munajat, 2017). By the end of November 2019, Gojek and Grab's apps' had been downloaded for over 125million and 138 million times respectively on Playstore (Hastuti, 2019) (Astutik, 2019). As the number of customer increases, these companies require more driver/rider and open many new job vacancies. Relatively easy and simple job requirements to be employed, the number of driver/rider of this service grows rapidly throughout the years and by the 2019, it was counted at over 2 million drivers/rider around Indonesia (Hastuti, 2019). This trend undeniably has helped to solve the unemployment issue significantly that Indonesia Government has been facing since a long time ago. This online transportation service provide two payment methods where the customer can pay the service by cash or e-money. During payment after receiving the service, some customers tend to give tip to reward the service, and this leads to the addition of Analysis Of Tipping Behaviour Towards Online…. (Husein,Warmana,(556)(557)(558)(559)(560)(561)(562)(563)(564)(565)(566)(567)(568)(569)(570)(571) 1 new feature of Tipping within the application by some of the online transportation companies (Gojek.com, 2018;Grab.com, 2019). Harsadakara (2019) reported that some Indonesian famous person are very generous when giving tips from leftover change to the rider or driver after receiving the service. COVID-19 outbreak in Indonesia has damaged Indonesia's economy situation severely. Due to some policies ruled out by the government to decrease the spread of this virus, such as physical distancing, travel restrictions, public policy, many companies and business experience a great depression such as private consumption, retail sales, and tourists arrival which are recorded experiencing a significant fall (Muzakki, 2020). Another impact is on the employment issue. It has been announced by Indonesia's Minister of Finance that the number of unemployment since the COVID-19 outbreak has reached up to nine million and this may result to a great number of people living in a poverty line due to this pandemic (Gorbiano, 2020). Purnama (2020) reported that many partners of this service were complaining with the significant decrease of order that they received since the outbreak of COVID-19. In response to this issue, one of the online transportation companies make a campaign to give tip to partners were spread in social media with hashtag #kasihLebihan since March 2020 to help the life of partners who have been impacted by COVID-19 (Kholisdinuka, 2020).
To the author's knowledge, this is the first study that explore customer tipping behaviour in online transportation industry particularly during the COVID-19 outbreak in Indonesia. Most studies about tipping are related to restaurant or hotel tipping and focusing on the motives or service quality. A study about restaurant tipping shows that customers are motivated to leave tip by they desire to help the servers' welfare, equitable relationship with servers, prefential future service, social approval and social obligation fulfilment (Lynn, 2015a). In line with the previous study, another study about customer tipping behaviour in hotel industry in Indonesia explains that service quality, social approval, server gender, customer empathy and payment methods are the factors that influence the tip (Priscilia et al, 2020). However, no published studies has investigated the customer tipping behaviour in online transportation service and therefore more research is needed to assess this behaviour particularly the change of behaviour during the COVID-19 outbreak. In order to understand more about this behaviour development, this study aims to examine the change of customer tipping behaviour to partners of online transportation service before and during the COVID-19 outbreak particularly in the province of South Sulawesi and Southeast Sulawesi.

LITERATURE REVIEW Tipping Behaviour
Some economists think that tipping can also be considered a social norm. This is because the customers leave tips after the service has been provided, proving that tips are not given to get good service in advance (Lynn, 2015b). A social norm is not the only reason. Another reason is that customers want to reward workers for the service delivered (Lynn & McCall, 2000). Most customers stated that they leave tips to compensate servers for their work, and this motive is also supported by research that found that the larger customers leave tips, the better the service that they receive (Lynn & Grassman, 1990). Consistent with this finding, many economist also believe that tipping can be utilised to monitor and reward workers performance (e.g. (Bodvarsson & Gibson, 1994); (Hemenway, 1983)) and as a result it will help companies to monitor and control the service quality Analysis Of Tipping Behaviour Towards Online…. (Husein,Warmana,(556)(557)(558)(559)(560)(561)(562)(563)(564)(565)(566)(567)(568)(569)(570)(571) delivered by their employees (Lynn & McCall, 2000). Some studies have also examined the relationship between some variables and tip size and found that Bill Size, Server Friendliness, Service Quality, Server Attractiveness, Customer Sex, Dining-Party Size, Patronage Frequency, Payment Method, and Miscellaneous may determine size of the tip given by the customer (Lynn et al, 1993).
Tipping is considered by many economists as an interesting behaviour because customer usually prefer to purchase something for the lowest available price and not to pay more than necessary (Lynn & McCall, 2000). Tipping has a large economic magnitude because it has become one of the major source of income for millions of workers yet it is often unreported to the tax authorities (Azar, 2011) thus it may be considered as an illegal income (Hemenway, 1983).

METHOD Data And Sample
This study collected data from online questionnaire spread to two Provinces which are South Sulawesi and Southeast Sulawesi. Author decided to examine these two area not only because that some online transportation service companies have entered these two provinces, but also because it is easier to collect the data by the author in the middle of COVID-19 physical distancing policy by the government. Participants for the online questionnaires were customers who had used transportation online service and given tip to the driver or rider. The questionnaires were then distributed randomly to the two provinces from 28th of July 2020 to 30th of July and 74 responses had been recorded over the time.

Researh Instrument
The questionnaires were made from Google Spreadsheet and consisted of 6 Identity Questions, and then divided to two timeframe categories as shown in

Data analysis
To measure the change of behaviour descriptively, data collected were converted into ordinal numbers and to measure the change of customer tipping behaviour from the respondents before and during the COVID-19 outbreak Wilcoxon Signed Rank Test was used because the data were measured before and during COVID-19 outbreak and do not normally distributed, IBM SPSS Statistics Version 22 was utilised to run the calculation. The procedure of Wilcoxon Signed analysis is as follow: 1. Determine the sign of the difference (Di) between each pair of observations. 2. Examine the distribution of the differences. If it is skewed, a logarithmic transformation of the (raw) data may make the distribution of the differences more symmetrical. 3. Rank the differences in order of absolute size with a rank of 1 assigned to the smallest difference. Differences of zero are (usually) dropped from the analysis. The rank assigned to tied ranks is the mean of the ranks that would have been given if the observations had not been tied. 4. Reassign the signs of the differences to their respective ranks (Ri). 5. Then calculate the appropriate test statistic.

Sum of rank statistics (small samples with no ties) formula
Calculate S + and Swhich are the sums of positive and negative ranks respectively.
For when ties are present where z is tested againts the standard normal deviate Z, n is the number of pairs, R i is the (signed) Rank of the absolute difference between the i-th pair of valueswhere its sign is the same as that of the i-th difference.
Hypothesis: H0 = Before -After = 0 H1 = Before -After ≠ 0 Customers for frequency of giving tip and size of tip are categorised to Customer from South Sulawesi, Customer from Southeast Sulawesi, Combined Provinces of Male Customer, Female Customer, Customer with no income, Customer with income less than IDR. 2.000.000 (1$ = IDR 14.000), Customer with income between IDR. 2.000.000 to IDR. 4.000.000, and Customer with income more than IDR. 4.000.000. In order to gain more understanding about the motive behind the given tips, the qualitative data, which were the reason for giving tips, were described graphically as supporting data of the quantitative data.
Some limitations for this study is that, due to the COVID-19 health protocol, the sampling method was done purposively and not based on randomized principle and as a result the samples cannot be generalized as the representative of the two provinces population.

RESULTS AND DISCUSSION Results
There were significant changes of tipping behaviour particularly in the size of the tip of Customer with No Income and Customer from South East Sulawesi category with p-value 0.046 and 0.021 lower than α = 0.05. However, other customer categories such as Customer from South Sulawesi, Male Customer, Female Customer, Customer with Income below IDR. 2.000.000, Customer with Income between IDR. 2.000.000 -IDR. 4.000.000, and Customer with Income more than IDR. 4.000.000 did not experience any significant change neither in the frequency nor the size of the tip (  Source: Primary data analyzed, 2020  Table 3, 20.5% of customers were identified to give tip more frequent before the COVID-19 outbreak while 14.7% of customers gave tip more often during the outbreak and 64.7% of customers gave tip in the same frequency before and during the outbreak. In addition, 17.6% of customers gave bigger tip before the outbreak while 11.7% of customers gave Analysis Of Tipping Behaviour Towards Online…. (Husein,Warmana,(556)(557)(558)(559)(560)(561)(562)(563)(564)(565)(566)(567)(568)(569)(570)(571) bigger tip during the outbreak and 70.5% customers gave the same amount of tip before and during the outbreak.

Service
Almsgiving   Table 4, 22.5% of customers were identified to give tip more frequent before the COVID-19 outbreak while 25% of customers gave tip more often during the outbreak and 52.5% of customers gave tip in the same frequency before and during the outbreak. In addition, 5% of customers gave bigger tip before the outbreak while 25% of customers gave bigger tip during the outbreak and 60% of customers gave the same amount of tip before and during the outbreak.  In the rank of Table 5, 26.5% of customers were identified to give tip more frequent before the COVID-19 outbreak while 16.3% of customers gave tip more often during the outbreak and 57.1% customers gave tip in the same frequency before and during the outbreak. In addition, 12.2% of customers gave bigger tip before the outbreak while 22.4% of customers gave bigger tip during the outbreak and 65.3% of customers gave the same amount of tip before and during the outbreak.  In the rank of Table 6, 12% of customers were identified to give tip more frequent before the COVID-19 outbreak while 28% of customers gave tip more often during the outbreak and 60% of customers gave tip in the same frequency before and during the outbreak. In addition, 8% of customers gave bigger tip before the outbreak while 12% of customers gave bigger tip during the outbreak and 80% of customers gave the same amount of tip before and during the outbreak.  In the rank of Table 7, 28.5% of customers were identified to give tip more frequent before the COVID-19 outbreak while 14.2 of customers gave tip more often during the outbreak and 57.1% of customers gave tip in the same frequency before and during the outbreak. In addition, 0% customers gave bigger tip before the outbreak while 57.1% of customers gave bigger tip during the outbreak and 42.8% of customers gave the same amount of tip before and during the outbreak.  In the rank of Table 8, 14.2% of customers were identified to give tip more frequent before the COVID-19 outbreak while 28.5% of customers gave tip more often during the outbreak and 57.1% of customers gave tip in the same frequency before and during the outbreak. In addition, 100% of customers gave the same amount of tip before and during the outbreak.  In the rank of Table 9, 25% of customers were identified to give tip more frequent before the COVID-19 outbreak while 25% of the other customers gave tip more often during the outbreak and 50% of customers gave tip in the same frequency before and during the outbreak. In addition, 16.7% of customers gave bigger tip before the outbreak while16.7% of other customers gave bigger tip during the outbreak and 16 customers gave the same amount of tip before and during the outbreak.  In the rank of Table 10, 19.4% of customers were identified to give tip more frequent before the COVID-19 outbreak while 16.7% of customers gave tip more often during the outbreak and 63.8% of customers gave tip in the same frequency before and during the outbreak. In addition, 11.1% of customers gave bigger tip before the outbreak while 16.7% of customers gave bigger tip during the outbreak and 72.2 customers gave the same amount of tip before and during the outbreak. From figure 1 -8, it can be seen that Almsgiving motive experience a significant increase during the COVID-19 outbreak and there was a Health Protocol motive identified during the outbreak. Meanwhile, Service Quality and Change Leftover motive experience a decrease.

Discussion
The findings from this study suggest that during the COVID-19 outbreak, customer with no income and customer from Southeast Sulawesi experience a significant change in their tipping behaviour, more specifically in the size of the tip, where most of the customers from these categories give bigger tip to the online transportation partners. Moreover, new motives in giving tips were also identified which include Almsgiving, Leftover Change and Health Protocol.
Analysis Of Tipping Behaviour Towards Online…. (Husein,Warmana,(556)(557)(558)(559)(560)(561)(562)(563)(564)(565)(566)(567)(568)(569)(570)(571) 13 Surprisingly, none of the customers with no income gave higher tip before the COVID-19 outbreak where most of them gave higher tip during the outbreak and the rest gave the same amount as before the outbreak. This behaviour is highly influenced by the motive of almsgiving since the number of almsgiving motives in this category increased significantly during the outbreak. The insignificant changes of the other variables' behaviour also suggest that the decreasing economy growth due to the COVID-19 Social and physical distancing policy does not bring any significant change to this unique behaviour eventhough Fadlan (2020) stated that COVID-19 outbreak in Indonesia has caused private consumption to contract and retail sales to fall. Another issue in the economic aspect is on the employment which has been announced by Indonesia's Minister of Finance that the number of unemployment since the COVID-19 outbreak has reached up to nine million and this may result to a great number of people living in a poverty line due to this pandemic (Gorbiano, 2020). Despite these economic issues, this study results show that the behaviour is not effected significantly by these constrains and most customers are still leaving tips considerably the same way and amount as before the COVID-19 outbreak eventhough, some customer categories show an increasing size of tip such as customer with no income and customer from Southeast Sulawesi.
The increasing motive of almsgiving during the outbreak suggests that this motive brings more influence in the customers tipping behaviour and as a result encourages the customers to keep giving tips or even to give bigger tip size during the COVID-19 outbreak. Almsgiving may be seen as an act for the customer to help balancing the life of the online transportation partners that has become severe since the COVID-19 outbreak. This idea is consistent with Victor (2011) who stated that almsgiving or charity is encouraged by all religions of the world as a way of bringing justice to society. In line with this, Azar (2005) also suggested that social norms of tipping can increase welfare by improving service quality. This trend may indicate the potential of an upcoming social norm in tipping behaviour within the online transportation industry in Indonesia in the future.

CONCLUSION
To conclude, this study found that the economic issues caused by COVID-19 do not bring many significant changes in the behaviour of customer tipping for the service of online transportation. Some customer categories do not experience any significant change neither in frequency of tipping nor in size of the tip, these categories are customer in South Sulawesi, combinedprovinces of male and female customer, customer with income less than IDR. 2.000.000, customer with income between IDR. 2.000.000 -IDR. 4.000.000, customer with income more than IDR. 4.000.000. However, two customer categories are found to experience a significant change in tipping behaviour. Overall customer with no income and customer in Southeast Sulawesi experience a significant increase in the tip size. Moreover, new motives behind the customer tipping behaviour are found and these motives experience a significant change before and during the outbreak; almsgiving, change leftover and health protocol. Ultimately, the almsgiving motive experiences a significant increase during the outbreak.
The findings of this study provide potential implication for business executives and managers of online transportation companies by providing several information about their customers' tipping behaviour as a consideration and direction in identifying the best policy for the companies. Related to the study result, this study suggests for future study to examine more thoroughly the customers who experience significant changes in the behaviour using Regression Analysis.