Exploring Nudges of Tipping Behavior in Online Services: A Cross-cultural Investigation

About this project

Project description

Tipping is a pervasive phenomenon and amounts to around an estimated $46 billion a year across foodservice workers in the United States (Azar, 2011). Tipping usually forms a route for customers to reward or punish service employees (Kwortnik, Lynn, and Ross, 2009). Traditionally, a tip was a discretionary amount paid by customers upon completion of service such as in table service restaurants (Becker, Bradley, and Zantow, 2012). For example, although tipping is optional, it has importance as a social norm with consumers paying tips to servers to compensate for guilt (Lin, 2007).
With the advent of online services, however, businesses have evolved to using mobile applications and technology to prompt customers to tip. For example, ride-sharing app like Ola in India and food delivery app like Uber Eats now prompt customers and request tips after the ordering process. Of note, is a pattern of requesting for a default percentage of 10%, 15% or a customizable amount. Recent research with data from a laundry pick-up, cleaning, and delivery service in the United States shows that larger suggestions in tip sizes increases amount of tips, but does not affect customer satisfaction or spending as such (Alexander et al., 2021). However, the insensitivity to tipping costs is not consistent across studies. For instance, recent studies on data from NYC taxicab rides (Haggag & Paci, 2014) and even manipulating default tipping options on the Uber app (Chander et al., 2019) showed that increasing default tipping options reduces likelihood of tipping but elicits higher amount from those who did tip. Notably, since attitudes to tipping is not part of cultural expectations varies throughout countries such as Australia and India, it is an interesting prospect to investigate in such a context where tipping is not mandatory but customary.
Thus, we focus on the managerially relevant service-related concept – tip format, and attempt to understand the following questions:
1. How do customers evaluate tipping format presented by service providers?
2. What factors do or do not influence automated tipping in an online context? In other words, what psychological aspects motivate consumers to tip in exchange for online services, where they are not bound by feelings of social guilt, unfairness, and embarrassment?
3. What other nudges (such as probability of tipping percentage, message framing, etc.) should be considered by online services to motivate tipping?
This research aims to contribute to the literature in three important ways. First, we will examine tip format (absolute value vs. percentage) as a variable to consider for online service providers. This is an important aspect to consider for service providers and findings from this research will inform current understanding not only in the online context, but also for traditional service settings. Second, we answer recent calls for research to integrate technologies in frontline services (MSI, 2018), and hope that this research will provide insights to understand what factors prompt customers to reward service.
Third, we aim to uncover psychological determinants that motivate tipping behaviour to assess what can help or hurt tipping motivations. In doing so, we aim to extend prior research on persuasion knowledge (Campbell and Kirmani, 2000), and shed light on what factors can influence tipping across cultures. In sum, we hope to investigate how tip requests can be more effective in online contexts, so that service providers adopting automated tipping technology can do so mindfully.
One of the major repercussions of the pandemic has been to trigger a global job crisis whereby an increasing number of people are taking up on-demand work. For instance, India’s online food delivery sector’s rapid growth has created a $8 billion market in 2022 (Boston Consulting Group, 2020). However, platform-based food delivery workers continue to work in economically unstable situations with low wages and benefits (Parwez, 2022). In such a situation, customers support to workers in the hospitality industry can help to ensure that the economic crisis does not turn into a social crisis. One way to do so is for consumers to engage in tipping behaviour to support the meagre incomes of hospitality workers (Nguyen, 2021). Though literature suggests linkages between quality of service (Furnham, 2021), appearance (Basnyat et al., 2021) and behaviour of the service provider (Hsiao et al., 2022) and tipping, tipping behaviour largely remains subjective and context dependant. For example, the customers’ tipping behaviour in a dine out context on receipt of face-to-face service might be different from that in in-contact (home delivery) or no-contact (leave at the door) food delivery services by technology enabled platforms (Swiggy, Zomato, UberEats etc.). However, the global pandemics have made it an emergent phenomenon which remains understudied and requires exploratory, survey and experimental studies to uncover the promises.

Alexander, Damon, Christopher Boone, and Michael Lynn (2021), “The Effects of Tip Recommendations on Customer Tipping, Satisfaction, Repatronage, and Spending,” Management Science, 67 (1), 146-165.
Azar, Ofer (2011), “Business Strategy and the Social Norm of Tipping,” Journal of Economic Psychology, 32(3), 515-525.
Basnyat, S., Che, I. T., & Ip, K. H. (2021). Gender roles and the commodification of beauty and physical attractiveness in restaurants: Perspectives of female servers. Tourism and Hospitality Research, 21(4), 447-460.
Becker, Cherylynn, Gregory T. Bradley, and Ken Zantow (2012), “The Underlying Dimensions of Tipping Behavior: An Exploration, Confirmation, and Predictive Model,” International Journal of Hospitality Management, 31, 247-256.
Boston Consulting Group (2020), “Demystifying the Online Food Consumer,” Available at https://web-assets.bcg.com/img-src/Demystifying-the-Online-Food-Consumer_tcm9-238295.pdf
Campbell, Margaret C., and Amna Kirmani (2000), “Consumers’ Use of Persuasion Knowledge: The Effects of Accessibility and Cognitive Capacity on Perceptions of an Influence Agent,” Journal of Consumer Research, 27 (1), 69–83.
Chandar, Bharath, Uri Gneezy, John A. List, Ian Muir (2019), “The Drivers of Social Preferences: Evidence from a Nationwide Tipping Field Experiment. NBER Working Paper 26380, National Bureau of Economic Research, Cambridge, MA.

Haggag, Kareem and Giovanni Paci (2014), “Default Tips,” American Economic Journal: Applied Economics, 6(3), 1-19.
Furnham, A. (2021). Attitudes to Tipping. Psychology, 12(5), 805-816.
Kwortnik Jr., Robert J., Michael W. Lynn, and William T. Ross Jr. (2009), “Buyer Monitoring: A Means to Insure Personalized Service,” Journal of Marketing Research, 46(5), 573–583.
Hsiao, C. H., Chien, C. H., Yeh, S. S., & Huan, T. C. (2022). Smiling for tips? Will restaurant servers’ actions affect customers’ emotional contagion and tipping behavior?. Tourism Review, (ahead-of-print).
Lin, Tin-Chun (2007), “Economic Behavior of Restaurant Tipping,” Economics Bulletin, 4(2), 1-10.
MSI (2018), “2018-2020 Research Priorities,” Marketing Science Institute. Available at https://www.msi.orghttps://www.msi.org/research/2018-2020-research-priorities.
Parwez, Sazzad (2022). COVID-19 pandemic and work precarity at digital food platforms: A delivery worker’s perspective. Social Sciences & Humanities Open, 5(1), 100259.
Nguyen T. (2021, May 21). The pandemic should permanently change how we tip. https://www.vox.com/the-goods/22446361/pandemic-gratuity-covid-service-work


● Presentation of research outputs in academic conferences.
● Scientific articles in high impact journals in the field of marketing and psychology.
● Provide insights into tipping behavior that can influence government and policymakers.
● The findings from this study will be documented and published in international and national reputed journals.

Information for applicants

Essential capabilities

Expertise in Qualitative and Quantitative research methods, SPSS, AMOS, proficiency in research writing

Desireable capabilities

Expertise in market research method and data analytics desirable.

Expected qualifications (Course/Degrees etc.)

B.Tech, MBA, M.Phil, M.Tech, M. Sc., M.A., Bachelor’s degree (honours) with a first class or Masters degree by research (with a formal thesis).

Additional information for applicants

note: i-students must have own scholarship to apply (CSIR, UCG-NET, etc)

Project supervisors

Principal supervisors

UQ Supervisor

Dr Srinwanti H. Chaudhury

UQ Business School
IITD Supervisor

Assistant Professor Biswajita Parida

Department of Management Studies