Feeling Manipulated: How Tip Request Sequence Impacts Customers and Service Providers?

Abstract

Technology is changing frontline service scripts. Businesses are now using mobile point-of-sale applications (e.g., Square) and mobile technology (e.g., iPad) to prompt customers for tips. Tip requests are occurring more frequently at the start of service transactions, before any service has been provided. This research examines how requesting a tip either before or after service completion affects customers and service providers. We test the effects of preservice versus postservice tip sequence in four studies (a natural experiment in the field and three controlled experiments) across food and beauty service contexts. Findings reveal that requesting a tip before (vs. after) completing a service leads to smaller tips, reduced return intentions, diminished word-of-mouth intentions, and lower online ratings. Inferred manipulative intent is revealed as the psychological mechanism underlying the harmful effects of requesting a tip before service. Findings suggest that emphasizing the benefits of automated point-of-sale systems can reduce, but not eliminate, the negative effects of preservice tip requests. Contrary to norms within the service industry, we find that service providers should avoid requesting tips before serving customers.

Keywords

tipping tip sequence service script manipulativeness

Tipped service scripts are being reimagined. Traditionally, customers have been prompted for a tip after a service is completed, such as a tip request via the bill in table service restaurants (Becker, Bradley, and Zantow 2012). New automated technologies (e.g., iPads, tipping apps, online ordering) are changing the sequence of tip requests. With increasing frequency, firms prompt customers to provide a tip at the start of the service encounter, before any service has been performed. For example, online delivery orders by both Jimmy John’s sandwiches and Papa John’s pizza now request tips as part of the ordering and payment process, before the food is made and delivered.

Press accounts indicate that customers have mixed reviews of these changes to tipping scripts. The Today Show recently asked, “Has ‘guilt tipping’ gone too far?” The segment described the proliferation of technology-driven tip requests into business sectors that have not traditionally involved tips, including quick service restaurants and retail shops (Kim 2018). New point-of-sale technologies prompt customers to tip employees who perform simple tasks that were not historically tipped, such as handing a customer a premade muffin from behind a counter (Levitz 2018). Often, these tip requests occur before the service provider has performed any service, forcing customers into a dilemma:

After swiping your credit or debit card, do you agree to a 10, 15, or 20 percent tip for something you have yet to receive—or do you hit the “no tip” button and brace yourself for inferior service from an insulted cashier? (Kim 2018)

The popular press also indicates that customers may have negative impressions of preservice tip requests, suggesting that they evaluate the practice as an unjust instance of persuasion (Campbell 1995; Friestad and Wright 1994). While anecdotal evidence highlights the negative aspects of changes to tipping, it also describes positive changes enabled by technology-facilitated tip requests. The proliferation of technology-facilitated tipping is praised for its convenience and efficiency, while it also allows customers to be more generous and supportive of local service providers (Kim 2018; Levitz 2018). In sum, there is little clarity into how changes to the sequence of tip requests may help or hurt firms or how managers can best integrate new technology into service interactions.

Despite the call for research on changing frontline services and the customer-technology interface (MSI 2018; Ostrom et al. 2015; Singh et al. 2017), and the increasing importance of emerging technologies in service interactions (Blut, Wang, and Schoefer 2016), no research has examined how the sequence of a tip request (i.e., before or after the service) impacts customers and service providers. Prior research on tipped services has largely assumed a postservice tip sequence (Azar 2007; Becker, Bradley, and Zantow 2012; Lynn and McCall 2016; Seiter, Givens, and Weger 2016). As technology leads service providers to adopt new tipping sequences, understanding how tip sequence affects the highly interpersonal and interactive relationships between customers and employees in tipped services is critical for service providers (Gremler and Gwinner 2000).

In this article, we focus on a theoretically understudied and managerially relevant service-related concept—tip sequence—and aim to answer three questions:

How do customers evaluate service providers that request tips before (vs. after) providing a service and do their evaluations impact tip amounts?

What consumer processes explain the influence of tip sequence on service provider outcomes?

What factors should service providers consider as they integrate new tipping technologies into service scripts?

Our research contributes to the literature in four important ways. First, we introduce tip sequence (i.e., pre- vs. postservice) as an important variable for service providers to consider. We examine how tip sequence impacts direct financial outcomes (e.g., actual and intended tip amounts) as well as broader behavioral measures of customer engagement (i.e., “behavioral manifestations toward the brand or firm, beyond purchase”; van Doorn et al. 2010, p. 253), including online ratings as well as customers’ return and word-of-mouth (WOM) intentions (Brodie et al. 2011; Kumar et al. 2010). While our studies focus on services that have adopted automated point-of-sale systems, the sequence of the tip request also has important implications in traditional service settings, where a paper receipt may be used to prompt a customer for a tip either before or after service.

Second, by focusing our studies on contexts where emerging technologies are used to automate tip requests, we add new complexity to the domain of service automation and technology-facilitated service interactions (Giebelhausen et al. 2014; Larivière et al. 2017; Parasuraman 2000), which has generally revealed positive outcomes (Collier and Kimes 2013; Collier and Sherrell 2010; Meuter et al. 2000; van Beuningen et al. 2009). Our exploration of tip sequence echoes the limited work demonstrating that technology-facilitated services can hurt service providers, particularly when the technology is unfamiliar or feels forced (Dabholkar and Bagozzi 2002; Reinders, Dabholkar, and Frambach 2008). In doing so, we shed new light on the difficulties of incorporating new technology within the service space.

Third, we examine the psychological process that underlies the effects of tip sequence, adding theoretical depth to the multidimensional nature of tipping motivations (Azar 2007; Becker, Bradley, and Zantow 2012; Lynn 2006b). Our research extends prior research on persuasion knowledge, specifically consumer inferences of manipulative intent (Campbell 1995; Campbell and Kirmani 2000; Friestad and Wright 1994). We bring enhanced understanding of inferences of manipulative intent into the domain of tipped services, where such inferences and evaluations have been largely overlooked. Further, our studies discover that consumer inferences of manipulative intent may be inadvertently redirected toward service providers even when the manipulative intervention (e.g., tip sequence) is created by a third party (e.g., the technology partner). Finally, for service providers adopting automated tipping technology, we investigate the managerially relevant moderating effect of justifying the tip request by emphasizing the benefits of automated point-of-sale systems as a way to attenuate the harmful effects of a preservice tip sequence.

In the following section, we begin by reviewing existent literature on tipped services. We then describe an exploratory qualitative study, which guides our subsequent review of services, hospitality, and persuasion knowledge literature. Next, we hypothesize the effects of tip sequence and the psychological mechanism explaining customer responses to tip sequence (i.e., inferred manipulative intent). We report one naturally occurring field experiment and three experimental studies to test our hypotheses. To close, we discuss the theoretical and managerial implications of this work and propose several promising avenues for future research.

Theoretical Development

To Insure Promptitude

Tipping originated in British pubs during the 16th century. Patrons could choose to tip service workers in advance “To Insure Promptitude” (i.e., tip) and to generally incentivize service quality (Azar 2004). Over time, tipping norms changed so that tipping before service became rare, with hotel concierge services still a major exception.

There are many reasons why customers may decide to tip, including heuristics based on social norms, a desire to impress others or control the quality of service, and feelings of reciprocal reward, social obligation, and generosity (e.g., Azar 2007; Becker, Bradley, and Zantow 2012; Lynn 2006b). Prior research on tipped service work has primarily focused on increasing revenue and identifying customer characteristics predictive of revenue. Firms can increase tip revenue by manipulating the service environment, for example, by playing prosocial music or using gold colored tablecloths or bill folders (Jacob, Guéguen, and Boulbry 2010; Lee, Noble, and Biswas 2016). Waitstaff seeking to subtly influence customers into providing higher tips also engage in a variety of tactics, from wearing more makeup (for waitresses) to forecasting good weather to writing patriotic messages on the check (Jacob et al. 2010; Lynn 2011). A small number of studies have explored employees’ purposeful use of emotionally manipulative tactics, or “venture emotionalism,” to elicit higher tips through power dynamics and feigned intimacy, especially in the sex work industry (Deshotels and Forsyth 2006; Thompson 2015). A number of individual customer differences, including race, gender, and nation of origin, play significant roles in customers’ tipping decisions (Azar 2007; Lynn, Zinkhan, and Harris 1993). For example, customers who are more educated, wealthier, middle-aged, urban dwelling, or living in the Northeast of the United States have been shown to leave higher tips (Lynn 2006a).

Still, customers’ reactions to and evaluations of different tip-elicitation strategies, particularly the sequence of the tip request, remain unexplored in the marketing and services literature. As such, prior to our theorizing, we sought insights qualitative consumer surveys in developing our hypotheses, turning to phenomena to construct exploratory theory (Haig 2005).

Exploratory Study of Preservice Tipping

To gain a preliminary understanding of consumers’ evaluations of pre- versus postservice tip sequences, we conducted an online survey (Amazon Mechanical Turk, N = 113) in which respondents were asked to respond in writing to an online pizza delivery scenario. As food delivery services move to online platforms, service providers have adopted a wide range of fee and tipping formats, notoriously exemplified by the 2019 GrubHub scandal (Glaser 2019). In the study, participants read that they were prompted for a tip either while they were placing the order (i.e., preservice tipping) or after the delivery person arrived with the pizza (i.e., postservice tipping).

Results of this exploratory study indicated that preservice tip requests are negatively evaluated by consumers, who qualitatively reported selecting lower tip amounts and feeling forced to tip. One respondent’s comment was echoed by many others:

I would not appreciate being asked to tip before I had received the service. I would err on the side of a lower tip just in case service was bad if I was forced to select my tip before delivery.

Another participant expressed frustration with preservice tip sequences: “I MIGHT GIVE MORE IF THEY WAITED UNTIL THE PIZZA CAME.” Elaborating further on how preservice tip sequences may place customers in an uncomfortable bind where they feel obligated to tip, one respondent stated, “I’m a little weary of tipping before I have received services, but I would be afraid that if I didn’t tip beforehand that I wouldn’t get very good service either.” More directly addressing the emotional reactions to preservice tip sequences, another said, “I hate tipping before I have received any service!”

Unsurprisingly, customer’s disapproval of preservice tip requests may lead customers to bring their business elsewhere, as one participant wrote: “I don’t like the pizza store’s policy of tipping before the pizza is delivered because then I don’t have any control over the service. It is unlikely that I would go to this pizza place again.” Combined with the press accounts discussed earlier, this exploratory survey indicates that preservice tip sequences may upset some customers, leading them to tip less and to patronize other businesses.

In the following sections, we review services, hospitality, and sales literature to build off our initial qualitative findings and develop the hypothesis that tip sequence affects consumer engagement and financial outcomes for firms. Specifically, we suggest that preservice tip requests result in inferences of manipulative intent, which negatively impact tip amounts, online ratings of the service provider, and customer intentions (i.e., WOM and return intentions). Further, we hypothesize that justifying the tip request by emphasizing the benefits of automation attenuates the negative effects of preservice tip sequence on service provider outcomes. See Figure 1 for a visual representation of the conceptual framework we propose.

Figure 1.

Conceptual framework.

The Main Effect of Tip Sequence

Do customers respond differently to tip requests that occur at the beginning or the end of a service interaction? Prior research has demonstrated that postservice tipping provides customers with increased feelings of fairness, generosity, and freedom, while also reducing feelings of guilt (Azar 2007; Greenberg 2014). On the other hand, preservice tipping may preemptively incentivize good service (Brenner 2001; Star 1988). However, existing studies have not directly compared different tip sequences, but rather compared particular tipping schemes to no tipping. As such, the suggested effects are best attributed to the mere existence of tips, rather than the specific sequence.

Research on tipping has primarily focused on and assumed that service providers use a postservice tip sequence, commonly referred to as a gratuity (Lynn 2006b). Compared to nontipped services, mandatory tipping, and other involuntary service charges, postservice tipping has been connected with improved service quality and higher ratings of service providers (Azar 2007; Kwortnik, Lynn, and Ross 2009; Lynn and Kwortnik 2015). In postservice tip settings, service failures that lack adequate service recovery result in decreased tips, indicating that customers use postservice tips as a means to address service quality failures (Roschk and Gelbrich 2017). In sum, tipping after service allows customers the opportunity to reward good service or punish bad service.

On the other hand, tipping before service is linked with mixed outcomes and little to no empirical work. For example, it has been suggested that tipping before service may increase employee opportunism, as employees who are tipped before service receive the same tip regardless of the quality of service they provide (Azar 2002). Collectively, research on the positive outcomes of postservice tipping, press accounts, and our exploratory study suggest generally negative evaluations of preservice tip requests. We propose that these negative evaluations of tip requests before service will negatively impact firms’ direct financial outcomes and broader customer engagement outcomes (Kumar et al. 2010). Formally stated:

Hypothesis 1: Compared to a postservice tip request, a preservice tip request will lead to (a) smaller actual and intended tip amounts, (b) lower customer intentions, and (c) lower online ratings.

The Mediating Effect of Inferred Manipulative Intent

Why does requesting tips before service lead customers to tip less and negatively evaluate service providers? We suggest that preservice tip requests lead customers to infer that service providers have manipulative intentions. To develop this hypothesis, we turn to the literature on persuasion and inferred manipulative intent.

Friestad and Wright’s (1994) persuasion knowledge model (PKM) argued that interpreting and coping with marketers’ sales tactics is an essential aspect of being a consumer. The PKM demonstrates that consumers develop context-dependent beliefs about the fairness and manipulativeness of persuasion attempts (Friestad and Wright 1994). Extending from the PKM is Campbell’s (1995) work on inferences of manipulative intent, a measure that involves varying levels of four key subcomponents: acceptability, appropriateness, fairness, and manipulativeness. To understand why customers respond negatively to preservice tip sequences, we examine tip sequence through these subcomponents.

First, we suggest that a postservice tip sequence is the norm that customers expect, especially in the United States (Azar 2007; Lynn 2006b). Postservice tipping is generally considered acceptable and appropriate by customers (Lynn 2006b). Thus, compared to postservice tipping, we suggest that preservice tipping violates tipping norms and induces inferences of manipulative intent, as this tipping sequence is less acceptable and appropriate.

Second, customers believe that the traditional model of tipping (i.e., postservice tip requests) facilitates customer control and therefore is considered fair (Azar 2005; Lynn and Wang 2013). While hospitality research suggests that preservice tip requests shift control over the service interaction from customers to service workers (Azar 2002), this is not empirically explored. Extending this finding, we propose that removing the customers’ ability to tip after service may be evaluated as unfair, which is a key component of inferred manipulative intent.

Most importantly, as discussed earlier, research in the services and hospitality literature has shown that actions by employees or changes to the service environment may be evaluated as manipulative by customers (Lunardo and Mbengue 2013). For example, customers who consider the atmosphere of a retail setting incongruent, such as bakeries that smell of freshly baked bread but where no oven or baker is present, will evaluate service providers as more manipulative (Lunardo and Mbengue 2013). Prior research in frontline service settings provides insight into situations in which persuasion attempts before a sale may be manipulative (Campbell and Kirmani 2000; Isaac and Grayson 2017; Main, Dahl, and Darke 2007). For example, Campbell and Kirmani (2000) found that when a salesperson complimented a customer before the purchase (e.g., while trying on an expensive coat), she was rated as more manipulative and less sincere than a salesperson who complimented a customer after the purchase is completed. Connecting these findings to the context of tipping, we hypothesize that customers infer greater manipulative intent when service providers request tips before, rather than after, a service is completed. Following from the relationship between tip sequence and manipulativeness, we propose that such manipulativeness perceptions directly impact outcomes important to the firm.

Customer inferences of firms’ manipulative intentions have been connected to negative firm outcomes in a wide variety of contexts, especially in the advertising (Campbell 1995) and sales (Campbell and Kirmani 2000) domains. It follows that customers who evaluate a tip request as manipulative will negatively evaluate the service provider who is requesting the tip. Service research has demonstrated that negative evaluations of service providers, including inferences of manipulative intent, lead to negative service provider outcomes (Han, Kwortnik, and Wang 2008; Schoefer and Diamantopoulos 2008). The negative outcomes of customer inferences of manipulative intent may include direct financial impacts on service providers in the form of tip amounts (Bodvarsson and Gibson 1999) or broader impacts on measures of customer engagement, including return intentions, WOM intentions, and online ratings of the firm (van Doorn et al. 2010). In sum, we propose that the effects of tip sequence on service providers’ financial outcomes and customer engagement will be mediated by inferences of manipulative intent. Formally stated:

Hypothesis 2: The negative impact of preservice tip requests is mediated by consumer’s inferences of manipulative intent.

The Moderating Effect of Justification for Automation

Inferences of manipulative intent depend on the assumptions that customers make about service provider motives. Consumers may be skeptical of service providers that have firm-serving motives (Campbell and Kirmani 2000), such as a desire to collect larger tips, but this skepticism may be discounted by beliefs that the service provider also has customer-serving motives (Kelley 1987), such as providing customers with a more convenient service encounter. Justifying a firm behavior by stating a customer-serving motive, such as improved customer convenience, may reduce customer inferences of self-serving motives (Kelley 1987). As long as consumers do not evaluate the service provider as trying to deceive consumers by masking firm-serving motives behind customer-serving motives, customers generally have positive attitudes toward such motives (Forehand and Grier 2003).

More relevant to technology-facilitated service interactions, service providers are evaluated negatively when customers believe price increases are due to profit-seeking motivations rather than due to increased costs, such as the cost of new technology (Campbell 2007). Since customers consider new point-of-sale technologies efficient and convenient (Bean and Wallendorf 2017), and customers generally prefer convenient technologies (Collier and Kimes 2013; Collier and Sherrell 2010), we suggest that service providers who justify their adoption of point-of-sale technologies for tip requests and who emphasize the customer-serving benefits of these technologies may attenuate the negative effects of preservice tip requests.

Specifically, we hypothesize that providing a justification attenuates the negative effect of preservice tip requests on firm outcomes, as customers will discount the service provider’s firm-serving motives and inferences of manipulative intent. Thus, justification will moderate the indirect effects of tip sequence on firm outcomes, which are mediated by inferences of manipulative intent. Formally stated:

Hypothesis 3: Providing a justification for tip automation moderates the effects of tip request sequence, such that the presence of a justification reduces the inferred manipulative intent of preservice tip requests and, thereby, attenuates the effects on (a) actual and intended tip amounts, (b) customer intentions, and (c) online ratings.

Study Overview

To test our hypotheses, we conducted four studies: one natural experiment in the field and three scenario-based experimental studies across food and beauty service contexts. In Study 1, we tested the effect of tip sequence on tip amounts using actual customer data (Hypothesis 1). Studies 2a and b tested the psychological mechanism mediating the effect of tip sequence on intentions—inferred manipulative intent (Hypothesis 2). Study 2a compared inferred manipulative intent to possible alternative mediation explanations in a quick service restaurant context. Study 2b extended Study 2a by including a broader measure of inferred manipulative intent and testing the effect of sequence in a haircutting context. Finally, Study 3 tested the full conceptual model outlined in Figure 1 by measuring two additional outcome variables, online rating and intended tip amount,¹ both of which have significant consequences for service providers. Study 3 also tested whether providing a justification for service automation moderates the effect of tip sequence on inferred manipulative intent and service provider outcomes (Hypothesis 3).

Study 1—Main Effect of Tip Sequence

Design and Procedure

The setting for Study 1 involved partnering with a local business in the Eastern United States to conduct a natural experiment that tested the impact of pre- versus postservice tip sequence on actual tip amounts. The local business—a fresh-made juice and smoothie shop—maintains two locations with different tip sequences. One location utilizes a preservice tip request sequence, such that the tip request occurs after the customer orders their juice or smoothie, but before receiving it. The other location utilizes a postservice tip request sequence, such that the tip request occurs after the customer is served their juice or smoothie. Both locations are owned and managed by the same entrepreneur. As such, they have identical menus, identical service provider training, and identical expectations for service providers.

Our central aim was to determine how tip sequence impacted tip amounts. As such, we sourced tip data from the local business via its point-of-sale software device (i.e., debit/credit card transactions, not cash). We also gathered transaction totals in an effort to control for the total amount spent by the customer in comparing tip totals. The data we were able to obtain spanned 35 days, a limitation we address in the discussion for this study. The data analyzed in this natural experiment involved a total of 7,523 transactions, with 4,704 from the location utilizing a preservice tip sequence and 2,819 from the location utilizing a postservice tip sequence.

Results and Discussion

Tip Amount

An independent samples t test revealed that average tip amounts were less at the preservice tip location, compared to the postservice location where the tip request was made after service was provided, M _Pre = US$0.90 versus M _Post = US$1.58, t(7,521) = −15.97, p < .001. Additionally, a χ² test of difference showed that customers at the preservice tip sequence location were more likely to leave a tip of US$0 than customers at the postservice tip sequence location, 31.9% versus 13.5%, χ²(1) = 155.94, p < .001. The differences in transaction totals at the two locations were not significantly different, M _Pre = US$15.05 versus M _Post = US$15.98, t(7,521) = −1.55, p > .1, suggesting that the greater tip mean at the postservice tip location was not due to greater overall transactions totals.

Discussion

Using actual customer tip amounts, Study 1 provides initial support for Hypothesis 1. Specifically, Study 1 found that customers tipped less when they were prompted for a tip before (vs. after) service. Certainly, a natural experiment such as the one conducted here, and field data in general, experience shortcomings from a number of uncontrollable factors that prevent causal inferences from being made, including the availability of data and differences between the locations beyond tip sequence (e.g., staff friendliness, service visibility, and customer loyalty). As such, we take the findings from Study 1 as illustrative evidence, which will be causally investigated in the following controlled laboratory experiments. We also conducted a follow-up study with a randomized pre- versus postservice tip request experimental design (see Web Appendix), which similarly demonstrates that preservice tip requests in a food delivery context have detrimental impacts on customers’ WOM and return intentions.

In the next study, we extend our inquiry to a new context—a quick service food counter—and also clarify via controlled stimuli where the tip request is coming from (i.e., the online system, the service provider, or the employee) in an effort to strengthen our contribution.

Study 2a—Underlying Impact of Inferred Manipulative Intent in Quick Service Food Context

Study 2a examines multiple psychological constructs that could explain the negative effects of preservice tip requests, including the hypothesized mediator of inferred manipulative intent (i.e., manipulativeness). We also test four alternative psychological constructs that might explain how changing the sequence of a tip request affects customers and service providers: fear of negative evaluation (FNE), impression management, regulatory focus, and surprise. Due to the social nature of tipped service encounters (Azar 2007), we attempted to rule out FNE (Leary 1983) and impression management motivations (Grayson and Shulman 2000) as alternative explanations for the negative impact of preservice tip sequences. In addition, changing the timing of the tip request could also impact regulatory focus by changing whether customers focus on preventing bad service or promoting good service (Higgins 1998; Lynn 2016), and as such, we measured promotion–prevention focus. Postservice tip sequences allow customers to reward a server for services rendered, but rewarding for completed service is not possible with preservice tip requests. We reasoned that a preservice tip sequence could change the focus of the customer to a promotion focus, where the tip is used to incentivize good service, similar to tipping a hotel concierge. Alternatively, preservice tip sequencing could raise prevention-based fears in customers, who may worry that insufficient tip amounts would lead to reduced service quality. Finally, it is possible that customers would be surprised, for better or worse (Lindgreen and Vanhamme 2003), by a novel tip request that occurs before service (Bean and Wallendorf 2017).

Design and Procedure

Study 2a followed a scenario-based, two-condition (tip sequence: pre- vs. postservice) between-subjects experimental design. The stimuli used are included in Appendix A. Participants read a scenario describing a service interaction in which they were a customer. Prior research has found that participants find scenario-based studies believable (Bitner 1990) and that they are useful in examining consumer responses to changing service scripts (Roschk and Gelbrich 2017).

The scenario described ordering a drink and a sandwich at a counter service café. To manipulate tip sequence, we used a presentation order manipulation (Wagner, Lutz, and Weitz 2009). Specifically, participants in the preservice tip condition read that they were asked for payment and a tip before reading that the employee prepared the order. Participants in the postservice tip condition read about the payment and tip request after reading that the employee prepared the order.

To control for effects of imagined repeat service interactions, all participants were told that the café was a business that they “go to a few times each week.” To control for potential inferences about service quality and price (Cho 2014), the study minimized and standardized information regarding the quality of the service and identifying information about the service provider. For example, all participants were told that the drink and sandwich total was the same and that the employee took 2 min to prepare the drink and sandwich. To increase realism while controlling for service quality, visuals of the café and the iPad were void of any humans.

Following the scenario and tip sequence manipulation, participants completed a short survey. Unless otherwise noted, items were collected using Likert-type scales from 1 = strongly disagree to 7 = strongly agree. Similar to Meuter et al.’s (2000) construct of “future behaviors,” the measure of customer intentions is composed of WOM intentions and return intentions, combined as one composite measure of intentions (α = .91). The measure of WOM intentions consisted of 2 items (e.g., “I’m willing to say positive things about the café to others”) adapted from Zeithaml, Berry, and Parasuraman (1996). Return intentions were measured as a single item (“I would continue to do business with this café in the next few weeks”) adapted from Kukar-Kinney, Xia, and Monroe (2007). See Appendix B for details on all constructs, measures, and their sources.

Next, participants rated inferences of manipulative intent (i.e., manipulativeness). To measure customers’ evaluations of service provider manipulativeness, we used a single-item measure (“The café is manipulative”), which was similar to a measure of manipulativeness used in prior research (Campbell and Kirmani 2000; Isaac and Grayson 2017). To test the proposed mechanism against alternatives, manipulativeness was embedded in a series of measures including the alternative psychological reasons for their evaluations: FNE (Leary 1983), impression management (Grayson and Shulman 2000), regulatory focus (Higgins 1998), and surprise (Affectiva 2018).

Following the example of Leary (1983), FNE (4 items, α = .74, e.g., “If I know someone is judging me, it has little effect on me,” 1 = not at all to 5 = extremely) was measured using a 5-point Likert-style scale. Six impression management items (α = .85, e.g., “When I decide how much to tip at the café that I go to a few times each week, I normally think about: If the employee likes me”) were averaged to create a composite measure of impression management. Regulatory focus was measured and tested as distinct promotion and prevention focus variables. Participants responded to the prompts, “When I decide how much to tip at the café that I go to a few times each week, I normally think about: promoting good service/preventing bad service.” The surprise measure (i.e., “How surprised did you feel when reading the scenario?” 1 = not at all to 5 = extremely) was adapted from biometric analytics software developer Affectiva (2018). To confirm the effectiveness of the tip sequence manipulation, we asked, “When did the employee turn the iPad toward you, so that you could select a tip amount?” Participants then selected from two options, indicating that the tip request occurred either before or after the food and drink were served.

Participants were recruited using Amazon Mechanical Turk. For this and future studies, we excluded participants from the recruitment process who had completed related studies by creating a qualification that prohibited recruitment of those who had participated in prior studies. The results below analyze 416 participants (M _Age = 37.31, 52% female) who passed the attention check and completed the survey. Participants who failed the attention check (n = 26) or who failed to complete the survey were eliminated from all analyses (Oppenheimer, Meyvis, and Davidenko 2009).

Results and Discussion

Manipulation Check

The tip sequence manipulation was confirmed, as 88% of the participants reported the correct condition, χ²(1) = 240, p < .001. Participants who failed the manipulation check were included in the data analysis for this and all subsequent studies.²

Customer Intentions

An independent samples Welch t test revealed a significant difference between the tip sequence groups on intentions. Compared to participants in the postservice tip condition (M _Post = 5.31), participants in the preservice tip condition expressed less positive WOM and return intentions, M_Pre = 4.95, t(400) = −3.4, p < .001, d = 0.33, supporting Hypothesis 1.³

Inferred Manipulative Intent

We also found a significant difference between the groups on manipulativeness. Participants who received a preservice tip request reported greater manipulativeness (M _Pre = 3.39) than participants who received a postservice tip request, M _Post = 3.04, t(410) = 2.3, p = .02, d = 0.23.

Mediation Analysis

To test whether the effect of tip sequence on intentions is mediated by manipulativeness (Hypothesis 2), we used Model 4 of the PROCESS Version 3.0 macro (Hayes 2018) with 10,000 bootstrapped samples. The indirect effect would be significant, as predicted, if the 95% confidence interval (CI) did not include zero. Analysis confirmed that the total effect of tip sequence on intentions (c = −0.36, p < .001) was significantly mediated by manipulativeness (a × b = −0.12, 95% CI [−0.23, −0.02]). In summary, customers consider requesting a tip before providing a service to be manipulative, which negatively impacts their intentions.

Alternative Explanations

To test the possible alternative mechanisms of FNE, impression management, regulatory focus (prevention and promotion), and surprise, we added these variables and manipulativeness as competing mediators to the PROCESS mediation procedure predicting intentions described above. All alternative mechanisms that were tested had nonsignificant CIs that included zero, indicating that the tested alternative mediators were not affected by tip sequence. The results of the alternative explanation mediation test are reported in Table 1.

Table 1.

Indirect Effects of Hypothesized and Alternative Mediation Explanations.

Construct	M _Post	M _Pre	b	SE	95% CI
Manipulativeness (Hypothesis 2)	3.04	3.39	−.113	.052	[−.225, −.016]
Fear of negative evaluation	2.99	2.82	.000	.011	[−.021, .024]
Impression management	3.04	3.02	−.001	.011	[−.024, .021]
Prevention focus	3.65	3.58	.002	.007	[−.011, .018]
Promotion focus	5.02	4.93	−.012	.021	[−.062, .024]
Surprise	1.94	1.95	.000	.010	[−.021, .021]

Note. Competing mediation tested using Model 4 of the Hayes (2018) SPSS PROCESS macro with 10,000 bootstrapped samples. CI = confidence interval.

Discussion

Study 2a demonstrated the robustness of the negative effect that a preservice tip sequence has on the firm, extending to a quick service food context. This study also provided initial evidence for manipulativeness as the psychological mechanism underlying negative customer responses to preservice tip requests and ruled out FNE, impression management, regulatory focus, and surprise as alternative mediators. Managerially, this study suggests that customers have more positive return intentions and WOM intentions when service providers request payment and tips after, rather than before, completing service.

The next study extends our research in two important ways. First, we attempted to increase the internal validity of our theory by incorporating a broader operationalization of manipulativeness. Second, we wanted to extend our findings into a tipped service context outside of the food industry. Press accounts have described how technology-facilitated tip requests have expanded into many new industries, including the broad personal beauty services industry (Kim 2018).

Study 2b—Underlying Impact of Inferred Manipulative Intent in Beauty Service Context

Design and Procedure

Study 2b followed the same scenario-based, two-condition (tip sequence: pre- vs. postservice) between-subjects experimental design as Study 2a but in a beauty services context. Tips for beauty services, including massages, nail services, and haircuts, have traditionally occurred after the service was completed. As beauty service providers adopt point-of-sale apps, they are also relying on these apps to request tips. In many cases, this means that tips are now requested with payment, which sometimes occurs before service.

The beauty service scenario asked participants to imagine that they were traveling (i.e., to minimize and control for loyalty effects) and decide to get a quick trim haircut (i.e., a gender-neutral scenario). The scenario described selecting a business with good online reviews that offers quick trim haircuts for US$18, for both men and women. Next, participants were asked to imagine arriving at the salon where they were greeted by an employee. The scenario included a picture of a clean, well-lit haircutting salon with no people in it. After describing the setting, the employee charged the customer US$18 for the haircut. The charge for services was followed by the tip sequence manipulation. In the preservice (postservice) tip condition, the participant is informed by the employee that they will decide on a tip amount before (after) the haircut. For the full stimuli, see Web Appendix.

Participants then answered questions evaluating the scenario. Similar to the prior studies, the intentions measure was an average of WOM and return intentions measures (α = .96). To capture a more encompassing construct of inferences of manipulative intent, including aspects of (un)fairness, (un)acceptability, and (in)appropriateness, we adapted the Campbell (1995) 6-item Inferences of Manipulative Intent Scale (α = .94) to fit the tip request scenario (e.g., “The way the tip was requested tries to manipulate customers in ways that I do not like”).

To control for the possibility that familiarity with preservice tip sequence explained the effects of tip sequence, we asked, “How normal is it for an employee to request a tip before cutting your hair?” To test for possible gender effects, at the end of the study, participants indicated their gender and the inferred gender of the service provider. The manipulation check was similar to Study 2a but modified to fit the beauty services context. The results below analyze 218 Amazon Mechanical Turk participants (M _Age = 35.39, 38% female) who passed the attention check (removed 22 responses) and completed the survey.

Results and Discussion

Manipulation Check

The tip sequence manipulation was confirmed, as 90% of the participants reported the correct condition, χ²(1) = 140, p < .001.

Customer Intentions

As in Study 2a, we found a significant difference between the tip sequence groups on intentions. Participants who received a preservice tip request reported lower intentions to spread positive WOM or to return (M _Pre = 3.65) than participants who received a postservice tip request, M _Post = 4.84, t(220) = −5.9, p < .001, d = −0.80.

Inferred Manipulative Intent

Replicating our findings from Study 2a, participants in the preservice tip condition rated the service encounter as more manipulative (M _Pre = 5.23) than participants in the postservice tip condition, M _Post = 3.43, t(200) = 9, p < .001, d = 1.35.

Control Variables

To test for possible gender effects and effects of tip sequence norms, we reran the same analyses, alternately including participant gender, inferred service provider gender, and normative tip sequence beliefs as control variables. The results for both intentions and manipulativeness remained significant (p < .001) when controlling for participant gender and inferred gender of the service provider. Further, no significant gender effects were observed. Not surprisingly, there were main effects of normative beliefs on both intentions (p = .014) and manipulativeness (p = .004), though these did not alter the significance (p < .001) nor the directionality of the effects of sequence on intentions and manipulativeness.

Mediation Analysis

Using the same mediation procedure as Study 2a, we found that the indirect effect of tip sequence on intentions was significant through manipulativeness (a × b = −1.25, 95% CI [−1.58, −0.94]).

Discussion

Studies 1, 2a, and 2b together establish the detrimental effect of preservice tip requests on both actual tip amounts and customer intentions. Study 2b further reveals inferred manipulative intent as the psychological mechanism driving the effect of tip sequence.

Study 3—The Moderating Impact of Justification

The final study extends our findings by testing whether customer inferences of manipulative intent mediates the effect of tip sequence on tip amounts (extending Study 1), and by including the managerially relevant and consequential firm outcome of online rating (e.g., Yelp review). We also explore a managerially relevant intervention in which the negative outcome of preservice tip requests may be attenuated by testing whether providing justification for the automated tip collection moderates the effects of tip sequence.

While our earlier findings indicate that requesting a tip after service is preferable, in certain service contexts, requesting a tip after service may prove disfluent and logistically challenging. For example, when a customer purchases multiple visits to a masseuse, the customer is choosing to pay for numerous service encounters at one time; as such, prompting the customer for a tip during later service encounters may interrupt the flow of service. Similarly, when customers order and pay for food at a counter, then food is handed to the customer, requesting additional payment in the form of a tip requires a second payment. Redesigning the service flow of a counter service eatery to request payment and tips after the food is prepared (i.e., postservice tipping) is possible but may be difficult for many service providers. Therefore, Study 3 tests the managerially relevant intervention of tip-request justification, a relatively easy-to-implement procedure, as a way to reduce the negative impacts of preservice tip requests on service providers.

Design and Procedure

Study 3 adopted a 2 (tip sequence: pre- vs. postservice) × 2 (justification: yes vs. no) between-subjects design. The scenario introduction and tip sequence manipulations were identical to the haircutting scenario in Study 2b. Participants were told that the employee rang them up for the haircut using a tablet and that the employee then turned the tablet toward them. To manipulate justification, half of the participants read a message from the service provider on the tablet. The justification message emphasized the convenience and speed of the automated tip collection process. Press accounts suggest that customers appreciate the speed and convenience that new tipping technologies provide (Kim 2018). See Appendix C for justification stimuli. The participants in the control condition did not see any justification for the tip request.

In addition to measuring intentions (α = .97) and manipulativeness (α = .94) using the measures from Study 2b, Study 3 included two additional consequential outcome variables: intended tip amount and online rating. To test our full theoretical model, we collected participants’ intended tipping and online rating behaviors using measures designed to replicate marketplace formats. The measure of intended tip amount was designed to mimic the tip request screen that customers are presented with by service providers who use the Square app. After reading the scenario, participants were prompted to select a tip. They were presented with four options: 15%, 20%, 25%, or custom tip amount. Participants who selected the custom option were then prompted to type in a tip amount in an open response text box. Online rating was operationalized as a single-item measure asking participants to rate the business using a five-star scale similar to the one used by the online review app, Yelp.

To address the possibility that consumers may feel a lack of control or feel forced to tip in the preservice tip condition (Becker, Bradley, and Zantow 2012; Reinders, Dabholkar, and Frambach 2008), we asked, “When the employee requested the tip, I felt that the business was trying to force me to do something” (1 = strongly disagree to 7 = strongly agree).

The tip sequence manipulation check was identical to the check used in Study 2b. To increase the generalizability of our findings, we used a different pool of online participants. The results below consider 383 Prolific (https://prolific.ac) participants (M_age = 32.04, 46% female) who passed the attention check (removed 22 responses) and completed the survey.

Results

Manipulation Check

The manipulation of tip sequence was confirmed, as 91% of the participants reported the correct condition, χ²(1) = 130, p < .001. The manipulation of the justification condition was also confirmed, such that 98% of participants correctly identified the correct condition.

Customer Intentions

A two-way factorial analysis of variance (ANOVA) on intentions revealed a marginally significant two-way interaction, F(1, 379) = 2.76, p = .098, $η_{p}^{2}$ = .007 (see Figure 2). Supporting Hypothesis 1, a significant main effect of tip sequence was revealed, M _Pre = 3.11 versus M _Post = 5.11, F(1, 379) = 143.2, p < .001, $η_{p}^{2}$ = .274, as well as a marginally significant main effect of justification, (M _{Justification} = 4.28 versus M _Control = 4.08, F(1, 379) = 2.77, p = .097, $η_{p}^{2}$ = .007. To better understand the marginally significant predicted interaction and to be in line with similar studies (e.g., Eggert, Steinhoff, and Garnefeld 2015; Schaefers et al. 2016), we analyzed the simple effects of justification. As predicted, among participants who received a preservice tip request, providing a justification led to more positive intentions, M _PreControl = 3.04 versus M _{PreJustification} = 3.55, F(1, 379) = 5.71, p = .017, $η_{p}^{2}$ = .015. However, providing a justification did not impact intentions among participants in the postservice tip condition, M _PostControl = 5.11 versus M _{PostJustification} = 5.11, F(1, 379) < 1, p = 1, $η_{p}^{2}$ = .000.

Figure 2.

Intentions as a function of tip sequence and justification.

Inferred Manipulative Intent

A two-way factorial ANOVA on manipulativeness revealed a significant two-way interaction, F(1, 379) = 4.77, p = .029, $η_{p}^{2}$ = .012 (see Figure 3), supporting Hypothesis 3. In support of Hypothesis 2, the main effect of tip sequence on manipulativeness was also significant, M _Pre = 5.24 versus M _Post = 3.36, F(1, 379) = 170.5, p < .001, $η_{p}^{2}$ = .31, while the main effect of justification was directional but not significant, M _{Justification} = 4.42 versus M _Control = 4.24, F(1, 379) = 2.66, p = .1, $η_{p}^{2}$ = .007.

Figure 3.

Manipulativeness as a function of tip sequence and justification.

Next, we analyzed the interactive effect by conducting planned contrasts within the pre- and postservice tip request conditions. When the customer benefits of the automated tip request were emphasized, participants in the preservice tip condition were less likely to report feeling manipulated than participants who did not receive the justification, M _PreControl = 5.53 versus M _{PreJustification} = 4.98, F(1, 379) = 7.53, p = .006, $η_{p}^{2}$ = .019. Manipulativeness did not differ when the tip request occurred postservice, regardless of whether or not emphasized justification for the tip request was provided, M _PostControl = 3.32 versus M _{PostJustification} = 3.40, F(1, 379) = 0.15, p = .7, $η_{p}^{2}$ = .000.

Online Rating

An analysis of the two-way interaction on participants’ review of the service provider revealed a significant main effect of tip sequence, F(1, 379) = 134.2, p < .001, $η_{p}^{2}$ = .261, and a significant main effect of justification, F(1, 379) = 6.45, p = .011, $η_{p}^{2}$ = .017, on participants’ online rating. The interaction was not significant (F < 1), indicating that providing a justification increases online ratings regardless of tip sequence. We theorize that an online rating is evaluated as a distant, holistic evaluation of a service encounter and that tip sequence only interacts with relatively close evaluations of service, such as customer intentions and tipping behaviors (Trope and Liberman 2010). As predicted by Hypothesis 1c, participants who received a preservice tip request rated the service provider on average 1.2 of the 5 stars lower than participants who received a postservice tip condition (M _Pre = 2.77 vs. M _Post = 3.99). Online ratings were also significantly greater when the firm provided a justification for the tip request (M _{Justification} = 3.47 vs. M _Control = 3.24).

Intended Tip Amount

A marginally significant two-way interaction (Tip Sequence × Justification) emerged for the measure of intended tip amount, F(1, 379) = 3.65, p = .057, $η_{p}^{2}$ = .010. Patterns follow the results for intentions. Analysis also revealed main effects of tip sequence, M _Pre = 11.4% versus M _Post = 17.1%, F(1, 379) = 69.7, p < .001, $η_{p}^{2}$ = .155, and justification, M _{Justification} = 15.2% versus M _Control = 13.0%, F(1, 379) = 12.0, p = .001, $η_{p}^{2}$ = .031.

Next, we conducted planned contrasts within the preservice and postservice tip request conditions. When a preservice tip request was presented along with a justification for the tip request, participants selected higher tips than participants who did not receive a justification for the tip request, M _{PreJustification} = 13.2% versus M _PreControl = 9.5%, F(1, 379) = 15.0, p < .001, $η_{p}^{2}$ = .038. When the tip request occurred postservice, justifying the decision to automate the tip request did not affect intended tip amounts when compared to the control condition, M _{PostJustification} = 17.6% versus M _PostControl = 16.5%, F(1, 379) = 1.18, p = .279, $η_{p}^{2}$ = .003.

Mediation Analysis

To test our full theoretical model of moderated mediation, we ran three separate analyses using the moderated mediation Model 7 of the PROCESS macro (Hayes 2018) with 10,000 bootstrapped samples. All three models use tip sequence as the predictor, justification as the moderator, and manipulativeness as the mediator. We first used intentions as the outcome variable, then repeated the same analysis with online rating and then intended tip amount as the outcome variables. Consistent with our prior results, we predicted that mediation would be significant (i.e., the 95% CI would not include zero) in the preservice tip condition. Further, we predicted that the difference between the preservice justification and the preservice control conditions (i.e., the index of moderated mediation) would be significant, indicating that justification moderates the effect of tip sequence on manipulativeness, such that the negative effects of preservice tip requests were attenuated.

In support of Hypotheses 1b and 2, the indirect effect of preservice tip requests on intentions through manipulativeness was significant in both the control (β_PreControl = −1.57, 95% CI [−1.90, −1.26]) and justification (β_{PreJustification} = −1.12, 95% CI [−1.45, −0.80]) conditions. Although preservice tip requests had a negative effect on intentions in both justification conditions (i.e., justification provided vs. no justification), justifying the tip request attenuated the negative impact of preservice tip requests on intentions, as measured by the difference between the conditional indirect effects (β_{ModeratedMediation} = 0.45, 95% CI [0.05, 0.87]). The results support the hypothesized indirect effect of tip sequence on intentions through manipulativeness and suggest that the effect of preservice tip requests on manipulativeness can be lessened, though not eliminated, by providing a justification for the tip request. The moderated mediation results with online rating and tip amount as the outcome variables followed similar patterns to the results of intentions (see Figure 4). Analysis testing lack of control as an alternative mediator did not reveal significant results and manipulativeness remained a significant mediator in each of the models.

Figure 4.

Study 3 moderated mediation analysis testing different outcome variables.

Discussion

The results of Study 3 reaffirm that the effects of tip request sequence are consequential for frontline service providers. Requesting tips prior to serving customers increases customers’ inferences that the service provider has manipulative intentions (Hypothesis 2), which creates a series of harmful downstream consequences for service providers. Preservice tip requests decrease customer’s intentions to return to the business and decrease customer’s intentions to speak positively about the service provider (Hypothesis 1b). Further, requesting tips before service leads to lower online ratings of the service provider (Hypothesis 1c) and smaller intended tips (Hypothesis 1a). For service providers who choose to request tips at the beginning of a service transaction, we find that providing a justification for the tip request may offset some, but not all, of the harmful effects of preservice tip requests (Hypothesis 3).

General Discussion

Automated point-of-sale technologies are changing the way customers and service providers interact in service settings. As functions that were typically performed by employees are shifted to technology, it is important to consider how those shifts affect the relationships between customers, employees, and firms (Larivière et al. 2017). In frontline services, the customer–employee relationship has important customer engagement consequences for firms through customer’s direct and indirect voluntary contributions to service providers (Jaakkola and Alexander 2014; Kumar et al. 2010). New technology has led to the proliferation of tipping into diverse service settings (Kim 2018; Levitz 2018). Therefore, the ways that managers integrate technology into tipped service scripts have important consequences for service providers.

Our research shows that the sequence of the tip request is an important feature of service scripts that service providers should consider. The proper implementation of tip sequence is particularly relevant as service firms adopt new technologies. Specifically, we show that requesting tips before completing service leads to negative outcomes for service providers, including declines in tip amounts and customer engagement. The effects of preservice tip requests are demonstrated in the field and the laboratory, across multiple populations and diverse service contexts. Studies that controlled for service provider variation and service quality repeatedly revealed customer inferences of manipulative intent as mediating the effects of tip sequence on service provider outcomes. However, our results also indicate that service providers who choose to request tips before serving customers can reduce negative consequences if they justify their tip requests by emphasizing the benefits of automated point-of-sale systems.

Theoretical Contributions

We contribute to the services literature by introducing tip sequence as an important variable of interest. Request sequence and request timing are understudied variables in marketing generally and in services specifically. The increase of preservice tip requests by service providers indicates that tip sequence is a variable that should be explored theoretically. Inconsistent tip sequencing across service scripts suggests that service providers are unsure how to best incorporate new technology into their service scripts. When and how to request tips is especially important as an increasing number of businesses, across diverse industries, integrate tip requests into service scripts.

While the tipping literature has focused on diverse tactics that service providers can use to elicit larger tips, very little research has explored consumer’s psychological responses to these tactics or service experiences more generally (Lemon and Verhoef 2016). Contributing to the broader literature on inferences of manipulative intent, our findings suggest that consumers find preservice tip sequencing a manipulative tip elicitation technique. To our knowledge, despite the abundance of tip elicitation tactics that service providers use, this is the first study to explore inferences of manipulative intent in tipped services.

Our findings also contribute to the literature on technology-facilitated service encounters (Parasuraman 2000). Previous findings have suggested that introducing technology has a generally positive impact on service encounters (Meuter et al. 2000; van Beuningen et al. 2009). However, automation may not be a panacea for service providers. Recent research has suggested that service automation may also lead to detrimental outcomes for customers and service providers (Anderson and Ostrom 2015; Brodie et al. 2011; Giebelhausen et al. 2014; Reinders, Dabholkar, and Frambach 2008). Our research begins to provide clarification, suggesting that in high-touch frontline service settings (Reynolds and Beatty 1999; Singh et al. 2017), customers may negatively evaluate certain technology-facilitated service interactions.

In sum, this research contributes to marketing theory by demonstrating the importance of tip request sequence in service scripts. Further, we uncover an important psychological mechanism—inferred manipulative intent—that helps to explain why pre- versus postservice tip sequences are evaluated differently. Our findings suggest the importance of further examining sequence in service scripts, automation of service scripts, and consumers’ inferences of manipulative intent in both automated service scripts and tipped services more generally.

Managerial Implications

The proliferation of point-of-sale apps, such as Square, has contributed to the expansion of tip requests into diverse domains. By default, many of these apps prompt customers for a tip as part of the service transaction. This practice has likely led to the success of point-of-sale apps and increased revenue for service providers that had not previously requested tips (Kim 2018). However, until our studies, research has not investigated the impacts of tip sequence on managerial outcomes or consumer psychological processes. As automated point-of-sale platforms, such as Square, are integrated into service scripts, understanding the effects of changing service scripts is vitally important for managers seeking to maximize profits, employee pay, and customer engagement. We find that the sequence of the tip request plays an important role in how consumers respond to the request and how consumers evaluate the service interaction. This suggests that managers should pay careful attention to the sequence of the tip request, be especially cautious when integrating new technology into service scripts, and request tips at the end of service whenever possible.

In some industries, including quick service restaurants, the speed and efficiency of the service transaction are vital to the success of service providers. In these instances, requesting a tip after service may prove cumbersome or impractical. Our findings suggest that these service providers should first consider charging customers and requesting a tip together after the service is completed. If this is not possible, our findings suggest that service providers who provide a justification for automating the tip collecting process, for example, by emphasizing the convenience benefits of automation, can reduce the harmful impact of preservice tip sequencing. Service providers may also choose to emphasize other benefits of automated tip requests, including enabling customers to support local service providers (Reich, Beck, and Price 2018). Prior research suggests that how and when service providers justify automation may further shape customer responses (Campbell, Mohr, and Verlegh 2013; Forehand and Grier 2003). Collectively, these findings may prove particularly relevant for businesses where tips cannot easily be requested after a service is completed.

In sum, we suggest that, when possible, managers request tips at the end of service encounters, regardless of payment type, tip request format, or degree of service automation. Further, we suggest that managers use automated technologies to increase efficiency and that they justify automation decisions by emphasizing the benefits of new technology.

Areas of Future Research

The diversity of service scripts and contexts where automated tip requesting has been adopted raises many questions that are outside the scope of the current research. In our operationalization, we assumed that the respondent was paying for the service and that the service was performed immediately following the service request. In some situations, such as preservice tip requests when reserving an airport shuttle online, the effects of tip sequence are unclear, especially if the customer is not paying for the service because it is a business expense. Similarly, if the service is paid for days or weeks ahead of the service, such as when customers prepay for a package of massages or beauty services, is requesting a tip before service evaluated by customers as convenient or manipulative?

This research begins to offer suggestions to service providers who choose to implement a preservice tip sequence into service scripts. However, we have only started to uncover how other aspects of service encounters, such as service transparency (Liu et al. 2015), may moderate the effect of tip sequence. How service contexts and automated service scripts interact remains largely unknown. For example, how does the visibility of the tip request affect service outcomes? If an employee walks away from the tip/payment device while the customer completes the transaction, is the tip request considered less manipulative than if the employee is present when the customer decides on the tip amount? What outcomes are affected if service providers emphasize that employees do not see how much individual customers tip? For example, a customer may feel especially manipulated if they feel that their tip choice, which may be seen by the employee, impacts the employee’s actions (e.g., they provide a smaller portion of the food order).

Our findings demonstrate consequential effects of tip sequence on service providers in general. As we are the first to explore the impact of tip sequence, we do not investigate the nuanced effects of tip sequence on different customers, employees, managers, and firms. Future research should investigate the specific impacts of tip sequence on various stakeholders. In particular, the consequences that tip sequence and automated tip elicitation may have on the interaction between customers and frontline employees remains an important question for future research. For example, press accounts indicate that tip sequence may have emotional impacts on frontline employees who consider asking customers for tips to be awkward or rude (Elejalde-Ruiz 2018; Levitz 2018). On the other hand, adopting preservice tipping as a means of removing the customer’s ability to use tips as a way to punish or reward employees (e.g., Brenner 2001) could reduce the emotional impacts, both good and bad, of working in tipped services. Similarly, the addition of a technology, and the accompanying technology firm, into the service encounter may affect who consumers believe is “in charge of” the tip request script. Do consumers respond differently if they think the technology firm or the service provider is responsible for determining tip sequence? Finally, while our findings suggest that customers are not surprised by preservice tip requests, it is possible that the detrimental effects of preservice tips could diminish as they become normalized in service scripts.

Importantly, our studies compared tip requests that occurred before versus after a service was completed and did not make any comparisons to older cash and receipt-based tip requesting techniques. Beyond suggesting that all tips, regardless of payment and request format, be collected after services have been completed, we cannot offer specific advice to managers considering the elimination of tip requests altogether or to managers who continue to rely on sequentially agnostic tip elicitation techniques, such as a tip jar. Further research should address these managerially relevant questions.

Supplemental Material

Supplemental Material, Executive_Summary_ASI-18-015_121019_v3 - Feeling Manipulated: How Tip Request Sequence Impacts Customers and Service Providers?

Supplemental Material, Executive_Summary_ASI-18-015_121019_v3 for Feeling Manipulated: How Tip Request Sequence Impacts Customers and Service Providers? by Nathan Warren, Sara Hanson and Hong Yuan in Journal of Service Research

Supplemental Material

Supplemental Material, Please_share_with_ten_academics_ASI-18-015_120819 - Feeling Manipulated: How Tip Request Sequence Impacts Customers and Service Providers?

Supplemental Material, Please_share_with_ten_academics_ASI-18-015_120819 for Feeling Manipulated: How Tip Request Sequence Impacts Customers and Service Providers? by Nathan Warren, Sara Hanson and Hong Yuan in Journal of Service Research

Footnotes

Appendix A

Appendix B

Appendix C

Acknowledgments

The authors are very grateful to the attendees of the 2018 SERVSIG Conference; Linda L. Price; and the Journal of Service Research review team, the guest editors, and the editor for their helpful and constructive guidance throughout the review process.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Nathan Warren

Supplemental Material

Supplemental material for this article is available online.

Notes

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