the effects of team-based recognition on employee
TRANSCRIPT
The Effects of Team-Based Recognition on Employee Engagement, Employee Effort, and Performance: A Field Study
Adam Presslee University of Waterloo
Greg Richins
University of New South Wales
Sasan Saiy University of Waterloo
Alan Webb
University of Waterloo
November 2020 We thank the two franchisee owners for allowing us access to their data and assisting us with designing and conducting the study. We also thank Leslie Berger, Anne Farrell, Lan Guo, Asis Martinez-Jerez, and workshop participants at Brigham Young University, Saint Maryâs University, University of New South Wales, Wilfrid Laurier University, and two anonymous reviewers for the 2020 MAS Midyear Meeting for comments on earlier drafts. We also appreciate financial support received from the CPA Ontario Centre for Performance Management Research and Education.
The Effects of Team-Based Recognition on Employee Engagement, Employee Effort, and Performance: A Field Study
Abstract Firms have long used recognition programs to engage and motivate employees. However, minimal research has tested their effectiveness in actual organizational settings. Moreover, to our knowledge no study has examined the effects of team-based recognition in settings known to have chronic problems with employee engagement such as the fast food industry. We predict that adoption of a team-based recognition program will be associated with significant improvements in employee engagement, employee effort, customer satisfaction, and restaurant-level financial performance. We test our predictions using a pre-post research design administered at six fast food franchise restaurant locations for a 12-week period. Employees at each location were eligible to receive non-monetary recognition (i.e., thank-you card, token gift) as a team every two weeks. The results support our predictions and we conduct additional analyses to rule out several plausible alternative explanations. Our findings should be of particular interest to compensation scheme designers in settings where employee motivation may be low and individual performance is costly or difficult to measure. We show that team-based recognition can be effective in such settings. In so doing, we also extend the academic literature that has primarily focused on the behavioral effects of individual-level recognition. Keywords: Effort, engagement, motivation, performance, team recognition.
1. Introduction
Non-monetary recognition (e.g., thank-you cards, token gifts) has long been used by
organizations as part of a management control system to motivate and reward employees, and recent
evidence shows widespread adoption of employee recognition programs (SHRM, 2018; WorldatWork, 2017,
2019). For example, a WorldatWork 2017 survey finds that 89% of respondents indicate their
organization uses recognition programs in some form and 67% of respondents indicate their
organization uses a team-level program. Commonly cited reasons for using recognition according to
human resource professionals include improving employee engagement and motivating employee effort
and performance (SHRM, 2018; WorldatWork, 2017). Despite the widespread use of recognition programs,
only a small number of studies have examined their impact in organizational settings and those that have
generally find recognition programs have a positive effect on behavior (e.g., Markham, Scott, & McKee,
2002; Lourenço, 2016). While this research has provided important insights, with few exceptions it has
focused on the effects of providing recognition at the individual-level based on individual performance
(e.g., sales, attendance at work, etc.).
In some settings providing individual-based recognition may be impractical because individual
performance is difficult or costly to measure. Alternatively, management may not want to recognize
individual effort when teamwork is important to the companyâs success. Accordingly, there is considerable
scope for research examining whether the positive consequences of providing individual-based recognition
generalize to programs recognizing team-based performance. Indeed, Dematteo, Eby, & Sundstrom (1998,
p. 155) claim that ânonmonetary, recognition-based rewards may be effective in team-based environments,
although there is virtually no data addressing the effectiveness of these types of rewardsâ (emphasis added).
This paucity of data still remains over 20 years later. Accordingly, we examine whether the adoption of a
team-based recognition program will be associated with positive effects on fast food restaurant employee
motivation and performance.
We examine the efficacy of team-based recognition in the fast food industry for two reasons. First,
the work performed by fast food restaurant employees is highly team-oriented (Perez & Mirabella 2013;
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Ryan, Ghazali, & Mohsin, 2011). Consistently delivering a positive customer experience requires
employees to coordinate their efforts across numerous interrelated tasks including taking orders accurately,
preparing orders quickly with minimal waste, and keeping restaurant facilities clean (Reiter, 1997). Thus,
employing team-based recognition fits well with the nature of the fast food restaurant setting. Second, in
addition to the challenges of using team-based rewards created by factors such as social loafing (e.g.,
Albanese and Van Fleet 1985), the likelihood that team-based recognition programs will be effective in our
setting is unclear. In particular, there are chronic problems with fast food employee engagement (Park &
Shaw 2013; Perez & Mirabella 2013; Peterson & Luthans 2006; Rosenbaum, 2019), which in part can be
attributed to the low skill nature of the tasks they perform and the limited opportunities (desire) for career
advancement (Perez & Mirabella 2013).1 Therefore, to the extent we do find positive behavioral
consequences arising from the use of team-based recognition in our fast food restaurant setting, we believe
this will be persuasive evidence of the efficacy of such programs.
Our first hypothesis examines the relation between adopting a team-based recognition program and
employee engagement. We focus on employee engagement because engaged employees bring a higher level
of effort and dedication to their work (Schaufeli & Bakker, 2004). Further, employee engagement has also
been linked to broader organizational outcomes such as customer satisfaction, productivity and financial
performance (Harter, Schmidt, & Hayes, 2002). Kahn (1990) offers that employees are more likely to be
engaged in the workplace when they believe their work and work-related outcomes are meaningful. We
expect that team-based recognition programs will increase employeesâ sense of meaningfulness because
the recognition is a symbol of approval and connectedness with the company and because the recognition
is a shared team outcome (Kosfeld & Neckermann, 2011). Accordingly, our first prediction is that adoption
of a team-based recognition program will be positively associated with improved employee engagement.
Prior research has documented a positive association between employee engagement and various
effort-related performance dimensions (e.g., Christian, Garza, & Slaughter, 2011). Moreover, the potential
1 Cross (2017) reports that voluntary turnover, one indicator of low employee engagement, in the fast food industry results in annual costs of $10 billion in the United States.
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to receive recognition may also have direct effects on effort because a recognition program can serve as a
cultural control, motivating peer monitoring and task coordination among employees (Malmi & Brown,
2008; Merchant & Van der Stede, 2017). Thus, our second prediction is that adoption of a team-based
recognition program will be positively associated with increased employee effort. Lastly, we leverage the
link between employee effort and restaurant-level outcomes in our setting to develop our final two
predictions that team-based recognition will be associated with improvements in restaurant-level customer
satisfaction and financial performance.
We employ a pre-post research design to test our hypotheses using data from six locations of a fast
food franchise, all operated by the same franchisee (âManagementâ) in the same geographic region in
Canada.2 Management decided to adopt a formal, team-based recognition program to address low employee
engagement where they: (a) used specific performance measures to determine recognition recipients; (b)
provided recognition at regular intervals; and (c) made employees aware of program details. 3 The program
ran for 12 consecutive weeks with team-level recognition (if earned) provided every two weeks.
Recognition was based on two restaurant-level measures: employee productivity and task completion.
Importantly, each restaurant could receive recognition for each two-week period based on Managementâs
evaluation of performance on the two measures. Recognition consisted simply of a thank-you card signed
by Management and the restaurant manager, and gift-wrapped chocolate of nominal monetary value. In
keeping with the team-based approach, both the card and the gift were individually presented to each
employee at the location by a member of Management and the restaurant manager.
Our results support each prediction. We observe a significant improvement in our proxies for
employee engagement, employee effort, and restaurant-level customer satisfaction and financial
performance during the 12-week period after the recognition program was adopted compared to the 12-
2 Our pre-post design used an active manipulation of recognition program adoption but did not allow for an independent (randomized) control group. Similar pre-post designs have been used to study the efficacy of goal-based incentive program adoption (Anderson, Dekker, & Sedatole, 2010) and piece-rate incentive program adoption (Lazear 2000). 3 Some companies have adopted peer-to-peer recognition programs where employee are empowered to bestow recognition on their co-workers (Brun and Dugas 2008). Given the chronic issues with employee engagement in the fast food industry and the potential risks associated with giving disengaged employees decision rights over recognition, Management chose to adopt a top-down, team-based recognition program.
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week period immediately preceding program adoption. Supplemental analysis of the same 24-week period
in the prior year (i.e., 12-week âadoptionâ period, 12-week âpre-adoptionâ period) when no recognition
program was in place indicates that the results supporting our predictions are unlikely to be attributable to
seasonality effects. Moreover, we find no evidence that our results can be explained by trends that may
have begun either before the recognition program began or after the program commenced.
Our study contributes to the literature on employee recognition programs in several ways. First,
demonstrating that team-based recognition can motivate desired behavior should be of interest to reward
scheme designers in settings where measuring and evaluating individual behaviour is challenging and where
the nature of the work is team-oriented. Importantly, our results show that the benefits of adopting a
recognition program can extend beyond individual-level outcomes documented in prior research (e.g.,
Lourenço, 2016; Gallani, 2017). In settings where performance outcomes such as sales or profits can be
influenced by coordinated employee effort and behavior, our findings show that a team-based recognition
program is associated with positive effects on these aggregate outcomes.
Second, while our findings are in keeping with the positive behavioral effects of providing
individual recognition documented in prior research, we extend this work in two important ways. First, we
show that team-based recognition can be effective despite the possibility that well-established group
dynamics such as social loafing may have worked against our predictions. This finding answers the call by
Sprinkle (2003, p. 300) for future research to study incentive issues in team-based settings because of the
unique issues only present in those settings. Second, to our knowledge we are the first to show positive
effects of team-based recognition in a fast food restaurant setting, a large industry in North America known
to face considerable challenges with employee motivation (Coleman, 2018).4 However, we believe our
theory is generalizable to other task settings that also involve highly repetitive jobs, limited opportunities
4The fast food industry is important to study as it employs 3.8 million individuals in the U.S. and 425,000 in Canada (Gaille, 2018; Lock, 2019) with over 232,000 (27,000) fast food establishments in the U.S. (Canada). See https://www.ibisworld.com/canada/market-research-reports/fast-food-restaurants-industry/ for more information.
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for advancement, and low employee motivation. Indeed, it is settings such as these where interventions
designed to boost motivation may be most needed.
Finally, our field-based study addresses recent concerns about the generalizability of theory from
lab and other controlled experiments to actual work settings (Gneezy & List, 2006; Al-Ubaydli, List, &
Suskind, 2019). In a similar vein, Kleingeld, van Mierlo, and Arends (2011, p.1289) stress the âstriking
lack of recent field studies in organizational settingsâ examining team performance outcomes. We believe
our findings, based on actual employees performing their real jobs, provide compelling evidence of the
efficacy of a team-based recognition program.
2. Research Site, Background, and Hypotheses
2.1 Research Site and the Fast food Industry Setting
To establish the context for our hypotheses, we briefly describe our research site, key features of
the recognition program employed, and aspects of the fast food industry setting that render it useful for
testing interventions aimed at improving employee motivation. We worked closely with Management who
own a franchise license to operate six fast food restaurants in the same geographic area in Canada. The
restaurant is part of a major chain operating throughout North America. Management described their belief
to us that fast food restaurants are âthe factories of the 21st centuryâ because of the well-defined operational
processes (e.g., order-taking, food preparation, cleaning schedule, inventory management, etc.) utilized to
ensure consistent delivery of the product/service over time and across locations.5 Management also
expressed their concern that a significant threat to the success of fast food restaurants is a lack of employee
effort, which is consistent with research in the fast food industry documenting similar challenges (Cross,
2017; Peterson & Luthans, 2006). In particular, Management noted that they believe low employee
engagement has negatively affected their restaurantsâ operational and financial performance over an
extended period of time.
5 We had numerous in-person discussions with Management during the various phases of the study with at least two co-authors present at each meeting. We did not record (or transcribe) any of these discussions but kept detailed notes, which form the basis of our references to Managementâs beliefs and observations.
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Management decided to implement a team-based recognition program in an attempt to increase
employee engagement and effort, which in turn they believed would positively impact restaurant-level
performance.6 We worked closely with Management to develop all details of the formal recognition
program, the key features of which are as follows:7
⢠Employees were made aware that recognition was based on restaurant-level performance in two key operational areas: customer satisfaction and service efficiency. Importantly, these two areas were also emphasized by Management prior to the adoption of the recognition program such that the recognition program did not convey new objectives to employees. Further, the operational areas were vaguely described to avoid employees focusing intensely on a single dimension of their job.8
⢠Management used objective measures and their discretion to determine whether performance at each restaurant in these two operational areas warranted recognition for each two-week period. If a restaurant was selected for recognition, all employees at that location received the same non-monetary award.
⢠The recognition program ran for 12 consecutive weeks with recognition provided every two weeks
where earned.
⢠To minimize competition among various restaurants, recognition was not based on relative performance among the six restaurants such that it was possible that each restaurant could receive recognition for each of the six recognition periods. Further, restaurant employees were not informed about whether other restaurants received recognition.
⢠All employees were informed about the recognition program immediately prior to its
implementation. However, limited feedback accompanied recognition or failure to receive recognition (See Appendix A for the bi-weekly feedback script).
⢠In an effort to strengthen the development of inferences regarding the effects of team-based recognition, prior to the commencement of the recognition program, restaurant managers were asked by Management not to make any changes to their ânormalâ interactions with employees (e.g., task assignments, feedback, training, mentoring) while the recognition program was in place.9
6 Management had implemented a profit-sharing program with annual payouts at all restaurants over a year before the beginning of our study. Management concluded that it had negligible effects on employee motivation, which they attributed in part to the high rate of restaurant employee turnover. Indeed, it was the ineffectiveness of the profit sharing program that lead Management to consider other approaches for improving employee motivation. However, because restaurant managers were also eligible for the profit-sharing program and tended to have much longer employment tenure, Management did not discontinue or change any of the eligibility details of the profit-sharing incentives during the recognition program. Accordingly, the profit-sharing program was present in both the pre and post recognition program periods we used to test our hypotheses. We discuss the implications this may have for our inferences in the conclusions section. 7 Informal recognition is more spontaneous in nature and can involve a simple gesture of appreciation such as a verbal thank-you from one individual to another for doing a good job (Gallani, 2017). While surveys show companies often employ both formal and informal recognition, there is evidence that the use of formal recognition programs is increasing (WorldatWork, 2019). 8 Management used the same measures pre and post recognition program adoption to evaluate restaurant performance. To the extent these measures are incongruent with program objectives, we believe this biases against finding support for our hypotheses. 9 Management expressed confidence that the restaurant managers would comply with this request but we have no way of verifying whether or not they did. If managers perceived personal benefits (e.g., their reputation with Management) arising from their restaurant being recognized, it could be that the adoption of the recognition program motivated them to change how they managed their employees. That said, restaurant managers were already incentivized to motivate their employees to perform well via the profit-sharing plan described in footnote 6. As such it is unclear that the adoption of the recognition program would have much
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Several features of the fast food restaurant setting more generally make it conducive for studying
the effects of team-based recognition. First, the nature of the work is team-oriented whereby consistently
providing a good customer experience requires a coordinated effort among employees in taking orders,
preparing the food, ensuring supplies are available (e.g., napkins, condiments, etc.) and keeping the facility
clean (Perez & Mirabella, 2013; Ryan et al., 2011). Moreover, while employees typically are assigned
individual responsibilities (e.g., order taking) there is a need to coordinate efforts to ensure all required
tasks are being done on a timely basis (Reiter, 1997). Second, objectively measuring performance at an
individual-level is challenging, largely because of the interdependent nature of the work tasks and because
employees within a single shift frequently rotate responsibilities. Third, individual employee effort can
impact aggregate measures of performance. For example, efficient order processing and facility cleanliness
can affect customer satisfaction, which in turn is likely to affect future revenues (Bernhardt, Donthu, &
Kennett, 2000). As such, âteam-levelâ performance can be meaningfully measured in a fast food restaurant
and is likely sensitive to individual employee effort.
2.2 Background and Hypotheses
2.2.1 Employee Recognition
Non-monetary recognition is âthe assignment of personal non-monetary rewards (i.e., interest,
approval and appreciation) for individual efforts and work accomplishment to recognize and reinforce the
desired behaviors displayed by an employeeâ (Montani, Boudrias, & Pigeon, 2017, p. 1). A key
motivational element of recognition is that it can provide social esteem for the recipient (Frey, 2006;
Lourenço, 2016). Recent survey evidence shows recognition programs are used extensively by
organizations, in part, because they represent a potentially cost-effective approach to motivate employees
(SHRM, 2018; WorldatWork, 2019).
Recognition programs vary considerably regarding their design and use (WorldatWork, 2019). For
incremental scope to influence managersâ behavior. However, we acknowledge that it is possible that any observed positive consequences of adopting team-based recognition in our setting, may in part be indirect vis-Ă -vis the effects of the program on managersâ efforts to motivate their employees to increase customer satisfaction and provide more efficient service.
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example, recognition can be provided on an informal basis such as a supervisor providing a spontaneous
verbal âthank-youâ to an employee for finishing a project ahead of schedule. Alternatively, recognition
programs can be highly formalized with specific criteria for receiving recognition (e.g., sales goals),
different âlevelsâ of recognition (e.g., bronze, silver, gold) with employees recognized at pre-determined
intervals (Ashraf, Bandiera, & Jack, 2014; Lourenço, 2017; Markham et al., 2002). Recognition also varies
as to whether it is provided privately or publicly and as to who provides the recognition (e.g., supervisor,
peers, subordinate) (Brun and Dugas, 2008). Companies have employed recognition programs for decades
but there is surprisingly little academic research examining their consequences in actual organizational
settings (Ashraf et al., 2014; Lourenço, 2016). No study that we are aware of has examined the
consequences of team-based recognition programs.
The small number of field studies examining recognition program effects in company settings
generally show that recognition of individual employees for specific individual-level outcomes (e.g., sales,
absenteeism) has positive effects on behavior. In a quasi-experimental field study in a manufacturing
company, Markham et al. (2002) find that providing individual recognition for employee attendance leads
to lower absenteeism than a feedback intervention. Using a field experiment, Ashraf et al. (2014) find that
individual recognition for sales performance leads to better individual performance than financial incentives
in a setting involving pro-social behavior. Lourenço (2016) uses a field experiment and finds that individual
recognition for achieving specific sales goals and financial incentives for achieving sales goals act as
substitutes leading to similar levels of sales performance. Finally, in a healthcare setting, Gallani (2017)
finds that provision of informal individual recognition to physicians by other healthcare workers for good
hand hygiene habits, leads to lasting improvements in this important behavior.
Relatedly, two controlled field experiments provide evidence that employee recognition decoupled
from financial incentives can increase individual effort (e.g., Bradler, Dur, Neckermann, & Non, 2016;
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Kosfeld & Neckermann, 2011).10 Kosfeld and Neckermann (2011) show that, when the best performer of
a group is awarded a congratulatory thank you card, performance increases by 12% across all individuals.
Bradler et al. (2016) also finds that performance increases when the best performers are recognized.
Interestingly, the authors find that it is individuals who did not receive recognition that are mainly
responsible for the overall performance increase.
Collectively, the studies discussed above have improved our understanding of the potential for
individual employee recognition programs to positively affect individual employee behavior. We seek to
build on these findings by examining team-based recognition rather than individual-level recognition in a
fast food restaurant setting where the challenges of motivating employees are well documented.
2.2.2 Team-Based Recognition and Employee Engagement
As noted earlier, low employee engagement is particularly problematic in the fast food industry
and thus identifying approaches to address this issue is of considerable practical importance (Peterson &
Luthans, 2006). Schaufeli and Bakker (2004) define employee engagement as âa positive, fulfilling, work-
related state of mind that is characterized by vigor, dedication, and absorption (p. 295)â and that it is âa
persistent and pervasive affective-cognitive state that is not focused on any particular object, event, or work-
related behavior (p. 295).â Put another way, employee engagement is the extent to which employees
simultaneously dedicate physical, cognitive, and emotional energy towards their job (Kahn, 1990; Kahn,
1992; Rich, Lepine, & Crawford, 2010). Although no prior study that we are aware of has examined the
relationship between team-based recognition and employee engagement, there is some evidence showing a
positive association between individual-based recognition and engagement (e.g., Grawitch, Gottschalk, &
Munz, 2006). Whether or not these results demonstrating a link between individual-based recognition and
engagement will generalize to team-based recognition programs is an empirical question. However, as
described below, there are reasons to believe they may.
10 Bradler et al. (2016) describe their study as a âcontrolled field experimentâ (p. 3085) where they hired student participants to perform a real data-entry task. While the task was real, the participants were not actually employees of the organization for which the task was being performed.
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A team-based recognition program that acknowledges employees for their important contributions
to the teamâs success can increase employeesâ sense of meaningfulness, which is a central component to
employee engagement (Kahn, 1990; Montani et al., 2017). Employees ascribe meaning to workplace
outcomes such as recognition since they are symbols both of appreciation by management of their efforts,
as well as the connectedness they feel with the company (Grandey, Chi, & Diamond, 2013; Kelly, Liu, &
Presslee, 2020; Mickel & Barron, 2008). Consistent with this, recent research on individual recognition
programs emphasizes the symbolic value that employees place on recognition (Kosfeld & Neckermann,
2011; Lourenço, 2016). Further, the team-based nature of the recognition program can engender in-group
affiliation by stressing task interdependence and shared goals, which can also increase the meaningfulness
of team-based outcomes (Evans & Eys, 2015; Lee, Shin, & Kim, 2018). Important to this reasoning is that
employees perceive recognition to be a sincere acknowledgement and appreciation of their efforts that is
merit-based and not gratuitous (Kosfeld & Neckermann, 2018; Tessema, Ready, & Embaye, 2013). Thus,
a key assumption underlying all of our predictions is that employees in our setting believe the team-based
recognition program reflects a genuine attempt by Management to acknowledge restaurants that have
performed well. Our first prediction, stated in the alternative form is:
H1: Adoption of a team-based employee recognition program will be positively associated with employee engagement.
Our first prediction contains tension for two reasons. First, the nature of the work environment in
fast food restaurants has been attributed to be a cause of low engagement among employees given the
repetitive tasks, the high time pressure, and limited opportunities for professional growth (Perez &
Mirabella, 2013; Reiter, 1997). The lack of employee engagement has in turn been linked to the high rate
of annual turnover in fast food restaurants, which evidence shows was 94% in the United States in 2014
(Coleman, 2018).11 Accordingly, it is not obvious that team-based employee recognition programs in such
settings will yield benefits similar to those observed in other organizational settings using individual-level
11 According to the U.S. Labor Bureau, employee turnover in the accommodation and food services industry in 2018 was nearly 75%, second only to the arts, entertainment and leisure industry (87%). See https://www.bls.gov/news.release/jolts.t16.htm.
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recognition where those individuals (health care workers, sales professionals) may have been more
intrinsically motivated to perform well (e.g., Gallani, 2017; Lourenço, 2016). Second, harder working or
more capable employees may resent the use of team-based recognition if they believe that some of their co-
workers are engaging in social loafing, thereby reducing the likelihood recognition will be received by the
team (Albanese & Van Fleet, 1985; Nagatsu et al., 2018). If so, the recognition program may be
unsuccessful in influencing employee engagement.
2.2.3 Team-Based Recognition and Employee Effort
The positive effects of employee engagement on employee effort are well-established (e.g.,
Christian et al., 2011; Rich et al., 2010). Engaged employees exert greater effort and are more committed
to performing well and to company objectives than disengaged employees (Ashforth & Humphrey, 1995).
Research finds employee engagement is positively associated with several performance dimensions such
as employee productivity, employee satisfaction, workplace safety, employee retention, and customer
satisfaction (Christian et al., 2011; Harter et al., 2002; Kruse, 2012; Xanthopoulou, Bakker, Demerouti, &
Schaufeli, 2009).
In addition to a team-based recognition program positively affecting employee effort by increasing
engagement, it may also have direct effects on effort. Team-based recognition is a type of cultural control,
which are mechanisms organizations use to shape employeesâ values, beliefs, and social norms, and to
encourage employees to monitor and influence each otherâs behaviors (Malmi & Brown, 2008; Merchant
& Van der Stede, 2017). Examples of cultural controls that can increase employee effort include tone from
the top (Deal & Kennedy, 1982; Kim & Brymer, 2011; Stewart, Volpone, Avery, & McKay, 2011; Peters
& Waterman, 1982), physical arrangements (Newman, Stikeleather, & Waddoups, 2017; Merchant & Van
der Stede, 2017), and most relevant to our setting, team-based reward and recognition programs (Bamberger
& Levi, 2009; Chen, Williamson, & Zhou, 2012). In a setting involving teamwork, basing recognition on
collective performance is likely to encourage constructive mutual monitoring whereby employees exert
social pressure on their co-workers to exert effort to perform well. In a somewhat related example,
Welbourne et al. (2010) find that adoption of team-based incentives (i.e., a gainsharing program) in a
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manufacturing company resulted in a significant increase in self-reported mutual monitoring among
employees.
In our setting, there is considerable scope for variation in employee effort as it relates to various
customer-facing tasks and other aspects of the service being provided. For example, customer-facing tasks
such as taking orders quickly and accurately requires cognitive (e.g., concentration) and physical effort.
Ensuring the effective and efficient performance of other tasks such as food preparation, facility cleaning,
condiment re-stocking, etc., also require physical and cognitive effort. As such, to the extent that a team-
based recognition program impacts engagement or otherwise directly impacts effort through the means
discussed above, these effects should be observable. Moreover, as discussed above, employees in our
setting know that recognition is based, in part, on service efficiency, and aware of the behaviors involved
in providing efficient service. Accordingly, our next prediction, stated in the alternative form is:
H2: Adoption of a team-based employee recognition program will be positively associated with employee effort.
2.2.4 Team-Based Recognition and Restaurant-Level Performance
In team-based settings, the efforts of individual team members can affect aggregate outcomes
(Kozlowski & Bell, 2012). Accordingly, our final two predictions examine the relation between adoption
of a team-based recognition program and two important outcomes at the restaurant-level, customer
satisfaction and financial performance. In a fast food restaurant setting, the efforts of individual employees
can directly impact customer satisfaction (Peterson & Luthans, 2005). For example, customer satisfaction
can be positively impacted by efficient (fast), accurate and courteous service, and clean restaurant facilities.
Thus, to the extent receiving team-based recognition is based, at least in part, on employees directing their
efforts towards delivering a positive experience to customers, we expect it will be associated with improved
customer satisfaction. As noted earlier, in our setting employees are aware that team-based recognition is
based, in part, on both customer satisfaction and service efficiency. Given this, our third prediction stated
in the alternative form is:
H3: Adoption of a team-based employee recognition program will be positively associated with customer satisfaction.
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Adoption of team-based recognition is also likely to impact financial performance at the restaurant
level for two reasons. First, satisfied customers are more likely to visit the restaurant again when considering
fast food options in the future, leading to higher revenues (Allon, Federgruen, & Pierson, 2011). Consistent
with this reasoning, Bernhardt et al. (2000) find a positive association between improvements in customer
satisfaction and future changes in sales and profits. Thus, to the extent team-based recognition has a positive
impact on customer satisfaction as predicted by H3, we also expect positive effects on financial
performance. Second, gross margin can be improved through reduced wastage of food and other materials.
Peterson and Luthans (2005) provide evidence from the fast food restaurant setting that employing a team-
based incentive system, with financial rewards contingent on employees exhibiting certain behaviors (e.g.,
repeating customersâ orders back to them, quickly processing orders, etc.), resulted in significant
improvements to gross profit. Thus, team-based recognition program can also have positive effects on
financial performance via motivating employees to work more efficiently and be more attentive to cost
management. This linkage is likely in our setting since employees are aware that recognition is based in
part on service efficiency. Given the foregoing, our final prediction, stated in the alternative form is:
H4: Adoption of a team-based employee recognition program will be positively associated with restaurant-level financial performance.
3. Method
3.1 Restaurant Characteristics
The six restaurants included in our sample operate in the same geographic area in Canada. Because
they are part of a major franchise, each restaurant offers the same menu choices, has nearly identical
facilities, and uses the same set of operational procedures. The restaurants all offer both drive-through and
in-restaurant services. Each restaurant has a full-time Manager, responsible for the locationâs operations,
including employee training and involvement in the hiring decisions. Each restaurant has up to 32
employees working on a part-time and full-time basis; however, the actual number of employees varies
over time during our sample period given on-going turnover and hiring. All employees, except the Manager,
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are paid hourly with individual hourly wages based on experience.12 Based on a survey we conducted prior
to commencing the study, 41% of employees are male, mean (standard deviation) employee age is 23.7
(9.2) years old, months employed is 16 (29) months [median 9 months], and hours worked per week is 28
(12) hours.13
3.2 Recognition Program Details
In September 2018, Management implemented a team-based recognition program whereby all
employees at any of its six restaurants could earn recognition based on restaurant-level performance. The
recognition program lasted for twelve weeks with recognition potentially awarded every two weeks (i.e.,
six recognition periods) based on their performance for the previous two-week period. Because recognition
was not based on relative performance, each restaurant could be recognized up to six times over the 12
weeks of the program. Prior to our study, Management had not used a formal recognition program to
motivate its employees.
Management used discretion in determining whether or not to recognize a restaurant in a given
two-week period based on restaurant-level performance for two key operational metrics that collectively
employees could directly influence. The first metric, Productivity, represents a measure of the speed of
dine-in and drive-through services, which are important determinants of future revenues in the fast food
industry (Allon et al., 2011). The second metric, Task Completion, represents the percentage of required
daily tasks (e.g., cleaning washrooms, taking out the garbage, etc.) done on time. The same tasks and
deadlines are assigned to each restaurant each day, and employees are expected to complete those tasks on-
time, in addition to carrying out their other duties. Using a standardized checklist for all six restaurants,
Managers digitally record whether or not each task is completed on time throughout the day. This
12 There were no changes made to managersâ compensation during the pre or post recognition program adoption periods. Managers earned a base salary and additional incentive pay based on individual restaurant performance. 13 We obtained demographic information by surveying restaurant employees just prior to the start of our study. In total, 122 employees completed our survey, approximately a 64% response rate. In the survey, we asked employees questions about their engagement. Our intent was to compare responses to this first survey with those from a second administration of the same survey at the end of the recognition program. One hundred and two employees completed the second survey (about a 54% response rate). Unfortunately, we experienced issues with the two surveys (e.g., different sets of employees, social desirability, etc.) that limit the usefulness of the inferences.
15
information is then uploaded to the database of operational metrics maintained by Management.
Importantly, employees can strongly influence outcomes on both of these measures through their effort
choices. See Appendix B for definitions of each measure.
Management chose not to tell employees the specific measures being used in the recognition
program because of their concern that doing so would lead to excessive focus on those dimensions of
performance at the expense of other important behaviors. For example, informing employees that speed of
service (Productivity) was a criterion for recognition could have led them to ignore other aspects of
customer service such as order accuracy or politely interacting with customers. Instead, all employees were
informed that recognition would be given for high âcustomer satisfaction and service efficiencyâ (see
Appendix A).14 Importantly, there was no deception in this communication since customer satisfaction and
service efficiency are closely related to the Productivity and Task Completion measures Management
actually used in determining which restaurants to recognize.15
Management used discretion in determining which restaurants to recognize every two weeks for
two reasons. First, discretion allowed Management to adjust for factors that could potentially negatively
affect Productivity or Task Completion on any given day but that are outside of employeesâ control as a
group (e.g., equipment problems, technology issues, staff shortages, etc.). Second, Management believed
restaurant employees would perceive discretionary recognition as more sincere than recognition based on
a simple âdo this get thatâ rule (e.g., a sales goal). Given the importance of recognition being perceived as
14 This design feature is in contrast to other recent field studies where recognition criteria were made explicit to employees (e.g., Ashraf et al., 2014; Lourenço, 2016). However, in those studies, the recognition program was individual-based and designed to motivate a single, specific behavior (i.e., sales effort) that represented the primary responsibility of interest for the employee. By contrast, in our setting employees had multiple task responsibilities all of which were deemed important by Management. Moreover, given the highly standardized nature of the work environment, employees understood how their efforts affect customer satisfaction and service efficiency. As a result, Management felt that providing more explicit recognition criteria was neither necessary nor desirable. We do not view the lack of specificity in the criteria communicated to employees as impairing our ability to develop inferences about the effects of the recognition program on engagement, effort and restaurant-level performance. If anything, it may bias against our predictions to the extent employees were uncertain as to how to direct their efforts in attempting to earn recognition. 15 Consistent with Managementâs stated criteria for determining which locations would receive recognition, results (untabulated) show that for all six recognition periods, restaurants that were recognized for a given period had a larger improvement in Productivity relative to the prior two weeks, than restaurants that were not. For Task Completion, results (untabulated) show little difference between restaurants that received recognition versus those that did not for any two-week period during the time the recognition program was in place.
16
sincere (e.g. Tessema et al. 2013), Management felt strongly that allowing some discretion in their judgment
was desirable.
When recognition was earned, Management sent all employees an email telling them of their
restaurantâs accomplishment (see Appendix A). In addition to the email, all employees at the restaurant
received a handmade thank you card signed by both Management and the local restaurant Manager as prior
research has found thank you cards can be effective forms of recognition (Bradler et al., 2016; Kosfeld &
Neckermann, 2001). The thank you card was accompanied by gift-wrapped, locally sourced organic
chocolate bar and was individually hand delivered to each employee by one of the companyâs owners.16
Importantly, the cash value of the reward was negligible to avoid the recognition itself becoming secondary
to a financial reward. Every element of the recognition program was designed to communicate to employees
that Management was sincerely appreciative of their efforts during the two-week period. When recognition
was not earned, Management sent all employees an email to tell them that their efforts, although âgoodâ,
were not sufficient to earn recognition for the period (Appendix A). The purpose of the email was to remind
those employees that did not receive recognition that the program was still on-going.
Table 1 reports the frequency of recognition rewarded to each restaurant across the six periods.
Management recognized one or two restaurants each period, and the most (least) a restaurant was
recognized during the six-period program was three (none). Employees were only made aware of whether
or not their restaurant had been recognized, not the outcomes at other locations.17
[Insert Table 1 here]
3.3 Measures of Engagement, Effort and Restaurant Performance
16 Prior field studies examining the efficacy of individual recognition examine settings where employees received recognition publicly as part of a ceremony (e.g., Markham, 2002; Ashraf et al., 2014; Lourenco, 2016). The nature of the fast food industry (e.g., part-time labor) prevented Management from using such public recognition ceremonies. However, to the extent making receipt of recognition public increases its meaningfulness, the private delivery of recognition in our setting biases against finding support for our hypotheses. 17 We cannot rule out the possibility that employees across the six locations interacted with each other and thus may have learned about recognition outcomes for other restaurants. However, even if this happened, it is unclear that such information sharing would have systematically biased outcomes in favor of our predictions. For example, employees at a restaurant which had not been recognized for a given two-week period could either have been demoralized and reduced effort, or motivated to work harder upon learning that other locations had been recognized for that period.
17
We worked with Management to identify proxies for our dependent variables. We report all
measures at the restaurant level given that most of the metrics are not available at the individual employee-
level (e.g., customer satisfaction, financial performance, employee turnover). Complete variable definitions
for each measure are provided in Appendix B.
We obtained data from Management regarding three proxies for employee engagement commonly
used in prior research (e.g., Al Mehrzi & Singh, 2016; Potoski & Callery, 2018; Robinson, 2012; Saks,
2006; Soane et al. 2013). Absences is the number of employees who did not show up for an assigned shift
per restaurant per week. Turnover is the number of employee resignations per restaurant per week and
includes both voluntary and involuntary departures. Lates is the number of employees arriving late (5
minutes or more) for their shift per restaurant per week. We use these three measures to test H1with higher
engagement indicated by lower values for each. We use the measures described above for Productivity and
Task Completion as our proxies for employee effort. Both are collected daily at each restaurant and we take
the daily average for the week. Because each measure is calculated at the restaurant-level they capture effort
for the team of employees. We use these measures to test H2.
We use two proxies for customer satisfaction, Guest_Index and Guest_Happy. Guest_Index
indicates the percentage growth in customer volume over the previous year. The index indicates each
restaurantâs growth relative to that of 51 other franchise restaurants of similar size in the same geographic
market.18 The index is calculated daily and we use the daily average for the week. Using an index for this
and the other similarly indexed measures (sales and profit) described below controls for overall consumer
and economic trends affecting franchise performance in the same geographic market during the period of
our study. As such it strengthens the inferences we can draw from our analysis of these measures.
Guest_Happy is a customer satisfaction measure collected from customers just before they exit the
restaurant after a visit. Specifically, customers have the opportunity to rate their satisfaction with the
restaurant and its service using a touch screen located near the restaurant exit. Customers rate their
18 The average percentage change of the 51 restaurants included in the index is set to 100 for purposes of interpreting the scores used in our analysis. Index variables are computed by franchisor head office based on data received from the various restaurants.
18
happiness for several aspects of their experience (e.g., restaurant cleanliness, order accuracy, food quality,
etc.) using a simple two-item response scale indicating either that they were satisfied or dissatisfied. Our
measure is the percentage of total weekly responses for which the customer indicated that they were
satisfied.19 We use both measures to test H3.
We use Sales_Index and Profit_Index as measures of restaurant-level financial performance. Both
indices are comprised of the same set of 51 market restaurants referred to above for Guest_Index and are
calculated in the same manner (i.e., 100 = market average). Sales_Index is a measure of sales growth
percentage over the previous year. Profit_Index is calculated as [(gross sales â actual food cost â oil cost â
paper cost â wages â benefits)/gross sales]. Prior to our study, Management relied on both Sales_Index and
Profit_Index to evaluate all six restaurantsâ financial performance and believe both are impacted by
employee effort. We use both measures to test H4.
4. Results
4.1 Descriptive Statistics
Table 2 presents the means (standard deviations) of each of our dependent variables pre and post
adoption of the team-based recognition program. Table 3 presents a correlation matrix including all of our
dependent variables. Generally, the descriptive results and correlations are consistent with the theory
underlying our hypotheses and provide evidence of construct validity for our key measures (Shadish, Cook,
& Campbell, 2002). Per Table 2, mean employee engagement (Panel A, Absences and Turnover), employee
effort (Panel B, Productivity and Task Completion), and restaurant-level performance (Panel C,
Guest_Index, Guest_Happy, Sales_Index, and Profit_Index) all show improvement in the post-recognition
period compared to the pre-recognition period. It is also noteworthy that the standard deviation for our two
measures of employee effort (Table 2, Panel B) are lower in the post-recognition period relative to the pre-
recognition period. Results (not tabulated) show that the decline in the standard deviation for Task
19 According to Management, Guest_Happy is based on about 200 customer responses per week per location. Each location in our sample has a video monitor in the kitchen area (out of sight from customers) that reports in real-time the number of customer responses in the two categories. These monitors were in place before our study began and were operational the entire 24-weeks of our study. Thus, employees were accustomed to having customer satisfaction (which incorporates service efficiency) monitored.
19
Completion is significant (p < 0.01). This is suggestive of more consistent effort by employees after the
recognition program was adopted.
Table 3 shows that both Turnover and Lates are significantly (p < 0.10) negatively correlated with
Productivity and Sales_Index. The negative sign reflects the fact that higher values for our engagement
proxies (e.g., Turnover and Lates) indicate lower engagement. We also find that both measures of employee
effort are significantly (p < 0.10) positively correlated with restaurant-level performance (e.g., Sales_Index,
Guest_Happy). This is in keeping with our arguments above that employee effort in our setting is likely to
be associated with both customer satisfaction and financial performance. Finally, we observe a significant
(p < 0.10) positive correlation between Sales_Index and Guest_Happy, consistent with more satisfied
customers being associated with better restaurant-level sales performance.
[Insert Table 2 here]
[Insert Table 3 here]
4.2 Hypotheses Tests
We test our hypotheses using a multi-level mixed model (Klein & Kozlowski, 2000; Luke, 2004).
Multi-level mixed models employing random effects are commonly used to correct standard errors for
group level effects.20 In our setting, a multi-level mixed model addresses the potential for correlated error
terms caused by our repeated-measures (nested) design in which multiple measurements are used for each
of the six restaurants for 24 consecutive weeks (West et al. 2014). We employ the following multi-level
mixed model to test our hypotheses:
đˇđ!" = đ˝# +đ˝$đđđđđđ đĄ!" +đ˘#! + đ˘$!đđđđđđ đĄ!" +đ!% (1)
đˇđ!" represents our nine dependent variables described earlier for restaurant đ (đ = 1 to 6) in week
đĄ (đĄ = 1 to 24). PrePost, our test variable, equals 0 for observations during the 12 weeks prior to the
recognition program, and 1 for observations during the 12 weeks subsequent to the beginning of the
20 Various commonly used methods for estimating the standard errors for group-level clusters produce no meaningful difference under ideal conditions (Arceneaux & Nickerson, 2009). However, in our setting, we have few (n = 6) well-defined clusters such that using clustered robust standard errors is not an appropriate technique (Petersen, 2009).
20
recognition program. Regarding the right-hand side details of the model, đ˝# +đ˝$đđđđđđ đĄ!" represents the
fixed component that fits an overall regression line representing the population average, whereas đ˘#! +
đ˘$!đđđđđđ đĄ!" is the random component of the model. The term đ˘#!represents the random intercept at the
restaurant level (đ = 1 to 6), which explicitly models the repeated measures intra-class correlation.21 The
term đ˘$!đđđđđđ đĄ!" allows for a random slope on PrePost. Specifically, it permits the slope of the regression
line in the pre-period to be different from the slope in the post-period. We use Poisson multi-level mixed
models for our three proxies of employee engagement (Absences, Turnover, and Lates) as they are count
variables. We use linear multi-level mixed models for the remaining six dependent variables as they are all
continuous measures.
H1 predicts that the adoption of a team-based recognition program will be associated with increased
employee engagement. We proxy for employee engagement using Absences, Turnover, and Lates. 22 Panel
A of Table 4 reports the results. Consistent with H1, the coefficients onđ˝$ are negative and statistically
significant for Absences and Turnover (đ˝$= -0.20, p < 0.05; đ˝$= -0.35, p < 0.10, respectively) indicating
that there are fewer absences and turnover, in the post-recognition period than the pre-recognition period.23,
The coefficient on đ˝$ is negative, but insignificant for Lates (đ˝$= -0.16, p = 0.28).24 Overall, these results
provide support for H1.
[Insert Table 4 here]
H2 predicts that the adoption of a team-based recognition program will be positively associated
with increased employee effort. We proxy for employee effort using Productivity and Task_Completion.
21 Given that individual observations are nested within restaurant, we examine the various intra-class correlation coefficients (ICC), which represent the proportion of dependent variable variance that is accounted for by a specific restaurant (Luke, 2004). We find that restaurant accounts for a significant portion of the variability in all the dependent variables used to test the hypotheses (not tabulated; ICC varies from 0.05 to 0.67, all p < 0.01). Based on the significant ICCs, our multi-level mixed model approach to estimating the standard errors attributed to restaurants is necessary. 22 Note that Absences, Turnover, and Lates are inverse proxies of engagement. Thus, lower values of these proxies indicate higher engagement. 23 We report one-tailed p-values in our main analyses since all hypotheses are directional predictions (Jobson, 1991). 24 Poisson regression analysis (used for Absences, Turnover and Lates) models the log expected value of the metric of interest. Thus, the reported coefficients are on the log scale, which we can exponentiate to get the actual values. For example, the coefficient estimate on đ˝! for Absences is -0.20, which is the log expected difference in absences between the pre-recognition period and the post-recognition period. To express the difference in terms of the actual number of Absences between the two periods, we exponentiate this value (e-0.20), which results in a value of 0.82.
21
Panel B of Table 4 reports the results. Consistent with H2, the coefficients on đ˝$ are positive and statistically
significant for Productivity and Task_Completion (đ˝$= 4.86, p < 0.05; đ˝$= 0.06, p < 0.01, respectively)
suggesting that the post-recognition period exhibits higher employee effort than the pre-recognition period.
Overall, these results provide support for H2.
Our final two predictions examine the effects of adopting a team-based recognition program on
restaurant-level performance. H3 predicts that the adoption of an employee recognition program will be
positively associated with improved customer satisfaction. We proxy for customer satisfaction using
Guest_Index and Guest_Happy. Panel A of Table 5 documents the results. Consistent with H3, đ˝$is positive
and statistically significant for Guest_Index (đ˝$= 1.60, p < 0.10), and while positive, is insignificant for
Guest_Happy (đ˝$= 1.87, p = 0.11). Overall, these results provide mixed support for H3. H4 predicts that
the adoption of an employee recognition program will be positively associated with improved restaurant-
level financial performance. We proxy for restaurant-level financial performance using Sales_Index and
Profit_Index. Panel B of Table 5 reports the results. Consistent with H4, the coefficients on đ˝$ are positive
and statistically significant for both Sales_Index and Profit_Index (đ˝$ = 4.03, p < 0.01; đ˝$ = 1.34, p < 0.05,
respectively) indicating that financial performance is higher in the post-recognition period than the pre-
recognition period. Overall, these results provide support for H4.
[Insert Table 5 here]
4.2.1 Robustness Analysis
Our predictions are based on behavioral responses to the adoption of the recognition program rather
than simply employeesâ responses to actually receiving or not receiving recognition. However, as a
robustness check, we examine whether our main findings differ between restaurants receiving frequent
versus infrequent recognition. To do so we create a new indicator variable, High_Recog, coded 0 for
restaurants receiving recognition less than two times, and 1 for restaurants receiving recognition two or
more times during the 12 weeks the program was used (see Table 1). In an alternative version of the analysis
used to evaluate our hypotheses (i.e., equation 1), we include both High_Recog and PrePost x High_Recog
22
in the model. A significant coefficient on PrePost x High_Recog would indicate an incremental effect of
receiving recognition more frequently on our measures of interest overall for the 12-week period the
program was in place.25 We observe a significant coefficient for the PrePost x High_Recog interaction in
two noteworthy instances. First, the reduction in Turnover in response to adopting the recognition program
(see Table 4, Panel A) is significantly lower (two-tailed, p < 0.05) for restaurants receiving recognition
more frequently. Second, the improvement in Productivity documented in Table 4 (Panel B), is also
significantly lower (two-tailed, p < 0.01) for those restaurants recognized more frequently. In both of these
cases the coefficient for PrePost is significant (two-tailed, p < 0.01) in the predicted direction indicating
that restaurants recognized less frequently subsequently have reduced Turnover and improved
Productivity.26
We offer two observations based on our robustness analyses. First, it yields no systematic evidence
that the support for our predictions is driven by those locations recognized more frequently. Had we
observed such evidence, a possible interpretation would have been that more frequent recognition lead to
positive reciprocity effects. This does not appear to be the case based on our data. Second, the evidence
shows that restaurants recognized less frequently, have larger improvements in engagement (Turnover) and
effort (Productivity) subsequent to the adoption of the program. Our small sample warrants considerable
caution in interpreting this result, but in our setting, employees receiving infrequent recognition did not
appear to become less engaged or exert less effort in response.
4.3 Supplemental Analysis
Because our research design does not include an independent control group of restaurants operating
without a recognition program, it is possible that the changes we observe between the 12-week pre-
25 We acknowledge that an alternative and more sophisticated way of conducting the analysis would be to examine our measures of interest in the two-week period immediately following the period for which recognition was received or not received. However, the possibility of dynamic effects (e.g., responses to receiving recognition the second time) would make it challenging to employ this approach and our small sample size precludes us from doing so. 26 We also find a marginally significant (two-tailed, p < .010) PrePost x High_Recog coefficient in our analysis of Guest_Index indicating a positive incremental effect of more frequent recognition on customer volume. However, we are reluctant to draw conclusions based on this finding given the weak overall effect (p < 0.10) of the recognition program on Guest_Index reported in Table 5 (Panel A).
23
recognition period and the 12-week post-recognition period in support of our predictions are due to other
factors. Four such factors are seasonality effects, the continuation or commencement of a performance
improvement trend for our dependent measures, hiring more engaged employees during the 12-week
recognition period, and recognition program novelty effects. We next discuss our approach to ruling out
each of these alternative explanations for our results.
4.3.1 Seasonality Effects
It may be that the improvements in engagement, effort, customer satisfaction and financial
performance we observe reflect a seasonal trend for these metrics. In an attempt to mitigate this concern,
we conduct a series of placebo tests to test for seasonality effects. We use the same 12-week pre and post
recognition periods from the previous year (i.e., 2017) when there was no recognition program in use. If
our results are attributable to seasonality then our placebo tests should show similar increases in employee
engagement, employee effort, customer satisfaction, and financial performance for the placebo post-
recognition period in 2017 as we observe for the post- recognition period when the recognition program
was actually in place (i.e., 2018).
As with our main analysis, we use the same multi-level mixed model approach in our placebo tests.
The results for employee engagement are reported in Table 6, Panel A. Contrary to our main findings, Panel
A of Table 6 shows that the means for Absences and Turnover in the post period are not significantly
different than the respective means in the pre-period. Moreover, the mean of Lates in the placebo post-
recognition period is significantly higher (p < 0.05) than the mean of Lates in the placebo pre-recognition
period consistent with lower engagement in the post-recognition period. These results suggest the support
we find for H1 does not appear to be attributable to seasonality effects.
[Insert Table 6 here]
The results of the placebo tests for employee effort are reported in Table 6, Panel B and show that
both Productivity and Task Completion are significantly lower in the placebo post-recognition period than
the placebo pre-recognition period (both p < 0.01). These results are in the opposite direction of the support
we document for H2, indicating that seasonality is unlikely to be an alternative explanation for our findings.
24
Finally, the placebo test results related to H3 (customer satisfaction) and H4 (restaurant financial
performance) are presented in Table 7. The results show significant declines in both customer satisfaction
and financial performance for the placebo post-recognition period compared to the placebo pre-recognition
period (all p < 0.01). As such, seasonality does not appear to explain our results in support of H3 and H4.
However, these placebo results are consistent with Managementsâ observation that employee effort,
customer satisfaction, and financial performance all typically decrease in the latter part of each year because
of high employee turnover during this period as many part-time employees return to school. Indeed, it was
Managementâs belief that performance was unlikely to improve because of seasonality effects alone during
the fall period that lead us to implement the recognition program during that specific time of year.
[Insert Table 7 here]
4.3.2 Trend Continuation or Trend Commencement
A second alternative explanation for our results is that the improved performance we observe
during the 12-week post recognition period reflects a continuation of a trend that began prior to the
introduction of the program. Although we allow for a random slope for PrePost in our main analyses, we
attempt to further mitigate this concern by performing a time series regression for each of our dependent
measures with time (12 weeks pre-recognition period) as the independent variable (Time). The results
(untabulated) show no evidence of improving performance during the 12-week pre-recognition period.
Except for Profit_Index, the coefficient for Time is insignificant in each of the time series models (all p-
values > 0.39). For Profit_Index, the coefficient for Time is negative and significant (p < 0.01) indicating a
downward trend in financial performance over the 12-week period prior to adoption of the recognition
program. Accordingly, it does not appear that the results supporting our predictions are attributable to a
pre-existing trend of improving performance across the six restaurants.
Rather than reflecting a trend that began prior to the start of the recognition program, it is also
possible that our results represent a trend of improved engagement, effort and restaurant-level performance
that commenced during the period the program was in place, for reasons unrelated to its adoption. To
examine this possibility, we analyze results for the 12-week period immediately following the end of the
25
recognition program (January â March 2019) for all dependent variables used in our main analysis except
for Task Completion, which Management stopped collecting during that period.27 We use the same multi-
level mixed model approach (i.e., equation 1) to analyzing the data where PrePost, the test variable, equals
0 for observations during the 12 weeks the recognition program was in effect, and 1 for observations during
the 12 weeks immediately after the end of the recognition program. The results (untabulated) show a
marginally significant increase in Lates and a significant decrease in Productivity (respectively two-tailed
p-values < 0.10, and < 0.05). The only other significant PrePost result is an improvement in Guest_Index
(two-tailed, p-value < 0.01).
These results have two implications for the interpretation of our main findings. First, they suggest
that the positive effects of adopting the recognition program on engagement and employee effort that we
observe for our main hypotheses tests, do not appear to be attributable to a positive trend that began during
the 12 weeks the program was in place.28 Since both Lates and Productivity are direct measures of employee
behavior in our setting, the deterioration in performance on each measure is consistent with a negative
response (i.e., a reduction in effort) to the discontinuation of the recognition program. In particular, the
decline in Productivity following the end of the recognition program is in keeping with it being a key
indicator of service efficiency used by Management in determining which restaurants to recognize. Once
the recognition program ended, employees may have felt less motivation to continue their efforts related to
providing efficient service.
Second, the significant increase in Guest_Index during the post-program period, requires that our
results supporting H3 be interpreted with caution. It could be that the post-program improvement is a
continuation of a trend that started because the recognition program was put in place. That is, the marginally
significant increase for Guest_Index that we observe while the recognition program was in place because
27 Management indicated that they decided that the cost of the third-party system used to compile the data needed for the Task Completion metric exceeded the benefits of the measure. 28 To examine the possibility that the observed increase in Lates and the decrease in Productivity for the post-program period reflect seasonal declines in engagement and effort during the January to March period, we conduct a placebo test similar to that reported in section 4.3.1. The results (untabulated) show that relative to the September to December 2017 period, Lates decreased significantly (p < 0.05) during January to March 2018 with no significant change in Productivity. These results are inconsistent with our post-program results reflecting a seasonal trend in Lates and Productivity.
26
of improved employee engagement and effort, spilled over to the post-program period. Such spillover
effects are plausible to the extent customers experienced better (e.g., faster) service during the 12 weeks
the program was in place, which increased the likelihood they would visit the restaurant again in the future,
perhaps more frequently. Alternatively, the increase in customer volume that we observe during the
recognition program period might be unrelated to the program itself, instead simply reflecting the
commencement of a trend that continued after the program was discontinued.29
4.3.3 Employee Hiring
Based on our discussions with Management, there were no changes to the employee hiring process
or to the compensation provided to new hires during the 12-week post recognition period that would result
in hiring employees that were systematically more engaged than those they were replacing. However, the
possibility remains that by chance, Management was able to hire employees during the period of our study
that were considerably more engaged and motivated than employees who left either during the 12-week pre
recognition period, or soon after our study began. If so, our results may be attributable to fortuitous hiring
rather than the efficacy of the recognition program. To examine this possibility, we examine the total
number of new hires during the 12-week post recognition period. The greater the number of new hires, the
more plausible this alternative explanation becomes. In total, 38 employees were hired during this period,
which represents only about 21% of the total employee group across all six locations. Also, over 40% of
these new hires occurred during the final six weeks of the recognition period. As such, it seems unlikely
that this subset of employees hired after the recognition program began represents a plausible alternative
explanation for our observed results.30
4.3.4 Novelty Effects
29 Placebo tests (see footnote 30) indicate that seasonality is unlikely to explain our results as we observe an insignificant change in Guest_Index January to March 2018 compared to September to December 2017. Moreover, the continued improvement in Guest_Index in the post-program period cannot be explained by franchise-wide initiatives (e.g., new menu choices) since it is an indexed (i.e., relative) measure (see Appendix B for the variable definition). 30 Of the 38 new hires during the post-recognition period, 12 were for location 4 and 9 were for location 1, which per Table 1 both received recognition twice. As a precaution, we re-ran the analyses used to evaluate our hypotheses excluding these two locations and our inferences are unaffected except that we no longer find a significant association between the adoption of the recognition program and the change in Guest_Index (H3).
27
Finally, it is possible that our results are attributable to the novelty employees experienced from
having their performance (or behavior) monitored for purposes of potentially receiving recognition. Simply
put, just knowing that Management was monitoring their performance may have led employees to be more
engaged and exert more effort to perform their assigned tasks, which in turn affected restaurant-level
performance. We think novelty effects of this nature are unlikely for several reasons. First, Management
closely monitors numerous metrics related to customer satisfaction and service efficiency performance
(e.g., Guest_Happy, Task Completion, etc.) at each location on a weekly (and in some cases daily) basis
and regularly provides feedback to the restaurant Managers, who in turn share information with their
employees. Relatedly, store Managers record and monitor key metrics such as Task Completion intra-day
and communicate with employees when tasks have not been completed. These practices were in existence
well before the recognition program began.
Second, as noted above, Guest_Happy information is reported in real-time in every location and
employees know that Management uses this information in evaluating restaurant performance. As such,
performance monitoring was likely highly salient to employees. Third, prior to the recognition program
implementation, Management had used a profit-sharing plan that included restaurant employees, which was
based on achieving targeted performance for a variety of operational, customer and financial metrics. Thus,
employees eligible for profit-sharing and still employed at the restaurants during the recognition program
would have been highly familiar with having performance monitored by Management.
Finally, any such âManagement monitoringâ effects cannot explain the reduction in employee
turnover we observe during the 12-weeks post recognition compared to the 12-weeks prior to the
recognition program (Table 4, Panel A). That is, it seems highly unlikely that any novelty associated with
a perceived increase in performance (or behavior) monitoring by Management would influence employeesâ
decision to resign versus stay with the company. Overall, because performance monitoring by Management
was an on-going feature of the work environment in our setting, in place well before the recognition
program began, we do not view it as a substantial novelty factor that might explain our results.
5. Discussion
28
There is considerable use of formal, non-financial recognition programs by various types of
organizations to engage and motivate employees (SHRM, 2018; WorldatWork, 2019). We conduct a field
study at six fast food restaurant locations to investigate the effects of adopting a team-based recognition
program on employee engagement, employee effort and restaurant-level performance. We find evidence
consistent with adoption of the recognition program being associated with significant positive effects on
behavior and performance in our setting. Further, results from our robustness analysis suggest it was the
adoption of the program itself, rather than employee responses to receiving or not receiving recognition that
accounts for our main findings.
Our study contributes to both theory and practice related to recognition programs in several
important ways. Prior research on employee recognition has focused on the consequences of providing
individual-level recognition (e.g., Kosfeld & Neckerman, 2011). However, to the best of our knowledge,
we provide the first evidence that team-based recognition can be effective in strengthening engagement and
improving effort. By using aggregate measures of performance as the basis for determining recognition,
team-based recognition avoids the need for the potentially more costly assessment of individual
performance. Moreover, we observe these positive behavioral consequences despite well-known challenges
to using team-based incentives such as social loafing that can arise (Albanese & Van Fleet, 1985). This
finding answers the call by Sprinkle (2003, p. 300) for future research studying incentive issues within
teams because of the unique issues only present in team-based settings. Thus, we believe our results are
relevant to reward program designers in other settings involving teamwork and where individual
performance is costly or difficult to measure. Relatedly, our setting also allowed us to examine the extent
to which a team-based recognition program can impact performance outcomes at a more aggregate
(restaurant) level. Prior field research on recognition programs has focused on individual-level outcomes
(e.g., Lourenço, 2016), but we provide evidence that the benefits can extend to firm-level outcomes in
settings where individual effort directly influences those outcomes. Thus, we believe our results provide
new insights regarding the broader consequences of adopting an employee recognition program.
29
Two additional aspects of our study represent important contributions. First, while prior field
research has demonstrated that individual-level recognition can be effective, many of those studies examine
settings (e.g., healthcare, sales) where employees were likely more intrinsically motivated to perform their
jobs (Gallani, 2017; Lourenço, 2016) than employees in our setting. Indeed, the highly repetitive nature of
the tasks employees perform in fast food restaurants, the considerable time pressure they face, and their
limited opportunities for advancement all likely serve to limit intrinsic motivation and engagement
(Peterson and Luthans 2006). As such, we believe our findings extend prior research by demonstrating the
potential for recognition programs to be effective in work environments where motivating employees can
be particularly challenging (Coleman 2018; Perez and Mirabella 2013). Second, we add to a small number
of field studies examining the effects of employee recognition in a ânatural settingâ (e.g., Ashraf et al., 2014;
Lourenço, 2016; Markham et al., 2002). In so doing, our methodological approach overcomes concerns
about the generalizability of theory from lab experiments and controlled field studies that are not conducted
in actual organization (Al-Ubadydili et al., 2019).
Our studyâs limitations provide opportunities for further research. First, we were unable to capture
process measures of employee engagement, and instead rely on commonly used proxies. Future studies
examining the relation between team-based recognition and employee engagement, could measure
engagement more directly using a validated scale (e.g., Schaufeli, Bakker, & Salanova, 2006; Schaufeli,
Salanova, GonzĂĄlez-RomĂĄ, & Bakker, 2002) and use experimental methods to better understand the
processes underlying the efficacy of team-based recognition. Second, our small sample prevents us from
using more sophisticated analytical techniques such as mediation analysis to better understand how
recognition directly and indirectly (i.e., through engagement) affects restaurant-level financial performance.
Future studies employing larger samples could better isolate the causal mechanisms underlying some of the
overall outcomes we observe. Third, our research design does not have a randomized control group and
thus alternative explanations are possible regarding our observed results. While we believe we rule out
several likely alternative explanations for our findings, future studies could use an experimental design that
includes a control group and employs random assignment to provide causal inferences. Fourth, the existence
30
of a profit-sharing program throughout the entire period the recognition program was used leaves open the
possibility that it was the combination of the two incentive mechanisms that is responsible for our results.
That said, Managementâs assessment that the profit-sharing plan had negligible effects on employee
motivation makes it seem unlikely that it played a role in the results we observe during the period the
recognition program was in place. However, future research examining team-based recognition without
other incentive schemes in place is needed to shed light on this issue. Finally, our recognition program ran
for only 12-weeks so it is possible that over a longer period, any novelty effects related to the first-time use
of such a program would diminish. Future research examining the persistence of the effects we observe in
our setting would be valuable.
31
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Appendix A: Email Script Initial Email Script Dear COMPANY NAME Employees, COMPANY NAME management is committed to recognizing all of our employees for providing customers with an outstanding guest experience. As such, we have designed a new recognition program that will start on Monday, September 10th. From that date forward, every two weeks we will evaluate each restaurantâs guest experience in terms of customer satisfaction and service efficiency. We will individually recognize all employees at restaurants achieving outstanding guest experience and provide each of them with a token of our appreciation and gratitude. We look forward to recognizing your contributions to COMPANY NAMEâs success. Thank you. COMPANY NAME Management Recognition Earned Email Script Dear COMPANY NAME (location) Employees, COMPANY NAME management is committed to recognizing employees at restaurants we evaluate as having provided customers with an outstanding guest experience. Our recognition program runs every two weeks. All employees at restaurants being recognized will be provided with a token of our appreciation and gratitude. We are delighted to inform you that your teamâs efforts to improve overall service time and customer satisfaction was commendable over the past two weeks and deserving of recognition. Thank you! COMPANY NAME Management No Recognition Earned Email Script Dear COMPANY NAME (location) Employees, COMPANY NAME management is committed to recognizing employees at restaurants we evaluate as having provided customers with an outstanding guest experience. Our recognition program runs every two weeks. All employees at restaurants being recognized will be provided with a token of our appreciation and gratitude. Your team made a good effort on overall service time and customer satisfaction over the past two-weeks, but there is room for improvement. So, you will not be receiving recognition for the past two-week period. COMPANY NAME Management
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Appendix B: Variable Definitions
Variables Definitions Absences
Number of employee absences per restaurant per week.
Turnover Number of employee turnovers per restaurant per week.
Lates Number of employees arriving late (5 minutes or more) to work per restaurant per week.
Productivity
Productivity is a score based on the number of seconds takes in serving guests (i.e., speed of service). The speed of service for the Counter and Drive-Through operations are tracked separately. Both the Counter and Drive-Through operations are broken down into Morning (Open-11:30), Lunch (11:30-13:30), Afternoon (13:30-17:00), Supper (17:00-20:00), and Evening (20:00-23:00) day parts. Each day part gets a score based on the number of seconds that the restaurant takes in serving the guest. The faster the speed of service the higher is the productivity score. The speed of service for the Drive-Through operation is expected to be faster than the speed of service for the Counter operation in each day part. For example, the Counter operation in the morning will receive a score of 12 for service time ⤠145 seconds, while the Drive-Through operation in the morning will receive a score of 12 for service time ⤠100 seconds. The maximum daily productivity score for Counter is 60/day and for Drive-Through is 60/day for a total of 120 possible points per restaurant per day. The management uses the weekly average of the total possible daily score as a measure of restaurant productivity. We use this weekly average as our measure of restaurant productivity.
Task Completion The percentage of tasks completed on time by each restaurant each week.
Sales_Index Sales_Index is based on the Net sales Growth % over the previous year. Index indicates the restaurant performance compared to the average of the Market restaurants (i.e., restaurants in the same market region and with the same concept but excluding the store for which the measure is being calculated). An Index of 100 is equal to the average of Market restaurants, while scores above 100 exceed the Market average. The higher the Index the better as compared to previous year.
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Appendix B continued
Profit_Index Profit_Index is calculated as (Gross Sales â Actual Food Cost â Oil Cost â Paper Cost â (Wage + Benefit)) / Gross Sales. Index indicates the restaurant performance compared to the average of the Market restaurants (i.e., restaurants in the same market region and with the same concept but excluding the store for which the measure is being calculated). An Index of 100 is equal to the average of Market restaurants, while scores above 100 exceed the Market average. The higher the Index the better as compared to previous year.
Guest_Index
Guest_Happy
Guest_Index indicates the percentage of guest growth over the previous year. Index indicates the restaurant performance compared to the average of the Market restaurants (i.e., restaurants in the same market region and with the same concept but excluding the store for which the measure is being calculated). An Index of 100 is equal to the average of Market restaurants, while scores above 100 exceed the Market average. The higher the Index the better as compared to previous year. The guest happiness percentage per week per restaurant based on guest input on the guest connect system. The percentage is calculated as the number of customers responses indicating satisfaction with their service experience divided by total responses for the period.
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Table 1: Recognition Summary for Each Restaurant Location
Restaurant Period 1 Period 2 Period 3 Period 4 Period 5 Period 6 Total
1 X X 2
2 X 1
3 X X X 3
4 X X 2
5 X X 2
6 0 Table 1 reports which locations received recognition in each of the six recognition periods. Last column indicates total number of recognitions for each location.
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Table 2: Descriptive Statistics Panel A: Means (Standard Deviations) Engagement (1) (2) (3) Absences Turnover Lates Pre Period (N = 72) 4.06 0.75 1.61 (3.05) (0.95) (2.34) Post Period (N = 72) 3.33 0.53 1.63 (2.55) (0.79) (2.26)
Panel B: Means (Standard Deviations) Effort (1) (2) Productivity Task Completion Pre Period (N = 72) 39.39 0.70 (24.01) (0.24) Post Period (N = 72) 44.22 0.76 (21.61) (0.15)
Panel C: Means (Standard Deviations) Performance (1) (2) (3) (4) Guest_Index Guest_Happy Sales_Index Profit_Index Pre Period (N = 72) 95.38 79.44 95.53 94.15 (12.80) (14.42) (16.13) (5.42) Post Period (N = 72) 96.97 81.32 99.56 95.49 (11.22) (6.92) (14.56) (4.31)
Table 2 reports the mean (standard deviations) of each of our dependent variables. Appendix B contains the variable definitions.
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Table 3: Correlation Matrix Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (1) Productivity 0.44 0.33 0.10 0.31 0.39 0.20 -0.19 -0.25 (2) Task Completion 0.45 0.52 -0.08 0.55 0.42 -0.09 -0.01 -0.23 (3) Sales_Index 0.36 0.57 0.22 0.92 0.21 -0.26 -0.05 -0.14 (4) Profit_Index 0.10 -0.02 0.18 0.17 -0.20 -0.14 0.03 -0.10 (5) Guest_Index 0.35 0.60 0.91 0.11 0.26 -0.29 0.03 -0.11 (6) Guest_ Happy 0.29 0.30 0.16 -0.15 0.19 -0.03 0.01 -0.08 (7) Absences 0.21 -0.07 -0.22 -0.14 -0.25 0.04 -0.12 -0.14 (8) Turnover -0.23 -0.08 -0.03 0.06 -0.01 -0.01 -0.12 0.15 (9) Lates -0.26 -0.35 -0.18 -0.10 -0.20 0.04 -0.13 0.22
Table 3 reports correlations between the performance and engagement measures. N = 144. Coefficients above the diagonal are Spearman rank-order, and those below diagonal are Pearson correlations. The correlation coefficients in bold are significant at the 10% level or better. Appendix B contains the variable definitions.
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Table 4: Employee Engagement and Employee Effort Results Panel A: Employee Engagement (H1)
(1) (2) (3) Absences Turnover Lates PrePost -0.20** -0.35* -0.16 (0.11) (0.24) (0.29) Constant 1.17 -0.32 0.12 (0.13) (0.14) (0.20) Restaurant-level Random Effect Yes Yes Yes N 144 144 144
Panel B: Employee Effort (H2)
(1) (2) Productivity Task_Completion PrePost 4.86** 0.06*** (2.31) (0.02) Constant 39.39 0.70 (1.31) (0.02) Restaurant-level Random Effect Yes Yes N 144 144
Table 4 reports the Poisson mixed model regression results for the effects of employee recognition on employee engagement (Panel A) and employee effort (Panel B). The 12-week pre-recognition period runs from June 18, 2018 to September 3, 2018, while the 12-week post- recognition period runs from September 10, 2018 to December 3, 2018. Standard errors are reported in parentheses below each coefficient and are robust and clustered at the restaurant-level using random effects. Statistical significance is based on one-sided t-tests and is indicated as follows: *** p-value < 0.01; ** p-value < 0.05; * p-value < 0.10. Appendix B contains the variable definitions.
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Table 5: Restaurant-Level Performance Results Panel A: Customer Satisfaction (H3)
(1) (2) Guest_Index Guest_Happy PrePost 1.60* 1.87 (1.00) (1.54) Constant 95.38 79.44 (0.75) (1.46) Restaurant-level Random Effect Yes Yes N 144 144
Panel B: Financial Performance (H4)
(1) (2) Sales_Index Profit_Index PrePost 4.03*** 1.34** (1.53) (0.60) Constant 95.53 94.14 (1.19) (0.52) Restaurant-level Random Effect Yes Yes N 144 144
Table 5 reports the linear mixed model regression results for the effect of employee recognition on customer satisfaction (Panel A) and financial performance (Panel B). The 12-week pre-recognition period runs from June 18, 2018 to September 3, 2018, while the 12-week post- recognition period runs from September 10, 2018 to December 3, 2018. Standard errors are reported in parentheses below each coefficient and are robust and clustered at the restaurant-level using random effects. Statistical significance is based on one-sided t-tests and is indicated as follows: *** p-value < 0.01; ** p-value < 0.05; * p-value < 0.10. Appendix B contains the variable definitions.
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Table 6: Placebo Tests for Employee Engagement and Employee Effort Panel A: Employee Engagement
(1) (2) (3) Absences Turnover Lates PrePost 0.08 0.03 0.49** (0.14) (0.29) (0.23) Constant 1.03 -0.81 -0.42 (0.10) (0.16) (0.19) Restaurant-level Random Effect Yes Yes Yes N 144 144 144
Panel B: Employee Effort
(1) (2) Productivity Task_Completion PrePost -10.57*** -0.12*** (2.38) (0.02) Constant 57.83 0.79 (1.63) (0.01) Restaurant-level Random Effect Yes Yes N 144 144
Table 6 reports the results of the placebo test using the 2017 pre- and post-recognition periods to rule out seasonality for the analysis reported in Table 3. Panel A reports the Poisson mixed model regression results for the effect of employee recognition on employee engagement. Panel B reports the linear mixed model regression results for the effect of employee recognition on employee effort. The 12-week pre-recognition period runs from June 19, 2017 to September 4, 2017, while the 12-week post- recognition period runs from September 11, 2017 to December 4, 2017. Standard errors are reported in parentheses below each coefficient and are robust and clustered at the restaurant-level using random effects. Statistical significance is based on two-sided t-tests and is indicated as follows: *** p-value < 0.01; ** p-value < 0.05; * p-value < 0.10. Appendix B contains the variable definitions.
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Table 7: Placebo Tests for Restaurant-Level Performance Panel A: Customer Satisfaction
(1) (2) Guest_Index Guest_Happy PrePost -7.79*** -13.82*** (1.73) (3.67) Constant 109.54 80.46 (1.69) (1.58) Restaurant-level Random Effect Yes Yes N 144 144
Panel B: Financial Performance
(1) (2) Sales_Index Profit_Index PrePost -5.42*** -0.19** (1.56) (0.07) Constant 104.97 101.30 (1.52) (0.04) Restaurant-level Random Effect Yes Yes N 144 144
Table 7 reports the results of the placebo test using the 2017 pre- and post-recognition periods to rule out seasonality for the analysis reported in Table 4. Panel A reports the linear mixed model regression results for the effect of employee recognition on customer satisfaction. Panel B reports the regression results for the effect of employee recognition on restaurant performance. The 12-week pre-recognition period runs from June 19, 2017 to September 4, 2017, while the 12-week post- recognition period runs from September 11, 2017 to December 4, 2017. Standard errors are reported in parentheses below each coefficient and are robust and clustered at the restaurant-level using random effects. Statistical significance is based on Two-sided t-tests and is indicated as follows: *** p-value < 0.01; ** p-value < 0.05; * p-value < 0.10. Appendix B contains the variable definitions.