Reynolds, D., & Biel, D. (2007). Incorporating satisfaction measures into a restaurant productivity index. International Journal of Hospitality Management, 26, 352-361

(Awarded Best Paper of the Year 2007 by IJHM).

INCORPORATING SATISFACTION MEASURES INTO A RESTAURANT PRODUCTIVITY INDEX

Dennis ReynoldsCollege of Business and Economics

Washington State UniversityTodd Hall 477

Pullman, WA 99164509-335-4344

509-335-3857 (fax)reynolds@cbe.wsu.edu

David BielSchool of Hotel Administration

Cornell University119 The Knoll

Ithaca, NY 14850drb38@cornell.edu

An earlier version of this manuscript received the “Editor’s Choice Award” at the 2005 International Conference on Services Management in Delhi, India. We thank the meeting-paper reviewers, and especially Dr. Vinnie Jauhari, Conference Program Chair, as well as the IJHM

editor and his reviewers for their advice and counsel.

Dennis Reynolds, Ph.D., is the Ivar Haglund Distinguished Professor of Hospitality Management at the Washington State University School of Business and Economics in

Pullman, Washington, USA.

David Biel is a research assistant and student at the Cornell University School of Hotel Administration in Ithaca, New York, USA.

INCORPORATING SATISFACTION MEASURES INTO A RESTAURANT PRODUCTIVITY INDEX

ABSTRACT

The increasing stature of the foodservice industry in the global service economy suggests

that productivity analyses—similar to those performed in non-service-based settings—would

benefit multiunit operators by maximizing their desirable operational outcomes while

minimizing expenses and other detrimental conditions such as low job satisfaction. This paper

suggests that such analyses might be possible through the application of a holistic productivity

metric—one that includes traditional operational variables such as revenue, profit, food cost, and

labor cost, and previously ignored variables such as guest and employee satisfaction as well as

retention equity. Through data gathered from a single chain’s 36 corporate-owned, same-brand

casual-theme restaurants located in metropolitan centers across the United States, we found that

factors leading to maximum outputs such as controllable profit and retention equity include

employee satisfaction in addition to expected variables such as cost of goods sold and number of

seats. Most notably, employee satisfaction as an input proved to be the most volatile variable in

maximizing operational outputs.

Keywords: Productivity, data envelopment analysis, employee satisfaction, guest satisfaction

1

INCORPORATING SATISFACTION MEASURES INTO A RESTAURANT PRODUCTIVITY INDEX

In today’s highly competitive global foodservice arena, multi-unit restaurant operators

are embracing every opportunity to maximize their operational efficiency. The methods used to

benchmark and assess productivity have been limited, however, to overly simplistic approaches

and, as a result, have offered limited utility. As Sigala (2004) noted, “Although there have been

attempts to identify satisfactory productivity-monitoring procedures, these have been heavily

criticized and no generally accepted means of productivity measurement exists” (p. 39).

Consider also the increasing complexity of restaurant operations, which, conjoined with

constantly escalating consumer expectations and demands, adds to the challenges faced by

foodservice operators. Traditional partial-factor productivity statistics, such as meals-per-labor

hour, simply do not reflect adequately the many factors that influence the metric. Moreover,

service-process matrices, such as those detailed by Schmenner (1986), offer constructive

guidelines for assessing productivity but are difficult to integrate into some distinct service-

industry segments such as foodservice.

Following Reynolds’s (1999) call for a productivity index that is truly holistic and Kohli

and Jaworski’s (1990) assertions regarding business analyses that include a customer-service

orientation, this study explores several input and output variables while introducing a

comprehensive productivity metric. In particular, we investigate the effects of the customary

unit-level financial indicators (e.g., profit, revenue, cost of goods, labor cost, occupancy cost),

constraining variables (e.g., number of seats, square footage), and previously unexplored

variables such as employee and guest satisfaction; we also consider related factors such as

retention equity. The study makes its primary contribution in developing a more holistic

2

productivity metric than those considered previously. We also evaluate the importance of guest

and employee satisfaction data in assessing unit-level restaurant productivity.

LITERATURE REVIEW

Researchers have demonstrated that superior financial performance correlates strongly

with productivity. Most notably, Schmenner (2004) provided theoretical support for this

relationship using the service businesses as the primary focus. Others have presented empirical

support using a variety of service-industry segments including lodging (Morey and Dittman

1995; Hu and Cai 2004), midscale restaurants (Reynolds 2004), and on-site foodservice

(Reynolds 1998).

Correspondingly, the use of and focus on productivity has evolved dramatically during

the last 25 years. This development has been fueled largely by increasingly stringent resource

constraints with a disproportionate rise in labor-related expenses. Given the labor intensiveness

of hospitality-related businesses, interest in productivity analyses has focused predominately on

labor and its corollaries: service outcomes per employee (e.g., rooms cleaned, meals served),

labor hour, or labor-dollar value (Ball, Johnson, and Slattery 1986).

Variable Identification

Building on Reynolds’s (1998) definition of productivity as the effective use of resources

to achieve operational goals, researchers and practitioners have recently acknowledged the

importance of productivity analyses that are more comprehensive than any single-factor indices.

Brown and Dev (1999), for example, posited the use of capital productivity output by

considering three single-factor statistics, including income before fixed charges per full-time

equivalent employee. Similarly, Jones and Hall (1996) used multiple single-factor statistics as a

3

means to consider desirable service-delivery outcomes such as perceived service relative to labor

expenditure.

In moving toward what Bucklin (1978) termed “multi-factor” productivity analysis,

Sigala (2004) aptly explained that the largest problem is the identification of inputs and outputs.

Reynolds (1998) added that any meaningful productivity statistic must not only accurately

identify inputs and outputs, but must integrate all critical variables if such a measure is used to

assess overall operational productivity.

So which variables are critical for a holistic productivity measure in a restaurant setting?

Ball, Johnson, and Slattery (1986) suggested that three broad categories of variables are

essential: financial, physical, and composite (reflecting financial and physical variables).

Furthermore, many researchers (e.g., Rimmington and Clark 1996) have explained that discreet

quantitative measures, such as revenue and net profit, are ideal since these encapsulate broader

aspects of the operation.

Regarding outputs, then, several researchers have demonstrated the criticality of revenue

(e.g., Pilling, Donthu, and Henson 1999; Thore, Phillips, Ruefli, and Yue 1996). Perhaps of

greater importance to operators, profit—often calculated as controllable income to reflect profit

before corporate overhead costs are allocated—has proved vital to any robust measure of unit-

level productivity (Grifell-Tatjé and Lovell 1999). Moreover, consideration of both revenue and

profit is considered paramount to gauging an individual operating unit’s financial viability

(Ingenito and Trehan 1996).

Guest satisfaction has been the most elusive output variable, yet many consider it the

most important as an indicator of long-term success. Lee-Ross (1994) and Witt and Witt (1989)

underscored the importance of service quality and guest satisfaction even before complex

4

analyses of productivity in the service industry had been developed. This output has also been

identified as a consequence of service quality that explains a considerable portion of customers’

intentions to purchase (Brady, Cronin, and Brand 2002). Central to the notion that quality service

leads to positive guest satisfaction, Mohr and Bitner (2000) reported that the effort of the service

provider has a strong positive influence on satisfaction with the transaction as an output.

Finally, two empirical studies focusing on guest satisfaction as a viable output variable in

productivity analyses offered strong evidence of its significance (Lothgren and Tambour 1999;

Parasuramam, Zeithaml, and Berry 1994). Heskett et al. (1994) highlighted this finding by noting

how one quick-service restaurant chain has found units in the top quadrant in guest-service

ratings outperform the others by all measures. Ojasalo (2003) summarized the point well: “Due

to customers’ increasing influence . . . they cannot be regarded as passive recipients of the

provider’s outputs, but should be seen as an integrated part” (p. 14).

Related to guest satisfaction is retention equity. Rust, Zeithaml, and Lemon (2000) define

retention equity as the strength of the relationship between the customer and the firm. Retention

equity is a potentially valuable output variable, as these authors note, since high retention equity

indicates “the customer’s tendency to stick with the brand, above and beyond objective and

subjective assessments of the brand” (p. 95). Furthermore, retention equity is linked to emotional

ties that the customer has with the brand. Typically measured through purchasing frequency,

retention equity indicates customers’ perception of extraordinary benefits and relational linkages

that make them very reluctant to switch to another dining option (Blattberg, Getz, and Thomas

2001).

As for inputs, financial measures that have proved important to productivity analysis

include labor cost (Burritt 1967; Yoo, Donthu, and Pilling 1997), cost of goods sold (Brown and

5

Hoover 1990; Burritt 1967), controllable fixed expenses (Sarkis 2000), and uncontrollable

expenses (Reynolds and Thompson 2002). Physical inputs that have proved important include

service capacity, such as square footage or number of seats (Doutt 1984), and environmental

characteristics, such as proximity to shopping centers and competitive conditions (Goldman

1992; Ortiz-Buonafina 1992).

Employee satisfaction intrigues us in having been posited as critical while going largely

untested in productivity-analysis studies. Koys’s (2003) comprehensive study provided evidence

of the strong relationship between employee satisfaction and restaurant performance. Similarly,

Rucci, Kim, and Quinn (1998) offered both theoretical and case-study evidence of the effect of

employee satisfaction on sales and profit. Spinelli and Cavanos (2000) identified a significant

correlation between employee and customer satisfaction in a hospitality company. As

substantiation for including this variable in productivity analyses, Kennedy, Lassk, and Goolsby

(2002) presented evidence of an indisputable relationship between employee satisfaction and

organizational goals such as sales and profit.

Data-Envelopment Analysis

Given the large number of aforementioned output and input variables, the challenge is to

integrate and analyze them simultaneously so as to identify meaningful differences among

operating units. As Caplow notes (1983), “An organization is efficient if, among similar

organizations, its output is relatively high in relation to its input” (pp. 80-81). A widely accepted

approach is Data-Envelopment Analysis (DEA), a non-parametric method that considers both

controllable and uncontrollable variables and produces a single relative-to-best productivity

index corresponding to each unit under comparison. Such a metric also allows operators, as

6

recommended decades ago by Farrell (1957), to use the best performing units as the bases for

evaluation.

As fully described by Charnes, Cooper, Lewin, and Seiford (2001), DEA extends the

productivity ratio analysis by integrating the weighted sum of outputs to the weighted sum of

inputs. In applying DEA, the weights are estimated separately for each restaurant to maximize

efficiency. Moreover, the weights estimated for restaurant i are such that when they are applied

to corresponding outputs and inputs from other units in the analysis, the ratio of weighted outputs

to weighted inputs is less than or equal to 1 (interpreted as a percentage). On a more general

basis, assuming that the number of outputs and inputs is infinite, the maximum efficiency of

restaurant o as compared with n other restaurants is calculated as follows:

Maximum Po = subject to 1 for all j = 1,…n

Ur, Vi > 0; r = 1,…, s; i = 1,…m

where

Yrj is the rth output for the jth restaurant

Xij is the ith input for the jth restaurant

Ur and Vi are the variable weights estimated and used to determine the relative efficiency of o

s is the number of outputs

m is the number of inputs

As Avkiran (2002) noted, DEA benchmarks units by comparing their ratios of multiple

inputs to produce corresponding outputs and plotting them on a multidimensional frontier. Such

7

a frontier allows for units that are most similar to be assessed by comparison with the top

performers in their peer groups. Wöber (2002) explained that benchmarking is useful particularly

when indicators span operations that are dissimilar but compete for similar target-market

constituents. Using DEA-generated productivity indices facilitates such comparison.

Furthermore, the method’s ability to take into account such a wide variety of output and input

variables makes it ideal for hospitality applications (Reynolds 2003).

METHODOLOGY

The sample consisted of all 36 same-brand corporate units of a casual-theme restaurant

chain with stores located in major metropolitan centers across the United States. The company

was selected in part for the geographic diversity of its locations; the company also allowed us

access to all financial information required for this study. These financial data were gathered

from month-ending financial statements. Physical characteristics (e.g., number of seats, square

footage) were also provided by the privately held corporation. The chain has positioned each unit

in densely populated metropolitan settings using a uniform location-selection strategy; thus,

environmental characteristics are similar across units.

Guest satisfaction data were obtained through a random sample of guests visiting each

store during the period corresponding to the aforementioned financial information. The voluntary

survey, which was distributed randomly to guests (one per table) and did not include any

incentive except for the opportunity to provide feedback, included questions corresponding to the

firm’s primary objectives: food quality, service quality, ambiance, value, and overall dining

experience (α. = .91). We also asked questions regarding patronage frequency as a measure of

retention equity, as discussed by Blattberg et al. (2001). On average, 32 surveys were completed

8

per store with an average response rate of 72%. (One unit within the chain failed to complete this

aspect of the study and was therefore dropped from the analyses.)

Employee satisfaction was assessed though a confidential, anonymous survey. The

survey included questions corresponding to the job-descriptive index (see Kinicki, et al. 2002)

and featured a summated five-point Likert scale (α. = .93). Owing to differing staffing levels, all

front-of-the house employees (n = 37.8 per unit) were surveyed with a 100% response rate

(average surveys per unit = 33.7).

In addition to traditional financial indicators such as revenue and cost of goods, we

included rent and taxes and insurance as a method for adjusting for regional economies.

Similarly, constraining variables such as number of seats and square footage were considered

since larger restaurants should be expected to produce larger sales, which are proportional to

greater expenses. Table 1 presents the complete list of factors for which we obtained data.

Table 1: Inputs and Outputs Considered

Variable Input/Output Measured As Controllable/Uncontrollable

Revenue Output $ for the period N/AControllable Income (profit)

Output $ for the period N/A

Guest Satisfaction Output Summated scale N/A

Retention Equity Output Average purchase

frequency N/A

Cost of goods sold Input $ for the period Controllable

Labor cost Input $ for the period Controllable

Employee satisfaction Input Summated scale Controllable

9

Rent Input $ for the period Uncontrollable

Taxes and Insurance Input $ for the period Uncontrollable

Square Footage Input Ft2 Uncontrollable

Number of Seats Input Number Uncontrollable

Prior to applying DEA, we ensured that each input was related to at least one output, that

the inputs are independent, and that the outputs are independent. This analysis indicated a very

strong relationship between total income and controllable income. While most firms pursue

maximization of both sales and profit (e.g., Ingenito and Trehan 1996), the ultimate goal of this

(and other) firms is improving the bottom line. Moreover, since the controllable input variables

provide adequate scaling indices for subsequent steps in the analyses, we removed total sales as

an output variable prior to proceeding.

Our next step, then, was to perform stepwise multiple regressions, using the output

variables as dependent variables and the input variables as independent variables. As

demonstrated in Table 2, while guest satisfaction did not appear to play a significant role,

retention equity did contribute to the model. Similarly, rent and square footage proved

unimportant for the subsequent DEA application.

As a confirmatory step, the data were then fit with a semiparametric regression model. As

a variation to the more traditional linear model, this approach allows us to evaluate the empirical

evidence with a broader application of the causal linkages previously suggested. Unlike classic

regression methods, semiparametric methods automatically fit linear and nonlinear functional

relationships (Ruppert, Wand, & Carroll, 2003). Thus, semiparametric regression models allow

10

for better estimation since they can be formulated to include fewer unjustified assumptions than

traditional regression models. These models supported the relationships identified in Table 2.

Furthermore, there were no substantial violations of the regression assumptions

underlying this analysis. Additionally, no individual units were found to be overly influential in

the analysis. We performed a Bootstrap model validation, with 5000 trials, to check the

robustness of the model fit (Harrell 2001). The bias-corrected estimate of the coefficient of

determination and the estimated global shrinkage parameters for the intercept and slope are -

suggest a robust model fit with little evidence of being overfit.

Table 2: Relationships between Inputs and Outputs

Variables Controllable Income

Guest Satisfaction

Retention Equity

Cost of Goods Sold 1.36*** N/A .019**

Labor Cost 1.71*** N/A N/AEmployee Satisfaction N/A N/A 3.455**

Rent N/A N/A N/ATaxes and Insurance 9.08** N/A N/A

Square Footage N/A N/A N/ANumber of Seats 1,134.84*** N/A N/A

*p<0.05, **p<0.01, ***p<0.001

As model specification, we employed an output-oriented model drawing from the

assumptions discussed earlier. Radial efficiency measures were taken using the CCR model to

provide an aggregate measure of technical efficiency, which lead to the unit-efficiency scores

described in the following section. Borger, Ferrier, and Kerstens (1998) demonstrated this logical

11

approach, which is particularly appropriate here as the inputs are not subject to radial reductions.

Furthermore, the constant returns to scale is suitable since the observed variability of outputs and

inputs is homothetic. As a test for nonradial slack, we also applied the BCC model, which

offered sufficient evidence for the associated rationale.

RESULTS

Average sales for this chain for the given 31-day period were $627,779 (SD = $120,630)

while controllable income averaged $181,149 (SD = $55,303). Average cost of goods, at 31.8%,

was $199,601 (SD = $38,751) and average total labor cost, at 27.5%, was $172,375 (SD =

$22,096). Taxes and insurance per unit averaged $15,267 (SD = $2,722). The chain averages 206

seats per store (SD = 27.5) occupying an average of 7,517 square feet (SD = 732).

According to guests’ self-reports, they typically patronize a given unit 5.7 times per

month. In terms of employees, the average front-of-house worker is 26.7 years of age and has

worked for the company some 2.8 years. Women represent a slight majority of the staff (59.4%);

a large number of the employees are single (82.9%).

Building on the regression results from Table 2, the final set of variables included five

input and two output variables (see Table 3). These were used to complete the data envelopment

analysis, which indicated that eight of the units were efficient (showing scores of 100%), with

the average efficiency score at 86%. Figure 1 shows the efficiency scores of the restaurants,

which are rank-ordered based on declining efficiency scores. The lowest-scoring restaurant had

an efficiency score of 0.6184 (or 61.8%).

12

Table 3: Inputs and Outputs Used

Controllable Inputs Uncontrollable Inputs Outputs

Cost of goods sold

Labor cost Employee

satisfaction

Taxes and Insurance

Number of Seats

Controllable Income

Retention Equity

The analysis also indicates that retention equity could be increased, on average, by

34.75% while controllable income could be increased by 13.14% if inputs were used more

efficiently. In the most extreme case, retention equity could be enhanced by 54.02%.

Furthermore, the DEA suggests that the most critical variable contributing to these potential

improvements in almost every underperforming unit is employee satisfaction. In most cases, it

appears that food and labor costs are well managed and offer little room for improvement in

terms of increasing outputs, as dictated by DEA’s weighting schema.

Figure 1: DEA Efficiency Scores of the 35 Restaurants, Rank-ordered in Terms of Declining Score

13

505560657075

80859095100

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

Ranked Restaurants

Effic

ienc

y Sc

ores

DISCUSSION

The findings regarding traditional productivity inputs, such as cost of goods sold and

labor costs, are not surprising, particularly given the variety of productivity approaches reported

during the past few decades (e.g., Ball, Johnson, and Slattery 1986; Morey and Dittman

1995; .Reynolds 2004). Similarly, the inclusion of constraining uncontrollable variables, such as

number of seats, offers little revelation. The importance of employee satisfaction, however,

represents an important contribution to the area of productivity analysis.

While many have explained the likely importance of the linkage between employee

satisfaction and the bottom line, most researchers have focused on such relationships more from

the service-profit-chain perspective than from an operations-management, efficiency perspective

(e.g., Allen and Wilburn 2002). The findings reported here suggest that the role this variable

plays may have more pervasive impact organizationally with direct implications to efficiency—

and profit.

The related consideration of retention equity is also interesting. First, we must consider

the interplay between employee satisfaction and guest loyalty, operationalized here as retention

equity. Since front-line employees are often most involved with the employee-customer

interface, these individuals ultimately will determine the external customers’ impressions of the

organization (Hartline et al. 2000; Kelley 1992). Thus, we see how such a relationship offers

utility in a holistic productivity analysis. As Banker et al (2000) noted, “Customer satisfaction is

considered a key short-term measure that is a lead indicator of long-term performance,” (p. 72)

leading ultimately to retention equity. Thus, to ignore such variables in exploring true unit-level

efficiency is naïve, particularly given the implications of this study.

14

While provocative, this study is limited in having targeted a relatively small chain.

Furthermore, the number of guest satisfaction surveys per store, while providing reasonable

variability, was limited. Finally, other variables, such as competition—which was demonstrated

to be unimportant in earlier studies (e.g., Reynolds 2004)—was not included in this study

because of the uniformity of the site-selection process used by the firm. Nonetheless, it is

possible that such an input may contribute to the accuracy of the efficiency analysis.

Despite these limitations, this study provides empirical support for the efficacy of

integrating non-financial data into the productivity analysis. It also opens the door for further

research in this field. While we recognize that data such as employee-satisfaction and retention-

equity indices may not always be easy to obtain, we assert that these and other variables must be

integrated to complete a truly holistic—and accurate—productivity assessment.

15

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