Northwestern University

Employees, Customers, and the Bottom Line

Mathematical Methods in the Social Sciences Senior Thesis

Bhargav M. Rajamannar

June 6th, 2013

Advisor: Russell Walker

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Table of Contents

Acknowledgements……………………………………………………………………………..…3

Abstract……………………………………………………………………………………………4

I. Introduction………………………………………………………………………………..5

A. Background………………………………………………………………………...….5

B. Literature Review……………………………………………………………………...8

II. Methodology……………………………………………………………………………..13

A. Data………………………..…………………………………………………………13

B. Models………………………………………………………..………………………14

C. Results…………………………………..……………………………………………17

1. Model 1………………………………………………………………………18

2. Model 2………………………………………………………………………18

3. Model 3………………………………………………………………………18

i. 2009………………………………………………………………18

ii. 2010………………………………………………………………19

iii. 2011………………………………………………………………19

iv. 2012………………………………………………………………20

4. Model 4………………………………………………………………………20

i. 2009………………………………………………………………20

ii. 2010………………………………………………………………21

iii. 2011………………………………………………………………21

iv. 2012………………………………………………………………21

D. Explanation of Results……………………………………………………………….22

III. Discussion………………………………………………………………………………..24

IV. Areas for Improvement and Directions of Future Research……………………………..27

V. Concluding Remarks……………………………………………………………………..30

VI. Bibliography……………………………………………………………………………..31

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Acknowledgements

First and foremost, I would like to thank my parents. Without your love, support, guidance, and

encouragement I would not have made it even close to this far. Secondly, thank you to all my

friends who supported and helped me throughout these last four years. A special thank you to

Lauren DePaula for your encouragement and assistance throughout this thesis. Another special

thank you to Naveen Nallappa for helping me work through several of the problems that arose

throughout the course of the writing. Thirdly, thank you to all my professors in MMSS who

have taught me the invaluable skills that even allowed me to write this thesis. In particular,

thank you to Professor Rogerson for looking out for me, to my TA Derek Song for helping me

perform the analysis in this thesis, and to Professor Witte for helping me find the data so I could

perform the analysis in the first place. In addition, thank you to Sarah Muir Ferrer for your

assistance over the last four years. Lastly, thank you to Professor Walker for serving as my

advisor.

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Abstract

At the start of the 1990s, a new philosophy was being developed regarding the best way to

capture profitability and growth. This new philosophy, coined the “Service Profit Chain” in a

seminal paper published by a group of academics at the Harvard Business School in 1994, stated

that the management of a company must strive to improve employee satisfaction, which will in

turn drive customer satisfaction, which will result in the final goal of profitability and growth.

There is, of course, heavy debate regarding the nature of this relationship and the validity of the

service profit chain itself. The contribution of this paper to the existing literature is threefold:

firstly, to examine the effects of employee satisfaction and customer satisfaction on a company’s

revenue; secondly, to try and determine which has a greater impact on revenue; and thirdly, to

ascertain if there is an “optimal” level of employee satisfaction and customer satisfaction. To

achieve these goals, econometric models that regressed revenue on employee satisfaction and

customer satisfaction (along with certain control variables) were built using empirical data. The

results generated seem to indicate that customer satisfaction has a greater positive impact on a

company’s revenue – in fact, the results of the models concluded that employee satisfaction has a

negative effect on revenue. The potential reasons for such a surprising outcome are discussed in

this paper. While it is very inconclusive, the output from the models seems to point to there

being a point at which customer satisfaction experiences diminishing returns, it is not possible to

pinpoint where exactly that point is, however, for reasons explored in this thesis.

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I. Introduction

A. Background

From about the 1950s to the late 1980s, the prevailing ideology in American corporate

culture was that a company had to strive to achieve market dominance in order to ensure

profitability and growth. This meant that companies were spending millions of dollars on

becoming the number one or two ranked company in their industry, done primarily through

extensive marketing and mergers and acquisitions. It should come as no surprise then that the

1960s and 1980s were two of the most active periods in terms of M&A transactions, and that

much of modern marketing philosophy, prior to the recent trend towards big data analytics, was

developed during this time period. In the late 1980s, however, the focus began changing to

retaining current customers rather than the previous practice of trying to gain new customers –

the reason for the heavy investment in marketing and M&A. This change in perspective came

about as businesses and academics began to look at the lifetime value of a customer’s repeat

business, which was found to be generally greater than the value of a new customer who had no

relationship with the company. This research culminated in the publication of the paper Putting

the Service-Profit Chain to Work by James L. Heskett, Thomas O. Jones, Gary W. Loveman, W.

Earl Sasser Jr., and Leonard A. Schlesinger, colleagues at the Harvard Business School. The

“service profit chain” mentioned in the title is a backwards inductive method to find how a

company can generate profitability and growth, and it is based on the idea that a loyal customer

is more valuable than one who is not. The service profit chain, as described in the paper is as

follows:

1) The goal of a company is to achieve profitability and growth.

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2) This occurs when a company has loyal customers because it has done a good job to

retain those customers. In turn, these loyal customers drive profit and growth through

their referrals and repeat business.

3) Satisfied Customers turn into loyal customers because they have received “service

designed and delivered to meet” their needs.

4) Customers are satisfied when there is a high external service value. That is, the good or

service provided by a company has value to the customer at a level that at the very least

matches their expectations.

5) External service value is driven by employee productivity.

6) Productive employees are first loyal employees.

7) Satisfied employees become loyal employees.

8) Employee satisfaction is driven by internal service quality. Internal service quality

encompasses all factors and structures that relate to an employee’s job and his or her

ability to execute the responsibilities of the role. According to the paper, it is the only

link of the service profit chain that a company’s management has the ability to directly

influence and is thusly the starting point of the chain and company’s primary “input.”

Internal service quality includes things as broad as a company’s hiring process, the

workplace and job designs, recognition and rewards given to employees, and the tools for

serving customers.

Since the publication of this paper, the service profit chain has been extensively discussed in

academia and the corporate world, and has been put into practice all over the world (with varying

success) over the last two decades. Of course, there is no consensus as of yet as to the validity of

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the chain, whether these are even the appropriate links, the nature of the relationship of the links,

and possibly most importantly, the how the chain should even be quantitatively measured.

The goal of this paper is to contribute to the field by gathering and analyzing data through

the construction of econometric models that will answer some of these questions; namely:

1) What are the effects of customer satisfaction and employee satisfaction on revenue?

2) Which has a greater impact on revenue?

3) Does there exist and optimal level of satisfaction for either metric?

The reason that these links in the service profit chain were selected is because they have been

posited to be the most important in the broad literature of the field. If the chain is broken into

four general sections, the first is where a company’s management direct has input, with the end

being the outcome of the whole system; the two middle portions are employee and customer

driven. The employee portion of this system, according to the theory of the service profit chain,

is contingent upon the employee satisfaction values, which is mirrored in the next customer-

centric section of the chain. Whereas in the standard service profit chain model profit is the

desired outcome, for this analysis revenue will be used as the desired output due to the wider

availability of information pertaining to it.

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B. Literature Review

There exists a gamut of literature in this area written from the late 1980s – precursors to

the modern theory – through to the 1994 paper, and to the present day. Researchers have

discussed the chain endlessly from a qualitative standpoint and there are many how-to’s for

businesses that are interested in leveraging the chain in their enterprises. There is, however,

somewhat of a dearth in quantitative analyses – an area this paper hopes to add to. There is one

huge problem that faces researchers who are interested in a more data intensive study of the

topic: a serious lack of data. It is an issue that plagues many in the social sciences and has

seriously hampered a rigorous exploration of this topic. The problems that stem from poor data

or a complete lack thereof are frankly innumerable, but it is important to identify the most severe

issues. The first problem stems from the fact that it is extremely difficult for researchers to

obtain quality data from a wide range of companies. Businesses are reluctant to allow third-

parties access to existing customer and employee survey data or they simply do not allow

independent polling to be taken for a variety of issues, ranging from concerns over the privacy of

their employees and customers to anxieties about the public revelations of their practices,

resulting in the loss of competitive advantage. The outcome of this is that researchers are forced

to perform an analysis with a sample of a single company or just a small handful, which causes

the validity of their results to be questioned when trying to apply it to the wider population of

businesses in general. This is the most crippling obstacle and is probably a big reason for the

lack of consensus regarding the effects of employee and customer satisfaction, which will be

discussed later. In fact, there is no real consensus on what all the links of the service profit chain

even are and the nature of their interactions.

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The next two stumbling blocks that will addressed are discussed in greater depth in the

paper Assessing the Service-Profit Chain by Wagner A. Kamakura, Vikas Mittal, Fernando da

Rosa, and Jose Afonso Mazzon, and also stem from a lack of quality data. The first area of

trouble identified by the authors of the paper is that most of the time when data is collected, each

link in the service profit chain is extremely difficult to compare because each one is measured in

different units. For example, if one wanted to compare employee satisfaction effects and

customer satisfaction effects, it would be quite difficult to do so because both metrics would be

measured in different ways; for instance employee satisfaction may be measured in terms of a

willingness to remain in the job despite receiving the same compensation at an alternate place of

employment, while customer satisfaction will be measured in referrals. These two variables are

defined in such different terms that comparing them is tough. This leads into the next problem: it

is proving to be enormously difficult to build a unified model of the service profit chain. While

it is fairly simple to demonstrate the relationship between adjacent links of the service profit

chain, trying to compare how non-adjacent links interact and influence each other is much

harder, and modeling the entire chain in one formula has so far not yet been done. This is

important to the study of the service profit chain because it would allow researchers to see how

one link in the chain affects the whole system, and it would reveal which links do in fact even

belong in the chain. To clarify, it is important to discover which links in the chain drive profit

and growth and only keep those while excising those links that are extraneous. In addition, it

would make it possible for researchers to observe more complex interactions that would stem

from simultaneously manipulating multiple selections in the chain. This would also enable

researchers to more accurately assess which of the links have outsized or undersized effects – a

related question to one in this thesis that seeks to determine this fact for the employee

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satisfaction and customer satisfaction links. The main obstacle in the construction of a complete

formula is that there are far too many collinear relationships between the different variables.

Mitigating them all has so far been unsuccessful.

Given that the background of the service profit chain has been explored as have the

problems related to measuring and analyzing it, it is time to observe the works and insights of

previous researchers that have influenced this paper. Since there is no true consensus regarding

all the links of the chain and how they interact, this paper will be taking the lead of certain

researchers, such as Garry A. Gelade and Stephen Young in their paper Test of a service profit

chain model in the retail banking sector, or whittling the chain to its commonly agreed upon,

most important aspects: employee satisfaction, customer satisfaction, and profit (revenue in this

case). For the reasons previously mentioned, there is currently no uniform opinion regarding the

relationship between these factors, however, here is a brief overlook of the various theories that

are currently being debated and some papers that support each claim.

The first view is that there is a positive relationship between employee satisfaction,

customer satisfaction and profit, most famously posited in Putting the Service-Profit Chain to

Work by James L. Heskett, Thomas O. Jones, Gary W. Loveman, W. Earl Sasser Jr., and

Leonard A. Schlesinger, and again in the 1997 book, The Service Profit Chain, by Heskett,

Sasser, and Schlesinger. In both works the authors espouse this sentiment (employee satisfaction

leads to customer satisfaction, which in turn leads to profit); however, they demonstrate it using

case studies rather than hard data, although they do perform the cases on a variety of companies

in different industries. Given that their results relied on a qualitative analysis, it is difficult to

evaluate the validity of their findings in a very robust fashion.

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The next view is that there is a positive relationship between only frontline employee

satisfaction, customer satisfaction and profit. A frontline employee is defined as an employee

whose primary duty is to interact with customers. The paper mentioned before, Assessing the

Service-Profit Chain by Wagner A. Kamakura, Vikas Mittal, Fernando da Rosa, and Jose Afonso

Mazzon, arrived at this conclusion. While this paper did involve using data and the construction

of a model to try and observe the interactions between the three variables plus several

instruments, the researchers only had data from a Brazilian retail bank chain. This approach has

some serious drawbacks. While there may be some branch to branch variations, the bank as a

whole has a general policy of the way it conducts its business, which means that this model is

very specific to this bank and potentially cannot be successfully applied to other companies.

Furthermore, when the researchers tried to apply their model on a more granular, branch level,

rather than the bank as a whole, they found that their results became statistically insignificant.

The next view is there is no relationship between employee satisfaction, customer

satisfaction, and happiness. This view is put forth on the previously mentioned paper, Test of a

service profit chain model in the retail banking sector, by Garry A. Gelade and Stephen Young,

and by Gary W. Loveman, one of the co-authors of the original 1994 paper, in his independently

published 1998 paper, Employee Satisfaction, Customer Loyalty, and Financial Performance: An

Empirical Examination of the Service Profit Chain in Retail Banking. In the first paper, the

authors found a positive relationship between employee satisfaction and profit, but no

statistically significant relationship between employee satisfaction and customer satisfaction, or

customer satisfaction and profit. In Loveman’s paper, he found a positive relationship between

employee satisfaction and profit and customer satisfaction and profit, however, no statistically

significant relationship between employee satisfaction and customer satisfaction. Both these

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papers suffer from the same drawback as the paper by Kamakura et al, in that they only observe

one company.

The last view, which is discussed in the 2000 paper, Applying the service profit chain in a

retail environment: Challenging the “satisfaction mirror”, by Rhian Silvestro and Stuart Cross,

states that there is a negative correlation between employee satisfaction and profit and a positive

relationship between customer satisfaction and profit. Again, a drawback in this paper is that a

single company was looked at; in this case a grocery chain. The authors admit that this result

may not be reproduced in other industries or companies, due to the highly self-service nature of

grocery shopping, during which a customer may not even interact with an employee; therefore,

seemingly indicating that employee satisfaction negatively affects profit. Though the authors

themselves did not discuss this, it could be possible that in certain situations, different links of

the chain change in importance and ability to drive profit. This relates to one of the questions

being asked in this paper, which type of satisfaction has a greater impact on revenue.

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II. Methodology

A. Data

For this paper, data regarding employee satisfaction and customer satisfaction was

required. Data of a wide variety of companies was sought to add rigor to the findings so that

they can be better representative of businesses in general, unlike the narrow focus of most

previous researchers. For the data on employee satisfaction, the lists curated by Fortune

Magazine of the hundred best companies to work for were used. For the data on customer

satisfaction, lists from the University of Minnesota of the hundred companies with the highest

customer satisfaction were used. Both lists covered the years 2009 to 2012. In addition, the

sample was augmented with an additional fifty companies on the Fortune 500 list that were not

on either satisfaction list. Furthermore, data was collected for all those companies of their

annual revenues and their approximate average number of employees for the time period being

analyzed. Since the data spans multiple years, it can be utilized as panel data in a time series.

While the service profit chain generally uses profit as the end result, for the purposes of

this paper revenue was chosen as dependent variable due to the greater availability of the

pertinent information. This was the reason that approximate average number of employees over

the four years was chosen as well, since records of total strength of workforce are very difficult

to obtain and the figures often rounded anyway. With regards to the companies that appear on

the lists, there is not very much overlap. In any given year no more than about fifteen entries

appeared on both lists. The implications of the weaknesses in the data will be further discussed

later in this paper.

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B. Models

There are four closely related models that were built for this analysis – regressions that

were used to try and answer three questions: firstly, how do employee satisfaction and customer

satisfaction influence revenue; secondly, which has a greater impact on revenue; and thirdly,

does there exist an optimal level for either. As stated in the literature review, there is no

consensus as to how the two variables interact with each other. It is unknown whether employee

satisfaction drives customer satisfaction, which in turn drives profit – or in this case revenue – or

if they both simultaneously drive profit, or if there are other mediating factors present. As a

result, for the purposes of this analysis, the models will treat employee satisfaction and customer

satisfaction as being independent, especially since one of the goals of this research is to

understand which has a greater impact on a company’s revenue. The potential pitfalls of this

assumption will be discussed later in this paper.

All four models have the same basic construction; revenue is regressed upon one or two

employee satisfaction variables and one or two customer satisfaction variables, the reason for

which will be explained later, plus a control variable, which is the number of employees. This

was done to try and address other driving factors of revenue, and company size is definitely one

of them. Since there is not enough data to segment by industry and use market share, workforce

size was the best proxy. If the model is a time series regression, then it contains an additional one

period lagged revenue variable. The goal of all four models is to seek explanations as to how

employee satisfaction and customer satisfaction influence revenue, and answer the three crucial

questions.

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The first model that was built is a time series regression that utilizes the collected panel

data:

Revenue = β0 + β1(EmpSatTop) + β2(EmpSatBot) + β3(CusSatTop) + β4(CusSatBot)

+ β5(NumEmp) + β6(LagRev)

EmpSatTop = Appears in the top half of the lists of the best places to work

EmpSatBot = Appears in the bottom half of the lists of the best places to work

CusSatTop = Appears in the top half of the lists of companies with the highest customer

satisfaction

CusSatBot = Appears in the bottom half of the lists of companies with highest customer

satisfaction

NumEmp = The average number of employees working for a company between 2009 and 2012

LagRev = A lagged variable that is the previous year’s revenue

As shown, revenue is the dependent variable because it is easier to obtain than profit. Revenue is

regressed upon the six variables, most importantly the four dummy variables of employee and

customer satisfaction. These dummy variables were used to try and answer the third question: is

there an optimal level of either type of satisfaction? Since the data comprises lists of only the top

hundred companies in either category, the organizations in question have extremely high levels

of one or either type of satisfaction to even appear on a list. Furthermore, since these lists are

ordinal rankings, there is no way to exactly pinpoint the optimal level of satisfaction, so this

model attempts to find a ballpark range. The final two variables are controls and instruments;

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number of employees is to try and control for company size, while lagged revenue variable is to

remove simultaneity in the equation.

The second model is a simplification of the first model and is used to more accurately

answer the first and second questions: what are the effects of the two types of satisfaction on

revenue and which has a greater impact, respectively.

Revenue = β0 + β1(EmpSat) + β2(CusSat) + β3(NumEmp) + β4(LagRev)

EmpSat = Appears on lists of best places to work

CusSat = Appears on lists of companies with highest customer satisfaction

The sole difference between these two models is that there are only two dummy variables instead

of four and these dummy variables only look at whether or not a company simply is present on

either list. As was stated before, the companies on these lists have very high levels of

satisfaction, so to see the overall effects of high satisfaction and on revenue it makes sense to

look at each type of satisfaction as a whole. It is also important to try and answer those questions

with this model because one hundred companies is still too small a sample.

The third model is nearly identical to the first model, but it is not a time series equation.

This model was built as a reaction to the results obtained from the first model – which will be

discussed later – that seemed to have been very badly impacted by the effects of the Great

Recession beginning in 2008. This model was used to regress the revenue of one year on the

employee and customer satisfaction for that same year, and as such the only difference in the

equation itself is that the lagged revenue variable is dropped.

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Revenue = β0 + β1(EmpSatTop) + β2(EmpSatBot) + β3(CusSatTop) + β4(CusSatBot)

+ β5(NumEmp)

The fourth and final model is to the third model what the second model is to the first, and

as such does not require more explanation.

Revenue = β0 + β1(EmpSat) + β2(CusSat) + β3(NumEmp) + β4(LagRev)

C. Results

(Significant variables are highlighted)

1. Model 1

R2 = .6

Variable Coefficient Standard Error z P > | z | 95% Confidence Interval

Constant 10040.67 1866.26 5.38 0 [6382.88 13698.46]

EmpSatTop -301.73 3069.83 -0.10 0.92 [-6318.49 5715.03]

EmpSatBot -2792.97 2167.88 -1.29 .2 [-7041.9 1456]

CusSatTop -2680.17 2182.61 -1.23 0.22 [-6958.01 1597.68]

CusSatBot -3143.66 1615.83 -1.95 0.05 [-6310.6 23.3]

NumEmp .19 .02 11.82 0 [.16 .23]

LagRev .16 .02 6.55 0 [.11 .2]

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2. Model 2

R2 = .6

Variable Coefficient Standard Error z P > | z | 95% Confidence Interval

Constant 10297.41 1824.03 5.65 0 [6722.37 13872.45]

EmpSat -2204.14 2034.49 -1.08 0.28 [-6191.67 1783.38]

CusSat -3114.28 1563.55 -1.99 0.05 [-6178.79 -49.78]

NumEmp .19 .02 11.83 0 [.16 .22]

LagRev .16 .02 6.59 0 [.11 .2]

3. Model 3

i. 2009

R2 = .24

Variable Coefficient Standard Error t P > | t | 95% Confidence Interval

Constant 8107.15 7196.54 1.13 0.26 [-6153.2 22367.57]

EmpSatTop -8.85 11840.86 -0.00 0.999 [-23472. 23454.6]

EmpSatBot -443.16 11907.41 -0.04 0.970 [-24038.48 23152.17]

CusSatTop -5566.54 9666.639 -0.58 0.566 [-24721.63 13588.55]

CusSatBot 6893.96 9149.868 0.75 0.453 [-11237.11 25025.04]

NumEmp .27 .05 5.66 0 [.18 .36]

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ii. 2010

R2 = .48

Variable Coefficient Standard Error t P > | t | 95% Confidence Interval

Constant 9508.75 3025.34 3.14 0 [3513.842 15503.65]

EmpSatTop -40895.03 7927.542 -0.62 0.54 [-70045.49 13228.77]

EmpSatBot -2042.61 6828.94 -0.3 0.77 [-14912.19 5042.96]

CusSatTop 6635.34 5806.94 1.14 .26 [-9161.17 8027.65]

CusSatBot 6639.21 5700.06 1.15 0.25 [-11624.59 9453.99]

NumEmp .22 .02 9.78 0 [.18 .27]

iii. 2011

R2 = .49

Variable Coefficient Standard Error t P > | t | 95% Confidence Interval

Constant 19740.38 3435.05 4 0 [6933.61 20547.15]

EmpSatTop -6834.27 8740.48 -0.78 0.44 [-13466.3 11914.44]

EmpSatBot -9481.01 6944.31 -1.37 0.18 [-18375.09 1704.2]

CusSatTop 539.84 6233.33 .09 0.93 [-14108.94 4045.38]

CusSatBot 1183.5 6136.6 .19 0.85 [-15655.76 2354.435]

NumEmp .2324344 .02 9.98 0 [.19 .28]

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iv. 2012

R2 = .43

Variable Coefficient Standard Error t P > | t | 95% Confidence Interval

Constant 12843.27 3915.928 3.28 0 [5083.597 20602.94]

EmpSatTop -482.28 7498.04 -0.71 0.48 [-15340.14 14375.58]

EmpSatBot -8789.92 6360.67 -1.38 0.17 [-21394.01 3814.17]

CusSatTop -3218.14 5364.63 -0.21 0.55 [-13848.51 7412.24]

CusSatBot 1688.52 5343.521 1.32 0.75 [-8900.024 12277.06]

NumEmp .24 .03 8.62 0 [.18 .29]

4. Model 4

i. 2009

R2 = .23

Variable Coefficient Standard Error t P > | t | 95% Confidence Interval

Constant 8494.03 7102.01 1.20 0.23 [-5576.34 22564.39]

EmpSat -1224.06 9049.36 -0.35 0.89 [-19152.47 16704.36]

CusSat 1158.04 7905.66 0.98 0.33 [-14504.49 16820.58]

NumEmp .27 .05 5.7 0 [.18 .37]

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ii. 2010

R2 = .47

Variable Coefficient Standard Error t P > | t | 95% Confidence Interval

Constant 9882.52 2983.51 3.31 0 [3971.66 15793.38]

EmpSat -2195.47 4058.2 -0.59 0.56 [-10235.5 5844.55]

CusSat 6788.97 3583 1.45 0.55 [-9242.55 16054.6]

NumEmp .22 .02 9.78 0 [.17 .26]

iii. 2011

R2 = .48

Variable Coefficient Standard Error t P > | t | 95% Confidence Interval

Constant 13931.14 3417.35 4.08 0 [7160.75 20701.52]

EmpSat -5602.71 4338.62 -1.29 0.2 [-14198.3 2992.88]

CusSat 810.53 3851.99 .16 0.87 [-13605.02 1657.97]

NumEmp .23 .02 9.96 0 [.18 .28]

iv. 2012

R2 = .42

Variable Coefficient Standard Error t P > | t | 95% Confidence Interval

Constant 13100.16 3902.53 3.36 0 [5368.55 20831.78]

EmpSat -5786.66 5250.2 -1.1 0.27 [-16188.25 4614.93]

CusSat 814.01 4501.14 0.18 0.86 [-9731.57 8103.56]

NumEmp .24 .03 8.68 0 [.18 .29]

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D. Explanation of Results

The results of the models are somewhat surprising. First of all, the third and fourth

models were built as a result of the strange results derived from the first and second models.

Nowhere in the literature of the field was it indicated that both employee and customer

satisfaction would have a negative effect on profit or revenue. A probable cause of this outcome

is that the panel data that was used in constructing the time series regressions comes from the

Great Recession that began in 2008 and lasted until 2010, comprising half the years in the panel

data. Furthermore, the economy was only sluggishly recovering in 2011 and 2012; so many

companies were still registering low or negative. Based on this information, it was decided that

the time series models would be dropped and not analyzed any further.

The remaining models are standard OLS regressions and yield results more in line with

the extant literature of the field. For the models in which employee satisfaction and customer

satisfaction are not split into whether or not a company is in the top half of the list or in the

bottom half of the list (which will be called “single-list models” from here on out), in all years

except 2009, employee satisfaction is a statistically significant variable that negatively correlates

with revenue, a similar result as to what was found in the paper Applying the service profit chain

in a retail environment: Challenging the “satisfaction mirror”. In the model where the lists are

split (to be called “split-list models” from here on out) for the years of 2011 and 2012 both

employee satisfaction variables are statistically significant and negatively correlated to revenue,

and only the being in the top half of the employee satisfaction list is statistically significant and

also negatively correlated to revenue for the year of 2010. Employee satisfaction is simply not

significant in any respect for the year 2009.

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Customer satisfaction was a statistically significant variable in the years of 2009 and

2010 for the single-list models, and was positively correlated to revenue. For the split-list

models customer satisfaction, for the year 2009 being in the top half of the list is statistically

significant but negatively correlated to revenue. Being on the bottom half of the list is also

statistically significant and shares a positive correlated with revenue. For the year 2010, both

customer satisfaction variables are statistically significant and are positively correlated to

revenue. For the year 2012, only being on the bottom half of the list was statistically significant

and it was positively correlated to revenue.

Generally speaking, being on the bottom half of either list had a greater effect – whether

positive or negative – than being in the top half of either list. It should be noted, however, that

the coefficients of each variable are somewhat meaningless. The main reason for this is the

enormous range of revenues in the dataset. When regressing these revenues that range anywhere

from a couple billion dollars to over a hundred billion dollars on dummy variables, the

coefficients lose quite a bit of predictive power and meaning, as evidenced by the extremely high

standard error values and very wide confidence intervals.

Lastly, the for all years except 2009, the R2 value is right below .5, indicating that there

may be other drivers of revenue that have not been captured in these models. Perhaps there are

some overlooked links in the service profit chain that have not been getting the attention they

deserve, but a more likely reason could be that there are more control variables that are required,

either instead of or in addition to number of employees. This will be further discussed later.

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III. Discussion

Though the results that have been collected through the models may not be the most

conclusive, they still offer several insights in answering the three questions posed earlier in the

paper.

The first question asked: What is the impact of each type of satisfaction on revenue?

Based on the data, it is easy to conclude that employee satisfaction has a negative impact on

revenue while customer satisfaction has a positive impact on revenue. It should be noted,

however, that employee satisfaction could have an overall positive impact on revenue that is not

being captured in these models, if it does in fact drive customer satisfaction.

The second question asked: Which type of satisfaction has the greatest on revenue?

Based on the data, it would be employee satisfaction since it was a statistically significant

variable more often. However, as stated before, employee satisfaction has a negative effect on

revenue according to the model, so customer satisfaction is the variable with the greatest positive

impact on revenue.

The data would seem to indicate that a company should invest in customer satisfaction,

since investments into employee satisfaction drive down revenue. This is potentially not true

because there may exist certain drivers in employee satisfaction that improves customer

satisfaction, which in turn increases revenue. Assuming that it is possible to spend towards

improving customer satisfaction by itself without having to invest in employee satisfaction, it

may still be better to invest in customer satisfaction directly. This is because employee

satisfaction may not raise customer satisfaction at the same rate as a direct investment into

customer satisfaction. For example, a dollar investment into customer satisfaction may increase

revenue by two dollars but a dollar investment into employee satisfaction may increase customer

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satisfaction to the same level as a fifty-cent direct investment, which will in turn raise revenue by

a dollar. This is purely conjecture, however, as the data supports nothing but the fact that

customer satisfaction is a major driver of revenue. Although, based on the paper Applying the

service profit chain in a retail environment: Challenging the “satisfaction mirror”, there are

instances where employee satisfaction is not necessarily an important driver of revenue or profit.

In that paper, the justification for employee satisfaction having a negative relationship with profit

is that in the grocery chain they observed, customers do not interact very much with employees;

they have a very self-service experience. There are certainly companies in this dataset where it

is the case where customers hardly interact with employees, but that would also mean one agreed

with the current set-up of the service profit chain. An alternative explanation could be that since

lists of the one hundred companies with the happiest employees are used, those companies may

be spending too much money on improving employee satisfaction – more than the returns

generated from employee satisfaction. This could be exacerbated by the fact that many Fortune

500 companies were added to the data that are not on either list. Many of these companies are

old, respected entities that are so entrenched that they do not need to necessarily concern

themselves with being one of the best places to work, such as many of the national commercial

banks or big oil companies, yet they are amongst the most revenue generating corporations on

the planet. As a result of their enormous revenues and lack of appearance on the list for best

places to work, the models indicate employee satisfaction as detrimental. It must be noted,

however, that customer satisfaction should have similarly suffered, as all of the added Fortune

500 companies do not appear on either list. A possible explanation is that since there is very

little overlap of companies on both lists, the companies with the best customer satisfaction, as a

whole, generate more revenue than those appearing on the employee satisfaction lists.

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The final question asks: Does there exist an optimal level of either satisfaction. Since

employee satisfaction has a negative effect on revenue, according the models used for this paper,

there can be no commentary made regarding the optimal level of employee satisfaction, except to

have the very minimal level of satisfaction that would be required to retain a functioning

workforce. With regards to customer satisfaction, the data indicates that it is best for a company

to be on the bottom half of the list. This intuitively makes sense from a standpoint of

diminishing returns that states that after a certain point the returns of revenue from an increase of

marginal customer satisfaction begins to decrease. In the context of the data used for this

research, however, this result and explanation must be viewed with a degree of skepticism

because the lists of customer satisfaction that were used comprise only the hundred companies

with the highest amounts, ranked in an ordinal manner. What that means is that all of the

companies on the list have very high customer satisfaction to begin with and since we are

unaware of the range of customer satisfaction value that exists in the list and the differences in

value between each rank, it is quite difficult to say what an optimal level would be. For

companies already on the list, however, it could be comforting to know that falling out of the top

half of the list is not necessarily a terrible thing, while companies on the bottom half can take

solace in knowing that it is potentially not worthwhile to aim for the top half of the list.

In summary, the answers to the three questions, based on the results from the models, are

as follows:

1) What are the effects of employee satisfaction and customer satisfaction on revenue?

Employee satisfaction has a negative effect while customer satisfaction has a positive

effect.

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2) Which type of satisfaction has a greater effect on revenue?

Employee satisfaction has a greater effect on revenue, but it is a negative effect.

Customer satisfaction has a positive effect on revenue.

3) Does there exist an optimal level of either type of satisfaction?

Since employee satisfaction has a negative effect on revenue, the very minimal amount

that is required to maintain a workforce. For customer satisfaction, there is potentially

point of diminishing returns.

IV. Areas for Improvement and Directions of Future Research

The most crucial improvement that could be done to this research is obtaining high

quality data. The first step would be increasing the size of the database. One hundred

companies for each type of satisfaction are simply not enough. The second step would be

ranking the companies by some form of satisfaction coefficient rather than the opaque, ordinal

system currently used. A side-effect of using satisfaction coefficients would be that the

coefficients in the models that were built for this paper would be more meaningful. One could

say for example, an increase of x in customer satisfaction will lead to an increase in revenue by

y. A third enhancement is related to the R2 measurement, but would produce meaningful

betterments in other ways. As shown in the data, the R2 for the models was just under .5. This

means that there are other drivers of revenue. The R2 could be significantly improved, however,

if in addition to more companies being added to the dataset, if the dataset was actually divided by

industry and market cap. The controlling variable in this analysis was average number of

employees, which pales in comparison to the robustness that would be provided from division by

industry and market cap. The distinction by industry and market cap unfortunately could not be

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done in this paper due to the small sample size of companies. If it was done, it would also

address an issue that was mentioned in the paper Applying the service profit chain in a retail

environment: Challenging the “satisfaction mirror”, by Rhian Silvestro and Stuart Cross, where

they explain that a possible cause for employee satisfaction being negatively related to profit is

because they used a grocery chain as their source for observations. The grocery chain’s

customers apparently hardly needed to interact with employees, which led to employee

satisfaction being unable to act as a driver of customer satisfaction (unless the standard service

profit chain is wrong), and consequently profit. By having companies broken into separate

industries and market caps, the comparison between companies becomes much fairer and more

meaningful. In addition, it would give insights as to how the service profit chain operates in

different industries (thus either proving or disproving the reasoning in Silvestro and Cross’

paper) and in companies of different sizes. It could also offer major insights for companies

seeking to expand market cap on potential ways to manipulate either satisfaction to increase

profit. Another improvement to the data would be the use of data from a more economically

stable time period. Since companies were losing money regardless of what they did during the

economic downturn, the time series regression models completely discredited either type of

satisfaction. By using data from a different time period, these models would be usable and could

offer additional insights, such as the compounding effects that could rise from consistently

having high employee and/or customer satisfaction. It could potentially improve the results

obtained from the standard OLS models as well. A final improvement on the data would be

using profits instead of revenue. The service profit chain is meant to drive profit, which is a

better measure of a company’s success than revenue. Using profit is especially important to

effectively answering the third question asked in this paper – is there an optimal level of either

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kind of satisfaction? Since the models presently indicate that there are diminishing returns to

satisfaction, profit would better help pinpoint when that occurs by more accurately gauging

whether there has been an excessive spending towards improving satisfaction, above the returns

generated.

For the future, researchers must turn their direction on answering one very pressing

question, and it could be definitively answered with the dataset I have described above. That

question is what is the relationship between employee satisfaction and customer satisfaction?

This question is absolutely crucial to answer as it is essentially the basis of the whole service

profit chain itself. In addition, by answering this question, it would allow researchers to better

pursue what is the “holy grail” of this field: building a unified model that comprises the entire

chain. As discussed in the paper Assessing the Service-Profit Chain by Wagner A. Kamakura,

Vikas Mittal, Fernando da Rosa, and Jose Afonso Mazzon, the ideal and most robust way to

analyze the service profit chain is to build an equation the models the whole system and accounts

for all the relationships and collinearities, which would firstly prove the links that do in fact

belong in the chain and those which are extraneous, but would also allow for more complex

analyses through the manipulation of multiple links in the chain. Such a model would also very

easily the three questions posed in this paper.

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V. Concluding Remarks

Service profit chain, which started being developed in the late 1980s and early 1990s,

states that effective management raises employee satisfaction. This increase employee

satisfaction raises customer satisfaction, which in turn generates profit and growth for the

company. Despite a lack of consensus regarding the exact nature of the chain – from the links

that comprise, to their relationships with each other, and their effects on profit – it is a trendy

philosophy that many companies have been seeking to leverage. The biggest obstacle to a

rigorous analysis of the chain is a lack of quality data, a problem that has plagued many

researchers, who have sometimes opted to simply perform a qualitative analysis.

The objective of this thesis is to try and perform an econometric analysis using empirical

data in the hopes of contributing to the field by answering three questions about the most

important links of the service profit chain: employee satisfaction and customer satisfaction. The

three questions are what are the effects of each type of satisfaction on revenue, which has a

greater impact and is there an ideal level of either type of satisfaction? While this paper also

suffered from poor data, there were some interesting results. From the econometric models built

over the course of the research, customer satisfaction had a positive impact on revenue, while

employee satisfaction had a negative effect and an outsized effect compared to customer

satisfaction. The third question could not truly be answered, but from the models there is

probably a point of diminishing returns for customer satisfaction.

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