TOWARDS MORE EFFECTIVE SERVICE MANAGEMENT
DECISION MAKING:
DESIGN AND APPLICATION OF AN OPTIMIZATION FRAMEWORK
IN A FRONTLINE EMPLOYEE MANAGEMENT CONTEXT
PROF. DR. SANDRA STREUKENS
HASSELT UNIVERSITY
FACULTY OF APPLIED ECONOMICS
DEPARTMENT OF BUSINESS STUDIES

OUTLINE
 INTRODUCTION
 A primer in services marketing
 What we do (not) know
 Research objective
 Importance of this study
 MODEL DEVELOPMENT
 Overview of conceptual model
 Model development
 Estimation and calibration of the decision-making model
 Estimation of the behavioral model

Example application
 DISCUSSION
 Implications
 Limitations and further research

INTRODUCTION
A primer in services marketing
 “Services are processes” (van Looy et al. 2003)
 Pure services – services accompanying goods/products
 Flight on an airplane
 Consulting with an accountant
 Haircut
 Attending a university
 Training for a new manufacturing system
 Service delivery involves a game between people (employeecustomer interaction)
 In services the service employee plays a crucial role

INTRODUCTION
What do we know
 The key to an effective service organization starts with managing employees’ perceptions regarding their own organization (Schneider and Bowen, 1993; Rogg et al. 2001)
 More specifically, ample empirical evidence for the positive relationships between employee perceptions, customer evaluative judgments, and financial performance (de Jong et al. 2004a;
Schneider et al. 1998; Kamakura et al. 2002)

INTRODUCTION
What we do not know
 Despite the large body of knowledge regarding service management, there are hardly any practical decision making models that make use of this research.
 One the other hand, OR scholars call for the development of service decision making models that infuse behavioral data in their (so far) purely mathematical model (Bretthauer, 2004; Boudreau et al.
2003).

INTRODUCTION
Research objective
To develop and demonstrate a practical and versatile decision-making tool that assists managers in evaluating and optimizing service improvement initiatives in an economically justified, yet behavioral oriented manner.
More generally, the aim is to design a decision-making tool that assists managers in evaluating and optimizing decisions regarding “soft measures” (perceptions) using “hard modeling”.

INTRODUCTION
Importance of this study
 We live in a service economy
Currently, services make up approx. 75 % of the GDP in Belgium and of all workers approx. 70 % works in the service sector.
 Service managers should be increasingly results oriented
(1) slow growth mature markets
(2) increasing (inter)national competition.
 Customers become an increasingly scarce resource being pursued by an increasing number of service providers

INTRODUCTION
Importance of this study
CONTRIBUTION TO THE ACADEMIC LITERATURE
Model Example
Strategic trade-offs of any service investment?
ROI modeled and calculated?
Can be applied to most service industries
Yes, adapt to
Optimization of service investments?
Optimal allocation of service investments?
Assessment robustness of service investments?
Statistical details?
Service profit chain Loveman (1998)
Rucci et al. (1998)
Kamakura et al. (2002)
No No context if necessary
No No No Yes
Return on quality Rust et al. (1995)
Return on marketing a
Rust et al. (1999)
Rust et al. (2004)
No
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
Yes
Yes
Our decision making
Yes Yes Yes Yes Yes Yes Yes approach a
Features like explicitly modeling of competition and the modeling of brand switching at the customer level as proposed in Rust et al.’s (2004) Return on Marketing model can be easily incorporated in our model by defining the relevant variables as Markov switching matrices.

MODEL DEVELOPMENT
Conceptual model
AT A MACRO LEVEL
Link 2 (+)
Revenue generating process:
A behavioral model
Link 1 (+)
Investment profitability
Link 3 (-)

AT A MICRO LEVEL
MODEL DEVELOPMENT
Conceptual model
Behavioral model describing investment revenue generation process
Customer satisfaction
Employee perceived
Service Climate
Customer perceived service quality
Revenues over period T
Customer loyalty
REV

N
 i


I
 i
( y i
 y ( 0 ) i
)
Link 2 (+) y i
 a i
 b i d i x
 c i i x i c i
Investment effort i


I
( x i
 x ( 0 ) i
)
Link 1 (+)
Link 3 ( )
 i

I
( x i
 x ( 0 ) i
)
Investment profits over period T profit

N
 i

I
(
 i y i
 y ( 0 ) i
)
  i

I
( x i
 x ( 0 ) i
)

MODEL DEVELOPMENT
Modeling revenues – A behavioral approach
MODELING REVENUES – A BEHAVIORAL APPROACH


 i
 y ( 0 )

 i i i i i i i i i i i i i i i c i i i i i i c i i i i i Î Î I I I
( x i i i
x ( 0 ) i i i
)  i

I
( x i
 x ( 0 ) i
)


 i  I y ( 0 )

 i  I x ( 0 )

MODEL DEVELOPMENT
Modeling Revenues – A Behavioral Approach
GENERAL
 Behavioral approach is rooted in the SPC literature
 Operations researchers call for the infusion of perceptual data in decision making
 Employee – Customer – Revenues Chain
 A key role for employee well-being climate, service climate, and customer evaluative judgments
 The effects of the behavioral approach on investment profitability is reflected by link 1 in the conceptual model
CUSTOMER EVALUATIVE JUDGMENTS
 Customer evaluative judgments are predictors of financial performance (Kamakura et al. 2002)
 Pivotal constructs here are perceived quality, customer satisfaction, and behavioral intent (Cronin et al. 2002)

MODEL DEVELOPMENT
Modeling revenues – A behavioral approach
SERVICE CLIMATE
 One of the most relevant contributors in the forming favorable customer evaluative judgments (de Jong et al. 2004a)
EMPLOYEE CLIMATE
 Employee climate is a key determinant of service climate (Parker,
1999)
 Dimensions: rewards orientation, means emphasis, goal emphasis, management support, workgroup support, and interdepartment service (Burke et al. 1992; Schneider et al. 1998)
 Generalizable across settings (Kopelman et al. 1990)
 Can be effectively influenced by targeted investments (Harter et al.
2002)
An overview of the literature underlying these links is available upon request

MODEL DEVELOPMENT
Modeling revenues – A behavioral approach
THE REVENUES FUNCTION
 Using the approach developed by Streukens and de Ruyter (2004) we conclude that all relationships in our behavioral model are linear
 Hence, revenues vary as a linear function of changes in employee well-being dimensions and can be compactly expressed as:
REV

N
 i

I
 i
( y i
 y ( 0 ) i
)

MODEL DEVELOPMENT
Modeling revenues – A behavioral approach
PARAMETERS REVENUE FUNCTION y i
= Level of various employee climate dimension, or input variables i after improvement y ( 0 ) i
= Current level of various employee climate dimension, or input variables i
 i
=
Total effect of each input variable i and on revenues REV .
N = Total number of customers

MODEL DEVELOPMENT
Modeling effort – (In)direct effects
MODELING EFFORT – (IN)DIRECT EFFECTS






 i

I y ( 0 )





 i i i i i i i i i Î Î Î I I I
( x i i i
x ( 0 ) i i i
)
 i

I
( x i
 x ( 0 ) i
) profit

 i

I
( (
 y ( ( 0 ) ) ) )


 i

I
( ( x ( ( 0 ) ) ) )

MODEL DEVELOPMENT
Modeling effort – (In)direct effects
INVESTMENT EFFORT AND PROFITABILITY
 A positive indirect effect (i.e. link 2 in conceptual model)
 A negative direct effect (i.e. link 3 in conceptual model)
INDIRECT EFFECT
 Investment effort
 employee perceptions
 customer perceptions
 revenues
 profitability (all positive relationships)
DIRECT EFFECT
 Profits = Revenues – Investment effort

MODEL DEVELOPMENT
Modeling effort – Indirect effects
 Modeling the effect between investment effort and level of input variables
 Decision calculus approach
 ADBUDG-model developed by Little (1970)
 ABDUDG is “simple, robust, easy to control, adaptive, as complete as possible, and easy to communicate with” (Little, 1970 p.466)
 ABDUDG adheres to Blattberg and Deighton’s (1990) 50%-50% rule

MODEL DEVELOPMENT
Modeling Effort – Indirect effects
THE ADBUDG MODEL y i
 a i

( b i
 a i
) d i x i c i
 x i c i y i x i a i b i c i d i
= Level input variable
= Investment effort
= y
= y x x i i

0
 
= Shape parameter
= Shape parameter

MODEL DEVELOPMENT
Modeling effort – Direct effects
 Requires an estimate of the total investment effort
 x i direct investment effort equals y i
 i

I
( x i
 x ( 0 ) i
)
 x ( 0 ) the various input variables y ( 0 ) i
 To capture the direct effect of investment effort in our approach the total investment effort needs to subtracted from revenues (i.e., link 3)

PROFIT FUNCTION
MODEL DEVELOPMENT
Profit function
PROFIT

N
 i

I
(
 i y i
 y ( 0 ) i
)
  i

I
( x i
 x ( 0 ) i
)
PROFIT OPTIMIZATION
 Profit optimization crucial decision making theme in services (Zeithaml
2000).
 The above profit function will serve as an objective function is an optimization framework.
 Optimization of the profit function is subject to several constraints.

MODEL DEVELOPMENT
Profit function
CONSTRAINT 1
Total investment effort cannot exceed a pre-set budget or spending limit
(Budget constraint)
 i

I
( x i
 x ( 0 ) i
)

BUDGET
CONSTRAINT 2
Non-negativity constraint investment effort x i

0

MODEL DEVELOPMENT
Profit function
CONSTRAINT 3
Relationship between investment effort and the input variables y i
 a i

( b i
 a i
) d i x i c i
 x i c i
CONSTRAINT 4
The level of input variable after implementation of the investment strategy should be at least equal to its starting level y i
 y ( 0 ) i

MODEL DEVELOPMENT
Overview
OVERVIEW DECISION MAKING APPROACH max s .
t .
profit
 i

I
( x i
 x ( 0 ) i
) x i y i



 y i

N


 i

I
 i
( y i
 y ( 0 ) i
)


BUDGET
  i

I
( x i
 x ( 0 ) i
) a i
0

 b i
 a i
 d i x i c i
 x i c i y ( 0 ) i
( i

I )
( i

I )
( i

I )

MODEL DEVELOPMENT
Estimation behavioral model
EMPIRICAL STUDY
 Estimation revenue formation process (i.e. employee-customerrevenues chain)
 Actual data on employee perceptions, customer evaluative judgments, and revenues
Please note that all scale items used in this study are available upon request!

MODEL DEVELOPMENT
Estimation behavioral model
SAMPLING
 Employees and business customers from an internationally operating firm in office equipment.
 Census of 250 employees in 28 teams (on average n=8 per team).
Effective sample size n = 169.
 Random selection of 1500 customers meeting the following criteria
(1) active in retail setting; (2) at least 24 month customer; (3) at least two times contact with service employees during last 12 months.
Effective sample size n = 499. (Min. 5 customers / team; Max. 38 customers / team).

MODEL DEVELOPMENT
Estimation behavioral model
EMPLOYEE SURVEY
 Despite the fact that researchers agree upon the positive relationship between employee climate and service climate, there exists no measurement scale for employee climate (Parker, 1999).
 Careful investigation of the theoretical contents of the employee climate constructs (work of Burke et al. 1992; Schneider et al. 1998).
Find existing validated scales that cover the contents of the constructs

MODEL DEVELOPMENT
Estimation behavioral model
EMPLOYEE SURVEY
 Rewards orientation (4 items), Boshoff and Allen (2000).
 Means emphasis (4 items), Iverson (1992)
 Goal emphasis (4 items), Sawyer (1992)
 Management support (7 items), House and Dessler (1974)
 Work group support (7 items), Beehr (1976)
 Interdepartment service (5 items), adapted from Schneider et al.
(1998)
 Service climate (8 items), Schneider et al. (1998)
All constructs measured on a 9-point Likert scale

MODEL DEVELOPMENT
Estimation behavioral model
CUSTOMER SURVEY
 Perceived quality (9 items), self designed cf. Rust et al. (1995)
 Overall satisfaction (1 item), Anderson et al. (1997)
 Behavioral intent (2 items), Zeithaml et al. (1996)
All constructs measured on a 9-point Likert scale
FINANCIAL DATA
 Internal company records on each customer’s sales history (i.e. revenues). Data covering a 12 months period after the questionnaires were sent out.
DATA LINKAGE
 Employee perceptual data , customer perceptual data, and customer financial data were linked by means of the customer’s unique client number. Providing client number on questionnaire = incentive.

MODEL DEVELOPMENT
Estimation behavioral model
ASSESSMENT PSYCHOMETRIC PROPERTIES
 Partial Least Squares (PLS) estimation
 For the employee data the 1-to-10 parameter to sample size ratio was not met (cf. Raykou and Widaman, 1995; Bentler and Chou,
1987).
 Both reflective and formative were employed in our study.
UNIDIMENSIONALITY
 First eigenvalue greater than 1 criterion (cf. Tenenhaus et al. 2005)
 All reflective scales met this criterion
INTERNAL CONSISTENCY RELIABILITY
 For all reflective constructs ρ > 0.70 (cf. Nunnally and Bernstein,
1994)

MODEL DEVELOPMENT
Estimation behavioral model
CONVERGENT VALIDITY
 Tested for all reflective scales
 All loadings significant and > 0.50 (cf. Anderson and Gerbing, 1988)
 All average variance extracted value > 0.50
CONTENT VALIDITY
 Key validity type for formative scales
 Scale designed to cover all relevant aspects of the construct (cf.
Jarvis et al., 2003)
 Magnitude and significance of the loadings defining the formative relationships evidence relevance of the indicators (cf.
Diamantopoulos and Winklhofer, 2001)

MODEL DEVELOPMENT
Estimation behavioral model
DISCRIMINANT VALIDITY
 Correlations between construct pairs did not include an absolute value of 1 in their 95% confidence intervals (both reflective and formative scales).
 Average variance extracted > squared value correlation coefficient
(only for reflective scales).

MODEL DEVELOPMENT
Estimation behavioral model
COMPLEX DATA STRUCTURE
 Employee part: employees nested within teams
 Linkage part: customers are nested within teams
 Customer part: between-person structure
RESULTING ANALYSIS STRATEGY
 Employee part: 2-level HLM (cf. de Jong et al., 2004a & b)
 Linkage part: 3-level HLM (cf. de Jong et al., 2004a & b)
 Customer part: SUR
ANALYTICAL SOFTWARE
 HLM models estimated in Mlwim
 SUR model estimated using SAS PROC SYSLIN

MODEL DEVELOPMENT
Estimation behavioral model
ASSESSING THE DATA’S SUITABILITY FOR HLM
 Interrater-agreement r(WG) (cf. James et al., 1993)
 Intra Class Correlation ICC(1) and ICC(2) (cf. Bliese, 2000)
 All three measures provide justification for aggregation of the data
Minimum r
WG ( J )
ROR 0.81
MEMP
GEMP
MSUP
WGS
0.78
0.81
0.87
0.85
IDS 0.78
SERVCLIM 0.88
Maximum r
WG ( J )
0.99
0.96
0.96
0.99
0.98
0.96
0.99
Mean r
WG ( J )
0.93
0.90
0.91
0.94
0.94
0.88
0.94
Median r
WG ( J )
0.94
0.92
0.93
0.94
0.95
0.89
0.95
ICC(1) ICC(2) F
(27,141)
0.14
0.15
0.18
0.43
0.10
0.27
0.17
0.50
0.52
0.57
0.82
0.40
0.69
0.55
2.003
2.072
2.327
5.486
1.666
3.265
2.207 p-value
< 0.01
< 0.01
< 0.01
< 0.01
< 0.05
< 0.01
< 0.01

MODEL DEVELOPMENT
Estimation behavioral model
2-LEVEL HLM EMPLOYEE PART
SERVCLIM ij
 
50
WGS ij
 
 
00

60
IDS ij

10
ROR ij
 
 
01
ROR j
20
MEMP ij
 
 
30
GEMP ij
02
MEMP j
 
03
GEMP j
 
40
MSUP ij
 
04
MSUP j
 
05
WGS j
 
06
IDS j
 u
0 j
 u
1 j
 u
2 j
 u
3 j
 u
4 j
 u
5j
 u
6 j
 e ij
.

MODEL DEVELOPMENT
Estimation behavioral model
3-LEVEL HLM LINKAGE PART
 Level 1: perceived service quality (m = qual01 – qual09)
 Level 2: individual customer (i = 1 – 499)
 Level 3: team which serves customer (j = 1-28) d shij

1
0 h
 h
 s s .
,
Y hij
 s m 

1
γ
0 s d shij
 k p 

1 s m 

1
γ ks d shij x kij
 s m 

1 u sj d shij
 s m 

1 e sij d shij
.

MODEL DEVELOPMENT
Estimation behavioral model
SUR MODEL CUSTOMER PART
QUAL p
   r r qual r
 
1
SAT p
 
1
 
1
QUAL p
 
2
INT p
 
2
 
2
SAT p
 
3
QUAL p
 
3
REV p
 
3
 
4
INT p
 
4

MODEL DEVELOPMENT
Results behavioral model
EMPLOYEE PART
 At the individual level “rewards orientation” (b= 0.23); “goal emphasis” (b = 0.13); “management support” (b = 0.23); “work group support” (b = 0.10); and “interdepartment service” (b = 0.20) have significant impact on service climate.
 At the group level none of the hypothesized antecedents has a significant impact on service climate
LINKAGE PART
 Service climate has a positive and significant impact on all quality dimensions (“qual01” (b = 0.90); “qual02” (b = 0.74); “qual03” (b =
0.76); “qual04” (b = 0.57);“qual05” (b = 0.39); “qual06” (b = 0.36);
“qual07” (b = 0.40); “qual08” (b = 0.42); “qual09” (b = 0.50))

MODEL DEVELOPMENT
Results behavioral model
CUSTOMER PART
 “Perceived quality” has a positive and significant impact on “overall satisfaction” (b = 0.73)
 “Behavioral intentions” is positively and significantly influenced by
“perceived quality” (b = 0.19) and “overall satisfaction” (b = 0.51)
 “Behavioral intentions” has a positive and significant impact on
“revenues” (b = 1092.80)
OVERALL
 We find empirical support for an employee-customer-revenues chain of effects
 Using these empirical results we can determine how much revenues vary as a function of changes in the employee climate perceptions
 We have insight in the revenue part of our decision making model
(i.e. link 1)

SERVICE MANAGEMENT DECISION MAKING
Example application
EXAMPLE ILLUSTRATION OF DECISION MAKING MODEL
 An exact description of the investment actions and the involved costs and profits were not allowed to be made public by the company at which we collected data.
 Hence, fictive numbers are used demonstrating the decision making model (i.e. regarding link 2 and link 3)
 This is no problem, as in contrast to the empirical study described above the figures on the investment actions are completely company specific and do not allow for making generalization to other settings.

SERVICE MANAGEMENT DECISION MAKING
Example application
DECISION MAKING MODEL
 Determining optimal level investment effort
 Calculation rate of return (ROI)
 Determining optimal allocation of the investment efforts
 Assessing the robustness of the optimal solution (risk)

SERVICE MANAGEMENT DECISION MAKING
Example application
INVESTMENT STRATEGY
 Emphasis on revenues expansion rather than cost reduction (cf.
Rust et al. 2000).
 In line with the customization-standardization trade-off explained by
Anderson et al. 1997)
 The literature shows that revenue expansion, customization, and satisfaction are related
 Focus on defensive strategy (cf. Fornell and Wernerfelt 1987, 1988)
 Thus, maximize profitability through increasing revenues from existing customers

SERVICE MANAGEMENT DECISION MAKING
Example application
OPTIMIZATION FRAMEWORK: REVENUE FUNCTION
REV

N



 i

I
 i
( y i
 y ( 0 ) i
)



 Parameters δ i and y(0) i follow directly from the empirical study
δ
1
δ
4
(ROR) = 436.74 ; δ
2
(GEMP) = 246.86; δ
3
(MSUP) = 436.74;
(WGS) = 189.89; δ
5
(IDS) = 379.78
y(0)
1 y(0)
4
(ROR) = 5.60 ; y(0)
2
(WGS) = 5.68; y(0)
5
(GEMP) = 5.12; y(0)
(IDS) = 3.97
3
(MSUP) = 5.10;
 The value for parameter y i is determined via the ADBUDG function
 N = 10,000

SERVICE MANAGEMENT DECISION MAKING
Example application
OPTIMIZATION FRAMEWORK: REVENUE FUNCTION
 Some background info on calculating the δ i
 Assume the following (a-cyclical) model parameter y
1
β
2
β
3
β
1 y
2
β
4 q
1
β
5
β
6 q
2
β
7 rev
 The impact of variable y i connecting y i and rev on rev (i.e., δ
 Thus, Δ
Δ
2
= (β
3
1
= (β
*β
6
1
* β
6
)+(β
3
)+(β
1
* β
5
* β
5
* β
7
* β
)+(β
4
7
)+(β
* β
7
)
2
* β i
7
) is the sum of all paths
) and

SERVICE MANAGEMENT DECISION MAKING
Example application
OPTIMIZATION FRAMEWORK: COST FUNCTION y i
 a i

( b i
 a i
) d i x i c i
 x i c i
 Calibration by means of the 4 standard ADBUDG questions
1. If effort is reduced to 0 what will than be the evaluation regarding the input variable? This provides the value for parameter a i
. The value of a i is typically the lowest value of the scale used to assess the perceptions regarding . In this case 1.

SERVICE MANAGEMENT DECISION MAKING
Example application
OPTIMIZATION FRAMEWORK: COST FUNCTION
2. If effort approaches infinity what will be the value of the input variable? This answer provides the value for parameter b i
. The value of b i is typically the highest value of the scale used to assess the perceptions regarding . In this case 9.
3. Regarding input variable i; what is the current level of effort and to what evaluation does that lead?
4. If compared to the current situation effort is doubled to what level of input variable would that lead?
Questions 1 and 2 restrict function to meaningful range
Questions 3 and 4 determine shape of the function (S-shaped or concave)

SERVICE MANAGEMENT DECISION MAKING
Example Application
OPTIMIZATION FRAMEWORK: COST FUNCTION
 Having calibrated the ADBUDG functions for the various input variables (ROR, GEMP, MSUP, WGD, and IDS) automatically provides all input for the total level of investment effort (i.e., direct effect or link 3)
METHODOLOGY
 Solving the optimization framework
 Non-linear programming using AIMMS

SERVICE MANAGEMENT DECISION MAKING
Example application
OPTIMIZATION ANALYSIS
 Investments remain feasible when the derivative of the objective function is positive
 Optimum of objective function is reached when its derivative is equal to zero
 Optimum of objective function is maximum level profitability
 Derivative profit function max (
 y x i i i
 x i
) '
 max



 b c d i i i i x i c i

1
( d i
 x i c i )
2

1




SERVICE MANAGEMENT DECISION MAKING
Example application
18.000.000
16.000.000
14.000.000
12.000.000
10.000.000
8.000.000
6.000.000
4.000.000
2.000.000
0
0,0
INVESTMENT EFFORT-REVENUES-PROFITS
INVESTMENT REVENUES
1,7 3,3
INVESTMENT PROFITS
5,0 6,6 8,2
INVESTMENT EFFORT (*1,000,000 $)
9,9 11,5 13,2 14,8

SERVICE MANAGEMENT DECISION MAKING
Example application
RATE OF RETURN
ROI

N
 i

I
 i
( y i
 y ( 0 ) i
)
  i

I
 i

I x i
 x ( 0 ) i x i
 x ( 0 ) i
OPTIMAL SOLUTION
 Investment effort = $ 23,000,000
 Profits = $ 7,298,500
 Rate of return = 31.71 %

SERVICE MANAGEMENT DECISION MAKING
Example application
OPTIMAL ALLOCATION
 Effort level and allocation of effort are equally important matters in making investment decisions (Mantrala et al. 1992).
 Question now is how to allocate the optimal effort level to indeed obtain the maximum level of profitability
 Guidance regarding the allocation of the investment effort can be directly obtained from the relative magnitudes of the derivatives.
 Remember that the partial derivative of the profit function with regard to y i reflects the change in profits obtained by investing an additional monetary unit in variable y i.

SERVICE MANAGEMENT DECISION MAKING
Example application
OPTIMAL ALLOCATION
 Thus, optimal allocation starts with directing all efforts to the input variable with the highest partial derivative
 Note that as investments are subject to diminishing returns, the partial derivative decreases
 When the highest partial derivative equals the second highest derivative, optimal allocation is obtained by spreading effort over the various alternatives as follows
 p
( b p d
 a
 p
) c x p  c p
2
 p p d p x p c p

1

 q
( b q d
 a
 q
) c x q c q q  
2 q d q x q c q

1

SERVICE MANAGEMENT DECISION MAKING
Example application
OPTIMAL ALLOCATION
ALLOCATION OF EFFORT
100
90
80
70
60
30
20
10
50
40
0
0,5 2,2 3,9
IDS
MSUP
5,5
OPTIMAL EFFORT LEVEL
ROR
7,2
EFFORT (*1,000,000$)
8,8 10,4 12,1
WGS
13,7
GEMP

SERVICE MANAGEMENT DECISION MAKING
Example application
OPTIMAL SOLUTION
Input variable Optimal level input variable y i
ROR ( i

1
GEMP ( i

MSUP ( i

)
2
3 )
)
WGS ( i

IDS ( i

4
5 )
)
5.83
5.12
5.83
5.68
5.60
Initial level input variable y ( 0 ) i
5.60
5.12
5.10
5.68
3.97
Optimal effort level x i
$ 8,360,000
$ 5,830,000
$ 8,360,000
$ 7,755,000
$ 7,425,000
Maintenance effort level x ( 0 ) i
$ 7,425,000
$ 5,830,000
$ 5,775,000
$ 7,755,000
$ 3,245,000
Optimal investment level x i
 x ( 0 ) i
$ 935,000
Proportional optimal investment level
( x i
 x ( 0 ) i
)
 i

I
( x i
12.14 %
 x ( 0 ) i
)
$ 0
$ 2,585,000
$ 0
$ 4,180,000
0 %
33.57 %
0 %
54.29 %
What the various amounts mean in practical investment actions (e.g.
Specific reward system) can be derived from the ADBUDG function

SERVICE MANAGEMENT DECISION MAKING
Example application
ROBUSTNESS / INVESTMENT RISK
 All investments are characterized by uncertainty regarding the projected outcome
 This uncertainty or variability concerning the projected outcome is referred to as risk (comparable to the definition of risk in finance)
 Robustness assessment by means of sensitivity analysis. That is: how does the optimal solution respond to changes in the model parameters?
 Robustness assessment by means of calculation switching values.
That is, how much can a coefficient drop until the investment results become economically infeasible / negative?

SERVICE MANAGEMENT DECISION MAKING
Example application
ROBUSTNESS: SENSITIVITY ANALYSIS
 Numerical experiments
 Deviation in coefficient (5%, 10%) and the resulting percentual change in optimal solution.
ROBUSTNESS: SWITCHING VALUES
 Solving the optimization framework to determine per coefficient when profitability becomes zero.
Note that the robustness is assessed by altering the δ i decision making approach parameter in our

SERVICE MANAGEMENT DECISION MAKING
Example application
RESULTS ROBUSTNESS ASSESSMENT when β i i when β i i

DISCUSSION
 Decision making model that allows to evaluate the financial consequences of service improvement initiatives in an economically sound manner, whilst guarding the firm’s key assets: its employee and customers
 The model merges knowledge from service research with mathematical rigor
 Profit maximization
 Allocation of investment effort
 Risk analysis
 Integral empirical assessment employee-customer-revenues chain

LIMITATIONS AND FUTURE RESEARCH
 Cross sectional approach – dynamic analysis
 Inclusion of customer characteristics
 Retention of customers and acquisition of new customers