Title: TOWARDS MORE EFFECTIVE SERVICE MANAGEMENT DECISION MAKING: DESIGN AND APPLICATION OF AN OPTIMIZATION FRAMEWORK IN A FRONTLINE EMPLOYEE MANAGEMENT CONTEXT
1TOWARDS MORE EFFECTIVE SERVICE MANAGEMENT
DECISION MAKINGDESIGN 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
2OUTLINE
- 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
3INTRODUCTIONA 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
(employee-customer interaction) - In services the service employee plays a crucial
role
4INTRODUCTIONWhat 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)
5INTRODUCTION 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).
6INTRODUCTIONResearch 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.
7INTRODUCTIONImportance 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
8INTRODUCTIONImportance of this study
- CONTRIBUTION TO THE ACADEMIC LITERATURE
9MODEL DEVELOPMENTConceptual model
10MODEL DEVELOPMENTConceptual model
11MODEL DEVELOPMENTModeling revenues A
behavioral approach
- MODELING REVENUES A BEHAVIORAL APPROACH
12MODEL DEVELOPMENTModeling 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)
13MODEL 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
14MODEL 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
15MODEL DEVELOPMENT Modeling revenues A
behavioral approach
- PARAMETERS REVENUE FUNCTION
16MODEL DEVELOPMENTModeling effort (In)direct
effects
- MODELING EFFORT (IN)DIRECT EFFECTS
17MODEL DEVELOPMENTModeling 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
18MODEL DEVELOPMENTModeling 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 Deightons (1990)
50-50 rule
19MODEL DEVELOPMENTModeling Effort Indirect
effects
Level input variable
Investment effort
Level of when
Level of when
Shape parameter
Shape parameter
20MODEL DEVELOPMENTModeling effort Direct effects
- Requires an estimate of the total investment
effort - As refers to the monetary investment
regarding input variable the total direct
investment effort equals -
- The term is the investment effort needed
to maintain the current level of the various
input variables - To capture the direct effect of investment effort
in our approach the total investment effort needs
to subtracted from revenues (i.e., link 3)
21MODEL DEVELOPMENTProfit function
- PROFIT FUNCTION
- 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.
22MODEL DEVELOPMENTProfit function
- CONSTRAINT 1
- Total investment effort cannot exceed a pre-set
budget or spending limit (Budget constraint) - CONSTRAINT 2
- Non-negativity constraint investment effort
23MODEL DEVELOPMENT Profit function
- CONSTRAINT 3
- Relationship between investment effort and the
input variables - CONSTRAINT 4
- The level of input variable after implementation
of the investment strategy should be at least
equal to its starting level
24MODEL DEVELOPMENTOverview
- OVERVIEW DECISION MAKING APPROACH
25MODEL DEVELOPMENTEstimation behavioral model
- EMPIRICAL STUDY
- Estimation revenue formation process (i.e.
employee-customer-revenues 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!
26MODEL DEVELOPMENTEstimation behavioral model
- SAMPLING
- Employees and business customers from an
internationally operating firm in office
equipment. - Census of 250 employees in 28 teams (on average
n8 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).
27MODEL DEVELOPMENTEstimation 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
28MODEL DEVELOPMENTEstimation 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
29MODEL DEVELOPMENTEstimation 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 customers 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 customers unique client number.
Providing client number on questionnaire
incentive.
30MODEL DEVELOPMENTEstimation 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 ? gt 0.70 (cf.
Nunnally and Bernstein, 1994)
31MODEL DEVELOPMENTEstimation behavioral model
- CONVERGENT VALIDITY
- Tested for all reflective scales
- All loadings significant and gt 0.50 (cf. Anderson
and Gerbing, 1988) - All average variance extracted value gt 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)
32MODEL DEVELOPMENTEstimation 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 gt squared value
correlation coefficient (only for reflective
scales).
33MODEL DEVELOPMENTEstimation 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
34MODEL DEVELOPMENTEstimation behavioral model
- ASSESSING THE DATAS 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
35MODEL DEVELOPMENTEstimation behavioral model
- 2-LEVEL HLM EMPLOYEE PART
36MODEL DEVELOPMENTEstimation 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)
37MODEL DEVELOPMENTEstimation behavioral model
38MODEL DEVELOPMENTResults 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))
39MODEL 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)
40SERVICE MANAGEMENT DECISION MAKINGExample
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.
41SERVICE MANAGEMENT DECISION MAKINGExample
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)
42SERVICE MANAGEMENT DECISION MAKINGExample
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
43SERVICE MANAGEMENT DECISION MAKING Example
application
- OPTIMIZATION FRAMEWORK REVENUE FUNCTION
- Parameters di and y(0)i follow directly from the
empirical study - d1(ROR) 436.74 d2(GEMP) 246.86 d3(MSUP)
436.74 d4(WGS) 189.89 d5(IDS) 379.78 - y(0)1(ROR) 5.60 y(0)2(GEMP) 5.12
y(0)3(MSUP) 5.10 y(0)4(WGS) 5.68 y(0)5(IDS)
3.97 - The value for parameter yi is determined via the
ADBUDG function - N 10,000
44SERVICE MANAGEMENT DECISION MAKINGExample
application
- OPTIMIZATION FRAMEWORK REVENUE FUNCTION
- Some background info on calculating the di
parameter - Assume the following (a-cyclical) model
- The impact of variable yi on rev (i.e., di) is
the sum of all paths connecting yi and rev - Thus, ?1 (ß1 ß6)(ß1 ß5 ß7)(ß2 ß7) and
- ?2 (ß3ß6)(ß3 ß5 ß7)(ß4 ß7)
45SERVICE MANAGEMENT DECISION MAKING Example
application
- OPTIMIZATION FRAMEWORK COST FUNCTION
- Calibration by means of the 4 standard ADBUDG
questions - If effort is reduced to 0 what will than be the
evaluation regarding the input variable? This
provides the value for parameter ai. The value of
ai is typically the lowest value of the scale
used to assess the perceptions regarding . In
this case 1.
46SERVICE MANAGEMENT DECISION MAKING Example
application
- OPTIMIZATION FRAMEWORK COST FUNCTION
- If effort approaches infinity what will be the
value of the input variable? This answer provides
the value for parameter bi. The value of bi is
typically the highest value of the scale used to
assess the perceptions regarding . In this case
9. - Regarding input variable i what is the current
level of effort and to what evaluation does that
lead? - 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)
47SERVICE MANAGEMENT DECISION MAKINGExample
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
48SERVICE MANAGEMENT DECISION MAKINGExample
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
49SERVICE MANAGEMENT DECISION MAKINGExample
application
50SERVICE MANAGEMENT DECISION MAKING Example
application
- RATE OF RETURN
- OPTIMAL SOLUTION
- Investment effort 23,000,000
- Profits 7,298,500
- Rate of return 31.71
51SERVICE MANAGEMENT DECISION MAKINGExample
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 yi reflects the
change in profits obtained by investing an
additional monetary unit in variable yi.
52SERVICE MANAGEMENT DECISION MAKINGExample
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
53SERVICE MANAGEMENT DECISION MAKINGExample
application
54SERVICE MANAGEMENT DECISION MAKINGExample
application
- OPTIMAL SOLUTION
- What the various amounts mean in practical
investment actions (e.g. Specific reward system)
can be derived from the ADBUDG function
55SERVICE MANAGEMENT DECISION MAKINGExample
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?
56SERVICE MANAGEMENT DECISION MAKINGExample
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 di parameter in our decision making approach
57SERVICE MANAGEMENT DECISION MAKINGExample
application
- RESULTS ROBUSTNESS ASSESSMENT
58DISCUSSION
- Decision making model that allows to evaluate the
financial consequences of service improvement
initiatives in an economically sound manner,
whilst guarding the firms 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-re
venues chain
59LIMITATIONS AND FUTURE RESEARCH
- Cross sectional approach dynamic analysis
- Inclusion of customer characteristics
- Retention of customers and acquisition of new
customers