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PROF. DR. SANDRA STREUKENS HASSELT UNIVERSITY FACULTY OF APPLIED ECONOMICS

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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

- 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

- “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

- 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)

- 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).

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”.

- 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

CONTRIBUTION TO THE ACADEMIC LITERATURE

AT A MACRO LEVEL

AT A MICRO LEVEL

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)

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

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:

PARAMETERS REVENUE FUNCTION

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

- 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

THE ADBUDG MODEL

- 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)

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.

CONSTRAINT 1

Total investment effort cannot exceed a pre-set budget or spending limit (Budget constraint)

CONSTRAINT 2

Non-negativity constraint investment effort

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

OVERVIEW DECISION MAKING APPROACH

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!

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).

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

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

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.

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)

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)

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).

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

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

2-LEVEL HLM EMPLOYEE PART

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)

SUR MODEL CUSTOMER PART

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))

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)

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.

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)

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

OPTIMIZATION FRAMEWORK: REVENUE FUNCTION

- Parameters δi and y(0)ifollow directly from the empirical study
δ1(ROR) = 436.74 ; δ2(GEMP) = 246.86; δ3(MSUP) = 436.74; δ4(WGS) = 189.89; δ5(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

β1

y1

q1

β6

β2

rev

β5

β3

y2

q2

β7

β4

OPTIMIZATION FRAMEWORK: REVENUE FUNCTION

- Some background info on calculating the δi parameter
- Assume the following (a-cyclical) model
- The impact of variable yi on rev (i.e., δi) is the sum of all paths connecting yi and rev
- Thus, Δ1 = (β1* β6)+(β1* β5* β7)+(β2 * β7) and
Δ2 = (β3*β6)+(β3* β5* β7)+(β4 * β7)

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.

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)

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

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

RATE OF RETURN

OPTIMAL SOLUTION

- Investment effort = $ 23,000,000
- Profits = $ 7,298,500
- Rate of return = 31.71 %

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.

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

OPTIMAL ALLOCATION

OPTIMAL SOLUTION

What the various amounts mean in practical investment actions (e.g. Specific reward system) can be derived from the ADBUDG function

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?

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 δi parameter in our decision making approach

RESULTS ROBUSTNESS ASSESSMENT

- 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-revenueschain

- Cross sectional approach – dynamic analysis
- Inclusion of customer characteristics
- Retention of customers and acquisition of new customers